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

 /*
  * Copyright (c) 2017-2018 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/NEWinogradLayerKernel.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 "support/ToolchainSupport.h"

 namespace arm_compute
 {
 //Batched Gemms
 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerKernel()
     : _gemms()
 {
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
     const unsigned int n_gemms,
     const int M, const int K, const int N,
     const int          a_matrix_stride,
     const int          a_row_stride,
     const int          b_matrix_stride,
     const int          b_row_stride,
     const int          c_matrix_stride,
     const int          c_row_stride,
     const float *const a_ptr,
     const float *const b_ptr,
     float *const       c_ptr)
 {
     _gemms = support::cpp14::make_unique<MultiGEMM>(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr);
     Window win;
     auto   win_last = _gemms->get_window();
     win.set(Window::DimX, Window::Dimension(0, win_last, 1));
     INEKernel::configure(win);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     const size_t first_gemm = window.x().start();
     const size_t last_gemm  = window.x().end();
     _gemms->run(first_gemm, last_gemm);
 }

 template class NEWinogradLayerKernel<2, 2, 3, 3>;

 // Weights transform

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 unsigned int NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels)
 {
     const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels);
     return static_cast<unsigned int>(
                // WinogradConv returns the size in bytes, we divide by `sizeof(float)` to
                // express that in units of float.
                WinogradConv::get_kernel_storage_size(shape) / sizeof(float));
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
     : _transform()
 {
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
     const ITensor *weights_hwio,
     float *const   output,
     const int      matrix_stride,     /** Stride across matrices in the output. */
     const int      n_output_channels, /** Number of filters. */
     const int      n_input_channels)  /** Number of channels in each filter. */
 {
     const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK);
     _transform                  = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<float *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels,
                                                                                 n_input_channels);
     Window win;
     auto   win_last = _transform->get_window();
     win.set(Window::DimX, Window::Dimension(0, win_last, 1));
     INEKernel::configure(win);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     const size_t fst = window.x().start();
     const size_t lst = window.x().end();
     _transform->run(fst, lst);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 bool NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
 {
     return false;
 }

 template class NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>;

 // Input transform

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 unsigned int NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
     int  n_batches,   /** Number of batches in the input tensor. */
     int  n_channels,  /** Number of feature maps in the input tensor. */
     int  n_rows,      /** Number of rows in each feature map. */
     int  n_cols,      /** Number of columns in each feature map. */
     bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
 )
 {
     // Construct shapes for the input and kernel tensors.
     const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels);
     const KernelShape   kern_shape(1, KernelRows, KernelCols, n_channels);
     const PaddingType   padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
     // Return the size, converted into units of TIn
     return static_cast<unsigned int>(
                WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(float));
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
     : _transform()
 {
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
     const float *const input,         /** Input tensor data */
     const int          n_batches,     /** Number of batches in input tensor. */
     const int          n_rows,        /** Number of rows in input tensor. */
     const int          n_cols,        /** Number of columns in input tensor. */
     const int          n_channels,    /** Number of channels in input tensor. */
     const PaddingType  padding,       /** Padding type. */
     float *const       output,        /** Base of output matrices. */
     const int          matrix_stride) /** Stride between output matrices. */
 {
     //  _input_matrix_row_stride(n_input_channels),
     _transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels);
     Window win;
     auto   win_last = _transform->get_window();
     win.set(Window::DimX, Window::Dimension(0, win_last, 1));
     INEKernel::configure(win);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     const size_t fst = window.x().start();
     const size_t lst = window.x().end();
     _transform->run(fst, lst);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 bool NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
 {
     return false;
 }

 template class NEWinogradLayerTransformInputKernel<2, 2, 3, 3>;

 // Output transform

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 unsigned int NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
     int  n_batches,         /** Number of batches in the output tensor. */
     int  n_rows,            /** Number of rows in each feature map of the input tensor. */
     int  n_cols,            /** Number of columns in each feature map of the input tensor. */
     int  n_output_channels, /** Number of feature maps in the output tensor. */
     bool same_padding       /** Use "SAME" padding, otherwise use "VALID". */
 )
 {
     // Construct shapes for the input and kernel tensors.
     const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1);
     const KernelShape   kern_shape(n_output_channels, KernelRows, KernelCols, 1);
     const PaddingType   padding = (same_padding) ? PADDING_SAME : PADDING_VALID;

     // Return the size, converted into units of TOut
     return static_cast<unsigned int>(
                WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(float));
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel()
     : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0)
 {
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
     const ITensor     *biases,
     const float *const output_workingspace,
     const int          matrix_stride,
     float *const       output,
     const int          n_batches,
     const int          n_rows,
     const int          n_cols,
     const int          n_channels)
 {
     _biases            = biases;
     _output_workspace  = output_workingspace;
     _matrix_stride     = matrix_stride;
     _matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK);
     _output            = output;
     _n_batches         = n_batches;
     _n_rows            = n_rows;
     _n_cols            = n_cols;
     _n_channels        = n_channels;

     // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
     OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels);
     Window          win;
     auto            win_last = output_transform.get_window();
     win.set(Window::DimX, Window::Dimension(0, win_last, 1));
     INEKernel::configure(win);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
 {
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_NULLPTR(_biases->buffer());
     ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
     ARM_COMPUTE_ERROR_ON_NULLPTR(_output);

     OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
                                      reinterpret_cast<float *>(_biases->buffer()), _output,
                                      _n_batches, _n_rows, _n_cols, _n_channels);

     // The code below cannot be moved to configure because biases hasn't been allocated at that point
     const size_t fst = window.x().start();
     const size_t lst = window.x().end();
     output_transform.run(fst, lst);
 }

 template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 bool NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
 {
     return false;
 }

 template class NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>;

 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2018 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/NEWinogradLayerKernel.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 "support/ToolchainSupport.h"

	namespace arm_compute
	{
	//Batched Gemms
	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerKernel()
	: _gemms()
	{
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
	const unsigned int n_gemms,
	const int M, const int K, const int N,
	const int a_matrix_stride,
	const int a_row_stride,
	const int b_matrix_stride,
	const int b_row_stride,
	const int c_matrix_stride,
	const int c_row_stride,
	const float *const a_ptr,
	const float *const b_ptr,
	float *const c_ptr)
	{
	_gemms = support::cpp14::make_unique<MultiGEMM>(n_gemms, M, K, N, a_matrix_stride, a_row_stride, b_matrix_stride, b_row_stride, c_matrix_stride, c_row_stride, a_ptr, b_ptr, c_ptr);
	Window win;
	auto win_last = _gemms->get_window();
	win.set(Window::DimX, Window::Dimension(0, win_last, 1));
	INEKernel::configure(win);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	const size_t first_gemm = window.x().start();
	const size_t last_gemm = window.x().end();
	_gemms->run(first_gemm, last_gemm);
	}

	template class NEWinogradLayerKernel<2, 2, 3, 3>;

	// Weights transform

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	unsigned int NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int n_output_channels, int n_input_channels)
	{
	const KernelShape shape(n_output_channels, KernelRows, KernelCols, n_input_channels);
	return static_cast<unsigned int>(
	// WinogradConv returns the size in bytes, we divide by `sizeof(float)` to
	// express that in units of float.
	WinogradConv::get_kernel_storage_size(shape) / sizeof(float));
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
	: _transform()
	{
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
	const ITensor *weights_hwio,
	float *const output,
	const int matrix_stride, /** Stride across matrices in the output. */
	const int n_output_channels, /** Number of filters. */
	const int n_input_channels) /** Number of channels in each filter. */
	{
	const int matrix_row_stride = roundup(n_output_channels, WinogradConv::N_BLOCK);
	_transform = support::cpp14::make_unique<WeightsTransform>(reinterpret_cast<float *>(weights_hwio->buffer()), output, matrix_stride, matrix_row_stride, n_output_channels,
	n_input_channels);
	Window win;
	auto win_last = _transform->get_window();
	win.set(Window::DimX, Window::Dimension(0, win_last, 1));
	INEKernel::configure(win);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	const size_t fst = window.x().start();
	const size_t lst = window.x().end();
	_transform->run(fst, lst);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	bool NEWinogradLayerTransformWeightsKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
	{
	return false;
	}

	template class NEWinogradLayerTransformWeightsKernel<2, 2, 3, 3>;

	// Input transform

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	unsigned int NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
	int n_batches, /** Number of batches in the input tensor. */
	int n_channels, /** Number of feature maps in the input tensor. */
	int n_rows, /** Number of rows in each feature map. */
	int n_cols, /** Number of columns in each feature map. */
	bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
	)
	{
	// Construct shapes for the input and kernel tensors.
	const Tensor4DShape input_shape(n_batches, n_rows, n_cols, n_channels);
	const KernelShape kern_shape(1, KernelRows, KernelCols, n_channels);
	const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;
	// Return the size, converted into units of TIn
	return static_cast<unsigned int>(
	WinogradConv::get_input_storage_size(kern_shape, input_shape, padding) / sizeof(float));
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
	: _transform()
	{
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
	const float const input, /* Input tensor data */
	const int n_batches, /** Number of batches in input tensor. */
	const int n_rows, /** Number of rows in input tensor. */
	const int n_cols, /** Number of columns in input tensor. */
	const int n_channels, /** Number of channels in input tensor. */
	const PaddingType padding, /** Padding type. */
	float const output, /* Base of output matrices. */
	const int matrix_stride) /** Stride between output matrices. */
	{
	// _input_matrix_row_stride(n_input_channels),
	_transform = support::cpp14::make_unique<InputTransform>(input, n_batches, n_rows, n_cols, n_channels, padding, output, matrix_stride, n_channels);
	Window win;
	auto win_last = _transform->get_window();
	win.set(Window::DimX, Window::Dimension(0, win_last, 1));
	INEKernel::configure(win);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	const size_t fst = window.x().start();
	const size_t lst = window.x().end();
	_transform->run(fst, lst);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	bool NEWinogradLayerTransformInputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
	{
	return false;
	}

	template class NEWinogradLayerTransformInputKernel<2, 2, 3, 3>;

	// Output transform

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	unsigned int NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
	int n_batches, /** Number of batches in the output tensor. */
	int n_rows, /** Number of rows in each feature map of the input tensor. */
	int n_cols, /** Number of columns in each feature map of the input tensor. */
	int n_output_channels, /** Number of feature maps in the output tensor. */
	bool same_padding /** Use "SAME" padding, otherwise use "VALID". */
	)
	{
	// Construct shapes for the input and kernel tensors.
	const Tensor4DShape input_shape(n_batches, n_rows, n_cols, 1);
	const KernelShape kern_shape(n_output_channels, KernelRows, KernelCols, 1);
	const PaddingType padding = (same_padding) ? PADDING_SAME : PADDING_VALID;

	// Return the size, converted into units of TOut
	return static_cast<unsigned int>(
	WinogradConv::get_output_storage_size(kern_shape, input_shape, padding) / sizeof(float));
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel()
	: _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0)
	{
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
	const ITensor *biases,
	const float *const output_workingspace,
	const int matrix_stride,
	float *const output,
	const int n_batches,
	const int n_rows,
	const int n_cols,
	const int n_channels)
	{
	_biases = biases;
	_output_workspace = output_workingspace;
	_matrix_stride = matrix_stride;
	_matrix_row_stride = roundup(n_channels, WinogradConv::N_BLOCK);
	_output = output;
	_n_batches = n_batches;
	_n_rows = n_rows;
	_n_cols = n_cols;
	_n_channels = n_channels;

	// We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
	OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels);
	Window win;
	auto win_last = output_transform.get_window();
	win.set(Window::DimX, Window::Dimension(0, win_last, 1));
	INEKernel::configure(win);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	void NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::run(const Window &window, const ThreadInfo &info)
	{
	ARM_COMPUTE_UNUSED(info);
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_NULLPTR(_biases->buffer());
	ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
	ARM_COMPUTE_ERROR_ON_NULLPTR(_output);

	OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
	reinterpret_cast<float *>(_biases->buffer()), _output,
	_n_batches, _n_rows, _n_cols, _n_channels);

	// The code below cannot be moved to configure because biases hasn't been allocated at that point
	const size_t fst = window.x().start();
	const size_t lst = window.x().end();
	output_transform.run(fst, lst);
	}

	template <int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
	bool NEWinogradLayerTransformOutputKernel<OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
	{
	return false;
	}

	template class NEWinogradLayerTransformOutputKernel<2, 2, 3, 3>;

	} // namespace arm_compute