src/runtime/NEON/functions/NEWinogradConvolutionLayer.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/runtime/NEON/functions/NEWinogradConvolutionLayer.h"

 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/NEON/AssemblyHelper.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"

 #include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"

 #include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"

 namespace
 {
 inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
 {
     const int in_width    = input->info()->dimension(0);
     const int in_height   = input->info()->dimension(1);
     const int in_batches  = input->info()->dimension(3);
     const int in_channels = input->info()->dimension(2);
     return Tensor4DShape({ in_batches, in_height, in_width, in_channels });
 }
 } /* namespace */

 namespace arm_compute
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported");
     ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }

     // Get parameters from conv_info
     unsigned int stride_x = 0;
     unsigned int stride_y = 0;
     std::tie(stride_x, stride_y) = conv_info.stride();
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides.");

     ARM_COMPUTE_UNUSED(output);
     return Status{};
 }
 } //namespace

 NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
     : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr),
       _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(),
       _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false)
 {
 } /* arm_compute */

 void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
     ARM_COMPUTE_UNUSED(conv_info);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));

     _weights = weights;
     _input   = input;
     _output  = output;

     std::unique_ptr<INEWinogradLayerTransformInputKernel<float>>   transform_input_kernel;
     std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
     std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>>  transform_output_kernel;

     const int weights_width  = weights->info()->dimension(0);
     const int weights_height = weights->info()->dimension(1);

     int output_tile_rows = 0;
     int output_tile_cols = 0;
     int n_gemms          = 0;
     int N_BLOCK          = 0; // Size of block used by GEMM.

     switch(weights_width)
     {
         case 3:
         {
             transform_input_kernel   = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>>();
             transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>>();
             transform_output_kernel  = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>>();
             output_tile_rows         = 2;
             output_tile_cols         = 2;
             n_gemms                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradBase::N_GEMMS;
             N_BLOCK                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradConv::N_BLOCK;
             break;
         }
         case 5:
         {
             transform_input_kernel   = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>>();
             transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>>();
             transform_output_kernel  = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>>();
             output_tile_rows         = 2;
             output_tile_cols         = 2;
             n_gemms                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradBase::N_GEMMS;
             N_BLOCK                  = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradConv::N_BLOCK;
             break;
         }
         default:
         {
             ARM_COMPUTE_ERROR("Not supported.");
             break;
         }
     }

     const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID;
     const bool        use_same_padding = use_padding_type == PADDING_SAME;

     // Get parameters from conv_info
     unsigned int stride_x = 0;
     unsigned int stride_y = 0;
     std::tie(stride_x, stride_y) = conv_info.stride();
     ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides.");

     // Get convolved dimensions
     const int in_channels  = input->info()->dimension(2);
     const int out_channels = output->info()->dimension(2);

     const Tensor4DShape in_shape(internal_get_input_shape(input));
     const size_t        data_type_size = input->info()->element_size();
     // Get the memory required to instantiate a new Winograd operator.
     constexpr size_t storage_alignment   = 64;
     const size_t     kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
     _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _kernel_storage.allocator()->allocate();
     // Input storage
     const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
     _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _input_workspace.allocator()->allocate();

     // Output storage
     const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size;
     _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _output_workspace.allocator()->allocate();

     // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
     TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
                                 _output->info()->dimension(1), _output->info()->dimension(3)),
                     1, _output->info()->data_type());
     _output_nhwc.allocator()->init(info);
     _output_nhwc.allocator()->allocate();

     // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
     _permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U));
     _weights_hwio.allocator()->allocate();

     // configure the kernel to transform the input tensor from NCHW -> NHWC
     _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
     _input_nhwc.allocator()->allocate();

     const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });

     // Configure the InputTransform
     const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
     transform_input_kernel->configure(reinterpret_cast<float *>(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
                                       reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride);

     // Configure WeightsTransform
     const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape);
     transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);

     // Configure OutputTransform
     //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
     const int  output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
     const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type));

     transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
                                        output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
                                        in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);

     // Configure GEMM
     const int    tile_rows                = iceildiv(output_shape.n_rows, output_tile_rows);
     const int    tile_cols                = iceildiv(output_shape.n_cols, output_tile_cols);
     const int    m                        = in_shape.n_batches * tile_rows * tile_cols;
     const int    k                        = in_shape.n_channels;
     const int    n                        = out_channels;
     const int    input_matrix_row_stride  = in_shape.n_channels;
     const int    kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
     const int    output_matrix_row_stride = kernel_matrix_row_stride;
     unsigned int num_threads              = NEScheduler::get().num_threads();

     _arm_gemm = arm_gemm::gemm<float, float>(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false);
     _arm_gemm->set_arrays(reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast<float *>(_kernel_storage.buffer()),
                           kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast<float *>(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride);

     auto acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<arm_gemm::GemmCommon<float, float>>>();
     acl_gemm_wrapper->configure(_arm_gemm.get());
     const size_t workspace_size = _arm_gemm->get_working_size();

     // Allocate workspace
     if(workspace_size > 0)
     {
         const unsigned int alignment = 4096;
         allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1);
         _arm_gemm->set_working_space(reinterpret_cast<float *>(_workspace.buffer()));
     }

     const unsigned int window_size = _arm_gemm->get_window_size();
     if(window_size < num_threads)
     {
         num_threads = window_size;
         _arm_gemm->set_nthreads(num_threads);
     }

     _gemm_kernel = std::move(acl_gemm_wrapper);

     // Reorder the convoluted output to ACL's ordering NCHW
     _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));

     _transform_input_kernel   = std::move(transform_input_kernel);
     _transform_weights_kernel = std::move(transform_weights_kernel);
     _transform_output_kernel  = std::move(transform_output_kernel);

     //Configure Activation Layer
     _is_activationlayer_enabled = act_info.enabled();
     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.configure(output, nullptr, act_info);
     }
 }

 void NEWinogradConvolutionLayer::run()
 {
     _memory_group.acquire();
     if(!_reshaped_kernel)
     {
         _reshaped_kernel = true;
         _permute_weights.run();
         NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
     }
     //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
     _permute_input.run();

     // Transform input tensor to the winograd domain
     NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);

     //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX);

     // Transform output tensor to the spatial domain
     NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);

     // Reorder the convoluted output to ACL's ordering NCHW
     _permute_output.run();

     if(_is_activationlayer_enabled)
     {
         _activationlayer_function.run();
     }
     _memory_group.release();
 }

 Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                             const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));

     // Get indices for the width and height
     const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
     // Input shape
     const TensorShape input_shape = input->tensor_shape();

     // Kernel size
     const unsigned int kernel_w = weights->tensor_shape()[idx_width];
     const unsigned int kernel_h = weights->tensor_shape()[idx_height];

     const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2),
                                                     Size2D(kernel_w, kernel_h),
                                                     Size2D(input_shape[idx_width], input_shape[idx_height]),
                                                     conv_info,
                                                     input->data_layout());

     // Validate input transform
     const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
     const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
     switch(weights->dimension(0))
     {
         case 3:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info)));
             break;
         }
         case 5:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info)));
             break;
         }
         default:
         {
             ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
             break;
         }
     }
     // Validate filter transform
     const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
     const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);

     switch(weights->dimension(0))
     {
         case 3:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info)));
             break;
         }
         case 5:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info)));
             break;
         }
         default:
         {
             ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
             break;
         }
     }
     // Validate batched matrix multiply
     TensorShape batched_mm_output_shape = input0.tensor_shape();
     batched_mm_output_shape[0]          = input1.tensor_shape()[0];
     const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
     switch(weights->dimension(0))
     {
         case 3:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
                                                                                                               true /* Reshape weights only for the first run*/))));
             // Validate output transform
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
             break;
         }
         case 5:
         {
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
                                                                                                               true /* Reshape weights only for the first run*/))));
             // Validate output transform
             ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info)));
             break;
         }
         default:
         {
             ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
             break;
         }
     }

     // Validate Activation Layer
     if(act_info.enabled())
     {
         NEActivationLayer::validate(output, nullptr, act_info);
     }
     return Status{};
 }

 } // 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/runtime/NEON/functions/NEWinogradConvolutionLayer.h"

	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/runtime/NEON/AssemblyHelper.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"
	#include "support/ToolchainSupport.h"

	#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h"

	#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"

	namespace
	{
	inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
	{
	const int in_width = input->info()->dimension(0);
	const int in_height = input->info()->dimension(1);
	const int in_batches = input->info()->dimension(3);
	const int in_channels = input->info()->dimension(2);
	return Tensor4DShape({ in_batches, in_height, in_width, in_channels });
	}
	} /* namespace */

	namespace arm_compute
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const PadStrideInfo &conv_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported");
	ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);

	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
	ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
	}

	// Get parameters from conv_info
	unsigned int stride_x = 0;
	unsigned int stride_y = 0;
	std::tie(stride_x, stride_y) = conv_info.stride();
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 \|\| stride_x != 1, "Winograd layer only supports unit strides.");

	ARM_COMPUTE_UNUSED(output);
	return Status{};
	}
	} //namespace

	NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
	: _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr),
	_activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(),
	_workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false)
	{
	} /* arm_compute */

	void NEWinogradConvolutionLayer::configure(const ITensor input, const ITensor weights, const ITensor biases, ITensor output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
	ARM_COMPUTE_UNUSED(conv_info);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info));

	_weights = weights;
	_input = input;
	_output = output;

	std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel;
	std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel;
	std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel;

	const int weights_width = weights->info()->dimension(0);
	const int weights_height = weights->info()->dimension(1);

	int output_tile_rows = 0;
	int output_tile_cols = 0;
	int n_gemms = 0;
	int N_BLOCK = 0; // Size of block used by GEMM.

	switch(weights_width)
	{
	case 3:
	{
	transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>>();
	transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>>();
	transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>>();
	output_tile_rows = 2;
	output_tile_cols = 2;
	n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradBase::N_GEMMS;
	N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradConv::N_BLOCK;
	break;
	}
	case 5:
	{
	transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>>();
	transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>>();
	transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>>();
	output_tile_rows = 2;
	output_tile_cols = 2;
	n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradBase::N_GEMMS;
	N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradConv::N_BLOCK;
	break;
	}
	default:
	{
	ARM_COMPUTE_ERROR("Not supported.");
	break;
	}
	}

	const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID;
	const bool use_same_padding = use_padding_type == PADDING_SAME;

	// Get parameters from conv_info
	unsigned int stride_x = 0;
	unsigned int stride_y = 0;
	std::tie(stride_x, stride_y) = conv_info.stride();
	ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 \|\| stride_x != 1, "Winograd layer only supports unit strides.");

	// Get convolved dimensions
	const int in_channels = input->info()->dimension(2);
	const int out_channels = output->info()->dimension(2);

	const Tensor4DShape in_shape(internal_get_input_shape(input));
	const size_t data_type_size = input->info()->element_size();
	// Get the memory required to instantiate a new Winograd operator.
	constexpr size_t storage_alignment = 64;
	const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
	_kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
	_kernel_storage.allocator()->allocate();
	// Input storage
	const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
	_input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
	_input_workspace.allocator()->allocate();

	// Output storage
	const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size;
	_output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
	_output_workspace.allocator()->allocate();

	// configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
	TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0),
	_output->info()->dimension(1), _output->info()->dimension(3)),
	1, _output->info()->data_type());
	_output_nhwc.allocator()->init(info);
	_output_nhwc.allocator()->allocate();

	// Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
	_permute_weights.configure(weights, &_weights_hwio, PermutationVector(3U, 2U, 0U, 1U));
	_weights_hwio.allocator()->allocate();

	// configure the kernel to transform the input tensor from NCHW -> NHWC
	_permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
	_input_nhwc.allocator()->allocate();

	const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });

	// Configure the InputTransform
	const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
	transform_input_kernel->configure(reinterpret_cast<float *>(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
	reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride);

	// Configure WeightsTransform
	const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape);
	transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels);

	// Configure OutputTransform
	//The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
	const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type);
	const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type));

	transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
	output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
	in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);

	// Configure GEMM
	const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows);
	const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols);
	const int m = in_shape.n_batches * tile_rows * tile_cols;
	const int k = in_shape.n_channels;
	const int n = out_channels;
	const int input_matrix_row_stride = in_shape.n_channels;
	const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
	const int output_matrix_row_stride = kernel_matrix_row_stride;
	unsigned int num_threads = NEScheduler::get().num_threads();

	_arm_gemm = arm_gemm::gemm<float, float>(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false);
	_arm_gemm->set_arrays(reinterpret_cast<float >(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast<float >(_kernel_storage.buffer()),
	kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast<float *>(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride);

	auto acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<arm_gemm::GemmCommon<float, float>>>();
	acl_gemm_wrapper->configure(_arm_gemm.get());
	const size_t workspace_size = _arm_gemm->get_working_size();

	// Allocate workspace
	if(workspace_size > 0)
	{
	const unsigned int alignment = 4096;
	allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1);
	_arm_gemm->set_working_space(reinterpret_cast<float *>(_workspace.buffer()));
	}

	const unsigned int window_size = _arm_gemm->get_window_size();
	if(window_size < num_threads)
	{
	num_threads = window_size;
	_arm_gemm->set_nthreads(num_threads);
	}

	_gemm_kernel = std::move(acl_gemm_wrapper);

	// Reorder the convoluted output to ACL's ordering NCHW
	_permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));

	_transform_input_kernel = std::move(transform_input_kernel);
	_transform_weights_kernel = std::move(transform_weights_kernel);
	_transform_output_kernel = std::move(transform_output_kernel);

	//Configure Activation Layer
	_is_activationlayer_enabled = act_info.enabled();
	if(_is_activationlayer_enabled)
	{
	_activationlayer_function.configure(output, nullptr, act_info);
	}
	}

	void NEWinogradConvolutionLayer::run()
	{
	_memory_group.acquire();
	if(!_reshaped_kernel)
	{
	_reshaped_kernel = true;
	_permute_weights.run();
	NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
	}
	//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
	_permute_input.run();

	// Transform input tensor to the winograd domain
	NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);

	//Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
	NEScheduler::get().schedule(_gemm_kernel.get(), Window::DimX);

	// Transform output tensor to the spatial domain
	NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);

	// Reorder the convoluted output to ACL's ordering NCHW
	_permute_output.run();

	if(_is_activationlayer_enabled)
	{
	_activationlayer_function.run();
	}
	_memory_group.release();
	}

	Status NEWinogradConvolutionLayer::validate(const ITensorInfo input, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo output, const PadStrideInfo &conv_info,
	const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info));

	// Get indices for the width and height
	const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
	const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
	// Input shape
	const TensorShape input_shape = input->tensor_shape();

	// Kernel size
	const unsigned int kernel_w = weights->tensor_shape()[idx_width];
	const unsigned int kernel_h = weights->tensor_shape()[idx_height];

	const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2),
	Size2D(kernel_w, kernel_h),
	Size2D(input_shape[idx_width], input_shape[idx_height]),
	conv_info,
	input->data_layout());

	// Validate input transform
	const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
	const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape);
	switch(weights->dimension(0))
	{
	case 3:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info)));
	break;
	}
	case 5:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info)));
	break;
	}
	default:
	{
	ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
	break;
	}
	}
	// Validate filter transform
	const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
	const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape);

	switch(weights->dimension(0))
	{
	case 3:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info)));
	break;
	}
	case 5:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info)));
	break;
	}
	default:
	{
	ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
	break;
	}
	}
	// Validate batched matrix multiply
	TensorShape batched_mm_output_shape = input0.tensor_shape();
	batched_mm_output_shape[0] = input1.tensor_shape()[0];
	const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape);
	switch(weights->dimension(0))
	{
	case 3:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
	true /* Reshape weights only for the first run*/))));
	// Validate output transform
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
	break;
	}
	case 5:
	{
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false,
	true /* Reshape weights only for the first run*/))));
	// Validate output transform
	ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info)));
	break;
	}
	default:
	{
	ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
	break;
	}
	}

	// Validate Activation Layer
	if(act_info.enabled())
	{
	NEActivationLayer::validate(output, nullptr, act_info);
	}
	return Status{};
	}

	} // namespace arm_compute