text: /*
   * Copyright (c) 2017-2021 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/NEWinogradConvolutionLayerKernel.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/Validate.h"
  #include "arm_compute/core/Window.h"
  #include "arm_compute/core/utils/misc/ShapeCalculator.h"
  #include "src/core/NEON/kernels/convolution/common/utils.hpp"
  #include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
  #include "src/core/helpers/AutoConfiguration.h"
  #include "src/core/helpers/WindowHelpers.h"
  
  #include <memory>
  
  namespace arm_compute
  {
  //Batched Gemms
  
  namespace
  {
  inline bool is_kernel_size_supported(DataType data_type, Size2D size)
  {
      const std::array<Size2D, 8> f32_support = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } };
      const std::array<Size2D, 8> f16_support = { { Size2D(3, 3) } };
  
      switch(data_type)
      {
          case DataType::F16:
              return std::end(f16_support) != std::find(std::begin(f16_support), std::end(f16_support), size);
          case DataType::F32:
              return std::end(f32_support) != std::find(std::begin(f32_support), std::end(f32_support), size);
          default:
              return false;
      }
  }
  
  Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
  
      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);
      const auto   input_width  = input->dimension(idx_width);
      const auto   input_height = input->dimension(idx_height);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(input_width, input_height)),
                                      "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported");
      ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
      const Size2D &output_tile = winograd_info.output_tile_size;
      const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } };
      ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile));
  
      // Checks performed when output is configured
      if(output->total_size() != 0)
      {
          const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info));
  
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
      }
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window_winograd_weight_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      // Output tensor auto inizialitation if not yet initialized
      auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_filter_transform_shape(*input, winograd_info)));
      const Window win = calculate_max_window(*input, Steps(), true /* skip border*/);
      return std::make_pair(Status{}, win);
  }
  
  Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      const Size2D        &kernel_dims = winograd_info.kernel_size;
      const PadStrideInfo &conv_info   = winograd_info.convolution_info;
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
                                      "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
  
      // Validate configured output
      if(output->total_size() != 0)
      {
          const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
  
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
      }
  
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window_winograd_input_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
      // Output auto inizialitation if not yet initialized
      auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape));
      return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
  }
  
  Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      const PadStrideInfo &conv_info   = winograd_info.convolution_info;
      const Size2D         kernel_dims = winograd_info.kernel_size;
  
      // Number of tiles along the X and Y direction
      const unsigned int num_tiles_x = std::ceil((winograd_info.input_dimensions.x() - (kernel_dims.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>
                                                 (winograd_info.output_tile_size.width));
      const unsigned int num_tiles_y = std::ceil((winograd_info.input_dimensions.y() - (kernel_dims.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>
                                                 (winograd_info.output_tile_size.height));
      const Size2D       num_tiles   = Size2D(num_tiles_x, num_tiles_y);
  
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area());
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(kernel_dims.width, kernel_dims.height)),
                                      "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
  
      const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } };
      ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2)));
      ARM_COMPUTE_UNUSED(kernel_dims);
      if(bias != nullptr)
      {
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, bias);
          ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0));
          ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != size_t(1));
      }
  
      // Checks performed when output is configured
      if(output->total_size() != 0)
      {
          const TensorInfo tensor_info_output = input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info));
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
          ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
      }
      return Status{};
  }
  
  std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      // Output tensor auto initialization if not yet initialized
      auto_init_if_empty(*output, input->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_winograd_output_transform_shape(*input, winograd_info)));
  
      return std::make_pair(Status{}, calculate_max_window(*input, Steps(), true));
  }
  } // namespace
  
  Status INEWinogradLayerTransformWeightsKernel::validate(const ITensorInfo *input, const ITensorInfo *weights)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
      const DataLayout   data_layout = input->data_layout();
      const unsigned int width_idx   = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
      const unsigned int height_idx  = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
      ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(input->data_type(), Size2D(weights->dimension(width_idx), weights->dimension(height_idx))),
                                      "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
      ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
      return Status{};
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
  {
      const KernelShape shape(num_output_channels, KernelRows, KernelCols, num_input_channels);
      return static_cast<unsigned int>(
                 // WinogradConv returns the size in bytes, we divide by `sizeof(T)` to express that in units of T
                 WinogradConv::get_kernel_storage_size(num_input_channels, num_output_channels) / sizeof(T));
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformWeightsKernel()
      : _transform(nullptr), _weights_hwio(nullptr), _output(nullptr), _matrix_stride(0), _num_output_channels(0), _num_input_channels(0)
  {
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(int num_output_channels, int num_input_channels) const
  {
      return WinogradConv::get_kernel_matrix_stride(num_input_channels, num_output_channels);
  }
  
  #ifndef DOXYGEN_SKIP_THIS
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
      const ITensor *weights_hwio,
      ITensor       *output,
      const int      matrix_stride,       /** Stride across matrices in the output. */
      const int      num_output_channels, /** Number of filters. */
      const int      num_input_channels)  /** Number of channels in each filter. */
  {
      _weights_hwio        = weights_hwio;
      _output              = output;
      _matrix_stride       = matrix_stride;
      _num_output_channels = num_output_channels;
      _num_input_channels  = num_input_channels;
      _transform           = std::make_unique<WeightsTransform>(num_output_channels, num_input_channels);
  
      Window win;
      auto   win_last = _transform->get_window();
      win.set(Window::DimX, Window::Dimension(0, win_last, 1));
      INEKernel::configure(win);
  }
  #endif /* DOXYGEN_SKIP_THIS */
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformWeightsKernel<T, 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->set_weight_tensor(_weights_hwio->buffer());
      const int matrix_row_stride = roundup(_num_output_channels, WinogradConv::N_BLOCK);
      _transform->set_output_matrices(_output->buffer(), _matrix_stride, matrix_row_stride);
      _transform->set_working_space(_output->buffer());
  
      _transform->run(fst, lst);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  bool NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::is_parallelisable() const
  {
      return false;
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output,
                                                                                                                    const WinogradInfo &winograd_info)
  {
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_weight_trans(input, output, winograd_info));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_weight_trans(input->clone().get(), output->clone().get(), winograd_info).first);
      return Status{};
  }
  
  template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>;
  template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>;
  template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>;
  template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>;
  template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>;
  
  template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>;
  template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>;
  template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>;
  template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>;
  
  #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  template class NEWinogradLayerTransformWeightsKernel<__fp16, 4, 4, 3, 3>;
  #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  
  // Input transform
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_input_storage_size(
      int  num_batches,  /* Number of batches in the input tensor. */
      int  num_channels, /* Number of feature maps in the input tensor. */
      int  num_rows,     /* Number of rows in each feature map. */
      int  num_cols,     /* Number of columns in each feature map. */
      bool same_padding  /* Use "SAME" padding, otherwise use "VALID". */
  ) const
  {
      // Construct shapes for the input and kernel tensors.
      const Tensor4DShape input_shape(num_batches, num_rows, num_cols, num_channels);
      const KernelShape   kern_shape(1, KernelRows, KernelCols, num_channels);
      // Return the size, converted into units of TIn
      return static_cast<unsigned int>(WinogradConv::get_input_storage_size(num_batches, num_rows, num_cols, num_channels, same_padding) / sizeof(T));
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  unsigned int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
  {
      return _transform->get_working_space_size(num_threads) / sizeof(T);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  int NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
      int  num_batches,  /* Number of batches in the input tensor. */
      int  num_channels, /* Number of feature maps in the input tensor. */
      int  num_rows,     /* Number of rows in each feature map. */
      int  num_cols,     /* Number of columns in each feature map. */
      bool same_padding /* Use "SAME" padding, otherwise use "VALID". */) const
  {
      return WinogradConv::get_input_matrix_stride(num_batches, num_rows, num_cols, num_channels, same_padding);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformInputKernel()
      : _transform(nullptr), _input_nhwc(nullptr), _num_batches(0), _num_rows(0), _num_cols(0), _num_channels(0), _padding(), _output(nullptr), _matrix_stride(0), _padding_top(), _padding_left(),
        _padding_right(), _padding_bottom(), _workspace(nullptr)
  {
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
      const ITensor    *input_nhwc,
      const int         num_batches,   /* Number of batches in input tensor. */
      const int         num_rows,      /* Number of rows in input tensor. */
      const int         num_cols,      /* Number of columns in input tensor. */
      const int         num_channels,  /* Number of channels in input tensor. */
      const PaddingType padding,       /* Padding type. */
      ITensor          *output,        /* Base of output matrices. */
      const int         matrix_stride, /* Stride between output matrices. */
      ITensor          *workspace)
  {
      _input_nhwc    = input_nhwc;
      _num_batches   = num_batches;
      _num_rows      = num_rows;
      _num_cols      = num_cols;
      _num_channels  = num_channels;
      _padding       = padding;
      _output        = output;
      _matrix_stride = matrix_stride;
      _workspace     = workspace;
  
      _padding_top    = (padding == PADDING_SAME) ? (KernelRows - 1) / 2 : 0;
      _padding_left   = (padding == PADDING_SAME) ? (KernelCols - 1) / 2 : 0;
      _padding_bottom = (padding == PADDING_SAME) ? iceildiv(KernelRows - 1, 2) : 0;
      _padding_right  = (padding == PADDING_SAME) ? iceildiv(KernelCols - 1, 2) : 0;
  
      _transform = std::make_unique<InputTransform>(
                       KernelRows,
                       KernelCols,
                       num_batches,
                       num_rows,
                       num_cols,
                       num_channels,
                       _padding_top,    /**< Padding to apply to the top of the image. */
                       _padding_left,   /**< Padding to apply to the left of the image. */
                       _padding_bottom, /**< Padding to apply to the bottom of the image. */
                       _padding_right   /**< Padding to apply to the right of the image. */
                   );
  
      Window win;
      auto   win_last = _transform->get_window();
      win.set(Window::DimX, Window::Dimension(0, win_last, 1));
      INEKernel::configure(win);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformInputKernel<T, 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(_workspace);
  
      const int  element_size_in_bytes = _input_nhwc->info()->element_size();
      const int  input_col_stride      = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
      const int  input_row_stride      = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
      const int  input_batch_stride    = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
      const auto input_nhwc_ptr        = reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes());
      auto       output_ptr            = reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes());
      ARM_COMPUTE_ERROR_ON_NULLPTR(output_ptr);
  
      _transform->set_input_tensor(input_nhwc_ptr, input_batch_stride, input_row_stride, input_col_stride);
      _transform->set_output_matrices(output_ptr, _matrix_stride, _num_channels);
  
      _transform->set_working_space(_workspace->buffer());
  
      // 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();
      _transform->run(fst, lst, info.thread_id);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
  {
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_input_trans(input, output, winograd_info));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_input_trans(input->clone().get(), output->clone().get(), winograd_info).first);
  
      return Status{};
  }
  
  template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>;
  template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>;
  template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>;
  template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>;
  template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>;
  
  template class NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>;
  template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>;
  template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>;
  template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>;
  
  #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  template class NEWinogradLayerTransformInputKernel<__fp16, 4, 4, 3, 3>;
  #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  
  // Output transform
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_storage_size(
      int num_batches,        /* Number of batches in the output tensor. */
      int num_rows,           /* Number of rows in each feature map of the input tensor. */
      int num_cols,           /* Number of columns in each feature map of the input tensor. */
      int num_output_channels /* Number of feature maps in the output tensor. */
  ) const
  {
      // Construct shapes for the input and kernel tensors.
      const Tensor4DShape input_shape(num_batches, num_rows, num_cols, 1);
      const KernelShape   kern_shape(num_output_channels, KernelRows, KernelCols, 1);
      // Return the size, converted into units of TOut
      return static_cast<unsigned int>(
                 WinogradConv::get_output_storage_size(num_batches, num_rows, num_cols, num_output_channels) / sizeof(T));
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::NEWinogradLayerTransformOutputKernel()
      : _transform(nullptr), _biases(nullptr), _transformed_output(nullptr), _workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output_nhwc(nullptr), _num_batches(0), _num_rows(0),
        _num_cols(0), _num_channels(0)
  {
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  unsigned int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_working_space_size(unsigned int num_threads) const
  {
      return _transform->get_working_space_size(num_threads) / sizeof(T);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  int NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_matrix_stride(
      int num_batches,        /* Number of batches in the output tensor. */
      int num_rows,           /* Number of rows in each feature map of the input tensor. */
      int num_cols,           /* Number of columns in each feature map of the input tensor. */
      int num_output_channels /* Number of feature maps in the output tensor. */
  ) const
  {
      return WinogradConv::get_output_matrix_stride(num_batches, num_rows, num_cols, num_output_channels);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  std::pair<unsigned int, unsigned int> NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_output_shape(
      int  num_rows, /* Number of rows in each feature map of the input tensor. */
      int  num_cols, /* Number of columns in each feature map of the input tensor. */
      bool padding_same) const
  {
      return WinogradConv::get_output_shape(std::make_pair<unsigned int, unsigned int>(num_rows, num_cols), padding_same);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::configure(
      const ITensor              *biases,
      const ITensor              *transformed_output,
      const int                   matrix_stride,
      ITensor                    *output_nhwc,
      const int                   num_batches,
      const int                   num_rows,
      const int                   num_cols,
      const int                   num_channels,
      ITensor                    *workspace,
      const arm_gemm::Activation &activation)
  {
      _biases             = biases;
      _workspace          = workspace;
      _transformed_output = transformed_output;
      _matrix_stride      = matrix_stride;
      _matrix_row_stride  = roundup(num_channels, WinogradConv::N_BLOCK);
      _output_nhwc        = output_nhwc;
      _num_batches        = num_batches;
      _num_rows           = num_rows;
      _num_cols           = num_cols;
      _num_channels       = num_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
      _transform = std::make_unique<OutputTransform>(num_batches, num_rows, num_cols, num_channels, activation);
      Window win;
      auto   win_last = _transform->get_window();
      win.set(Window::DimX, Window::Dimension(0, win_last, 1));
  
      INEKernel::configure(win);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  void NEWinogradLayerTransformOutputKernel<T, 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(_workspace);
      ARM_COMPUTE_ERROR_ON_NULLPTR(_transformed_output);
      ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc);
  
      const int out_batch_stride = _output_nhwc->info()->strides_in_bytes()[3] / sizeof(T);
      const int out_row_stride   = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
      const int out_col_stride   = _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
  
      _transform->set_input_matrices(_transformed_output->buffer(), _matrix_stride, _matrix_row_stride);
      _transform->set_bias((_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr));
      _transform->set_output_tensor(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes(), out_batch_stride, out_row_stride, out_col_stride);
      _transform->set_working_space(_workspace->buffer());
      // 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();
      _transform->run(fst, lst, info.thread_id);
  }
  
  template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
  Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
                                                                                                                   const WinogradInfo &winograd_info)
  {
      ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_winograd_output_trans(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
      ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_winograd_output_trans(input->clone().get(), output->clone().get(), winograd_info).first);
  
      return Status{};
  }
  
  template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>;
  template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>;
  template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>;
  template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>;
  template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>;
  
  template class NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>;
  template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>;
  template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>;
  template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>;
  
  #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  template class NEWinogradLayerTransformOutputKernel<__fp16, 4, 4, 3, 3>;
  #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
  } // namespace arm_compute
  