src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2023 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/gpu/cl/kernels/ClWinogradInputTransformKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/CL/OpenCL.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "src/core/AccessWindowStatic.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace opencl
 {
 namespace kernels
 {
 namespace
 {
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);

     const PadStrideInfo conv_info        = winograd_info.convolution_info;
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const Size2D        kernel_size      = winograd_info.kernel_size;
     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(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd input transform not supported");

     ARM_COMPUTE_UNUSED(conv_info);
     ARM_COMPUTE_UNUSED(output_tile_size);
     ARM_COMPUTE_UNUSED(kernel_size);

     // 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(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_UNUSED(output);
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

     bool window_changed                    = false;
     int  num_elems_processed_per_iteration = 1;

     if(input->data_layout() == DataLayout::NHWC)
     {
         // In the case of FP16 computation, we can perform more
         // output feature maps in a single work-item.
         // From experiments, num_elems_processed_per_iteration = 2 looks good for fp16 to
         // improve the performance. However, in order to make the implementation simpler,
         // we set num_elems_processed_per_iteration = 2 only when the OFMs are multiple of 2.
         // Note: At the moment, only Winograd Input Transform 3x3 can support N0 != 1
         const DataType dt   = input->data_type();
         const size_t   dim0 = input->dimension(0);
         const size_t   k_sz = winograd_info.kernel_size.area();
         const bool     cond = dt == DataType::F16 && ((dim0 % 2) == 0);
         if(cond)
         {
             if(k_sz == 3 || k_sz == 9)
             {
                 num_elems_processed_per_iteration = 2;
             }
         }
     }
     Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));

     if(input->data_layout() == DataLayout::NCHW)
     {
         const PadStrideInfo conv_info        = winograd_info.convolution_info;
         const Size2D        output_tile_size = winograd_info.output_tile_size;
         const Size2D        kernel_size      = winograd_info.kernel_size;

         unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1;
         unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1;

         AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y);
         window_changed = update_window_and_padding(win, input_access);
     }

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
 } // namespace

 ClWinogradInputTransformKernel::ClWinogradInputTransformKernel()
 {
     _type = CLKernelType::WINOGRAD;
 }

 BorderSize ClWinogradInputTransformKernel::border_size() const
 {
     return _border_size;
 }

 void ClWinogradInputTransformKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, winograd_info));

     auto padding_info = get_padding_info({ src, dst });

     const PadStrideInfo conv_info        = winograd_info.convolution_info;
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const Size2D        kernel_size      = winograd_info.kernel_size;

     _data_layout = src->data_layout();

     const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
     const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);

     // Compute the number of output tiles along the x and y direction of size "output_tile_size"
     const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(src->dimension(idx_w), src->dimension(idx_h)),
                                                                 kernel_size,
                                                                 output_tile_size,
                                                                 conv_info);

     _num_tiles_x = num_tiles.width;
     _num_tiles_y = num_tiles.height;

     const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*dst, src->clone()->set_tensor_shape(output_shape));

     ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(dst->dimension(1)));
     const size_t total_batches = src->tensor_shape().total_size_upper(3);

     // Create window and update padding
     auto win_config = validate_and_configure_window(src, dst, winograd_info);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     IClKernel::configure_internal(win_config.second, cl::NDRange(1, 1, 8));

     _src_width  = src->dimension(idx_w);
     _src_height = src->dimension(idx_h);

     CLBuildOptions build_opts;
     if(_data_layout == DataLayout::NHWC)
     {
         build_opts.add_option("-DNHWC");
         build_opts.add_option("-DN0=" + support::cpp11::to_string(win_config.second.x().step()));
         build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
         build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
         build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
         build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
         build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
         build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
         build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
         build_opts.add_option_if(total_batches > 1, "-DIS_BATCHED");
     }
     else
     {
         build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
         build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
         build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
         build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
         build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
         build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
         build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
         build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
         build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(2)));
     }

     // Create kernel
     std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();

     // Get the maximum dimension from the tile size
     const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);

     // Check optimized kernel if output_dims == 2x2
     if((tile_max_dim == 2) && (_data_layout == DataLayout::NCHW))
     {
         _step_z = (src->dimension(2) % 2) != 0 ? 1 : 2;
     }

     // Append stepz and data layout
     kernel_name += "_stepz";
     kernel_name += support::cpp11::to_string(_step_z);
     kernel_name += "_" + lower_string(string_from_data_layout(_data_layout));

     // A macro guard to compile ONLY the kernel of interest
     build_opts.add_option("-D" + upper_string(kernel_name));
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

     _border_size = BorderSize(src->padding());

     ARM_COMPUTE_ERROR_ON((src->data_layout() == DataLayout::NHWC) && has_padding_changed(padding_info));

     _config_id = kernel_name;
     _config_id += support::cpp11::to_string(src->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(src->dimension(1));
     _config_id += "_";
     _config_id += support::cpp11::to_string(src->dimension(2));
     _config_id += "_";
     _config_id += support::cpp11::to_string(conv_info.pad_left());
     _config_id += "_";
     _config_id += support::cpp11::to_string(conv_info.pad_top());
     _config_id += "_";
     _config_id += lower_string(string_from_data_layout(_data_layout));
 }

 Status ClWinogradInputTransformKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, winograd_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), winograd_info).first);
     return Status{};
 }

 void ClWinogradInputTransformKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     const size_t idx_w         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
     const size_t idx_h         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
     const size_t idx_c         = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
     const size_t total_batches = window.shape().total_size_upper(3);

     // Collapse window
     Window window_collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ);

     if(_data_layout == DataLayout::NHWC)
     {
         Window slice = window_collapsed.first_slice_window_3D();
         slice.set(1, Window::Dimension(0, _num_tiles_x * _num_tiles_y, 1));
         slice.set(2, Window::Dimension(0, total_batches, 1));

         unsigned int idx = 0;
         add_4D_tensor_argument(idx, src, slice);
         add_4D_tensor_argument(idx, dst, slice);
         _kernel.setArg<cl_uint>(idx++, _src_width);
         _kernel.setArg<cl_uint>(idx++, _src_height);
         _kernel.setArg<cl_uint>(idx++, _num_tiles_x);
         _kernel.setArg<cl_uint>(idx++, _num_tiles_y);
         enqueue(queue, *this, slice, lws_hint());
     }
     else
     {
         Window slice = window_collapsed.first_slice_window_3D();
         slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1));
         slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1));

         ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0);
         slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z));

         unsigned int idx = 2 * num_arguments_per_3D_tensor();
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3]));
         _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[3]));

         do
         {
             unsigned int idx = 0;
             add_3D_tensor_argument(idx, src, slice);
             add_3D_tensor_argument(idx, dst, slice);

             enqueue(queue, *this, slice, lws_hint());
         }
         while(window_collapsed.slide_window_slice_3D(slice));
     }
 }
 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2023 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/gpu/cl/kernels/ClWinogradInputTransformKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/CL/OpenCL.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Helpers.h"
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "src/core/AccessWindowStatic.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/Cast.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace opencl
	{
	namespace kernels
	{
	namespace
	{
	Status validate_arguments(const ITensorInfo input, const ITensorInfo output, const WinogradInfo &winograd_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);

	const PadStrideInfo conv_info = winograd_info.convolution_info;
	const Size2D output_tile_size = winograd_info.output_tile_size;
	const Size2D kernel_size = winograd_info.kernel_size;
	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(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd input transform not supported");

	ARM_COMPUTE_UNUSED(conv_info);
	ARM_COMPUTE_UNUSED(output_tile_size);
	ARM_COMPUTE_UNUSED(kernel_size);

	// 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(ITensorInfo input, ITensorInfo output, const WinogradInfo &winograd_info)
	{
	ARM_COMPUTE_UNUSED(output);
	ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);

	bool window_changed = false;
	int num_elems_processed_per_iteration = 1;

	if(input->data_layout() == DataLayout::NHWC)
	{
	// In the case of FP16 computation, we can perform more
	// output feature maps in a single work-item.
	// From experiments, num_elems_processed_per_iteration = 2 looks good for fp16 to
	// improve the performance. However, in order to make the implementation simpler,
	// we set num_elems_processed_per_iteration = 2 only when the OFMs are multiple of 2.
	// Note: At the moment, only Winograd Input Transform 3x3 can support N0 != 1
	const DataType dt = input->data_type();
	const size_t dim0 = input->dimension(0);
	const size_t k_sz = winograd_info.kernel_size.area();
	const bool cond = dt == DataType::F16 && ((dim0 % 2) == 0);
	if(cond)
	{
	if(k_sz == 3 \|\| k_sz == 9)
	{
	num_elems_processed_per_iteration = 2;
	}
	}
	}
	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));

	if(input->data_layout() == DataLayout::NCHW)
	{
	const PadStrideInfo conv_info = winograd_info.convolution_info;
	const Size2D output_tile_size = winograd_info.output_tile_size;
	const Size2D kernel_size = winograd_info.kernel_size;

	unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1;
	unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1;

	AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y);
	window_changed = update_window_and_padding(win, input_access);
	}

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}
	} // namespace

	ClWinogradInputTransformKernel::ClWinogradInputTransformKernel()
	{
	_type = CLKernelType::WINOGRAD;
	}

	BorderSize ClWinogradInputTransformKernel::border_size() const
	{
	return _border_size;
	}

	void ClWinogradInputTransformKernel::configure(const ClCompileContext &compile_context, ITensorInfo src, ITensorInfo dst, const WinogradInfo &winograd_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, winograd_info));

	auto padding_info = get_padding_info({ src, dst });

	const PadStrideInfo conv_info = winograd_info.convolution_info;
	const Size2D output_tile_size = winograd_info.output_tile_size;
	const Size2D kernel_size = winograd_info.kernel_size;

	_data_layout = src->data_layout();

	const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
	const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);

	// Compute the number of output tiles along the x and y direction of size "output_tile_size"
	const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(src->dimension(idx_w), src->dimension(idx_h)),
	kernel_size,
	output_tile_size,
	conv_info);

	_num_tiles_x = num_tiles.width;
	_num_tiles_y = num_tiles.height;

	const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*dst, src->clone()->set_tensor_shape(output_shape));

	ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(dst->dimension(1)));
	const size_t total_batches = src->tensor_shape().total_size_upper(3);

	// Create window and update padding
	auto win_config = validate_and_configure_window(src, dst, winograd_info);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	IClKernel::configure_internal(win_config.second, cl::NDRange(1, 1, 8));

	_src_width = src->dimension(idx_w);
	_src_height = src->dimension(idx_h);

	CLBuildOptions build_opts;
	if(_data_layout == DataLayout::NHWC)
	{
	build_opts.add_option("-DNHWC");
	build_opts.add_option("-DN0=" + support::cpp11::to_string(win_config.second.x().step()));
	build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
	build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
	build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
	build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
	build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
	build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
	build_opts.add_option_if(total_batches > 1, "-DIS_BATCHED");
	}
	else
	{
	build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
	build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
	build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
	build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
	build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type()));
	build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
	build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
	build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(2)));
	}

	// Create kernel
	std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();

	// Get the maximum dimension from the tile size
	const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);

	// Check optimized kernel if output_dims == 2x2
	if((tile_max_dim == 2) && (_data_layout == DataLayout::NCHW))
	{
	_step_z = (src->dimension(2) % 2) != 0 ? 1 : 2;
	}

	// Append stepz and data layout
	kernel_name += "_stepz";
	kernel_name += support::cpp11::to_string(_step_z);
	kernel_name += "_" + lower_string(string_from_data_layout(_data_layout));

	// A macro guard to compile ONLY the kernel of interest
	build_opts.add_option("-D" + upper_string(kernel_name));
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	_border_size = BorderSize(src->padding());

	ARM_COMPUTE_ERROR_ON((src->data_layout() == DataLayout::NHWC) && has_padding_changed(padding_info));

	_config_id = kernel_name;
	_config_id += support::cpp11::to_string(src->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src->dimension(2));
	_config_id += "_";
	_config_id += support::cpp11::to_string(conv_info.pad_left());
	_config_id += "_";
	_config_id += support::cpp11::to_string(conv_info.pad_top());
	_config_id += "_";
	_config_id += lower_string(string_from_data_layout(_data_layout));
	}

	Status ClWinogradInputTransformKernel::validate(const ITensorInfo src, const ITensorInfo dst, const WinogradInfo &winograd_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, winograd_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), winograd_info).first);
	return Status{};
	}

	void ClWinogradInputTransformKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
	const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
	const size_t idx_c = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
	const size_t total_batches = window.shape().total_size_upper(3);

	// Collapse window
	Window window_collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ);

	if(_data_layout == DataLayout::NHWC)
	{
	Window slice = window_collapsed.first_slice_window_3D();
	slice.set(1, Window::Dimension(0, _num_tiles_x * _num_tiles_y, 1));
	slice.set(2, Window::Dimension(0, total_batches, 1));

	unsigned int idx = 0;
	add_4D_tensor_argument(idx, src, slice);
	add_4D_tensor_argument(idx, dst, slice);
	_kernel.setArg<cl_uint>(idx++, _src_width);
	_kernel.setArg<cl_uint>(idx++, _src_height);
	_kernel.setArg<cl_uint>(idx++, _num_tiles_x);
	_kernel.setArg<cl_uint>(idx++, _num_tiles_y);
	enqueue(queue, *this, slice, lws_hint());
	}
	else
	{
	Window slice = window_collapsed.first_slice_window_3D();
	slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1));
	slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1));

	ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0);
	slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z));

	unsigned int idx = 2 * num_arguments_per_3D_tensor();
	_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3]));
	_kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[3]));

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src, slice);
	add_3D_tensor_argument(idx, dst, slice);

	enqueue(queue, *this, slice, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	}
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute