src/gpu/cl/operators/ClWinogradConv2d.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2018-2021 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/gpu/cl/operators/ClWinogradConv2d.h"

 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/experimental/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLScheduler.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
 #include "src/core/CL/kernels/CLFillBorderKernel.h"
 #include "src/core/helpers/MemoryHelpers.h"
 #include "src/gpu/cl/kernels/ClWinogradFilterTransformKernel.h"
 #include "src/gpu/cl/kernels/ClWinogradInputTransformKernel.h"
 #include "src/gpu/cl/kernels/ClWinogradOutputTransformKernel.h"
 #include "src/gpu/cl/utils/ClAuxTensorHandler.h"

 #include "src/common/utils/Log.h"
 #include "support/Cast.h"

 using namespace arm_compute::experimental;

 namespace arm_compute
 {
 namespace opencl
 {
 namespace
 {
 Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataLayout data_layout)
 {
     Size2D output_tile = Size2D{};

     const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height);

     // Check if the input spatial dimensions are smaller than 4
     const bool is_input_lt4_nchw = (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW);

     if(kernel_max_dim == 3U)
     {
         if(kernel_dims == Size2D(3U, 3U))
         {
             output_tile = is_input_lt4_nchw ? Size2D(2U, 2U) : Size2D(4U, 4U);
         }
         else if(kernel_dims == Size2D(3U, 1U))
         {
             output_tile = is_input_lt4_nchw ? Size2D(2U, 1U) : Size2D(4U, 1U);
         }
         else
         {
             output_tile = is_input_lt4_nchw ? Size2D(1U, 2U) : Size2D(1U, 4U);
         }
     }
     else if(kernel_max_dim == 5U)
     {
         output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U,
                              kernel_dims.height == 1 ? 1U : 4U);
     }
     else if(kernel_max_dim == 7U)
     {
         output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U,
                              kernel_dims.height == 1 ? 1U : 2U);
     }

     return output_tile;
 }

 bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size)
 {
     // Check if we want to configure a Winograd configuration which requires fast math
     using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;

     std::vector<WinogradConfiguration> fast_math_winograd =
     {
         WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)),
         WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7))
     };

     auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
                             std::pair<int, int>(kernel_size.width, kernel_size.height));

     return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();
 }

 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info,
                           const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     // Get indeces for the width and height
     const size_t idx_width  = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);

     // Input shape, kernel size and output tile
     const Size2D input_dims  = Size2D(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height]);
     const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
     const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout());

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_left() > (kernel_size.x() / 2u)) || (conv_info.pad_right() > (kernel_size.x() / 2u))), "Winograd only supports padding up to half kernel size");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_top() > (kernel_size.y() / 2u)) || (conv_info.pad_bottom() > (kernel_size.y() / 2u))), "Winograd only supports padding up to half kernel size");

     // Check if the Winograd configuration requires fast math
     if(!enable_fast_math)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
     }

     const WinogradInfo winograd_info = WinogradInfo(output_tile,
                                                     kernel_size,
                                                     input_dims,
                                                     conv_info,
                                                     src->data_layout());

     // Validate input transform
     const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
     const TensorInfo  input0       = src->clone()->set_tensor_shape(input0_shape);
     ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradInputTransformKernel::validate(src, &input0, winograd_info));

     // 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);
     ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradFilterTransformKernel::validate(weights, &input1, winograd_info));

     // 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);
     ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false,
                                                                                                                      GEMMLowpOutputStageInfo(), (src->data_type() == DataType::F16))));

     // Configure output transform
     ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradOutputTransformKernel::validate(&batched_mm_output, biases, dst, winograd_info, act_info));
     return Status{};
 }

 } // namespace

 ClWinogradConv2d::ClWinogradConv2d()
     : _batched_mm(),
       _input_transform(std::make_unique<kernels::ClWinogradInputTransformKernel>()),
       _filter_transform(std::make_unique<kernels::ClWinogradFilterTransformKernel>()),
       _output_transform(std::make_unique<kernels::ClWinogradOutputTransformKernel>()),
       _border_handler(),
       _input0(),
       _input1(),
       _batched_mm_output(),
       _is_prepared(false),
       _aux_mem()
 {
 }

 ClWinogradConv2d::~ClWinogradConv2d() = default;

 void ClWinogradConv2d::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst,
                                  const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math));
     ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);

     // Get indices for the width and height
     const size_t idx_width  = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);

     // Input shape, kernel size and output tile
     const Size2D input_dims  = Size2D(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height]);
     const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
     const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout());

     // Check if the Winograd configuration requires fast math
     if(!enable_fast_math)
     {
         ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
         ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
     }
     const WinogradInfo winograd_info = WinogradInfo(output_tile,
                                                     kernel_size,
                                                     input_dims,
                                                     conv_info,
                                                     src->data_layout());

     _is_prepared = false;

     // Configure input transform
     _input_transform->configure(compile_context, src, &_input0, winograd_info);
     _border_handler.configure(compile_context, src, _input_transform->border_size(), BorderMode::CONSTANT, PixelValue());

     // Configure filter transform
     _filter_transform->configure(compile_context, weights, &_input1, winograd_info);

     // Configure batched matrix multiply
     _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0,
                                                                                                                   false, false,
                                                                                                                   GEMMLowpOutputStageInfo(),
                                                                                                                   (src->data_type() == DataType::F16)));

     // Configure output transform
     _output_transform->configure(compile_context, &_batched_mm_output, biases, dst, winograd_info, act_info);

     _aux_mem                             = _batched_mm.workspace();
     const MemoryLifetime wino_wei_lifetm = std::any_of(std::begin(_aux_mem), std::end(_aux_mem), [](const auto & r)
     {
         return (r.lifetime == MemoryLifetime::Persistent) && (r.size > 0);
     }) ?
     MemoryLifetime::Prepare :
     MemoryLifetime::Persistent;
     _aux_mem.push_back(MemoryInfo(offset_int_vec(2), MemoryLifetime::Temporary, _input0.total_size()));
     _aux_mem.push_back(MemoryInfo(offset_int_vec(3), wino_wei_lifetm, _input1.total_size()));
     _aux_mem.push_back(MemoryInfo(offset_int_vec(4), MemoryLifetime::Temporary, _batched_mm_output.total_size()));
 }

 Status ClWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info,
                                   const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math));
     return Status{};
 }

 void ClWinogradConv2d::run(ITensorPack &tensors)
 {
     const bool is_gemm_reshaped = _aux_mem[3].lifetime == MemoryLifetime::Prepare;

     auto src    = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
     auto biases = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
     auto dst    = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     CLAuxTensorHandler input0(offset_int_vec(2), _input0, tensors, true);
     CLAuxTensorHandler input1(offset_int_vec(3), _input1, tensors, true, is_gemm_reshaped);
     CLAuxTensorHandler batched_mm_output(offset_int_vec(4), _batched_mm_output, tensors, true);

     prepare(tensors);

     // Run input transform
     ITensorPack pack_it
     {
         { TensorType::ACL_SRC, src },
         { TensorType::ACL_DST, input0.get() },
     };
     CLScheduler::get().enqueue_op(_border_handler, pack_it, false);
     CLScheduler::get().enqueue_op(*_input_transform, pack_it, false);

     // Run batched matrix multiplication
     ITensorPack pack_mm = tensors;
     pack_mm.add_const_tensor(TensorType::ACL_SRC_0, input0.get());
     pack_mm.add_tensor(TensorType::ACL_DST, batched_mm_output.get());
     is_gemm_reshaped ? pack_mm.remove_tensor(TensorType::ACL_SRC_1) : pack_mm.add_const_tensor(TensorType::ACL_SRC_1, input1.get());
     _batched_mm.run(pack_mm);

     // Run output transform
     ITensorPack pack_ot
     {
         { TensorType::ACL_SRC_0, batched_mm_output.get() },
         { TensorType::ACL_SRC_1, biases },
         { TensorType::ACL_DST, dst },
     };
     CLScheduler::get().enqueue_op(*_output_transform, pack_ot);
 }

 void ClWinogradConv2d::prepare(ITensorPack &tensors)
 {
     if(!_is_prepared)
     {
         auto       weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
         ICLTensor *in1_aux = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(3)));

         CLAuxTensorHandler input1(_input1, *in1_aux);
         ITensorPack        pack_ft
         {
             { TensorType::ACL_SRC, weights },
             { TensorType::ACL_DST, input1.get() },
         };
         // Run filter transform and mark original weights as unused
         CLScheduler::get().enqueue_op(*_filter_transform, pack_ft, false);
         weights->mark_as_unused();

         // Prepare GEMM and release reshaped weights if marked unused by ClGemm
         ITensorPack mm_prepare_pack = tensors;
         mm_prepare_pack.add_tensor(ACL_SRC_1, input1.get());
         _batched_mm.prepare(mm_prepare_pack);

         CLScheduler::get().queue().finish();
         _is_prepared = true;
     }
 }

 experimental::MemoryRequirements ClWinogradConv2d::workspace() const
 {
     return _aux_mem;
 }
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2021 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/gpu/cl/operators/ClWinogradConv2d.h"

	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/experimental/Types.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/runtime/CL/CLScheduler.h"
	#include "src/core/CL/kernels/CLFillBorderKernel.h"
	#include "src/core/CL/kernels/CLFillBorderKernel.h"
	#include "src/core/helpers/MemoryHelpers.h"
	#include "src/gpu/cl/kernels/ClWinogradFilterTransformKernel.h"
	#include "src/gpu/cl/kernels/ClWinogradInputTransformKernel.h"
	#include "src/gpu/cl/kernels/ClWinogradOutputTransformKernel.h"
	#include "src/gpu/cl/utils/ClAuxTensorHandler.h"

	#include "src/common/utils/Log.h"
	#include "support/Cast.h"

	using namespace arm_compute::experimental;

	namespace arm_compute
	{
	namespace opencl
	{
	namespace
	{
	Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataLayout data_layout)
	{
	Size2D output_tile = Size2D{};

	const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height);

	// Check if the input spatial dimensions are smaller than 4
	const bool is_input_lt4_nchw = (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW);

	if(kernel_max_dim == 3U)
	{
	if(kernel_dims == Size2D(3U, 3U))
	{
	output_tile = is_input_lt4_nchw ? Size2D(2U, 2U) : Size2D(4U, 4U);
	}
	else if(kernel_dims == Size2D(3U, 1U))
	{
	output_tile = is_input_lt4_nchw ? Size2D(2U, 1U) : Size2D(4U, 1U);
	}
	else
	{
	output_tile = is_input_lt4_nchw ? Size2D(1U, 2U) : Size2D(1U, 4U);
	}
	}
	else if(kernel_max_dim == 5U)
	{
	output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U,
	kernel_dims.height == 1 ? 1U : 4U);
	}
	else if(kernel_max_dim == 7U)
	{
	output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U,
	kernel_dims.height == 1 ? 1U : 2U);
	}

	return output_tile;
	}

	bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size)
	{
	// Check if we want to configure a Winograd configuration which requires fast math
	using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;

	std::vector<WinogradConfiguration> fast_math_winograd =
	{
	WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)),
	WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7))
	};

	auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
	std::pair<int, int>(kernel_size.width, kernel_size.height));

	return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end();
	}

	Status validate_arguments(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo dst, const PadStrideInfo &conv_info,
	const ActivationLayerInfo &act_info, bool enable_fast_math)
	{
	// Get indeces for the width and height
	const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
	const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);

	// Input shape, kernel size and output tile
	const Size2D input_dims = Size2D(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height]);
	const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
	const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout());

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_left() > (kernel_size.x() / 2u)) \|\| (conv_info.pad_right() > (kernel_size.x() / 2u))), "Winograd only supports padding up to half kernel size");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_top() > (kernel_size.y() / 2u)) \|\| (conv_info.pad_bottom() > (kernel_size.y() / 2u))), "Winograd only supports padding up to half kernel size");

	// Check if the Winograd configuration requires fast math
	if(!enable_fast_math)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
	}

	const WinogradInfo winograd_info = WinogradInfo(output_tile,
	kernel_size,
	input_dims,
	conv_info,
	src->data_layout());

	// Validate input transform
	const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
	const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape);
	ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradInputTransformKernel::validate(src, &input0, winograd_info));

	// 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);
	ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradFilterTransformKernel::validate(weights, &input1, winograd_info));

	// 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);
	ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false,
	GEMMLowpOutputStageInfo(), (src->data_type() == DataType::F16))));

	// Configure output transform
	ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradOutputTransformKernel::validate(&batched_mm_output, biases, dst, winograd_info, act_info));
	return Status{};
	}

	} // namespace

	ClWinogradConv2d::ClWinogradConv2d()
	: _batched_mm(),
	_input_transform(std::make_unique<kernels::ClWinogradInputTransformKernel>()),
	_filter_transform(std::make_unique<kernels::ClWinogradFilterTransformKernel>()),
	_output_transform(std::make_unique<kernels::ClWinogradOutputTransformKernel>()),
	_border_handler(),
	_input0(),
	_input1(),
	_batched_mm_output(),
	_is_prepared(false),
	_aux_mem()
	{
	}

	ClWinogradConv2d::~ClWinogradConv2d() = default;

	void ClWinogradConv2d::configure(const ClCompileContext &compile_context, ITensorInfo src, ITensorInfo weights, ITensorInfo biases, ITensorInfo dst,
	const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
	{
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math));
	ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);

	// Get indices for the width and height
	const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
	const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);

	// Input shape, kernel size and output tile
	const Size2D input_dims = Size2D(src->tensor_shape()[idx_width], src->tensor_shape()[idx_height]);
	const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]);
	const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, src->data_layout());

	// Check if the Winograd configuration requires fast math
	if(!enable_fast_math)
	{
	ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); //disable winograd for fp16 if fast math is false.
	ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true");
	}
	const WinogradInfo winograd_info = WinogradInfo(output_tile,
	kernel_size,
	input_dims,
	conv_info,
	src->data_layout());

	_is_prepared = false;

	// Configure input transform
	_input_transform->configure(compile_context, src, &_input0, winograd_info);
	_border_handler.configure(compile_context, src, _input_transform->border_size(), BorderMode::CONSTANT, PixelValue());

	// Configure filter transform
	_filter_transform->configure(compile_context, weights, &_input1, winograd_info);

	// Configure batched matrix multiply
	_batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0,
	false, false,
	GEMMLowpOutputStageInfo(),
	(src->data_type() == DataType::F16)));

	// Configure output transform
	_output_transform->configure(compile_context, &_batched_mm_output, biases, dst, winograd_info, act_info);

	_aux_mem = _batched_mm.workspace();
	const MemoryLifetime wino_wei_lifetm = std::any_of(std::begin(_aux_mem), std::end(_aux_mem), [](const auto & r)
	{
	return (r.lifetime == MemoryLifetime::Persistent) && (r.size > 0);
	}) ?
	MemoryLifetime::Prepare :
	MemoryLifetime::Persistent;
	_aux_mem.push_back(MemoryInfo(offset_int_vec(2), MemoryLifetime::Temporary, _input0.total_size()));
	_aux_mem.push_back(MemoryInfo(offset_int_vec(3), wino_wei_lifetm, _input1.total_size()));
	_aux_mem.push_back(MemoryInfo(offset_int_vec(4), MemoryLifetime::Temporary, _batched_mm_output.total_size()));
	}

	Status ClWinogradConv2d::validate(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo dst, const PadStrideInfo &conv_info,
	const ActivationLayerInfo &act_info, bool enable_fast_math)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math));
	return Status{};
	}

	void ClWinogradConv2d::run(ITensorPack &tensors)
	{
	const bool is_gemm_reshaped = _aux_mem[3].lifetime == MemoryLifetime::Prepare;

	auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
	auto biases = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	CLAuxTensorHandler input0(offset_int_vec(2), _input0, tensors, true);
	CLAuxTensorHandler input1(offset_int_vec(3), _input1, tensors, true, is_gemm_reshaped);
	CLAuxTensorHandler batched_mm_output(offset_int_vec(4), _batched_mm_output, tensors, true);

	prepare(tensors);

	// Run input transform
	ITensorPack pack_it
	{
	{ TensorType::ACL_SRC, src },
	{ TensorType::ACL_DST, input0.get() },
	};
	CLScheduler::get().enqueue_op(_border_handler, pack_it, false);
	CLScheduler::get().enqueue_op(*_input_transform, pack_it, false);

	// Run batched matrix multiplication
	ITensorPack pack_mm = tensors;
	pack_mm.add_const_tensor(TensorType::ACL_SRC_0, input0.get());
	pack_mm.add_tensor(TensorType::ACL_DST, batched_mm_output.get());
	is_gemm_reshaped ? pack_mm.remove_tensor(TensorType::ACL_SRC_1) : pack_mm.add_const_tensor(TensorType::ACL_SRC_1, input1.get());
	_batched_mm.run(pack_mm);

	// Run output transform
	ITensorPack pack_ot
	{
	{ TensorType::ACL_SRC_0, batched_mm_output.get() },
	{ TensorType::ACL_SRC_1, biases },
	{ TensorType::ACL_DST, dst },
	};
	CLScheduler::get().enqueue_op(*_output_transform, pack_ot);
	}

	void ClWinogradConv2d::prepare(ITensorPack &tensors)
	{
	if(!_is_prepared)
	{
	auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
	ICLTensor in1_aux = utils::cast::polymorphic_downcast<ICLTensor >(tensors.get_tensor(offset_int_vec(3)));

	CLAuxTensorHandler input1(_input1, *in1_aux);
	ITensorPack pack_ft
	{
	{ TensorType::ACL_SRC, weights },
	{ TensorType::ACL_DST, input1.get() },
	};
	// Run filter transform and mark original weights as unused
	CLScheduler::get().enqueue_op(*_filter_transform, pack_ft, false);
	weights->mark_as_unused();

	// Prepare GEMM and release reshaped weights if marked unused by ClGemm
	ITensorPack mm_prepare_pack = tensors;
	mm_prepare_pack.add_tensor(ACL_SRC_1, input1.get());
	_batched_mm.prepare(mm_prepare_pack);

	CLScheduler::get().queue().finish();
	_is_prepared = true;
	}
	}

	experimental::MemoryRequirements ClWinogradConv2d::workspace() const
	{
	return _aux_mem;
	}
	} // namespace opencl
	} // namespace arm_compute