src/cpu/operators/CpuWinogradConv2d.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2021-2022 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/cpu/operators/CpuWinogradConv2d.h"
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/Validate.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "arm_compute/runtime/FunctionDescriptors.h"
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "src/common/utils/Log.h"
 #include "src/core/CPP/Validate.h"
 #include "src/core/NEON/kernels/assembly/winograd.hpp"
 #include "src/core/NEON/kernels/convolution/common/tensor.hpp"
 #include "src/core/NEON/kernels/convolution/common/utils.hpp"
 #include "src/core/helpers/MemoryHelpers.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "src/core/utils/AssemblyUtils.h"
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
 #include "src/cpu/kernels/assembly/arm_gemm.hpp"
 #include "src/cpu/operators/CpuActivation.h"
 #include "src/cpu/operators/CpuPermute.h"
 #include "src/cpu/utils/CpuAuxTensorHandler.h"
 #include "support/Cast.h"

 namespace arm_compute
 {
 namespace cpu
 {
 using namespace arm_compute::experimental;
 using namespace arm_compute::utils::cast;

 namespace
 {
 inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
 {
     const DataLayout data_layout = in->data_layout();
     const int        in_width    = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
     const int        in_height   = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
     const int        in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
     const int        in_batches  = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));

     return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
 }

 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_UNUSED(dst, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
     return Status{};
 }

 bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
                                         const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
                                         arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
 {
     arm_conv::winograd::WinogradConfig winograd_cfg;
     arm_gemm::GemmConfig               cfg;

     const DataType data_type = src->data_type();
     Tensor4DShape  in_shape{ internal_get_shape(src) };
     Tensor4DShape  out_shape{ internal_get_shape(dst) };
     Tensor4DShape  kernel_shape{ internal_get_shape(weights) };
     uint32_t       nthreads = NEScheduler::get().num_threads();
     // Get configuration arguments for Winograd
     winograd_cfg.output_rows = 0;
     winograd_cfg.output_cols = 0;
     conv_args                = std::make_unique<arm_conv::ConvolutionArgs>(
                                    in_shape.n_batches,
                                    arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
                                    in_shape.n_channels,
                                    conv_info.pad_top(),
                                    conv_info.pad_left(),
                                    arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
                                    out_shape.n_channels,
                                    arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
                                    assembly_utils::map_to_arm_gemm_activation(act_info));

     bool success = false;
     if(data_type == DataType::F32)
     {
         success = arm_conv::winograd::get_implementation<float>(
                       *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
 #if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
     else if(data_type == DataType::F16)
     {
         success = arm_conv::winograd::get_implementation<__fp16>(
                       *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
 #endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
     else
     {
         success = false;
     }
     return success;
 }
 inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
 {
     return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
 }
 } // namespace

 CpuWinogradConv2d::CpuWinogradConv2d()

     : _gemm_function(std::make_unique<CpuGemm>()),
       _activation_func(std::make_unique<CpuActivation>()),
       _transform_input_kernel(nullptr),
       _transform_output_kernel(nullptr),
       _permute_input(std::make_unique<CpuPermute>()),
       _permute_output(std::make_unique<CpuPermute>()),
       _permute_weights(std::make_unique<CpuPermute>()),
       _aux_mem(AuxTensorIdx::Count),
       _conv_args{ nullptr },
       _winograd_impl{},
       _data_layout(),
       _winograd_transformed_input{},
       _winograd_transformed_output{},
       _winograd_transformed_weights{},
       _input_workspace(),
       _output_workspace(),
       _weights_hwio(),
       _input_nhwc(),
       _output_nhwc(),
       _is_prepared{ false },
       _run_activation{ false }
 {
 }

 CpuWinogradConv2d::~CpuWinogradConv2d() = default;

 void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, ITensorInfo *dst,
                                   const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
     ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
     ARM_COMPUTE_UNUSED(biases);
     const DataType data_type = src->data_type();
     uint32_t       nthreads  = NEScheduler::get().num_threads();
     _data_layout             = src->data_layout();
     const Tensor4DShape kernel_shape{ internal_get_shape(weights) };

     bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);

     ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());

     const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
     if(has_impl)
     {
         // Determine how much working space is required, allocate it.
         const size_t input_workspace_size  = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
         const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);

         TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
         TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
         _input_workspace  = input_workspace_info;
         _output_workspace = output_workspace_info;

         const auto &wds = _winograd_impl.winograd_spec;

         // Preparing winograd transformed input tensor
         const size_t     data_type_size    = src->element_size();
         const uint32_t   m                 = _winograd_impl.gemm_args->_Msize; // Total number of tiles
         const uint32_t   k                 = _winograd_impl.gemm_args->_Ksize; // Input channels
         const uint32_t   n                 = _winograd_impl.gemm_args->_Nsize; // Output channels
         const uint32_t   n_gemms           = _winograd_impl.gemm_args->_nmulti;
         const uint32_t   n_batches         = _winograd_impl.gemm_args->_nbatches;
         constexpr size_t storage_alignment = 64;

         const TensorShape a_shape(k, m, n_batches, n_gemms);
         Strides           a_strides(data_type_size);
         a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
         a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
         a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);

         const TensorShape b_shape(n, k, n_gemms);
         Strides           b_strides(data_type_size);
         b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
         b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);

         const TensorShape d_shape(n, m, n_batches, n_gemms);
         Strides           d_strides(data_type_size);
         d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
         d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
         d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);

         TensorInfo a_info{};
         TensorInfo b_info{};
         TensorInfo d_info{};
         a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
         b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
         d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);

         _winograd_transformed_input   = a_info;
         _winograd_transformed_weights = b_info;
         _winograd_transformed_output  = d_info;

         PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);

         // Configure the kernel to transform the input tensor from NCHW -> NHWC
         if(_data_layout == DataLayout::NCHW)
         {
             _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
             weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
         }

         // 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, weights_permutation_vector);

         // Reorder the convoluted output to ACL's ordering NCHW
         if(_data_layout == DataLayout::NCHW)
         {
             // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
             TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
                                         dst->dimension(1), dst->dimension(3)),
                             1, dst->data_type());
             _output_nhwc = info;
             _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
         }

         // Configure GEMM function
         _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);

         //Configure Activation Layer
         _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
         if(_run_activation)
         {
             _activation_func->configure(dst, nullptr, act_info);
         }

         auto asm_mem_req         = _gemm_function->workspace();
         _aux_mem[GemmWorkspace]  = asm_mem_req[GemmWorkspace];
         _aux_mem[Pretranspose]   = asm_mem_req[Pretranspose];
         _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
         _aux_mem[TransposedRHS]  = asm_mem_req[TransposedRHS];
         _aux_mem[TempResult]     = asm_mem_req[TempResult];

         // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
         _aux_mem[TransformedInput]   = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
         _aux_mem[TransformedOutput]  = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
         _aux_mem[WorkspaceIO]        = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
         _aux_mem[PermutedWeights]    = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
         _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
         if(_data_layout == DataLayout::NCHW)
         {
             _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
             _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
         }
     }
 }
 Status CpuWinogradConv2d::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_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));

     const Tensor4DShape              kernel_shape{ internal_get_shape(weights) };
     arm_conv::winograd::WinogradImpl winograd_impl{};

     std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
     const bool                                 success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);

     ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
     ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
     return Status{};
 }

 void CpuWinogradConv2d::run(ITensorPack &tensors)
 {
     prepare(tensors);
     auto   src    = tensors.get_const_tensor(ACL_SRC_0);
     auto   biases = tensors.get_const_tensor(ACL_SRC_2);
     auto   output = tensors.get_tensor(ACL_DST);
     Window win;

     const uint32_t nthreads = NEScheduler::get().num_threads();

     // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
     win.set(Window::DimX, Window::Dimension(0, nthreads, 1));

     // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
     CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
     CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
     CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
     const bool          is_nchw = _data_layout == DataLayout::NCHW;
     if(is_nchw)
     {
         //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
         ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
         _permute_input->run(pack);
     }

     CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
     CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
     CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);

     ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
     _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);

     NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);

     CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);

     // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     ITensorPack gemm_pack = tensors;
     gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
     gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
     gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
     gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
     _gemm_function->run(gemm_pack);

     // Output transform
     _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
     ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
     NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
     if(is_nchw)
     {
         // Reorder the convoluted output to ACL's ordering NCHW
         ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
         _permute_output->run(pack);
     }
     if(_run_activation)
     {
         ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
         _activation_func->run(pack);
     }
 }

 void CpuWinogradConv2d::prepare(ITensorPack &tensors)
 {
     if(!_is_prepared)
     {
         const ITensor *weights     = tensors.get_const_tensor(ACL_SRC_1);
         ITensor       *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));

         CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
         ITensorPack         permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
         _permute_weights->run(permute_tensors);
         const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
         // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
         const unsigned int height_idx  = 3; // H in HWIO
         const unsigned int width_idx   = 2; // W in HWIO
         const unsigned int channel_idx = 1; // I in HWIO

         const int permuted_weight_row_stride     = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
         const int permuted_weight_col_stride     = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
         const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;

         // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
         ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
         ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
         CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);

         const void *permuted_weights_ptr;
         void       *win_wght_transf_ptr;

         permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
         win_wght_transf_ptr  = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());

         // Prepare Weights
         _winograd_impl.weight_transform->execute(
             *_conv_args,
             permuted_weights_ptr,
             permuted_weight_row_stride,
             permuted_weight_col_stride,
             permuted_weight_channel_stride,
             win_wght_transf_ptr,
             _winograd_impl.winograd_spec,
             0, 1 // Thread 1 of 1
         );
         ITensorPack gemm_pack = tensors;
         gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
         _gemm_function->prepare(gemm_pack);
         _is_prepared = 1;
     }
 }
 experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
 {
     return _aux_mem;
 }

 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2021-2022 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/cpu/operators/CpuWinogradConv2d.h"
	#include "arm_compute/core/Error.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/Validate.h"
	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "arm_compute/runtime/FunctionDescriptors.h"
	#include "arm_compute/runtime/NEON/NEScheduler.h"
	#include "src/common/utils/Log.h"
	#include "src/core/CPP/Validate.h"
	#include "src/core/NEON/kernels/assembly/winograd.hpp"
	#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
	#include "src/core/NEON/kernels/convolution/common/utils.hpp"
	#include "src/core/helpers/MemoryHelpers.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "src/core/utils/AssemblyUtils.h"
	#include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
	#include "src/cpu/kernels/assembly/arm_gemm.hpp"
	#include "src/cpu/operators/CpuActivation.h"
	#include "src/cpu/operators/CpuPermute.h"
	#include "src/cpu/utils/CpuAuxTensorHandler.h"
	#include "support/Cast.h"

	namespace arm_compute
	{
	namespace cpu
	{
	using namespace arm_compute::experimental;
	using namespace arm_compute::utils::cast;

	namespace
	{
	inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
	{
	const DataLayout data_layout = in->data_layout();
	const int in_width = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
	const int in_height = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
	const int in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
	const int in_batches = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));

	return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
	}

	Status validate_arguments(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo biases, const ITensorInfo dst, const PadStrideInfo &conv_info)
	{
	ARM_COMPUTE_UNUSED(dst, weights);
	ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 \|\| conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
	if(biases != nullptr)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
	ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
	return Status{};
	}

	bool get_winograd_kernel_implementation(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo *dst,
	const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
	arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
	{
	arm_conv::winograd::WinogradConfig winograd_cfg;
	arm_gemm::GemmConfig cfg;

	const DataType data_type = src->data_type();
	Tensor4DShape in_shape{ internal_get_shape(src) };
	Tensor4DShape out_shape{ internal_get_shape(dst) };
	Tensor4DShape kernel_shape{ internal_get_shape(weights) };
	uint32_t nthreads = NEScheduler::get().num_threads();
	// Get configuration arguments for Winograd
	winograd_cfg.output_rows = 0;
	winograd_cfg.output_cols = 0;
	conv_args = std::make_unique<arm_conv::ConvolutionArgs>(
	in_shape.n_batches,
	arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
	in_shape.n_channels,
	conv_info.pad_top(),
	conv_info.pad_left(),
	arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
	out_shape.n_channels,
	arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
	assembly_utils::map_to_arm_gemm_activation(act_info));

	bool success = false;
	if(data_type == DataType::F32)
	{
	success = arm_conv::winograd::get_implementation<float>(
	winograd_impl, &CPUInfo::get(), conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
	}
	#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
	else if(data_type == DataType::F16)
	{
	success = arm_conv::winograd::get_implementation<__fp16>(
	winograd_impl, &CPUInfo::get(), conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
	}
	#endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
	else
	{
	success = false;
	}
	return success;
	}
	inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
	{
	return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU \|\| act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
	}
	} // namespace

	CpuWinogradConv2d::CpuWinogradConv2d()

	: _gemm_function(std::make_unique<CpuGemm>()),
	_activation_func(std::make_unique<CpuActivation>()),
	_transform_input_kernel(nullptr),
	_transform_output_kernel(nullptr),
	_permute_input(std::make_unique<CpuPermute>()),
	_permute_output(std::make_unique<CpuPermute>()),
	_permute_weights(std::make_unique<CpuPermute>()),
	_aux_mem(AuxTensorIdx::Count),
	_conv_args{ nullptr },
	_winograd_impl{},
	_data_layout(),
	_winograd_transformed_input{},
	_winograd_transformed_output{},
	_winograd_transformed_weights{},
	_input_workspace(),
	_output_workspace(),
	_weights_hwio(),
	_input_nhwc(),
	_output_nhwc(),
	_is_prepared{ false },
	_run_activation{ false }
	{
	}

	CpuWinogradConv2d::~CpuWinogradConv2d() = default;

	void CpuWinogradConv2d::configure(const ITensorInfo src, const ITensorInfo weights, const ITensorInfo biases, ITensorInfo dst,
	const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
	ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
	ARM_COMPUTE_UNUSED(biases);
	const DataType data_type = src->data_type();
	uint32_t nthreads = NEScheduler::get().num_threads();
	_data_layout = src->data_layout();
	const Tensor4DShape kernel_shape{ internal_get_shape(weights) };

	bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);

	ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());

	const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
	if(has_impl)
	{
	// Determine how much working space is required, allocate it.
	const size_t input_workspace_size = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
	const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);

	TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
	TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
	_input_workspace = input_workspace_info;
	_output_workspace = output_workspace_info;

	const auto &wds = _winograd_impl.winograd_spec;

	// Preparing winograd transformed input tensor
	const size_t data_type_size = src->element_size();
	const uint32_t m = _winograd_impl.gemm_args->_Msize; // Total number of tiles
	const uint32_t k = _winograd_impl.gemm_args->_Ksize; // Input channels
	const uint32_t n = _winograd_impl.gemm_args->_Nsize; // Output channels
	const uint32_t n_gemms = _winograd_impl.gemm_args->_nmulti;
	const uint32_t n_batches = _winograd_impl.gemm_args->_nbatches;
	constexpr size_t storage_alignment = 64;

	const TensorShape a_shape(k, m, n_batches, n_gemms);
	Strides a_strides(data_type_size);
	a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
	a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
	a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);

	const TensorShape b_shape(n, k, n_gemms);
	Strides b_strides(data_type_size);
	b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
	b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);

	const TensorShape d_shape(n, m, n_batches, n_gemms);
	Strides d_strides(data_type_size);
	d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
	d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
	d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);

	TensorInfo a_info{};
	TensorInfo b_info{};
	TensorInfo d_info{};
	a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
	b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
	d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);

	_winograd_transformed_input = a_info;
	_winograd_transformed_weights = b_info;
	_winograd_transformed_output = d_info;

	PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);

	// Configure the kernel to transform the input tensor from NCHW -> NHWC
	if(_data_layout == DataLayout::NCHW)
	{
	_permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
	weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
	}

	// 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, weights_permutation_vector);

	// Reorder the convoluted output to ACL's ordering NCHW
	if(_data_layout == DataLayout::NCHW)
	{
	// configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
	TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
	dst->dimension(1), dst->dimension(3)),
	1, dst->data_type());
	_output_nhwc = info;
	_permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
	}

	// Configure GEMM function
	_gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);

	//Configure Activation Layer
	_run_activation = act_info.enabled() && !fuse_function_supported(act_info);
	if(_run_activation)
	{
	_activation_func->configure(dst, nullptr, act_info);
	}

	auto asm_mem_req = _gemm_function->workspace();
	_aux_mem[GemmWorkspace] = asm_mem_req[GemmWorkspace];
	_aux_mem[Pretranspose] = asm_mem_req[Pretranspose];
	_aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
	_aux_mem[TransposedRHS] = asm_mem_req[TransposedRHS];
	_aux_mem[TempResult] = asm_mem_req[TempResult];

	// Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
	_aux_mem[TransformedInput] = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
	_aux_mem[TransformedOutput] = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
	_aux_mem[WorkspaceIO] = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
	_aux_mem[PermutedWeights] = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
	_aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
	if(_data_layout == DataLayout::NCHW)
	{
	_aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
	_aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
	}
	}
	}
	Status CpuWinogradConv2d::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_ERROR_ON_NULLPTR(src, weights, dst);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));

	const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
	arm_conv::winograd::WinogradImpl winograd_impl{};

	std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
	const bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);

	ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
	ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
	return Status{};
	}

	void CpuWinogradConv2d::run(ITensorPack &tensors)
	{
	prepare(tensors);
	auto src = tensors.get_const_tensor(ACL_SRC_0);
	auto biases = tensors.get_const_tensor(ACL_SRC_2);
	auto output = tensors.get_tensor(ACL_DST);
	Window win;

	const uint32_t nthreads = NEScheduler::get().num_threads();

	// The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
	win.set(Window::DimX, Window::Dimension(0, nthreads, 1));

	// Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
	CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
	CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
	CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
	const bool is_nchw = _data_layout == DataLayout::NCHW;
	if(is_nchw)
	{
	//Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
	ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
	_permute_input->run(pack);
	}

	CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
	CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
	CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);

	ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
	_transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);

	NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);

	CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);

	// Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
	ITensorPack gemm_pack = tensors;
	gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
	gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
	gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
	gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
	_gemm_function->run(gemm_pack);

	// Output transform
	_transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
	ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
	NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
	if(is_nchw)
	{
	// Reorder the convoluted output to ACL's ordering NCHW
	ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
	_permute_output->run(pack);
	}
	if(_run_activation)
	{
	ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
	_activation_func->run(pack);
	}
	}

	void CpuWinogradConv2d::prepare(ITensorPack &tensors)
	{
	if(!_is_prepared)
	{
	const ITensor *weights = tensors.get_const_tensor(ACL_SRC_1);
	ITensor weights_aux = utils::cast::polymorphic_cast<ITensor >(tensors.get_tensor(offset_int_vec(PermutedWeights)));

	CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
	ITensorPack permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
	_permute_weights->run(permute_tensors);
	const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
	// Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
	const unsigned int height_idx = 3; // H in HWIO
	const unsigned int width_idx = 2; // W in HWIO
	const unsigned int channel_idx = 1; // I in HWIO

	const int permuted_weight_row_stride = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
	const int permuted_weight_col_stride = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
	const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;

	// Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
	ITensor weights_transf = utils::cast::polymorphic_cast<ITensor >(tensors.get_tensor(offset_int_vec(TransformedWeights)));
	ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
	CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);

	const void *permuted_weights_ptr;
	void *win_wght_transf_ptr;

	permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
	win_wght_transf_ptr = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());

	// Prepare Weights
	_winograd_impl.weight_transform->execute(
	*_conv_args,
	permuted_weights_ptr,
	permuted_weight_row_stride,
	permuted_weight_col_stride,
	permuted_weight_channel_stride,
	win_wght_transf_ptr,
	_winograd_impl.winograd_spec,
	0, 1 // Thread 1 of 1
	);
	ITensorPack gemm_pack = tensors;
	gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
	_gemm_function->prepare(gemm_pack);
	_is_prepared = 1;
	}
	}
	experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
	{
	return _aux_mem;
	}

	} // namespace cpu
	} // namespace arm_compute