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Diffstat (limited to 'src/cpu/operators/CpuWinogradConv2d.cpp')
| -rw-r--r-- | src/cpu/operators/CpuWinogradConv2d.cpp | 478 |
1 files changed, 478 insertions, 0 deletions
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp new file mode 100644 index 0000000000..7d81aee0e9 --- /dev/null +++ b/src/cpu/operators/CpuWinogradConv2d.cpp @@ -0,0 +1,478 @@ +/* + * Copyright (c) 2021-2023 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/cpu/operators/CpuWinogradConv2d.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/core/Validate.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/helpers/MemoryHelpers.h" +#include "src/core/helpers/WindowHelpers.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/utils/AssemblyUtils.h" +#include "src/cpu/kernels/assembly/arm_gemm.hpp" +#include "src/cpu/kernels/CpuWinogradConv2dKernel.h" +#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(ENABLE_FP16_KERNELS) + 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(ENABLE_FP16_KERNELS) + 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(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); + 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 input transform kernel + _transform_input_kernel = + std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads); + + // Configure GEMM function + _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, + &_winograd_transformed_output, 1.0f, 0.f); + + // Configure output transform kernel + _transform_output_kernel = + std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads); + + //Configure Activation Layer + _run_activation = act_info.enabled() && !fuse_function_supported(act_info); + if (_run_activation) + { + _activation_func->configure(dst, nullptr, act_info); + } + + const auto mm_mem_req = _gemm_function->workspace(); + for (unsigned int slot = 0; slot < mm_mem_req.size(); ++slot) + { + _aux_mem[slot] = mm_mem_req[slot]; + } + + // 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)); + + // Disable winograd for fp16 if fast math is false. + if (!enable_fast_math) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32); + } + + 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()}}; + 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 + 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 |