| /* | |
| * 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(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); | |
| } | |
| 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)); | |
| // 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 |