diff options
Diffstat (limited to 'src/cpu/operators/CpuWinogradConv2d.cpp')
| -rw-r--r-- | src/cpu/operators/CpuWinogradConv2d.cpp | 263 |
1 files changed, 157 insertions, 106 deletions
diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp index c4edd89964..9d07736c13 100644 --- a/src/cpu/operators/CpuWinogradConv2d.cpp +++ b/src/cpu/operators/CpuWinogradConv2d.cpp @@ -22,23 +22,25 @@ * 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/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/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/kernels/CpuWinogradConv2dKernel.h" #include "src/cpu/operators/CpuActivation.h" #include "src/cpu/operators/CpuPermute.h" #include "src/cpu/utils/CpuAuxTensorHandler.h" @@ -56,21 +58,26 @@ 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)); + 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 }; + 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) +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_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); @@ -80,43 +87,46 @@ Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, co 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) +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) }; + 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)); + 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) + 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); + 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) + 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); + 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 @@ -127,7 +137,8 @@ bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInf } inline bool fuse_function_supported(const ActivationLayerInfo &act_info) { - return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU; + return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || + act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU; } } // namespace @@ -141,7 +152,7 @@ CpuWinogradConv2d::CpuWinogradConv2d() _permute_output(std::make_unique<CpuPermute>()), _permute_weights(std::make_unique<CpuPermute>()), _aux_mem(AuxTensorIdx::Count), - _conv_args{ nullptr }, + _conv_args{nullptr}, _winograd_impl{}, _data_layout(), _winograd_transformed_input{}, @@ -152,15 +163,20 @@ CpuWinogradConv2d::CpuWinogradConv2d() _weights_hwio(), _input_nhwc(), _output_nhwc(), - _is_prepared{ false }, - _run_activation{ false } + _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) +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)); @@ -169,21 +185,29 @@ void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *wei 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) + 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); + 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); @@ -232,7 +256,7 @@ void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *wei PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U); // Configure the kernel to transform the input tensor from NCHW -> NHWC - if(_data_layout == DataLayout::NCHW) + if (_data_layout == DataLayout::NCHW) { _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U)); weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U); @@ -242,28 +266,30 @@ void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *wei _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector); // Reorder the convoluted output to ACL's ordering NCHW - if(_data_layout == DataLayout::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()); + 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); + _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); + _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); + _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) + if (_run_activation) { _activation_func->configure(dst, nullptr, act_info); } @@ -276,40 +302,55 @@ void CpuWinogradConv2d::configure(const ITensorInfo *src, const ITensorInfo *wei _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[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) +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) + 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) }; + 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()); + 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{}; } @@ -328,24 +369,29 @@ void CpuWinogradConv2d::run(ITensorPack &tensors) // 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 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) + 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() } }; + 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 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() } }; + 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); + 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; @@ -356,30 +402,34 @@ void CpuWinogradConv2d::run(ITensorPack &tensors) _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() } }; + 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) + if (is_nchw) { // Reorder the convoluted output to ACL's ordering NCHW - ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } }; + ITensorPack pack{{ACL_SRC, output_nhwc.get()}, {ACL_DST, output}}; _permute_output->run(pack); } - if(_run_activation) + if (_run_activation) { - ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } }; + ITensorPack pack{{ACL_SRC, output}, {ACL_DST, output}}; _activation_func->run(pack); } } void CpuWinogradConv2d::prepare(ITensorPack &tensors) { - if(!_is_prepared) + 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))); + 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() } }; + 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. @@ -387,31 +437,32 @@ void CpuWinogradConv2d::prepare(ITensorPack &tensors) 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; + 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))); + 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()); + 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 + *_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()); |