| /* |
| * Copyright (c) 2017-2018 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 "arm_compute/runtime/NEON/functions/NEWinogradLayer.h" |
| |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "support/ToolchainSupport.h" |
| |
| #include "arm_compute/core/NEON/kernels/NEWinogradLayerKernel.h" |
| |
| #include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp" |
| |
| namespace |
| { |
| inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input) |
| { |
| const int in_width = input->info()->dimension(0); |
| const int in_height = input->info()->dimension(1); |
| const int in_batches = input->info()->dimension(3); |
| const int in_channels = input->info()->dimension(2); |
| return Tensor4DShape({ in_batches, in_height, in_width, in_channels }); |
| } |
| } /* namespace */ |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(0) != 3 && weights->dimension(0) != 5, "Only 3 and 5 kernels are supported"); |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| } |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); |
| |
| ARM_COMPUTE_UNUSED(output); |
| |
| return Status{}; |
| } |
| } //namespace |
| |
| NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _batched_gemm_kernel(nullptr), _transform_input_kernel(nullptr), _transform_output_kernel(nullptr), _transform_weights_kernel(nullptr), |
| _activationlayer_function(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), |
| _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) |
| { |
| } /* arm_compute */ |
| |
| void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_UNUSED(conv_info); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info)); |
| |
| _weights = weights; |
| _input = input; |
| _output = output; |
| |
| std::unique_ptr<INEWinogradLayerBatchedGEMMKernel<float, float>> batched_gemm_kernel; |
| std::unique_ptr<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel; |
| std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel; |
| std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> transform_output_kernel; |
| |
| switch(weights->info()->dimension(0)) |
| { |
| case 3: |
| { |
| batched_gemm_kernel = support::cpp14::make_unique<NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>>(); |
| transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>>(); |
| transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>>(); |
| transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>>(); |
| break; |
| } |
| case 5: |
| { |
| batched_gemm_kernel = support::cpp14::make_unique<NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>>(); |
| transform_input_kernel = support::cpp14::make_unique<NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>>(); |
| transform_weights_kernel = support::cpp14::make_unique<NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>>(); |
| transform_output_kernel = support::cpp14::make_unique<NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>>(); |
| break; |
| } |
| default: |
| { |
| ARM_COMPUTE_ERROR("Not supported."); |
| break; |
| } |
| } |
| |
| const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID; |
| const bool use_same_padding = use_padding_type == PADDING_SAME; |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| ARM_COMPUTE_ERROR_ON_MSG(stride_y != 1 || stride_x != 1, "Winograd layer only supports unit strides."); |
| |
| // Get convolved dimensions |
| const int in_channels = input->info()->dimension(2); |
| const int out_channels = output->info()->dimension(2); |
| |
| const Tensor4DShape in_shape(internal_get_input_shape(input)); |
| const size_t data_type_size = input->info()->element_size(); |
| // Get the memory required to instantiate a new Winograd operator. |
| constexpr size_t storage_alignment = 64; |
| const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size; |
| _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _kernel_storage.allocator()->allocate(); |
| // Input storage |
| const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size; |
| _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _input_workspace.allocator()->allocate(); |
| |
| // Output storage |
| const size_t output_storage_size = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, use_same_padding) * data_type_size; |
| _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8)); |
| _output_workspace.allocator()->allocate(); |
| |
| // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output() |
| TensorInfo info(TensorShape(_output->info()->dimension(2), _output->info()->dimension(0), |
| _output->info()->dimension(1), _output->info()->dimension(3)), |
| 1, _output->info()->data_type()); |
| _output_nhwc.allocator()->init(info); |
| _output_nhwc.allocator()->allocate(); |
| |
| // 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, PermutationVector(3U, 2U, 0U, 1U)); |
| _weights_hwio.allocator()->allocate(); |
| |
| // configure the kernel to transform the input tensor from NCHW -> NHWC |
| _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U)); |
| _input_nhwc.allocator()->allocate(); |
| |
| const int weights_width = weights->info()->dimension(0); |
| const int weights_height = weights->info()->dimension(1); |
| const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels }); |
| |
| // Configure the InputTransform |
| const int input_matrix_stride = transform_input_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); |
| transform_input_kernel->configure(reinterpret_cast<float *>(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, |
| reinterpret_cast<float *>(_input_workspace.buffer()), input_matrix_stride); |
| |
| // Configure WeightsTransform |
| const int kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(kernel_shape); |
| transform_weights_kernel->configure(&_weights_hwio, reinterpret_cast<float *>(_kernel_storage.buffer()), kernel_matrix_stride, out_channels, in_channels); |
| |
| // Configure OutputTransform |
| //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method |
| const int output_matrix_stride = transform_output_kernel->get_matrix_stride(kernel_shape, in_shape, use_padding_type); |
| const auto output_shape(transform_output_kernel->get_output_shape(kernel_shape, in_shape, use_padding_type)); |
| |
| transform_output_kernel->configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()), |
| output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()), |
| in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); |
| |
| // Configure Batched GEMMs |
| const int output_tile_rows = batched_gemm_kernel->get_output_tile_rows(); |
| const int output_tile_cols = batched_gemm_kernel->get_output_tile_cols(); |
| const int n_block = batched_gemm_kernel->get_number_blocks(); |
| const int tile_rows = iceildiv(output_shape.n_rows, output_tile_rows); |
| const int tile_cols = iceildiv(output_shape.n_cols, output_tile_cols); |
| const int m = in_shape.n_batches * tile_rows * tile_cols; |
| const int k = in_shape.n_channels; |
| const int n = out_channels; |
| const int input_matrix_row_stride = in_shape.n_channels; |
| const int kernel_matrix_row_stride = roundup(out_channels, n_block); |
| const int output_matrix_row_stride = kernel_matrix_row_stride; |
| const unsigned n_gemms = batched_gemm_kernel->get_number_gemms(); |
| |
| batched_gemm_kernel->configure(n_gemms, m, k, n, |
| input_matrix_stride, input_matrix_row_stride, |
| kernel_matrix_stride, kernel_matrix_row_stride, |
| output_matrix_stride, output_matrix_row_stride, |
| reinterpret_cast<float *>(_input_workspace.buffer()), |
| reinterpret_cast<float *>(_kernel_storage.buffer()), |
| reinterpret_cast<float *>(_output_workspace.buffer())); |
| |
| // Reorder the convoluted output to ACL's ordering NCHW |
| _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U)); |
| |
| _transform_input_kernel = std::move(transform_input_kernel); |
| _transform_weights_kernel = std::move(transform_weights_kernel); |
| _transform_output_kernel = std::move(transform_output_kernel); |
| _batched_gemm_kernel = std::move(batched_gemm_kernel); |
| |
| //Configure Activation Layer |
| _is_activationlayer_enabled = act_info.enabled(); |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.configure(output, nullptr, act_info); |
| } |
| } |
| |
| void NEWinogradLayer::run() |
| { |
| _memory_group.acquire(); |
| if(!_reshaped_kernel) |
| { |
| _reshaped_kernel = true; |
| _permute_weights.run(); |
| NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX); |
| } |
| //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC |
| _permute_input.run(); |
| |
| // Transform input tensor to the winograd domain |
| NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX); |
| |
| //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs |
| NEScheduler::get().schedule(_batched_gemm_kernel.get(), Window::DimX); |
| |
| // Transform output tensor to the spatial domain |
| NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX); |
| |
| // Reorder the convoluted output to ACL's ordering NCHW |
| _permute_output.run(); |
| |
| if(_is_activationlayer_enabled) |
| { |
| _activationlayer_function.run(); |
| } |
| |
| _memory_group.release(); |
| } |
| |
| Status NEWinogradLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); |
| ARM_COMPUTE_RETURN_ERROR_ON(validate_arguments(input, weights, biases, output, conv_info)); |
| |
| return Status{}; |
| } |
| |
| } // namespace arm_compute |