From 9fb1159e2501f276a27d32264bece54b3d42d258 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 26 Apr 2018 20:34:58 +0100 Subject: COMPMID-1074: Rename WinograLayer.cpp to WinogradConvolutionLayer.cpp Change-Id: Iccac7cd6cb458469568d0cd6fb36b262353f4188 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129261 Tested-by: Jenkins Reviewed-by: Pablo Tello --- .../NEON/functions/NEWinogradConvolutionLayer.cpp | 390 +++++++++++++++++++++ 1 file changed, 390 insertions(+) create mode 100644 src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp (limited to 'src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp') diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp new file mode 100644 index 0000000000..a1256ac8cb --- /dev/null +++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp @@ -0,0 +1,390 @@ +/* + * 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/NEWinogradConvolutionLayer.h" + +#include "arm_compute/core/Error.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/NEON/AssemblyHelper.h" +#include "arm_compute/runtime/NEON/NEScheduler.h" +#include "support/ToolchainSupport.h" + +#include "arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.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_NULLPTR(input); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(weights); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output); + 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 + +NEWinogradConvolutionLayer::NEWinogradConvolutionLayer(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _arm_gemm(nullptr), _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(), + _workspace(), _input(), _weights(), _output(), _reshaped_kernel(false), _is_activationlayer_enabled(false) +{ +} /* arm_compute */ + +void NEWinogradConvolutionLayer::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> transform_input_kernel; + std::unique_ptr> transform_weights_kernel; + std::unique_ptr> transform_output_kernel; + + const int weights_width = weights->info()->dimension(0); + const int weights_height = weights->info()->dimension(1); + + int output_tile_rows = 0; + int output_tile_cols = 0; + int n_gemms = 0; + int N_BLOCK = 0; // Size of block used by GEMM. + + switch(weights_width) + { + case 3: + { + transform_input_kernel = support::cpp14::make_unique>(); + transform_weights_kernel = support::cpp14::make_unique>(); + transform_output_kernel = support::cpp14::make_unique>(); + output_tile_rows = 2; + output_tile_cols = 2; + n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; + N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; + break; + } + case 5: + { + transform_input_kernel = support::cpp14::make_unique>(); + transform_weights_kernel = support::cpp14::make_unique>(); + transform_output_kernel = support::cpp14::make_unique>(); + output_tile_rows = 2; + output_tile_cols = 2; + n_gemms = NEWinogradLayerBatchedGEMMKernel::WinogradBase::N_GEMMS; + N_BLOCK = NEWinogradLayerBatchedGEMMKernel::WinogradConv::N_BLOCK; + 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 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(_input_nhwc.buffer()), in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type, + reinterpret_cast(_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(_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(_output_workspace.buffer()), + output_matrix_stride, reinterpret_cast(_output_nhwc.buffer()), + in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels); + + // Configure GEMM + 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; + unsigned int num_threads = NEScheduler::get().num_threads(); + + _arm_gemm = arm_gemm::gemm(NEScheduler::get().cpu_info(), m, n, k, 1, n_gemms, false, false, 1.f, 0.f, num_threads, false); + _arm_gemm->set_arrays(reinterpret_cast(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast(_kernel_storage.buffer()), + kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride); + + auto acl_gemm_wrapper = support::cpp14::make_unique>>(); + acl_gemm_wrapper->configure(_arm_gemm.get()); + const size_t workspace_size = _arm_gemm->get_working_size(); + + // Allocate workspace + if(workspace_size > 0) + { + const unsigned int alignment = 4096; + allocate_workspace(workspace_size, _workspace, _memory_group, alignment, 1); + _arm_gemm->set_working_space(reinterpret_cast(_workspace.buffer())); + } + + const unsigned int window_size = _arm_gemm->get_window_size(); + if(window_size < num_threads) + { + num_threads = window_size; + _arm_gemm->set_nthreads(num_threads); + } + + _gemm_kernel = std::move(acl_gemm_wrapper); + + // 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); + + //Configure Activation Layer + _is_activationlayer_enabled = act_info.enabled(); + if(_is_activationlayer_enabled) + { + _activationlayer_function.configure(output, nullptr, act_info); + } +} + +void NEWinogradConvolutionLayer::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(_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 NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info)); + + // Get indices for the width and height + const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + // Input shape + const TensorShape input_shape = input->tensor_shape(); + + // Kernel size + const unsigned int kernel_w = weights->tensor_shape()[idx_width]; + const unsigned int kernel_h = weights->tensor_shape()[idx_height]; + + const WinogradInfo winograd_info = WinogradInfo(Size2D(2, 2), + Size2D(kernel_w, kernel_h), + Size2D(input_shape[idx_width], input_shape[idx_height]), + conv_info, + input->data_layout()); + + // Validate input transform + const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); + const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel::validate(input, &input0, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + // Validate filter transform + const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); + const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); + + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel::validate(weights, &input1, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + // Validate batched matrix multiply + TensorShape batched_mm_output_shape = input0.tensor_shape(); + batched_mm_output_shape[0] = input1.tensor_shape()[0]; + const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); + switch(weights->dimension(0)) + { + case 3: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, + true /* Reshape weights only for the first run*/)))); + // Validate output transform + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); + break; + } + case 5: + { + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, + true /* Reshape weights only for the first run*/)))); + // Validate output transform + ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel::validate(&batched_mm_output, biases, output, winograd_info))); + break; + } + default: + { + ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported."); + break; + } + } + + // Validate Activation Layer + if(act_info.enabled()) + { + NEActivationLayer::validate(output, nullptr, act_info); + } + return Status{}; +} + +} // namespace arm_compute -- cgit v1.2.1