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author | Georgios Pinitas <georgios.pinitas@arm.com> | 2018-04-26 20:34:58 +0100 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:50:15 +0000 |
commit | 9fb1159e2501f276a27d32264bece54b3d42d258 (patch) | |
tree | 9b23fa7f12d889096b9fd36897f61f8d67f98a3b /src/runtime/NEON/functions/NEWinogradLayer.cpp | |
parent | 43f6afef70c29264c9c40032faf35a1f1d3379af (diff) | |
download | ComputeLibrary-9fb1159e2501f276a27d32264bece54b3d42d258.tar.gz |
COMPMID-1074: Rename WinograLayer.cpp to WinogradConvolutionLayer.cpp
Change-Id: Iccac7cd6cb458469568d0cd6fb36b262353f4188
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129261
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Diffstat (limited to 'src/runtime/NEON/functions/NEWinogradLayer.cpp')
-rw-r--r-- | src/runtime/NEON/functions/NEWinogradLayer.cpp | 390 |
1 files changed, 0 insertions, 390 deletions
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp deleted file mode 100644 index 7d93bcff07..0000000000 --- a/src/runtime/NEON/functions/NEWinogradLayer.cpp +++ /dev/null @@ -1,390 +0,0 @@ -/* - * 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/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/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_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 - -NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> 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 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<INEWinogradLayerTransformInputKernel<float>> transform_input_kernel; - std::unique_ptr<INEWinogradLayerTransformWeightsKernel<float>> transform_weights_kernel; - std::unique_ptr<INEWinogradLayerTransformOutputKernel<float>> 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<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>>(); - output_tile_rows = 2; - output_tile_cols = 2; - n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradBase::N_GEMMS; - N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 3, 3>::WinogradConv::N_BLOCK; - break; - } - case 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>>(); - output_tile_rows = 2; - output_tile_cols = 2; - n_gemms = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::WinogradBase::N_GEMMS; - N_BLOCK = NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::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<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 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<float, float>(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<float *>(_input_workspace.buffer()), input_matrix_row_stride, 0, input_matrix_stride, reinterpret_cast<float *>(_kernel_storage.buffer()), - kernel_matrix_row_stride, kernel_matrix_stride, reinterpret_cast<float *>(_output_workspace.buffer()), output_matrix_row_stride, 0, output_matrix_stride); - - auto acl_gemm_wrapper = support::cpp14::make_unique<NEGEMMAssemblyWrapper<arm_gemm::GemmCommon<float, float>>>(); - 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<float *>(_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 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(_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, - 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<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::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<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::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<float, float, 2, 2, 3, 3>::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<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info))); - break; - } - case 5: - { - ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerBatchedGEMMKernel<float, float, 2, 2, 5, 5>::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<float, 2, 2, 5, 5>::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 |