From d2fab7315bac3a586f2f1b1c8d64f2441f89ca64 Mon Sep 17 00:00:00 2001 From: Gian Marco Iodice Date: Fri, 2 Mar 2018 11:18:12 +0000 Subject: COMPMID-935 - Implementing Convolution with Winograd on OpenCL (part 4) Implemented Winograd Output Transform (2x2,3x3) on OpenCL Implemented CLWinogradConvolutionLayer on OpenCL Change-Id: I6a113fc5f052ca07f878d2b800d2ab003f84af65 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125148 Reviewed-by: Georgios Pinitas Tested-by: Jenkins --- .../CL/functions/CLWinogradConvolutionLayer.cpp | 146 +++++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp (limited to 'src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp new file mode 100644 index 0000000000..5081cbac4e --- /dev/null +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -0,0 +1,146 @@ +/* + * Copyright (c) 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/CL/functions/CLWinogradConvolutionLayer.h" + +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +using namespace arm_compute; + +CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr memory_manager) + : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _is_first_run(true) +{ +} + +void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) +{ + // TODO(COMPMID-1013): This part will be removed + // Get indeces for the width and height + const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); + const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); + + // Kernel size + const unsigned int kernel_w = weights->info()->tensor_shape()[idx_width]; + const unsigned int kernel_h = weights->info()->tensor_shape()[idx_height]; + + // Number of tiles along the X and Y direction + const unsigned int num_tiles_x = std::ceil((input->info()->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); + const unsigned int num_tiles_y = std::ceil((input->info()->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); + + // Compute output shape + const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input->info(), *weights->info(), conv_info); + + // Manage intermediate tensors + _memory_group.manage(&_input0); + _memory_group.manage(&_batched_mm_output); + + // Do not manage _input1 as it contains the weights + + // Configure input transform + _input_transform.configure(input, &_input0, conv_info, Size2D(kernel_w, kernel_h)); + + // Configure filter transform + _filter_transform.configure(weights, &_input1); + + // Configure batched matrix multiply + _batched_mm.configure(&_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + + // Configure output transform + _output_transform.configure(&_batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], output_convolved_shape[idx_height]), Size2D(num_tiles_x, + num_tiles_y)); + + // Allocate temporary tensors + _input0.allocator()->allocate(); + _input1.allocator()->allocate(); + _batched_mm_output.allocator()->allocate(); +} + +Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info) +{ + // TODO(COMPMID-1013): This part will be removed + // Get indeces 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); + + // Kernel size + const unsigned int kernel_w = weights->tensor_shape()[idx_width]; + const unsigned int kernel_h = weights->tensor_shape()[idx_height]; + + // Number of tiles along the X and Y direction + const unsigned int num_tiles_x = std::ceil((input->tensor_shape().x() - (kernel_w - 1) + conv_info.pad_left() + conv_info.pad_right()) / 2.f); + const unsigned int num_tiles_y = std::ceil((input->tensor_shape().y() - (kernel_h - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / 2.f); + + // Compute output shape + const TensorShape output_convolved_shape = misc::shape_calculator::compute_deep_convolution_shape(*input, *weights, conv_info); + + // Validate input transform + const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, conv_info, Size2D(kernel_w, kernel_h)); + const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, conv_info, Size2D(kernel_w, kernel_h))); + + // Validate filter transform + const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights); + const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1)); + + // Configure 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); + ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/))); + + // Configure output transform + ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, Size2D(kernel_w, kernel_h), Size2D(output_convolved_shape[idx_width], + output_convolved_shape[idx_height]), + Size2D(num_tiles_x, num_tiles_y))); + + return Status{}; +} + +void CLWinogradConvolutionLayer::run() +{ + if(_is_first_run) + { + // Run filter transform + CLScheduler::get().enqueue(_filter_transform, false); + + _is_first_run = false; + } + + _memory_group.acquire(); + + // Run input transform + _input_transform.run(); + + // Run batched matrix multiplication + _batched_mm.run(); + + // Run output transform + CLScheduler::get().enqueue(_output_transform); + + _memory_group.release(); +} -- cgit v1.2.1