/* * Copyright (c) 2017 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/CLConvolutionLayer.h" #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include #include using namespace arm_compute; CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights() : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) { } void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } const bool _has_bias = (biases != nullptr); _transpose1xW = transpose1xW; if(transpose1xW) { // Create tensor to store the reshaped weights const unsigned int mat_weights_cols = weights->info()->dimension(3); const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); TensorShape shape_wr(mat_weights_cols, mat_weights_rows); const DataType dt = weights->info()->data_type(); TensorInfo info_wr(shape_wr, 1, dt); _weights_reshaped.allocator()->init(info_wr); _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); _weights_transposed_kernel.configure(&_weights_reshaped, output); _weights_reshaped.allocator()->allocate(); } else { _weights_reshape_kernel.configure(weights, biases, output); } } void CLConvolutionLayerReshapeWeights::run() { cl::CommandQueue q = CLScheduler::get().queue(); CLScheduler::get().enqueue(_weights_reshape_kernel); if(_transpose1xW) { CLScheduler::get().enqueue(_weights_transposed_kernel); } } CLConvolutionLayer::CLConvolutionLayer() : _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) { } void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } _has_bias = (biases != nullptr); _are_weights_reshaped = weights_info.are_reshaped(); // Get parameters for conv_info unsigned int stride_x = 0; unsigned int stride_y = 0; unsigned int pad_x = 0; unsigned int pad_y = 0; std::tie(stride_x, stride_y) = conv_info.stride(); std::tie(pad_x, pad_y) = conv_info.pad(); // Get convolved dimensions unsigned int conv_w = 0; unsigned int conv_h = 0; const unsigned int kernel_width = _are_weights_reshaped ? weights_info.kernel_size() : weights->info()->dimension(0); std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, stride_x, stride_y, pad_x, pad_y, conv_info.round()); ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); // Check if its a "fully connected" convolution _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); // Create tensor to store the reshaped weights size_t mat_weights_cols = weights->info()->dimension(3); size_t mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + ((_has_bias) ? 1 : 0); if(_are_weights_reshaped) { mat_weights_cols = output->info()->dimension(2); const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols); } else { if(_is_fully_connected_convolution) { // Create tensor to store the reshaped weights TensorShape shape_wr(mat_weights_cols, mat_weights_rows); TensorInfo info_wr(shape_wr, 1, weights->info()->data_type()); _weights_reshaped.allocator()->init(info_wr); _reshape_weights.configure(weights, biases, &_weights_reshaped, false); weights = &_weights_reshaped; } else { // Create tensor to store transposed weights TensorShape shape_wt(mat_weights_rows * 4, static_cast(std::ceil(mat_weights_cols / 4.f))); TensorInfo info_wt(shape_wt, 1, weights->info()->data_type()); _weights_transposed.allocator()->init(info_wt); _reshape_weights.configure(weights, biases, &_weights_transposed, true); weights = &_weights_transposed; } } // Create tensor to store im2col reshaped inputs const size_t mat_input_cols = mat_weights_rows; const size_t mat_input_rows = conv_w * conv_h; TensorShape shape_im2col = input->info()->tensor_shape(); shape_im2col.set(0, mat_input_cols); shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, input->info()->data_type())); // Create tensor (interleave) to prepare input tensor for GEMM if(!_is_fully_connected_convolution) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(0, shape_interleaved.x() * 4); shape_interleaved.set(1, std::ceil(static_cast(shape_interleaved.y()) / 4.f)); _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, input->info()->data_type())); } // Create GEMM output tensor TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, input->info()->data_type())); // Configure kernels _input_im2col_kernel.configure(input, &_input_im2col_reshaped, std::make_pair(conv_w, conv_h), conv_info, _has_bias); _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); if(_is_fully_connected_convolution) { _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f); } else { _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); } if(!_are_weights_reshaped) { if(!_is_fully_connected_convolution) { _weights_transposed.allocator()->allocate(); } else { _weights_reshaped.allocator()->allocate(); } } _input_im2col_reshaped.allocator()->allocate(); if(!_is_fully_connected_convolution) { _input_interleaved_reshaped.allocator()->allocate(); } _gemm_output.allocator()->allocate(); } void CLConvolutionLayer::run() { // Run weights reshaping (Runs once for every configure) if(!_are_weights_reshaped) { _are_weights_reshaped = true; _reshape_weights.run(); } // Run input reshaping CLScheduler::get().enqueue(_input_im2col_kernel); if(!_is_fully_connected_convolution) { CLScheduler::get().enqueue(_input_interleave_kernel); } // Runs matrix multiply on reshaped matrices CLScheduler::get().enqueue(_mm_kernel); // Reshape output matrix CLScheduler::get().enqueue(_output_col2im_kernel, false); }