| /* |
| * 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/NEON/functions/NEConvolutionLayer.h" |
| |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include <cmath> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| NEConvolutionLayerReshapeWeights::NEConvolutionLayerReshapeWeights() |
| : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) |
| { |
| } |
| |
| void NEConvolutionLayerReshapeWeights::configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, 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::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, biases); |
| ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); |
| ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); |
| } |
| |
| // Check if bias are present, if yes they will be embedded to the weights matrix |
| 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); |
| TensorInfo info_wr(shape_wr, 1, weights->info()->data_type(), weights->info()->fixed_point_position()); |
| |
| _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 NEConvolutionLayerReshapeWeights::run() |
| { |
| NEScheduler::get().schedule(&_weights_reshape_kernel, 3); |
| if(_transpose1xW) |
| { |
| NEScheduler::get().schedule(&_weights_transposed_kernel, Window::DimY); |
| } |
| } |
| |
| NEConvolutionLayer::NEConvolutionLayer() |
| : _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), |
| _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) |
| { |
| } |
| |
| void NEConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(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::QS8, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(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); |
| } |
| |
| const DataType dt = input->info()->data_type(); |
| const int fixed_point_position = input->info()->fixed_point_position(); |
| |
| _has_bias = (biases != nullptr); |
| _are_weights_reshaped = weights_info.are_reshaped(); |
| |
| // Get parameters from 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)); |
| |
| unsigned int mat_weights_cols = weights->info()->dimension(3); |
| unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); |
| |
| // Reshape weights if needed |
| 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, dt, fixed_point_position); |
| _weights_reshaped.allocator()->init(info_wr); |
| _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */); |
| } |
| else |
| { |
| // Create tensor to store transposed weights |
| const float transpose_width = 16.0f / input->info()->element_size(); |
| TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width))); |
| TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); |
| _weights_reshaped.allocator()->init(info_wt); |
| _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */); |
| } |
| weights = &_weights_reshaped; |
| } |
| |
| // Create tensor to store im2col reshaped inputs |
| const unsigned int mat_input_cols = mat_weights_rows; |
| const unsigned int 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, dt, fixed_point_position)); |
| |
| // 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(shape_interleaved.y() / 4.f)); |
| _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); |
| } |
| |
| // 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, dt, fixed_point_position)); |
| |
| // Configure kernels |
| _input_im2col_kernel.configure(input, &_input_im2col_reshaped, std::make_pair(conv_w, conv_h), conv_info, _has_bias); |
| 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); |
| } |
| _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); |
| |
| // Allocate intermediate tensor |
| if(!_are_weights_reshaped) |
| { |
| _weights_reshaped.allocator()->allocate(); |
| } |
| _input_im2col_reshaped.allocator()->allocate(); |
| if(!_is_fully_connected_convolution) |
| { |
| _input_interleaved_reshaped.allocator()->allocate(); |
| } |
| _gemm_output.allocator()->allocate(); |
| } |
| |
| void NEConvolutionLayer::run() |
| { |
| // Run weights reshaping (Runs once for every configure) |
| if(!_are_weights_reshaped) |
| { |
| _are_weights_reshaped = true; |
| _reshape_weights.run(); |
| } |
| |
| // Run input reshaping |
| NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY); |
| if(!_is_fully_connected_convolution) |
| { |
| // Run interleave |
| NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY); |
| } |
| |
| // Runs matrix multiply on reshaped matrices |
| NEScheduler::get().schedule(&_mm_kernel, Window::DimY); |
| |
| // Reshape output matrix |
| NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY); |
| } |
