From 6ff3b19ee6120edf015fad8caab2991faa3070af Mon Sep 17 00:00:00 2001 From: Anthony Barbier Date: Mon, 4 Sep 2017 18:44:23 +0100 Subject: COMPMID-344 Updated doxygen Change-Id: I32f7b84daa560e460b77216add529c8fa8b327ae --- src/runtime/CL/functions/CLConvolutionLayer.cpp | 247 ++++++++++++++++++++++++ 1 file changed, 247 insertions(+) create mode 100644 src/runtime/CL/functions/CLConvolutionLayer.cpp (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp new file mode 100644 index 0000000000..f0bbc3514f --- /dev/null +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -0,0 +1,247 @@ +/* + * 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); +} -- cgit v1.2.1