| /* |
| * 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/GLES_COMPUTE/functions/GCConvolutionLayer.h" |
| |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/Size2D.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/GLES_COMPUTE/GCScheduler.h" |
| |
| #include <cmath> |
| #include <memory> |
| #include <tuple> |
| |
| using namespace arm_compute; |
| |
| GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights() |
| : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) |
| { |
| } |
| |
| void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); |
| ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); |
| |
| if(biases != nullptr) |
| { |
| ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type())); |
| 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 append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); |
| const unsigned bias_element = (append_biases) ? 1 : 0; |
| const IGCTensor *biases_to_use = (append_biases) ? 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) + bias_element; |
| TensorShape shape_wr(mat_weights_cols, mat_weights_rows); |
| const DataType dt = weights->info()->data_type(); |
| const int fixed_point_position = weights->info()->fixed_point_position(); |
| TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); |
| |
| _weights_reshaped.allocator()->init(info_wr); |
| _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); |
| _weights_transposed_kernel.configure(&_weights_reshaped, output); |
| _weights_reshaped.allocator()->allocate(); |
| } |
| else |
| { |
| _weights_reshape_kernel.configure(weights, biases_to_use, output); |
| } |
| } |
| |
| void GCConvolutionLayerReshapeWeights::run() |
| { |
| GCScheduler::get().dispatch(_weights_reshape_kernel); |
| if(_transpose1xW) |
| { |
| GCScheduler::get().dispatch(_weights_transposed_kernel); |
| } |
| } |
| |
| GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _input_im2col_reshaped(), |
| _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) |
| { |
| } |
| |
| void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed) |
| { |
| _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); |
| } |
| |
| void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *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_MISMATCHING_DATA_TYPES(input, weights); |
| 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_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); |
| } |
| |
| const DataType dt = input->info()->data_type(); |
| |
| _append_bias = (biases != nullptr); |
| _are_weights_reshaped = weights_info.are_reshaped(); |
| |
| const unsigned bias_element = (_append_bias) ? 1 : 0; |
| const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr; |
| |
| // Get parameters from conv_info |
| unsigned int stride_x = 0; |
| unsigned int stride_y = 0; |
| std::tie(stride_x, stride_y) = conv_info.stride(); |
| |
| // 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().first : weights->info()->dimension(0); |
| const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1); |
| std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, |
| conv_info); |
| |
| // Check if its a "fully connected" convolution |
| _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); |
| const bool run_interleaved = (!_is_fully_connected_convolution); |
| |
| 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) + bias_element; |
| |
| // Reshape weights if needed |
| if(_are_weights_reshaped) |
| { |
| if(_is_fully_connected_convolution) |
| { |
| mat_weights_cols = weights->info()->dimension(0); |
| mat_weights_rows = weights->info()->dimension(1); |
| } |
| else |
| { |
| mat_weights_cols = weights_info.num_kernels(); |
| const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; |
| mat_weights_rows = quarter_reshaped_cols + bias_element; |
| } |
| } |
| else |
| { |
| if(_is_fully_connected_convolution) |
| { |
| // Create tensor to store the reshaped weights |
| int num_elems_read_per_iteration_x = 1; |
| if(dt == DataType::F16) |
| { |
| num_elems_read_per_iteration_x = 2; |
| } |
| TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows); |
| _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr)); |
| _reshape_weights.configure(weights, biases_to_use, &_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))); |
| _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt)); |
| _reshape_weights.configure(weights, biases_to_use, &_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); |
| |
| // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. |
| TensorInfo im2col_reshaped_info(shape_im2col, 1, dt, input->info()->fixed_point_position()); |
| _input_im2col_reshaped.allocator()->init(im2col_reshaped_info); |
| _memory_group.manage(&_input_im2col_reshaped); |
| |
| // Create tensor (interleave) to prepare input tensor for GEMM |
| if(run_interleaved) |
| { |
| TensorShape shape_interleaved = shape_im2col; |
| shape_interleaved.set(0, shape_interleaved.x() * 4); |
| shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); |
| |
| // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. |
| TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position()); |
| _input_interleaved_reshaped.allocator()->init(interleaved_info); |
| _memory_group.manage(&_input_interleaved_reshaped); |
| } |
| |
| // 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); |
| const DataType gemm_data_type = dt; |
| |
| // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. |
| TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position()); |
| _gemm_output.allocator()->init(info_gemm); |
| _memory_group.manage(&_gemm_output); |
| |
| // Configure kernels |
| if(dt == DataType::F16) |
| { |
| BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()); |
| input->info()->extend_padding(border_size); |
| _fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border |
| } |
| _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); |
| |
| // Configure matrix multiply |
| if(run_interleaved) |
| { |
| _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); |
| configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); |
| _input_interleaved_reshaped.allocator()->allocate(); |
| } |
| else |
| { |
| configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); |
| } |
| _input_im2col_reshaped.allocator()->allocate(); |
| |
| // Configure Col2Im |
| _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); |
| _gemm_output.allocator()->allocate(); |
| |
| 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"); |
| |
| // Allocate intermediate tensor |
| if(!_are_weights_reshaped) |
| { |
| _weights_reshaped.allocator()->allocate(); |
| } |
| } |
| |
| void GCConvolutionLayer::run() |
| { |
| // Run weights reshaping (Runs once for every configure) |
| if(!_are_weights_reshaped) |
| { |
| _are_weights_reshaped = true; |
| _reshape_weights.run(); |
| } |
| |
| _memory_group.acquire(); |
| |
| // Run im2col |
| GCScheduler::get().dispatch(_fill_border); |
| GCScheduler::get().memory_barrier(); |
| GCScheduler::get().dispatch(_input_im2col_kernel); |
| |
| if(!_is_fully_connected_convolution) |
| { |
| GCScheduler::get().memory_barrier(); |
| // Run interleave4x4 |
| GCScheduler::get().dispatch(_input_interleave_kernel); |
| } |
| |
| GCScheduler::get().memory_barrier(); |
| // Runs matrix multiply on reshaped matrices |
| GCScheduler::get().dispatch(_mm_kernel); |
| |
| GCScheduler::get().memory_barrier(); |
| // Reshape output matrix |
| GCScheduler::get().dispatch(_output_col2im_kernel, false); |
| |
| _memory_group.release(); |
| } |