From e855c237a5b61c4ed5a5ab79dd4af27385cf72f5 Mon Sep 17 00:00:00 2001 From: Stephen Li Date: Thu, 4 Jan 2018 14:13:22 +0800 Subject: APPBROWSER-377: GCConvoutionLayer support for FP16 Change-Id: I801b5e393a16a9f92c062826e6fcfd5982ca7bb3 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116584 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- .../GLES_COMPUTE/functions/GCConvolutionLayer.cpp | 285 +++++++++++++++++++++ .../functions/GCFullyConnectedLayer.cpp | 4 +- 2 files changed, 287 insertions(+), 2 deletions(-) create mode 100644 src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp (limited to 'src/runtime/GLES_COMPUTE') diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp new file mode 100644 index 0000000000..5689722340 --- /dev/null +++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp @@ -0,0 +1,285 @@ +/* + * 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 +#include +#include + +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() + : _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(transpose_width), static_cast(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); + + // 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); + } + + // 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); + + // 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(); + } + + // 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); +} diff --git a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp index 041622d255..9e4f0f6c95 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCFullyConnectedLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -61,7 +61,7 @@ void GCFullyConnectedLayer::configure_conv_fc(const IGCTensor *input, const IGCT _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, dt)); // Configure im2col kernel - _im2col_kernel.configure(input, &_im2col_output, std::make_pair(1, 1), PadStrideInfo(1, 1, 0, 0), false); + _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false); // Configure matrix multiply kernel _mm_kernel.configure(&_im2col_output, weights, output, 1.0f, false); -- cgit v1.2.1