From f07d28d9ee8ae73a93fe433f72855b6dcf58ad90 Mon Sep 17 00:00:00 2001 From: Isabella Gottardi Date: Tue, 6 Feb 2018 14:52:43 +0000 Subject: COMPMID-845: Create a ConvolutionLayer for CL Change-Id: Ifcc406d2d0a99c911d6b6c875657b0e0028255d5 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/119148 Tested-by: Jenkins Reviewed-by: Anthony Barbier Reviewed-by: Georgios Pinitas --- .../CL/functions/CLGEMMConvolutionLayer.cpp | 353 +++++++++++++++++++++ 1 file changed, 353 insertions(+) create mode 100644 src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp (limited to 'src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp new file mode 100644 index 0000000000..c4cfe1e24c --- /dev/null +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -0,0 +1,353 @@ +/* + * 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/CL/functions/CLGEMMConvolutionLayer.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/core/utils/quantization/AsymmHelpers.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +#include +#include +#include + +using namespace arm_compute; + +CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _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_NULLPTR(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 ICLTensor *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); + _memory_group.manage(&_weights_reshaped); + _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); + } + + output->info()->set_quantization_info(weights->info()->quantization_info()); +} + +void CLConvolutionLayerReshapeWeights::run() +{ + _memory_group.acquire(); + + CLScheduler::get().enqueue(_weights_reshape_kernel); + if(_transpose1xW) + { + CLScheduler::get().enqueue(_weights_transposed_kernel); + } + + _memory_group.release(); +} + +CLGEMMConvolutionLayer::CLGEMMConvolutionLayer(std::shared_ptr memory_manager) + : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _interleave_kernel(), _mm_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), + _col2im_kernel(), _im2col_output(), _interleave_output(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _are_weights_reshaped(false), _is_quantized(false), + _is_interleaved_transposed(false) +{ +} + +void CLGEMMConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed, bool are_weights_reshaped) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); + if(_is_quantized) + { + if(are_weights_reshaped) + { + ARM_COMPUTE_ERROR("Weights already reshaped are not suppported with gemmlowp"); + } + else + { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); + weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + + _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + + // Revert back QuantizatioInfo as input and weights could be used in other convolution layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); + } + } + else + { + if(are_weights_reshaped) + { + // Configure matrix multiply kernel + _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + } + else + { + // Configure matrix multiply function + _mm_gemm.configure(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + } + } +} + +void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); + ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && CLScheduler::get().target() == GPUTarget::BIFROST); + 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); + ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type())); + + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + + if(biases != nullptr) + { + if(_is_quantized) + { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + 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(); + + // Set the GPU target for matrix multiply and im2col and col2im + _mm_kernel.set_target(CLScheduler::get().target()); + _im2col_kernel.set_target(CLScheduler::get().target()); + _col2im_kernel.set_target(CLScheduler::get().target()); + + const bool append_bias = (biases != nullptr) && (!_is_quantized); + _are_weights_reshaped = weights_info.are_reshaped(); + + const unsigned bias_element = (append_bias) ? 1 : 0; + const ICLTensor *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 + const bool is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); + _is_interleaved_transposed = (!is_fully_connected_convolution) && (!_is_quantized) && (_are_weights_reshaped); + + 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 || _is_quantized) + { + 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 + { + // _weights_reshaped will be auto configured in the kernel. + // Just append biases and do not transpose 1xW as it will be reshaped in CLGEMM + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false); + + 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()); + im2col_reshaped_info.set_quantization_info(input->info()->quantization_info()); + _im2col_output.allocator()->init(im2col_reshaped_info); + _memory_group.manage(&_im2col_output); + + // Create GEMM output tensor + TensorShape shape_gemm = _im2col_output.info()->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, mat_input_rows); + const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt; + // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. + // 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()); + info_gemm.set_quantization_info(output->info()->quantization_info()); + _gemm_output.allocator()->init(info_gemm); + _memory_group.manage(&_gemm_output); + + // Configure im2col + _im2col_kernel.configure(input, &_im2col_output, Size2D(kernel_width, kernel_height), conv_info, append_bias); + + // Configure matrix multiply + if(_is_interleaved_transposed) + { + // Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel + _memory_group.manage(&_interleave_output); + _interleave_kernel.configure(&_im2col_output, &_interleave_output); + + // Configure GEMM + configure_mm(&_interleave_output, weights, &_gemm_output, true, _are_weights_reshaped); + _interleave_output.allocator()->allocate(); + } + else + { + configure_mm(&_im2col_output, weights, &_gemm_output, false, _are_weights_reshaped); + } + _im2col_output.allocator()->allocate(); + + // Configure output stage for quantized case + if(_is_quantized) + { + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _memory_group.manage(&_tmp_output); + _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset); + } + + // Configure Col2Im + _col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h)); + if(_is_quantized) + { + _tmp_output.allocator()->allocate(); + } + _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 CLGEMMConvolutionLayer::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 + CLScheduler::get().enqueue(_im2col_kernel); + + // Note: _is_interleaved_transposed is true only if the weights passed to the function have been passed already reshaped + // and if we do not have QASYMM8 data type. If this flag is true, we need to run the + // gemm kernel instead of gemm function + if(_is_interleaved_transposed) + { + // Run interleave4x4 kernel + CLScheduler::get().enqueue(_interleave_kernel); + + // Run matrix multiply kernel + CLScheduler::get().enqueue(_mm_kernel); + } + else + { + // Runs CLGEMM or CLGEMMLowpMatrixMultiplyCore functions + if(_is_quantized) + { + // Run gemmlowp + _mm_gemmlowp.run(); + + // Run output stage + _gemmlowp_output_stage.run(); + } + else + { + // Run gemm + _mm_gemm.run(); + } + } + + // Reshape output matrix + CLScheduler::get().enqueue(_col2im_kernel, false); + + _memory_group.release(); +} -- cgit v1.2.1