From 5124be5d1caa70964d452cf9a8cc7c67df31fa9d Mon Sep 17 00:00:00 2001 From: Chunosov Date: Wed, 22 Nov 2017 20:42:13 +0700 Subject: COMPMID-661: Convolution quantized (#32) Change-Id: Id69df4ce98d1d89bdf9c9aa5c4d909659909b30f Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110456 Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com Reviewed-by: Georgios Pinitas Reviewed-by: Anthony Barbier --- src/runtime/CL/functions/CLConvolutionLayer.cpp | 153 ++++++++++++++++++------ 1 file changed, 115 insertions(+), 38 deletions(-) (limited to 'src/runtime/CL/functions/CLConvolutionLayer.cpp') diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 8d45416b30..66548d19b2 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -27,6 +27,7 @@ #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 @@ -42,19 +43,22 @@ CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_p 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::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, 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(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 _has_bias = (biases != nullptr); + 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; @@ -62,7 +66,7 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const { // 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); + 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(); @@ -70,13 +74,13 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const _weights_reshaped.allocator()->init(info_wr); _memory_group.manage(&_weights_reshaped); - _weights_reshape_kernel.configure(weights, biases, &_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, output); + _weights_reshape_kernel.configure(weights, biases_to_use, output); } } @@ -95,36 +99,73 @@ void CLConvolutionLayerReshapeWeights::run() } CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _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) + : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(), + _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), _is_quantized(false) { } +void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed) +{ + if(_is_quantized) + { + // 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 + { + _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + } +} + 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::QS8, DataType::QS16, DataType::F16, DataType::F32); + 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() && 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) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + 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(); - const int fixed_point_position = input->info()->fixed_point_position(); + const DataType dt = input->info()->data_type(); // Set the GPU target for matrix multiply _mm_kernel.set_target(CLScheduler::get().target()); - _has_bias = (biases != nullptr); + _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; @@ -141,36 +182,36 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig // 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 && !_is_quantized); 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); + 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) { mat_weights_cols = weights_info.num_kernels(); const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols); + mat_weights_rows = quarter_reshaped_cols + bias_element; } else { - if(_is_fully_connected_convolution) + if(_is_fully_connected_convolution || _is_quantized) { // 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 */); + _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))); - 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_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_reshaped.info()->set_quantization_info(weights->info()->quantization_info()); weights = &_weights_reshaped; } @@ -181,16 +222,16 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig 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)); + _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); _memory_group.manage(&_input_im2col_reshaped); // Create tensor (interleave) to prepare input tensor for GEMM - if(!_is_fully_connected_convolution) + 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)); - _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); + _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved)); _memory_group.manage(&_input_interleaved_reshaped); } @@ -198,30 +239,51 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig 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)); + 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. + TensorInfo info_gemm(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm).set_data_type(gemm_data_type).set_quantization_info( + output->info()->quantization_info())); + _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); // Configure kernels - _input_im2col_kernel.set_target(CLScheduler::get().target()); - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); // Configure matrix multiply - if(_is_fully_connected_convolution) + if(run_interleaved) { - // The matrix A and Matrix B have not been reshaped - _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f, false); + _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); + configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); + _input_interleaved_reshaped.allocator()->allocate(); } else { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); - _input_interleaved_reshaped.allocator()->allocate(); + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); } _input_im2col_reshaped.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); + _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset); + _gemm_output.allocator()->allocate(); + } + + // Configure Col2Im _output_col2im_kernel.set_target(CLScheduler::get().target()); - _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); - _gemm_output.allocator()->allocate(); + _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h)); + if(_is_quantized) + { + _tmp_output.allocator()->allocate(); + } + else + { + _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"); @@ -243,15 +305,30 @@ void CLConvolutionLayer::run() _memory_group.acquire(); - // Run input reshaping + // Run im2col CLScheduler::get().enqueue(_input_im2col_kernel); - if(!_is_fully_connected_convolution) + + if(!_is_fully_connected_convolution && !_is_quantized) { + // Run interleave4x4 CLScheduler::get().enqueue(_input_interleave_kernel); } // Runs matrix multiply on reshaped matrices - CLScheduler::get().enqueue(_mm_kernel); + if(_is_quantized) + { + _mm_gemmlowp.run(); + } + else + { + CLScheduler::get().enqueue(_mm_kernel); + } + + // Run output stage for quantized case + if(_is_quantized) + { + _gemmlowp_output_stage.run(); + } // Reshape output matrix CLScheduler::get().enqueue(_output_col2im_kernel, false); -- cgit v1.2.1