From 2d7e683e79c8ad328d4930c1f82a46827313faf4 Mon Sep 17 00:00:00 2001 From: George Wort Date: Fri, 22 Feb 2019 16:37:41 +0000 Subject: COMPMID-1694: Fuse offset contribution with the output stage when we use NEGEMMLowpMatrixMultiplyCore Change-Id: Ic1a681e4cc03e1eba3bf8485d9cdb17b3e926047 Signed-off-by: giuros01 Reviewed-on: https://review.mlplatform.org/c/561 Reviewed-by: Gian Marco Iodice Tested-by: Arm Jenkins --- .../NEON/functions/NEGEMMConvolutionLayer.cpp | 241 +++++++++------------ 1 file changed, 106 insertions(+), 135 deletions(-) (limited to 'src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp') diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index be7cc2d0e1..b6c37349c1 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -90,16 +90,17 @@ void NEConvolutionLayerReshapeWeights::run() } NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager) - : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _col2im_kernel(), _activationlayer_function(), - _add_bias_kernel(), _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), - _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false) + : _memory_group(memory_manager), _reshape_weights(), _im2col_kernel(), _mm_gemm(memory_manager), _mm_gemmlowp(memory_manager), _col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), + _reshape_layer(), _original_weights(nullptr), _im2col_output(), _weights_reshaped(), _gemm_output(), _tmp_output(), _data_layout(DataLayout::NCHW), _append_bias(false), _skip_im2col(false), + _skip_col2im(false), _is_quantized(false), _is_activationlayer_enabled(false), _is_prepared(false) { } -void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, int gemm_3d_depth) +void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info, int gemm_3d_depth) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); - ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info(), gemm_3d_depth, _skip_im2col)); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), biases == nullptr ? nullptr : biases->info(), output == nullptr ? nullptr : output->info(), act_info, gemm_3d_depth, + _skip_im2col)); const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, _skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); @@ -114,7 +115,40 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w 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, nullptr, output, gemm_info); + const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quantization_info : output->info()->quantization_info(); + + float multiplier = input_quantization_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + // Merge activation with output stage + int min_activation = 0; + int max_activation = 0; + + const std::set supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, + ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU + }; + if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0) + { + const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); + const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + + min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; + max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; + + _is_activationlayer_enabled = false; + } + + GEMMLowpOutputStageInfo output_info; + output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + output_info.gemmlowp_offset = output_quant_info.offset; + output_info.gemmlowp_multiplier = output_multiplier; + output_info.gemmlowp_shift = output_shift; + output_info.gemmlowp_min_bound = min_activation; + output_info.gemmlowp_max_bound = max_activation; + + _mm_gemmlowp.configure(input, weights, biases, output, GEMMInfo(false, false, true, gemm_3d_depth, _skip_im2col, false, output_info)); // Revert back QuantizatioInfo as input and weights could be used in other convolution layers input->info()->set_quantization_info(input_quantization_info); @@ -127,9 +161,11 @@ void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *w } } -Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth, bool skip_im2col) +Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info, + int gemm_3d_depth, bool skip_im2col) { - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + const bool is_activation_enabled = act_info.enabled(); const GEMMInfo &gemm_info = GEMMInfo(false, false, true /* Reshape weights only for the first run */, gemm_3d_depth, skip_im2col /* Reinterpret the input as 3D if im2col is skipped */); @@ -145,8 +181,39 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens input_qa->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quantization_info : output->quantization_info(); + + float multiplier = input_quantization_info.scale * weights->quantization_info().scale / output_quant_info.scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + + // Merge activation with output stage + int min_activation = 0; + int max_activation = 0; + + const std::set supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, + ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, + ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU + }; + if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0) + { + const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); + const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); + + min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; + max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; + } + + GEMMLowpOutputStageInfo output_info; + output_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + output_info.gemmlowp_offset = output_quant_info.offset; + output_info.gemmlowp_multiplier = output_multiplier; + output_info.gemmlowp_shift = output_shift; + output_info.gemmlowp_min_bound = min_activation; + output_info.gemmlowp_max_bound = max_activation; + // Perform validation step on GEMMLowp - return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), nullptr, output, gemm_info); + return NEGEMMLowpMatrixMultiplyCore::validate(input_qa.get(), weights_qa.get(), biases, output, GEMMInfo(false, false, true, gemm_3d_depth, skip_im2col, false, output_info)); } else { @@ -155,19 +222,18 @@ Status NEGEMMConvolutionLayer::validate_mm(const ITensorInfo *input, const ITens } } -Status NEGEMMConvolutionLayer::validate_gemm3d(DataType data_type, int gemm_3d_depth, bool skip_im2col) +Status NEGEMMConvolutionLayer::validate_gemm3d(const ITensorInfo *input_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col) { - const bool is_quantized = is_data_type_quantized_asymmetric(data_type); - const DataType output_gemm_data_type = is_quantized ? DataType::S32 : data_type; - const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth; - const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U; + const DataType data_type = input_info->data_type(); + const unsigned int mult_y = skip_im2col ? 1U : gemm_3d_depth; + const unsigned int mult_z = skip_im2col ? gemm_3d_depth : 1U; // Set dummy tensor shapes for the validation - const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type); + const TensorInfo dummy_input_info(TensorShape(4U, 4U * mult_y, 1U * mult_z), 1, data_type, input_info->quantization_info()); const TensorInfo dummy_weights_info(TensorShape(4U, 4U), 1, data_type); - const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, output_gemm_data_type); + const TensorInfo dummy_output_info(TensorShape(4U, 4U, gemm_3d_depth), 1, data_type, input_info->quantization_info()); - return validate_mm(&dummy_input_info, &dummy_weights_info, &dummy_output_info, gemm_3d_depth, skip_im2col); + return validate_mm(&dummy_input_info, &dummy_weights_info, nullptr, &dummy_output_info, act_info, gemm_3d_depth, skip_im2col); } void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, @@ -202,9 +268,8 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig _append_bias = (biases != nullptr) && (!_is_quantized); _is_activationlayer_enabled = act_info.enabled(); - const ITensor *gemm_input_to_use = input; - ITensor *gemm_output_to_use = output; - ITensor *gemm_output_staged_to_use = output; + const ITensor *gemm_input_to_use = input; + ITensor *gemm_output_to_use = output; // Get convolved dimensions unsigned int conv_w = 0; @@ -219,7 +284,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig // Check if GEMM3D is supported if(data_layout == DataLayout::NHWC) { - _skip_col2im = bool(validate_gemm3d(input->info()->data_type(), conv_h, true)); + _skip_col2im = bool(validate_gemm3d(input->info(), act_info, conv_h, true)); // If not supported, we need to perform im2col and col2im (or reshape layer) if(!_skip_col2im) { @@ -262,26 +327,17 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig } // Create temporary GEMM output tensor in case we cannot skip col2im - if(!_skip_col2im || _is_quantized) + if(!_skip_col2im) { - // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. - const DataType gemm_data_type = _is_quantized ? DataType::S32 : data_type; - TensorShape shape_gemm; + TensorShape shape_gemm; - if(_is_quantized && _skip_col2im) - { - shape_gemm = output->info()->tensor_shape(); - } - else - { - // Calculate GEMM output shape - shape_gemm = _im2col_output.info()->tensor_shape(); - shape_gemm.set(0, mat_weights_cols); - shape_gemm.set(1, conv_w * conv_h); - } + // Calculate GEMM output shape + shape_gemm = _im2col_output.info()->tensor_shape(); + shape_gemm.set(0, mat_weights_cols); + shape_gemm.set(1, conv_w * conv_h); // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo info_gemm(shape_gemm, 1, gemm_data_type); + TensorInfo info_gemm(shape_gemm, 1, data_type); info_gemm.set_quantization_info(output->info()->quantization_info()).set_data_layout(input->info()->data_layout()); _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); @@ -293,62 +349,24 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig // Configure GEMM // In case we need to skip col2im, GEMM3D (gemm_3d_depth != 0) must be called in order to avoid reshaping the output matrix const unsigned int gemm_3d_depth = _skip_col2im ? conv_h : 0; - configure_mm(gemm_input_to_use, &_weights_reshaped, gemm_output_to_use, gemm_3d_depth); + configure_mm(gemm_input_to_use, &_weights_reshaped, biases, gemm_output_to_use, act_info, gemm_3d_depth); if(!_skip_im2col) { _im2col_output.allocator()->allocate(); } - // Configure output stage for quantized case - if(_is_quantized) - { - const QuantizationInfo input_quant_info = input->info()->quantization_info(); - const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input_quant_info : output->info()->quantization_info(); - - float multiplier = input_quant_info.scale * weights->info()->quantization_info().scale / output_quant_info.scale; - int output_multiplier, output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - - if(!_skip_col2im) - { - _memory_group.manage(&_tmp_output); - gemm_output_staged_to_use = &_tmp_output; - } - - // Merge activation with output stage - int min_activation = 0; - int max_activation = 0; - - const std::set supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - if(_is_activationlayer_enabled && supported_acts.count(act_info.activation()) != 0) - { - const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); - const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); - - min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; - max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; - - _is_activationlayer_enabled = false; - } - - _gemmlowp_output_stage.configure(gemm_output_to_use, biases, gemm_output_staged_to_use, output_multiplier, output_shift, output_quant_info.offset, min_activation, max_activation); - } - if(!_skip_col2im) { if(_data_layout == DataLayout::NCHW) { // Configure col2im - _col2im_kernel.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output, Size2D(conv_w, conv_h)); + _col2im_kernel.configure(gemm_output_to_use, output, Size2D(conv_w, conv_h)); } else { // Configure reshape layer - _reshape_layer.configure(_is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, output); + _reshape_layer.configure(gemm_output_to_use, output); } } @@ -395,10 +413,9 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI const unsigned int kernel_height = weights->dimension(idx_height); TensorInfo im2col_reshaped_info, info_gemm, tmp_info, weights_reshaped_info; - const ITensorInfo *gemm_input_to_use = input; - const ITensorInfo *gemm_output_to_use = output; - const ITensorInfo *gemm_output_staged_to_use = output; - const ITensorInfo *weights_to_use = weights; + const ITensorInfo *gemm_input_to_use = input; + const ITensorInfo *gemm_output_to_use = output; + const ITensorInfo *weights_to_use = weights; const bool is_quantized = is_data_type_quantized_asymmetric(data_type); const bool append_bias = (biases != nullptr) && (!is_quantized); @@ -420,7 +437,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI bool skip_col2im = false; if(data_layout == DataLayout::NHWC) { - skip_col2im = bool(validate_gemm3d(input->data_type(), conv_h, true)); + skip_col2im = bool(validate_gemm3d(input, act_info, conv_h, true)); // If not supported, we need to perform im2col and col2im (or reshape layer) if(!skip_col2im) { @@ -431,7 +448,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI if(skip_col2im) { // If not supported, we need to perform im2col and col2im (or reshape layer) - if(!bool(validate_gemm3d(input->data_type(), conv_h, skip_im2col))) + if(!bool(validate_gemm3d(input, act_info, conv_h, skip_im2col))) { skip_im2col = false; skip_col2im = false; @@ -495,68 +512,25 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI } // Create temporary GEMM output tensor in case we cannot skip col2im - const DataType gemm_data_type = is_quantized ? DataType::S32 : data_type; if(!skip_col2im) { TensorShape shape_gemm = gemm_input_to_use->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, conv_w * conv_h); - info_gemm = TensorInfo(shape_gemm, 1, gemm_data_type); + info_gemm = TensorInfo(shape_gemm, 1, data_type); } else { - info_gemm = TensorInfo(output->tensor_shape(), 1, gemm_data_type); + info_gemm = TensorInfo(output->tensor_shape(), 1, data_type); } info_gemm.set_quantization_info(output->quantization_info()).set_data_layout(input->data_layout()); gemm_output_to_use = &info_gemm; - - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, gemm_output_to_use, skip_col2im ? conv_h : 0, skip_im2col)); - - if(is_quantized) - { - const QuantizationInfo input_quant_info = input->quantization_info(); - const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input_quant_info : output->quantization_info(); - const float multiplier = input_quant_info.scale * weights_to_use->quantization_info().scale / output_quant_info.scale; - int output_multiplier, output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - - if(!skip_col2im) - { - tmp_info = TensorInfo(gemm_output_to_use->tensor_shape(), 1, DataType::QASYMM8); - tmp_info.set_quantization_info(output->quantization_info()).set_data_layout(data_layout); - gemm_output_staged_to_use = &tmp_info; - } - - // Merge activation with output stage - int min_activation = 0; - int max_activation = 0; - - const std::set supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, - ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, - ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU - }; - - if(is_activation_enabled && supported_acts.count(act_info.activation()) != 0) - { - const int a_const_int = output_quant_info.quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); - const int b_const_int = output_quant_info.quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); - - min_activation = act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU ? output_quant_info.offset : b_const_int; - max_activation = act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU ? 255 : a_const_int; - - is_activation_enabled = false; - } - - // Validate output stage for quantized case - NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint::validate(gemm_output_to_use, biases, gemm_output_staged_to_use, min_activation, max_activation); - } + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases, gemm_output_to_use, act_info, skip_col2im ? conv_h : 0, skip_im2col)); // Validate Col2Im/ReshapeLayer if(!skip_col2im && (data_layout == DataLayout::NCHW)) { - ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(is_quantized ? gemm_output_staged_to_use : gemm_output_to_use, - output, - Size2D(conv_w, conv_h))); + ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(gemm_output_to_use, output, Size2D(conv_w, conv_h))); } //Validate Activation Layer @@ -586,9 +560,6 @@ void NEGEMMConvolutionLayer::run() { // Run gemmlowp _mm_gemmlowp.run(); - - // Run output stage - _gemmlowp_output_stage.run(); } else { -- cgit v1.2.1