From adb3291dda4e56de1af10e783b787445d6587a38 Mon Sep 17 00:00:00 2001 From: SiCong Li Date: Mon, 17 Feb 2020 16:39:27 +0000 Subject: COMPMID-3100 Fuse bias addition with fully connected layer NEON NEGEMM and NEGEMMLowpMatrixMultiplyCore are already fuse with bias addition. Expose them to NEFullyConnectedLayer. Change-Id: I42a909565bf49de1a019a07dc4dca11ae0981ada Signed-off-by: SiCongLi Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2769 Tested-by: Arm Jenkins Comments-Addressed: Arm Jenkins Reviewed-by: Gian Marco Iodice --- .../runtime/NEON/functions/NEFullyConnectedLayer.h | 14 +- examples/graph_deepspeech_v0_4_1.cpp | 5 +- .../NEON/functions/NEFullyConnectedLayer.cpp | 167 ++++++++------------- 3 files changed, 68 insertions(+), 118 deletions(-) diff --git a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h index 78f12daf9c..db09da45ee 100644 --- a/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h +++ b/arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h @@ -27,13 +27,11 @@ #include "arm_compute/runtime/IFunction.h" #include "arm_compute/core/NEON/kernels/NEFlattenLayerKernel.h" -#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.h" #include "arm_compute/core/NEON/kernels/NETransposeKernel.h" #include "arm_compute/runtime/MemoryGroup.h" #include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h" #include "arm_compute/runtime/NEON/functions/NEGEMM.h" #include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" -#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" #include "arm_compute/runtime/Tensor.h" namespace arm_compute @@ -107,7 +105,7 @@ private: * -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer) * -# @ref NEFullyConnectedLayerReshapeWeights (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once) * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric) - * -# @ref NEGEMMMatrixAccumulateBiasesKernel or @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is not equal to nullptr) + * -# @ref NEGEMMMatrixAdditionKernel or @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is not equal to nullptr) * * @note The fully connected layer accepts "weights" tensors only with 2 dimensions. */ @@ -164,9 +162,9 @@ public: void prepare() override; private: - void configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output); - void configure_conv_fc(const ITensor *input, const ITensor *weights, ITensor *output); - void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output); + void configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output); + void configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output); + void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output); MemoryGroup _memory_group; IWeightsManager *_weights_manager; @@ -177,17 +175,13 @@ private: weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged _reshape_weights_managed_function; NEGEMM _mm_gemm; NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp; - NEGEMMLowpOutputStage _gemmlowp_output_stage; - NEGEMMMatrixAccumulateBiasesKernel _accumulate_biases_kernel; Tensor _flatten_output; - Tensor _gemmlowp_output; Tensor _converted_weights_output; Tensor _reshape_weights_output; const ITensor *_original_weights; bool _are_weights_converted; bool _are_weights_reshaped; bool _is_fc_after_conv; - bool _accumulate_biases; bool _is_quantized; bool _is_prepared; }; diff --git a/examples/graph_deepspeech_v0_4_1.cpp b/examples/graph_deepspeech_v0_4_1.cpp index d2a4832bd1..ed44ffbee2 100644 --- a/examples/graph_deepspeech_v0_4_1.cpp +++ b/examples/graph_deepspeech_v0_4_1.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2019 ARM Limited. + * Copyright (c) 2019-2020 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -57,9 +57,6 @@ public: return false; } - // Checks - ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); - // Print parameter values std::cout << common_params << std::endl; diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp index 92ccd5d1cc..b5f406da8d 100644 --- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp +++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp @@ -39,24 +39,46 @@ using namespace arm_compute::misc::shape_calculator; namespace { -Status validate_mm(const ITensorInfo &input, const ITensorInfo &weights, const ITensorInfo &output) +Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output) { - if(is_data_type_quantized_asymmetric(input.data_type())) + if(is_data_type_quantized_asymmetric(input->data_type())) { // 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.quantization_info().uniform().scale, -input.quantization_info().uniform().offset); - const QuantizationInfo weights_quantization_info(weights.quantization_info().uniform().scale, -weights.quantization_info().uniform().offset); + const QuantizationInfo input_quantization_info(input->quantization_info().uniform().scale, -input->quantization_info().uniform().offset); + const QuantizationInfo weights_quantization_info(weights->quantization_info().uniform().scale, -weights->quantization_info().uniform().offset); + + const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); + + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int32_t output_multiplier; + int32_t output_shift; + ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); + + GEMMLowpOutputStageInfo gemmlowp_output_stage_info; + gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; + gemmlowp_output_stage_info.gemmlowp_shift = output_shift; + gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset; + gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + const auto min_max_bound = get_min_max(input->data_type()); + gemmlowp_output_stage_info.gemmlowp_min_bound = (std::get<0>(min_max_bound)).get(); + gemmlowp_output_stage_info.gemmlowp_max_bound = (std::get<1>(min_max_bound)).get(); + + GEMMInfo gemm_info; + gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); // Validate gemmlowp function - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input.clone()->set_quantization_info(input_quantization_info), - &weights.clone()->set_quantization_info(weights_quantization_info), - nullptr, - &output)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpMatrixMultiplyCore::validate(&input->clone()->set_quantization_info(input_quantization_info), + &weights->clone()->set_quantization_info(weights_quantization_info), + biases, + output, + gemm_info)); } else { - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(&input, &weights, nullptr, &output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */))); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMM::validate(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */))); } return Status{}; @@ -77,13 +99,12 @@ Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, c NEFullyConnectedLayer::NEFullyConnectedLayer(std::shared_ptr memory_manager, IWeightsManager *weights_manager) : _memory_group(std::move(memory_manager)), _weights_manager(weights_manager), _flatten_kernel(), _convert_weights(), _convert_weights_managed(), _reshape_weights_function(), - _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(), _gemmlowp_output_stage(), _accumulate_biases_kernel(), _flatten_output(), _gemmlowp_output(), - _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _accumulate_biases(false), - _is_quantized(false), _is_prepared(false) + _reshape_weights_managed_function(), _mm_gemm(nullptr, weights_manager), _mm_gemmlowp(), _flatten_output(), _converted_weights_output(), _reshape_weights_output(), _original_weights(nullptr), + _are_weights_converted(true), _are_weights_reshaped(false), _is_fc_after_conv(false), _is_quantized(false), _is_prepared(false) { } -void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output) +void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output) { if(_is_quantized) { @@ -95,8 +116,27 @@ void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *we input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); - // Configure gemmlowp function - _mm_gemmlowp.configure(input, weights, nullptr, output); + // Configure gemmlowp function and output stage for asymmetric quantized types + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); + + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int32_t output_multiplier; + int32_t output_shift; + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + + GEMMLowpOutputStageInfo gemmlowp_output_stage_info; + gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; + gemmlowp_output_stage_info.gemmlowp_shift = output_shift; + gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset; + gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; + const auto min_max_bound = get_min_max(input->info()->data_type()); + gemmlowp_output_stage_info.gemmlowp_min_bound = (std::get<0>(min_max_bound)).get(); + gemmlowp_output_stage_info.gemmlowp_max_bound = (std::get<1>(min_max_bound)).get(); + GEMMInfo gemm_info; + gemm_info.set_gemmlowp_output_stage(gemmlowp_output_stage_info); + _mm_gemmlowp.configure(input, weights, biases, output, gemm_info); // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers input->info()->set_quantization_info(input_quantization_info); @@ -105,11 +145,11 @@ void NEFullyConnectedLayer::configure_mm(const ITensor *input, const ITensor *we else { // Configure matrix multiply kernel - _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); + _mm_gemm.configure(input, weights, biases, output, 1.f, 1.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); } } -void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, ITensor *output) +void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output) { ARM_COMPUTE_ERROR_ON((weights->info()->dimension(1) != (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2)))); @@ -124,18 +164,18 @@ void NEFullyConnectedLayer::configure_conv_fc(const ITensor *input, const ITenso _flatten_kernel.configure(input, &_flatten_output); // Configure matrix multiply kernel - configure_mm(&_flatten_output, weights, output); + configure_mm(&_flatten_output, weights, biases, output); // Allocate the output tensor for flatten once all the configure methods have been called _flatten_output.allocator()->allocate(); } -void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, ITensor *output) +void NEFullyConnectedLayer::configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output) { ARM_COMPUTE_ERROR_ON(input->info()->dimension(0) != weights->info()->dimension(1)); // Configure matrix multiply kernel - configure_mm(input, weights, output); + configure_mm(input, weights, biases, output); } void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, @@ -152,7 +192,6 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh _are_weights_converted = true; _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; _is_fc_after_conv = true; - _accumulate_biases = false; _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); _original_weights = weights; @@ -161,21 +200,6 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh _weights_manager->manage(weights); } - // Configure gemmlowp output - if(_is_quantized) - { - _gemmlowp_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); - } - - // Configure accumulate biases kernel for non quantized asymmetric types - if(biases != nullptr && !_is_quantized) - { - _accumulate_biases = true; - - // Configure accumulate biases kernel - _accumulate_biases_kernel.configure(output, biases); - } - // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches // 2) Fully Connected layer -> Fully Connected layer without batches @@ -236,37 +260,15 @@ void NEFullyConnectedLayer::configure(const ITensor *input, const ITensor *weigh _are_weights_converted = false; } - ITensor *tmp_output = (_is_quantized) ? &_gemmlowp_output : output; if(_is_fc_after_conv) { // Fully Connected layer after a Convolution Layer without batches - configure_conv_fc(input, weights_to_use, tmp_output); + configure_conv_fc(input, weights_to_use, biases, output); } else { // Fully Connected layer after a Fully Connected Layer without batches - configure_fc_fc(input, weights_to_use, tmp_output); - } - - // Configure output stage for asymmetric quantized types - if(_is_quantized) - { - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int32_t output_multiplier; - int32_t output_shift; - quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); - - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; - gemmlowp_output_stage_info.gemmlowp_shift = output_shift; - gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset; - gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - _gemmlowp_output_stage.configure(&_gemmlowp_output, biases, output, gemmlowp_output_stage_info); - _gemmlowp_output.allocator()->allocate(); + configure_fc_fc(input, weights_to_use, biases, output); } _are_weights_reshaped = _are_weights_reshaped || fc_info.retain_internal_weights; @@ -283,19 +285,10 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; bool is_fc_after_conv = true; - bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); const ITensorInfo &flatten_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(input))); const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone()); - const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32)); - - // Configure accumulate biases kernel for non quantized asymmetric types - if(biases != nullptr && !is_quantized) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases)); - } // With the Fully Connected layer we can have 4 different cases: // 1) Convolution layer -> Fully Connected layer without batches @@ -305,7 +298,6 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn const ITensorInfo *input_to_use = input; const ITensorInfo *weights_to_use = weights; - const ITensorInfo *tmp_output = (is_quantized) ? &gemmlowp_output : output; // Check if we have a fully connected layer with batches const bool is_batched_fc_layer = output->dimension(1) > 1; @@ -353,27 +345,7 @@ Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorIn ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != weights_to_use->dimension(1)); } // Validate matrix multiply kernel - ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*input_to_use, *weights_to_use, *tmp_output)); - - // Validate output stage for asymmetric quantized types - if(is_quantized) - { - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int32_t output_multiplier; - int32_t output_shift; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); - - GEMMLowpOutputStageInfo gemmlowp_output_stage_info; - gemmlowp_output_stage_info.gemmlowp_multiplier = output_multiplier; - gemmlowp_output_stage_info.gemmlowp_shift = output_shift; - gemmlowp_output_stage_info.gemmlowp_offset = oq_info.offset; - gemmlowp_output_stage_info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMLowpOutputStage::validate(&gemmlowp_output, biases, output, gemmlowp_output_stage_info)); - } + ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(input_to_use, weights_to_use, biases, output)); return Status{}; } @@ -399,19 +371,6 @@ void NEFullyConnectedLayer::run() { _mm_gemm.run(); } - - // Accumulate biases if provided - if(_is_quantized) - { - _gemmlowp_output_stage.run(); - } - else - { - if(_accumulate_biases) - { - NEScheduler::get().schedule(&_accumulate_biases_kernel, Window::DimY); - } - } } void NEFullyConnectedLayer::prepare() -- cgit v1.2.1