diff options
Diffstat (limited to 'src/armnn/optimizations/FuseBatchNorm.hpp')
| -rw-r--r-- | src/armnn/optimizations/FuseBatchNorm.hpp | 152 |
1 files changed, 152 insertions, 0 deletions
diff --git a/src/armnn/optimizations/FuseBatchNorm.hpp b/src/armnn/optimizations/FuseBatchNorm.hpp new file mode 100644 index 0000000000..e8e8c5d77f --- /dev/null +++ b/src/armnn/optimizations/FuseBatchNorm.hpp @@ -0,0 +1,152 @@ +// +// Copyright © 2020 Arm Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#pragma once + +#include "Optimization.hpp" +#include <armnnUtils/DataLayoutIndexed.hpp> + +namespace armnn +{ +namespace optimizations +{ + +template <typename ConvLayer> +class FuseBatchNorm +{ +public: + /// Run for every exclusive connection between any base Convolution layer and a child BatchNorm layer for not + /// quantized layers. + /// The child will be removed, the base will be removed if it's left unconnected. A new Convolution layer will + /// be added, its weights and bias will be calculated using the weights and bias of the base Convolution layer + /// combined with the parameters of the child BatchNorm layer. + void Run(Graph& graph, InputSlot& connection) const + { + Layer& base = connection.GetConnectedOutputSlot()->GetOwningLayer(); + Layer& child = connection.GetOwningLayer(); + + ARMNN_ASSERT(base.GetType() == LayerType::Convolution2d); + ARMNN_ASSERT(child.GetType() == LayerType::BatchNormalization); + + if (base.GetDataType() == DataType::Float32 && child.GetDataType() == DataType::Float32) + { + OutputSlot* parentOut = base.GetInputSlot(0).GetConnectedOutputSlot(); + auto convLayer = PolymorphicDowncast<ConvLayer*>(&base); + auto batchNormLayer = PolymorphicDowncast<BatchNormalizationLayer*>(&child); + + // Read convolution and batch norm parameters + BatchNormalizationDescriptor batchNormDescriptor = batchNormLayer->GetParameters(); + auto epsilon = batchNormDescriptor.m_Eps; + IgnoreUnused(epsilon); + + ConstTensor betaTensor(batchNormLayer->m_Beta->GetTensorInfo(), batchNormLayer->m_Beta->Map(true)); + ConstTensor gammaTensor(batchNormLayer->m_Gamma->GetTensorInfo(), batchNormLayer->m_Gamma->Map(true)); + ConstTensor meanTensor(batchNormLayer->m_Mean->GetTensorInfo(), batchNormLayer->m_Mean->Map(true)); + ConstTensor varTensor(batchNormLayer->m_Variance->GetTensorInfo(), batchNormLayer->m_Variance->Map(true)); + + auto convDescriptor = convLayer->GetParameters(); + ConstTensor weightsTensor(convLayer->m_Weight->GetTensorInfo(), convLayer->m_Weight->Map(true)); + + armnnUtils::DataLayoutIndexed dataLayout(convDescriptor.m_DataLayout); + auto weightsShape = convLayer->m_Weight->GetTensorInfo().GetShape(); + const unsigned int outputChannels = weightsShape[0]; + const unsigned int inputChannels = weightsShape[dataLayout.GetChannelsIndex()]; + const unsigned int weightsHeight = weightsShape[dataLayout.GetHeightIndex()]; + const unsigned int weightsWidth = weightsShape[dataLayout.GetWidthIndex()]; + + const auto* weightsBuffer = static_cast<const float*>(weightsTensor.GetMemoryArea()); + const auto* betaBuffer = static_cast<const float*>(betaTensor.GetMemoryArea()); + const auto* gammaBuffer = static_cast<const float*>(gammaTensor.GetMemoryArea()); + const auto* meanBuffer = static_cast<const float*>(meanTensor.GetMemoryArea()); + const auto* varBuffer = static_cast<const float*>(varTensor.GetMemoryArea()); + + std::vector<float> weightsVector (weightsBuffer, weightsBuffer + weightsTensor.GetNumElements()); + std::vector<float> betaVector (betaBuffer, betaBuffer + betaTensor.GetNumElements()); + std::vector<float> gammaVector (gammaBuffer, gammaBuffer + gammaTensor.GetNumElements()); + std::vector<float> meanVector (meanBuffer, meanBuffer + meanTensor.GetNumElements()); + std::vector<float> varianceVector(varBuffer, varBuffer + varTensor.GetNumElements()); + + // fusedWeights = ( gamma * weights ) / ( std - epsilon); + std::vector<float> fusedWeightsVector(weightsVector.size()); + + unsigned int i = 0; + for (unsigned int cOut = 0; cOut < outputChannels; ++cOut) + { + auto mult = gammaVector[cOut] / sqrtf (varianceVector[cOut] + epsilon); + for (unsigned int cInput = 0; cInput < inputChannels; ++cInput) + { + for (unsigned int h = 0; h < weightsHeight; ++h) + { + for (unsigned int w = 0; w < weightsWidth; ++w) + { + fusedWeightsVector[i] = mult * weightsVector[i]; + i++; + } + } + } + } + ConstTensor fusedWeightsTensor(convLayer->m_Weight->GetTensorInfo(), fusedWeightsVector); + + // fusedBias = (gamma * (bias - mean)) / (variance - epsilon) + beta; + std::vector<float> fusedBiasVector(outputChannels); + if (convDescriptor.m_BiasEnabled) + { + ARMNN_ASSERT_MSG(convLayer->m_Bias != nullptr, + "FuseBatchNorm: Bias data should not be null if bias is enabled."); + + ConstTensor biasTensor(convLayer->m_Bias->GetTensorInfo(), convLayer->m_Bias->Map(true)); + const auto* biasBuffer = static_cast<const float*>(biasTensor.GetMemoryArea()); + std::vector<float> biasVector(biasBuffer, biasBuffer + biasTensor.GetNumElements()); + + for (unsigned int cOut = 0; cOut < outputChannels; ++cOut) + { + fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) / + sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut]; + } + } + else + { + convDescriptor.m_BiasEnabled = true; + std::vector<float> biasVector(outputChannels, 0); + + for (unsigned int cOut = 0; cOut < outputChannels; ++cOut) + { + fusedBiasVector[cOut] = ((gammaVector[cOut] * (biasVector[cOut] - meanVector[cOut])) / + sqrtf(varianceVector[cOut] + epsilon)) + betaVector[cOut]; + } + } + ConstTensor fusedBiasTensor(TensorInfo({outputChannels}, DataType::Float32), fusedBiasVector); + + // Insert the new convolution layer that has batch norm parameters fused into + const std::string name = std::string("fused-") + child.GetName() + std::string("-into-") + base.GetName(); + auto& newConv2dLayer = *graph.InsertNewLayer<ConvLayer>(base.GetInputSlot(0), + convDescriptor, + name.c_str()); + newConv2dLayer.m_Weight = std::make_unique<ScopedCpuTensorHandle>(fusedWeightsTensor); + newConv2dLayer.m_Bias = std::make_unique<ScopedCpuTensorHandle>(ConstTensor(fusedBiasTensor)); + + // Reconnects with original parent. + newConv2dLayer.GetOutputSlot().MoveAllConnections(*parentOut); + // Parent is now the new convolution2d layer. + parentOut = &newConv2dLayer.GetOutputSlot(); + + // Moves connections in child output to parent layer. + // Child layer will be removed as it's left unconnected. + // Base layer will be removed if left unconnected. + child.GetOutputSlot().MoveAllConnections(*parentOut); + } + } +protected: + FuseBatchNorm() = default; + ~FuseBatchNorm() = default; +}; + +using FuseBatchNormIntoConvolution2D = + OptimizeForExclusiveConnection<Convolution2dLayer, + BatchNormalizationLayer, + FuseBatchNorm<Convolution2dLayer>>; + +} // namespace optimizations +} // namespace armnn
\ No newline at end of file |