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author | kevmay01 <kevin.may@arm.com> | 2018-09-26 10:21:55 +0100 |
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committer | Matthew Bentham <matthew.bentham@arm.com> | 2018-10-10 16:16:57 +0100 |
commit | e448be3ac55897a3eabe85962891f8414f8e3cf9 (patch) | |
tree | 11e46d4979eb6d8d70c7f43d5cf690fc6f78d605 /src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp | |
parent | 9fc824a596d6bddd27e5aa1438b115e71a117aa3 (diff) | |
download | armnn-e448be3ac55897a3eabe85962891f8414f8e3cf9.tar.gz |
IVGCVSW-1927 Add Neon 8-bit FullyConnected support
Change-Id: Idf4cc7a9a7d3261b9eceb653b999257506cdae76
Diffstat (limited to 'src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp')
-rw-r--r-- | src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp | 110 |
1 files changed, 110 insertions, 0 deletions
diff --git a/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp b/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp new file mode 100644 index 0000000000..8cebb4f48f --- /dev/null +++ b/src/backends/NeonWorkloads/NeonFullyConnectedWorkload.cpp @@ -0,0 +1,110 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonFullyConnectedWorkload.hpp" + +#include <backends/aclCommon/ArmComputeTensorUtils.hpp> +#include <backends/aclCommon/ArmComputeUtils.hpp> +#include <backends/CpuTensorHandle.hpp> + +namespace armnn +{ +using namespace armcomputetensorutils; + +arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const TensorInfo& weights, + const TensorInfo& biases, + const FullyConnectedDescriptor& descriptor) +{ + const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output); + const arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights); + + arm_compute::TensorInfo aclBiases; + arm_compute::TensorInfo *optionalAclBiases = nullptr; + if (descriptor.m_BiasEnabled) + { + aclBiases = BuildArmComputeTensorInfo(biases); + optionalAclBiases = &aclBiases; + } + + const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo = + ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor); + + + return arm_compute::NEFullyConnectedLayer::validate(&aclInput, + &aclWeights, + optionalAclBiases, + &aclOutput, + fullyConnectedLayerInfo); +} + +NeonFullyConnectedWorkload::NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor, + const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) + : BaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info) + , m_FullyConnectedLayer(memoryManager) +{ + m_Data.ValidateInputsOutputs("NeonFullyConnectedWorkload", 1, 1); + + arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + + m_WeightsTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_WeightsTensor, m_Data.m_Weight->GetTensorInfo()); + + if (m_Data.m_Parameters.m_BiasEnabled) + { + m_BiasesTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo()); + } + + // Construct + arm_compute::FullyConnectedLayerInfo fc_info; + fc_info.transpose_weights = m_Data.m_Parameters.m_TransposeWeightMatrix; + m_FullyConnectedLayer.configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info); + + // Allocate + if (m_Data.m_Weight->GetTensorInfo().GetDataType() == DataType::QuantisedAsymm8) + { + InitialiseArmComputeTensorData(*m_WeightsTensor, m_Data.m_Weight->GetConstTensor<uint8_t>()); + } + else + { + InitializeArmComputeTensorDataForFloatTypes(*m_WeightsTensor, m_Data.m_Weight); + } + + if (m_BiasesTensor) + { + if (m_Data.m_Bias->GetTensorInfo().GetDataType() == DataType::Signed32) + { + InitialiseArmComputeTensorData(*m_BiasesTensor, m_Data.m_Bias->GetConstTensor<int32_t>()); + } + else + { + InitializeArmComputeTensorDataForFloatTypes(*m_BiasesTensor, m_Data.m_Bias); + } + } + + // Force Compute Library to perform the necessary copying and reshaping, after which + // delete all the input tensors that will no longer be needed + m_FullyConnectedLayer.prepare(); + FreeUnusedTensors(); +} + +void NeonFullyConnectedWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonFullyConnectedWorkload_Execute"); + m_FullyConnectedLayer.run(); +} + +void NeonFullyConnectedWorkload::FreeUnusedTensors() +{ + FreeTensorIfUnused(m_WeightsTensor); + FreeTensorIfUnused(m_BiasesTensor); +} + +} //namespace armnn + |