| // |
| // Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonFullyConnectedWorkload.hpp" |
| |
| #include "NeonWorkloadUtils.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <aclCommon/ArmComputeUtils.hpp> |
| |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| |
| #include <armnn/backends/TensorHandle.hpp> |
| |
| #include <arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h> |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| using ACLMemManagerOnDemand = std::shared_ptr<arm_compute::MemoryManagerOnDemand>; |
| |
| arm_compute::Status NeonFullyConnectedWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| const FullyConnectedDescriptor& descriptor, |
| const ActivationDescriptor* activationDescriptor) |
| { |
| const arm_compute::TensorInfo aclInput = BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutput = BuildArmComputeTensorInfo(output); |
| arm_compute::TensorInfo aclWeights = BuildArmComputeTensorInfo(weights); |
| aclWeights.set_are_values_constant(weights.IsConstant()); |
| |
| arm_compute::TensorInfo aclBiases; |
| arm_compute::TensorInfo* optionalAclBiases = nullptr; |
| if (descriptor.m_BiasEnabled) |
| { |
| ARMNN_ASSERT(biases.has_value()); |
| // Same for bias as weights. We don't currently support non const. |
| if (!biases.value().IsConstant()) |
| { |
| return arm_compute::Status{arm_compute::ErrorCode::RUNTIME_ERROR, |
| "Arm NN NeonFullyConnectedWorkload does not support non constant bias."}; |
| } |
| aclBiases = BuildArmComputeTensorInfo(biases.value()); |
| aclBiases.set_are_values_constant(biases.value().IsConstant()); |
| optionalAclBiases = &aclBiases; |
| } |
| |
| const arm_compute::FullyConnectedLayerInfo fullyConnectedLayerInfo = |
| ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor, activationDescriptor); |
| return arm_compute::NEFullyConnectedLayer::validate(&aclInput, |
| &aclWeights, |
| optionalAclBiases, |
| &aclOutput, |
| fullyConnectedLayerInfo); |
| } |
| |
| NeonFullyConnectedWorkload::NeonFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor, |
| const WorkloadInfo& info, |
| ACLMemManagerOnDemand& memoryManager) |
| : NeonBaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info) |
| { |
| m_Data.ValidateInputsOutputs("NeonFullyConnectedWorkload", 1, 1); |
| |
| arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| // Copy the weights' tensor into arm_compute tensor. |
| m_WeightsTensor = std::make_unique<arm_compute::Tensor>(); |
| m_WeightsTensorInfo = info.m_InputTensorInfos[1]; |
| BuildArmComputeTensor(*m_WeightsTensor, m_WeightsTensorInfo); |
| |
| if (m_Data.m_Parameters.m_BiasEnabled) |
| { |
| // Copy the biases tensor into arm_compute tensor. |
| m_BiasesTensor = std::make_unique<arm_compute::Tensor>(); |
| m_BiasesTensorInfo = info.m_InputTensorInfos[2]; |
| BuildArmComputeTensor(*m_BiasesTensor, m_BiasesTensorInfo); |
| } |
| |
| const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor); |
| arm_compute::FullyConnectedLayerInfo fc_info = |
| ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor.m_Parameters, activationInfo); |
| |
| auto layer = std::make_unique<arm_compute::NEFullyConnectedLayer>(memoryManager); |
| layer->configure(&input, m_WeightsTensor.get(), m_BiasesTensor.get(), &output, fc_info); |
| m_FullyConnectedLayer.reset(layer.release()); |
| |
| // Add details for profiling output |
| WorkloadInfo detailsInfo; |
| |
| detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos; |
| detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos; |
| detailsInfo.m_WeightsTensorInfo = armnn::Optional<armnn::TensorInfo>(info.m_InputTensorInfos[1]); |
| if (descriptor.m_Parameters.m_BiasEnabled) |
| { |
| detailsInfo.m_BiasTensorInfo = armnn::Optional<armnn::TensorInfo>(info.m_InputTensorInfos[2]); |
| } |
| |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonFullyConnectedWorkload_Construct", |
| descriptor.m_Parameters, |
| detailsInfo, |
| this->GetGuid()); |
| |
| // Force Compute Library to perform the necessary copying and reshaping. |
| } |
| |
| void NeonFullyConnectedWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonFullyConnectedWorkload_Execute", this->GetGuid()); |
| // The constant tensors may not be fully in place until the workload is Executed |
| if (!prepared) |
| { |
| InitializeArmComputeTensorData(*m_WeightsTensor, m_WeightsTensorInfo, m_Data.m_Inputs[1]); |
| |
| if (m_Data.m_Parameters.m_BiasEnabled) |
| { |
| InitializeArmComputeTensorData(*m_BiasesTensor, m_BiasesTensorInfo, m_Data.m_Inputs[2]); |
| } |
| m_FullyConnectedLayer->prepare(); |
| FreeTensorIfUnused(m_WeightsTensor); |
| FreeTensorIfUnused(m_BiasesTensor); |
| prepared = true; |
| } |
| m_FullyConnectedLayer->run(); |
| } |
| |
| } //namespace armnn |