| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ClFullyConnectedWorkload.hpp" |
| #include <backends/cl/ClTensorHandle.hpp> |
| #include <backends/CpuTensorHandle.hpp> |
| #include <backends/aclCommon/ArmComputeTensorUtils.hpp> |
| #include <backends/aclCommon/ArmComputeUtils.hpp> |
| #include <backends/cl/ClLayerSupport.hpp> |
| |
| #include "ClWorkloadUtils.hpp" |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| arm_compute::Status ClFullyConnectedWorkloadValidate(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::CLFullyConnectedLayer::validate(&aclInput, |
| &aclWeights, |
| optionalAclBiases, |
| &aclOutput, |
| fullyConnectedLayerInfo); |
| } |
| |
| ClFullyConnectedWorkload::ClFullyConnectedWorkload(const FullyConnectedQueueDescriptor& descriptor, |
| const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) |
| : BaseWorkload<FullyConnectedQueueDescriptor>(descriptor, info) |
| , m_FullyConnectedLayer(memoryManager) |
| { |
| m_WeightsTensor = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_WeightsTensor, m_Data.m_Weight->GetTensorInfo()); |
| |
| if (m_Data.m_Parameters.m_BiasEnabled) |
| { |
| m_BiasesTensor = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_BiasesTensor, m_Data.m_Bias->GetTensorInfo()); |
| } |
| |
| m_Data.ValidateInputsOutputs("ClFullyConnectedWorkload", 1, 1); |
| |
| arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| // 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); |
| |
| InitializeArmComputeClTensorData(*m_WeightsTensor, m_Data.m_Weight); |
| |
| if (m_BiasesTensor) |
| { |
| InitializeArmComputeClTensorData(*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 ClFullyConnectedWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_CL("ClFullyConnectedWorkload_Execute"); |
| m_FullyConnectedLayer.run(); |
| } |
| |
| void ClFullyConnectedWorkload::FreeUnusedTensors() |
| { |
| FreeTensorIfUnused(m_WeightsTensor); |
| FreeTensorIfUnused(m_BiasesTensor); |
| } |
| |
| } //namespace armnn |