| // |
| // Copyright © 2017-2018,2020-2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ClBatchNormalizationFloatWorkload.hpp" |
| #include "ClWorkloadUtils.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <aclCommon/ArmComputeUtils.hpp> |
| #include <armnn/backends/TensorHandle.hpp> |
| #include <cl/ClLayerSupport.hpp> |
| #include <cl/ClTensorHandle.hpp> |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| arm_compute::Status ClBatchNormalizationValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& mean, |
| const TensorInfo& var, |
| const TensorInfo& beta, |
| const TensorInfo& gamma, |
| const BatchNormalizationDescriptor& descriptor, |
| const ActivationDescriptor* activationDescriptor) |
| { |
| const arm_compute::TensorInfo aclInputInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclOutputInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclMeanInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(mean, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclVarInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(var, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclBetaInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(beta, descriptor.m_DataLayout); |
| const arm_compute::TensorInfo aclGammaInfo = |
| armcomputetensorutils::BuildArmComputeTensorInfo(gamma, descriptor.m_DataLayout); |
| |
| const arm_compute::ActivationLayerInfo activationInfo = ConvertActivationDescriptorToAclActivationLayerInfo( |
| activationDescriptor); |
| |
| return arm_compute::CLBatchNormalizationLayer::validate(&aclInputInfo, |
| &aclOutputInfo, |
| &aclMeanInfo, |
| &aclVarInfo, |
| &aclBetaInfo, |
| &aclGammaInfo, |
| descriptor.m_Eps, |
| activationInfo); |
| } |
| |
| ClBatchNormalizationFloatWorkload::ClBatchNormalizationFloatWorkload( |
| const BatchNormalizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info, |
| const arm_compute::CLCompileContext& clCompileContext) |
| : FloatWorkload<BatchNormalizationQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClBatchNormalizationWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| this->GetGuid()); |
| |
| m_Mean = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo()); |
| |
| m_Variance = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_Variance, m_Data.m_Variance->GetTensorInfo()); |
| |
| m_Gamma = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_Gamma, m_Data.m_Gamma->GetTensorInfo()); |
| |
| m_Beta = std::make_unique<arm_compute::CLTensor>(); |
| BuildArmComputeTensor(*m_Beta, m_Data.m_Beta->GetTensorInfo()); |
| |
| m_Data.ValidateInputsOutputs("ClBatchNormalizationFloatWorkload", 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(); |
| |
| arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); |
| input.info()->set_data_layout(aclDataLayout); |
| output.info()->set_data_layout(aclDataLayout); |
| |
| const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor); |
| |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID("ClBatchNormalizationFloatWorkload_configure"); |
| m_Layer.configure(clCompileContext, |
| &input, |
| &output, |
| m_Mean.get(), |
| m_Variance.get(), |
| m_Beta.get(), |
| m_Gamma.get(), |
| m_Data.m_Parameters.m_Eps, |
| activationInfo); |
| } |
| |
| InitializeArmComputeClTensorData(*m_Mean, m_Data.m_Mean); |
| InitializeArmComputeClTensorData(*m_Variance, m_Data.m_Variance); |
| InitializeArmComputeClTensorData(*m_Beta, m_Data.m_Beta); |
| InitializeArmComputeClTensorData(*m_Gamma, m_Data.m_Gamma); |
| |
| // Force Compute Library to perform the necessary copying and reshaping, after which |
| // delete all the input tensors that will no longer be needed |
| m_Layer.prepare(); |
| FreeUnusedTensors(); |
| } |
| |
| void ClBatchNormalizationFloatWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID("ClBatchNormalizationFloatWorkload_Execute"); |
| RunClFunction(m_Layer, CHECK_LOCATION()); |
| } |
| |
| void ClBatchNormalizationFloatWorkload::FreeUnusedTensors() |
| { |
| FreeTensorIfUnused(m_Mean); |
| FreeTensorIfUnused(m_Variance); |
| FreeTensorIfUnused(m_Gamma); |
| FreeTensorIfUnused(m_Beta); |
| } |
| |
| void ClBatchNormalizationFloatWorkload::ReplaceInputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) |
| { |
| ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; |
| this->m_Data.m_Inputs[slot] = tensorHandle; |
| try |
| { |
| Reconfigure(); |
| } |
| catch(armnn::UnimplementedException& e) |
| { |
| // Cannot reconfigure, revert the slot back and throw the exception. |
| this->m_Data.m_Inputs[slot] = backupHandle; |
| throw e; |
| } |
| } |
| |
| // Replace output tensor handle with the given TensorHandle |
| void ClBatchNormalizationFloatWorkload::ReplaceOutputTensorHandle(ITensorHandle* tensorHandle, unsigned int slot) |
| { |
| ITensorHandle* backupHandle = this->m_Data.m_Inputs[slot]; |
| this->m_Data.m_Inputs[slot] = tensorHandle; |
| try |
| { |
| Reconfigure(); |
| } |
| catch(armnn::UnimplementedException& e) |
| { |
| // Cannot reconfigure, revert the slot back and throw the exception. |
| this->m_Data.m_Inputs[slot] = backupHandle; |
| throw e; |
| } |
| } |
| |
| void ClBatchNormalizationFloatWorkload::Reconfigure() |
| { |
| throw armnn::UnimplementedException("Reconfigure not implemented for this workload"); |
| } |
| |
| } //namespace armnn |