// // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "ClFullyConnectedWorkload.hpp" #include #include #include #include #include #include "ClWorkloadUtils.hpp" namespace armnn { using namespace armcomputetensorutils; arm_compute::Status ClFullyConnectedWorkloadValidate(const TensorInfo& input, const TensorInfo& output, const TensorInfo& weights, const Optional& biases, const FullyConnectedDescriptor& descriptor, const ActivationDescriptor* activationDescriptor) { // The CL implemented workload does support both const and non const // weights. However, in the case of non const weights we'd have to call // prepare or configure for each inference which we're not setup to do just yet. if (!weights.IsConstant()) { return arm_compute::Status{arm_compute::ErrorCode::RUNTIME_ERROR, "Arm NN ClFullyConnectedWorkload does not support non constant weights."}; } 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 ClFullyConnectedWorkload 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::CLFullyConnectedLayer::validate(&aclInput, &aclWeights, optionalAclBiases, &aclOutput, fullyConnectedLayerInfo); } ClFullyConnectedWorkload::ClFullyConnectedWorkload( const FullyConnectedQueueDescriptor& descriptor, const WorkloadInfo& info, std::shared_ptr& memoryManager, const arm_compute::CLCompileContext& clCompileContext) : ClBaseWorkload(descriptor, info), m_FullyConnectedLayer(memoryManager) { // Add details for profiling output WorkloadInfo detailsInfo; detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos; detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos; detailsInfo.m_WeightsTensorInfo = armnn::Optional(descriptor.m_Weight->GetTensorInfo()); if (descriptor.m_Parameters.m_BiasEnabled) { detailsInfo.m_BiasTensorInfo = armnn::Optional(descriptor.m_Bias->GetTensorInfo()); } // Report Profiling Details ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClFullyConnectedWorkload_Construct", descriptor.m_Parameters, detailsInfo, this->GetGuid()); m_Data.ValidateInputsOutputs("ClFullyConnectedWorkload", descriptor.m_Parameters.GetNumInputs(), 1); arm_compute::ICLTensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ICLTensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); arm_compute::ICLTensor& weights = PolymorphicDowncast(m_Data.m_Inputs[1])->GetTensor(); arm_compute::ICLTensor* bias = nullptr; if (m_Data.m_Parameters.m_BiasEnabled) { bias = &PolymorphicDowncast(m_Data.m_Inputs[2])->GetTensor(); } const arm_compute::ActivationLayerInfo activationInfo = ConvertAdditionalInfoToAclActivationLayerInfo(descriptor); arm_compute::FullyConnectedLayerInfo fc_info = ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(descriptor.m_Parameters, activationInfo); { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClFullyConnectedWorkload_configure"); m_FullyConnectedLayer.configure(clCompileContext, &input, &weights, bias, &output, fc_info); } } void ClFullyConnectedWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClFullyConnectedWorkload_Execute", this->GetGuid()); RunClFunction(m_FullyConnectedLayer, CHECK_LOCATION()); } } //namespace armnn