src/armnn/LoadedNetwork.cpp

//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "LoadedNetwork.hpp"
#include "Layer.hpp"
#include "Graph.hpp"
#include <Processes.hpp>
#include "Profiling.hpp"
#include "HeapProfiling.hpp"
#include "WorkingMemHandle.hpp"

#include <armnn/BackendRegistry.hpp>
#include <armnn/Logging.hpp>
#include <armnn/utility/Assert.hpp>

#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/IMemoryManager.hpp>
#include <armnn/backends/MemCopyWorkload.hpp>
#include <backendsCommon/MemSyncWorkload.hpp>
#include <armnn/BackendHelper.hpp>

#include <fmt/format.h>

namespace armnn
{

using namespace std;
using namespace arm::pipe;

namespace
{

template <typename ExceptionType>
std::string ToErrorMessage(const char * prefix, const ExceptionType & error)
{
    std::stringstream ss;
    ss << prefix << " " << error.what();
    return ss.str();
}

void AddLayerStructure(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,
                       const Layer& layer,
                       ProfilingGuid networkGuid)
{
    // Add layer to the post-optimisation network structure
    std::string layerName = layer.GetNameStr().empty() ? "<Unnamed>" : layer.GetNameStr();
    timelineUtils->CreateNamedTypedChildEntity(layer.GetGuid(),
                                               networkGuid,
                                               layerName,
                                               LabelsAndEventClasses::LAYER_GUID);
    for (auto&& input : layer.GetInputSlots())
    {
        const IOutputSlot* source = input.GetConnectedOutputSlot();
        ARMNN_ASSERT(source != NULL);
        timelineUtils->CreateConnectionRelationship(ProfilingRelationshipType::RetentionLink,
                                                    source->GetOwningLayerGuid(),
                                                    layer.GetGuid());
    }
}

void AddWorkloadStructure(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,
                          std::unique_ptr<IWorkload>& workload,
                          const Layer& layer)
{
    // Add workload to the post-optimisation network structure
    timelineUtils->CreateTypedEntity(workload->GetGuid(), LabelsAndEventClasses::WORKLOAD_GUID);
    timelineUtils->MarkEntityWithLabel(workload->GetGuid(),
                                       layer.GetBackendId().Get(),
                                       LabelsAndEventClasses::BACKENDID_GUID);

    // Link the workload to the layer
    timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink,
                                      layer.GetGuid(),
                                      workload->GetGuid(),
                                      LabelsAndEventClasses::CHILD_GUID);
}

} // anonymous

std::unique_ptr<LoadedNetwork> LoadedNetwork::MakeLoadedNetwork(std::unique_ptr<IOptimizedNetwork> net,
                                                                std::string& errorMessage,
                                                                const INetworkProperties& networkProperties,
                                                                ProfilingService&  profilingService)
{
    std::unique_ptr<LoadedNetwork> loadedNetwork;

    auto Fail = [&](const std::exception& error) -> std::unique_ptr<LoadedNetwork>
    {
        errorMessage = ToErrorMessage("An error occurred when preparing the network workloads: ", error);
        ARMNN_LOG(error) << errorMessage;

        return std::unique_ptr<LoadedNetwork>();
    };

    try
    {
        loadedNetwork.reset(new LoadedNetwork(std::move(net), networkProperties, profilingService));
    }
    catch (const armnn::RuntimeException& error)
    {
        return Fail(error);
    }
    catch (const armnn::Exception& error)
    {
        return Fail(error);
    }
    catch (const std::runtime_error& error)
    {
        return Fail(error);
    }

    return loadedNetwork;
}

LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net,
                             const INetworkProperties& networkProperties,
                             ProfilingService&  profilingService) :
                             m_OptimizedNetwork(std::move(net)),
                             m_NetworkProperties(networkProperties),
                             m_TensorHandleFactoryRegistry(),
                             m_ProfilingService(profilingService)
{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadedNetwork");
    // Get the profiler and register it for the current thread.
    const std::shared_ptr<IProfiler>& profiler = m_OptimizedNetwork->GetProfiler();
    ProfilerManager::GetInstance().RegisterProfiler(profiler.get());

    profiler->EnableProfiling(networkProperties.m_ProfilingEnabled);

    profiler->EnableNetworkDetailsToStdOut(networkProperties.m_OutputNetworkDetailsMethod);

    //First create tensor handlers, backends and workload factories.
    //Handlers are created before workloads are.
    //Because workload creation can modify some of the handlers,
    //(for example the splitter and concat layers).

    bool useExternalMemoryManager = false;
    bool useInternalMemoryManager = false;
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

    if (!networkProperties.m_AsyncEnabled)
    {
        m_IsInputImported = std::vector<bool>(order.GetNumInputs(), false);
        m_IsOutputImported = std::vector<bool>(order.GetNumOutputs(), false);
    }

    for (auto&& layer : order)
    {
        auto const& backendId = layer->GetBackendId();
        if (m_Backends.count(backendId) == 0)
        {
            auto createBackend = BackendRegistryInstance().GetFactory(backendId);
            auto it = m_Backends.emplace(std::make_pair(backendId, createBackend()));

            IBackendInternal* backend = it.first->second.get();

            if (networkProperties.m_AsyncEnabled &&
                !HasCapability(BackendOptions::BackendOption{"AsyncExecution", true}, backend->GetCapabilities()))
            {
                std::string er = backend->GetId();
                er += " does not support AsyncExecution";
                throw BackendCapabilityException(er);
            }

            if (networkProperties.m_AsyncEnabled &&
                !HasCapability(BackendOptions::BackendOption{"ExternallyManagedMemory", true},
                backend->GetCapabilities()))
            {
                std::string er = backend->GetId();
                er += " does not support ExternallyManagedMemory\n";
                er += "AsyncEnabled networks require all backends to support ExternallyManagedMemory";
                throw BackendCapabilityException(er);
            }

            if (HasCapability(BackendOptions::BackendOption{"ExternallyManagedMemory", true},backend->GetCapabilities())
                && (m_NetworkProperties.m_ExternalMemoryManagementEnabled ||  m_NetworkProperties.m_AsyncEnabled))
            {
                m_SupportsExternallyManagedMemory[backend->GetId()] = true;
                useExternalMemoryManager = true;
            }
            else
            {
                m_SupportsExternallyManagedMemory[backend->GetId()] = false;
                useInternalMemoryManager = true;
            }

            IBackendInternal::IWorkloadFactoryPtr workloadFactory;
            if (backend->SupportsTensorAllocatorAPI())
            {
                workloadFactory = backend->CreateWorkloadFactory(
                    m_TensorHandleFactoryRegistry,
                    m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions(),
                    static_cast<MemorySourceFlags>(m_NetworkProperties.m_InputSource),
                    static_cast<MemorySourceFlags>(m_NetworkProperties.m_OutputSource));
            }
            else
            {
                m_BackendMemoryMangers.emplace_back(backend->CreateMemoryManager());
                workloadFactory = backend->CreateWorkloadFactory(
                        m_BackendMemoryMangers.back(), m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions());
            }
            m_WorkloadFactories[backendId ] = std::move(workloadFactory);
        }
    }

    if (!networkProperties.m_AsyncEnabled)
    {
        for (auto&& layer : order)
        {
            auto& workloadFactory = GetWorkloadFactory(*layer);
            bool supportsExternalManager = m_SupportsExternallyManagedMemory[layer->GetBackendId()];

            switch (layer->GetType())
            {
                case LayerType::Input:
                case LayerType::MemImport:
                {
                    // If IsImportEnabled is true then we need to set IsMemoryManaged
                    // to false when creating TensorHandles
                    layer->CreateTensorHandles(m_TensorHandleFactoryRegistry,
                                               workloadFactory,
                                               !supportsExternalManager && !m_NetworkProperties.m_ImportEnabled);
                    break;
                }
                case LayerType::Constant:
                {
                    layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory, true);
                    break;
                }
                default:
                {
                    // Look for a layer with 1 OutputSlot which has 1 connection and that connection is an Output Layer
                    // If Export is enabled disable memory management so we can export, otherwise we do a copy
                    if ((layer->GetNumOutputSlots() == 1) &&
                       (layer->GetOutputSlots()[0].GetNumConnections() == 1) &&
                       (layer->GetOutputSlots()[0].GetConnection(0)->GetOwningLayer().GetType() == LayerType::Output))
                    {
                        layer->CreateTensorHandles(m_TensorHandleFactoryRegistry,
                                                   workloadFactory,
                                                   !supportsExternalManager && !m_NetworkProperties.m_ExportEnabled);
                    }
                    else
                    {
                        layer->CreateTensorHandles(m_TensorHandleFactoryRegistry,
                                                   workloadFactory,
                                                   !supportsExternalManager);
                    }
                }
            }
        }
    }

    ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
    std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                        TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
    if (timelineUtils)
    {
        timelineUtils->CreateTypedEntity(networkGuid, LabelsAndEventClasses::NETWORK_GUID);
        // Mark the network with a start of life event
        timelineUtils->RecordEvent(networkGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);
        // and with the process ID
        int processID = armnnUtils::Processes::GetCurrentId();
        std::stringstream ss;
        ss << processID;
        timelineUtils->MarkEntityWithLabel(networkGuid, ss.str(), LabelsAndEventClasses::PROCESS_ID_GUID);
    }

    //Then create workloads.
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadNetwork_CreateWorkloads");
        for (auto&& layer: order)
        {
            if (timelineUtils)
            {
                // Add layer to the post-optimisation network structure
                AddLayerStructure(timelineUtils, *layer, networkGuid);
            }

            const IWorkloadFactory& workloadFactory = GetWorkloadFactory(*layer);

            switch (layer->GetType())
            {
                case LayerType::Input:
                case LayerType::Output:
                {
                    // Inputs and outputs are treated in a special way - see EnqueueInput() and EnqueueOutput().
                    break;
                }
                default:
                {
                    auto workload = layer->CreateWorkload(workloadFactory);

                    if (!workload)
                    {
                        const char* const layerName =
                                layer->GetNameStr().length() != 0 ? layer->GetName() : "<Unnamed>";
                        throw InvalidArgumentException(
                                fmt::format("No workload created for layer (name: '{0}' type: '{1}') (compute '{2}')",
                                            layerName, static_cast<int>(layer->GetType()), layer->GetBackendId().Get()
                                ));
                    }

                    if (timelineUtils)
                    {
                        // Add workload to the post-optimisation network structure
                        AddWorkloadStructure(timelineUtils, workload, *layer);
                    }

                    // For async networks ConstantWorkloads are managed exclusively by LoadedNetwork
                    // and are separated out from the other workloads
                    if((networkProperties.m_AsyncEnabled  || useExternalMemoryManager) &&
                        layer->GetType() == LayerType::Constant)
                    {
                        m_ConstantTensorHandles[layer->GetGuid()] =
                                layer->GetOutputSlot(0).GetOutputHandler().GetData();
                        m_ConstantWorkloads[layer->GetGuid()] = std::move(workload);
                    }
                    else
                    {
                        m_WorkloadQueue.push_back(std::move(workload));
                    }

                    // release the constant data in the layer..
                    layer->ReleaseConstantData();
                    break;
                }
            }
        }
    }

    // Gather information about workloads for inputs & outputs
    if (!networkProperties.m_AsyncEnabled && m_WorkloadQueue.size() != 0)
    {
        const int noOfInputs = armnn::numeric_cast<int>(order.GetNumInputs());

        // Get indices of all workloads connected to each input and
        // check if they support tensor handle replacement
        for (const BindableLayer* layer: order.GetInputLayers())
        {
            const auto bindingId = layer->GetBindingId();

            bool supportsReplacement = true;

            for (const auto inputSlot: layer->GetOutputSlot(0).GetConnections())
            {
                auto workloadIndex = std::distance(order.begin(), order.GetPosInGraph(inputSlot->GetOwningLayer()));
                workloadIndex -= noOfInputs;

                m_InputWorkloadSlotPairs[bindingId].emplace_back(WorkloadIndices{
                        armnn::numeric_cast<unsigned int>(workloadIndex), inputSlot->GetSlotIndex()});

                auto workload = m_WorkloadQueue[m_InputWorkloadSlotPairs[bindingId].back().m_WorkloadIndex].get();
                supportsReplacement &= workload->SupportsTensorHandleReplacement();
            }

            ITensorHandleFactory::FactoryId factoryId = layer->GetOutputSlot(0).GetTensorHandleFactoryId();
            // Get matching import factory Id
            ITensorHandleFactory::FactoryId importFactoryId =
                    m_TensorHandleFactoryRegistry.GetMatchingImportFactoryId(factoryId);

            ITensorHandleFactory *importFactory = m_TensorHandleFactoryRegistry.GetFactory(importFactoryId);

            if (supportsReplacement && importFactory)
            {
                m_PreImportedInputHandles.emplace_back(
                        bindingId, importFactory->CreateTensorHandle(layer->GetOutputSlot(0).GetTensorInfo(), false));
            }
            else
            {
                m_PreImportedInputHandles.emplace_back(bindingId, nullptr);
            }
        }

        // Get indices of all workloads connected to each output and
        // check if they support tensor handle replacement
        for (const BindableLayer* layer: order.GetOutputLayers())
        {
            const auto bindingId = layer->GetBindingId();

            const auto outputSlot = layer->GetInputSlot(0).GetConnectedOutputSlot();
            auto& indices = m_OutputWorkloadSlotPairs[bindingId];

            auto workloadIndex = std::distance(order.begin(), order.GetPosInGraph(outputSlot->GetOwningLayer()));
            workloadIndex -= noOfInputs;

            indices.m_OutputSlotIndices = WorkloadIndices{numeric_cast<unsigned int>(workloadIndex),
                                                          outputSlot->CalculateIndexOnOwner()};

            bool supportsReplacement = true;
            auto outputWorkload = m_WorkloadQueue[indices.m_OutputSlotIndices.m_WorkloadIndex].get();
            supportsReplacement &= outputWorkload->SupportsTensorHandleReplacement();

            for (auto &inputSlot: outputSlot->GetConnections())
            {
                if(inputSlot->GetOwningLayer().GetType() != LayerType::Output)
                {
                    auto inWorkloadIndex = std::distance(order.begin(),
                                                         order.GetPosInGraph(inputSlot->GetOwningLayer()));
                    inWorkloadIndex -= noOfInputs;
                    indices.m_InputSlotIndices.emplace_back(WorkloadIndices{numeric_cast<unsigned int>(inWorkloadIndex),
                                                            inputSlot->GetSlotIndex()});
                    auto inputWorkload = m_WorkloadQueue[indices.m_InputSlotIndices.back().m_WorkloadIndex].get();
                    supportsReplacement &= inputWorkload->SupportsTensorHandleReplacement();
                }
            }

            ITensorHandleFactory::FactoryId factoryId = outputSlot->GetTensorHandleFactoryId();
            // Get matching import factory Id
            ITensorHandleFactory::FactoryId importFactoryId =
                    m_TensorHandleFactoryRegistry.GetMatchingImportFactoryId(factoryId);
            ITensorHandleFactory *importFactory = m_TensorHandleFactoryRegistry.GetFactory(importFactoryId);

            if (supportsReplacement && importFactory)
            {
                m_PreImportedOutputHandles.emplace_back(
                        bindingId, importFactory->CreateTensorHandle(outputSlot->GetTensorInfo(), false));
            }
            else
            {
                m_PreImportedOutputHandles.emplace_back(bindingId, nullptr);
            }
        }
    }

    for (auto&& workloadFactory : m_WorkloadFactories)
    {
        workloadFactory.second->AfterWorkloadsCreated();
    }

    if (timelineUtils)
    {
        // Commit to send the post-optimisation network structure
        timelineUtils->Commit();
    }

    if (useExternalMemoryManager)
    {
        if (networkProperties.m_AsyncEnabled)
        {
            CreateMemoryProfileAsync();
        }
        else
        {
            CreateMemoryProfile();
        }

        auto backendStrategyMap = BackendRegistryInstance().GetMemoryOptimizerStrategies();
        for (auto& backendMemoryProfile : m_MemBlockMap)
        {
            const BackendId& backendId = backendMemoryProfile.first;
            if (backendStrategyMap.find(backendId) != backendStrategyMap.end())
            {
                m_MemBinMap[backendId] = backendStrategyMap[backendId]->Optimize(backendMemoryProfile.second);
            }
            else
            {
                m_MemBinMap[backendId] = m_ConstantStrategy->Optimize(backendMemoryProfile.second);
            }
        }

        if (!networkProperties.m_AsyncEnabled)
        {
            m_ExternalMemoryManager = CreateExternalMemoryManger(m_TensorMemory);

            // Sort m_TensorMemory, so it's order matches m_Tensorhandles
            std::sort(m_TensorMemory.begin(), m_TensorMemory.end(),
                      [](const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& lhs,
                         const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& rhs)
                      {
                          return lhs.first->m_OutputSlotId < rhs.first->m_OutputSlotId;
                      });
        }
    }

    // Now that the intermediate tensor memory has been set-up,
    // do any post allocation configuration for each workload.
    if (!networkProperties.m_AsyncEnabled)
    {
        if (useInternalMemoryManager)
        {
            // Set up memory.
            m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().AllocateDynamicBuffers();
        }

        for (auto &workload : m_WorkloadQueue)
        {
            workload->PostAllocationConfigure();
        }
    }

    if (useExternalMemoryManager)
    {
        if (!networkProperties.m_AsyncEnabled)
        {
            AllocateAndExecuteConstantWorkloads();
        }
        else
        {
            AllocateAndExecuteConstantWorkloadsAsync();
        }
    }
}

void LoadedNetwork::AllocateAndExecuteConstantWorkloads()
{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadNetwork_AllocateAndExecuteConstants");
    for (auto& pair : m_ConstantWorkloads)
    {
        auto tensorHandle = m_ConstantTensorHandles[pair.first];
        tensorHandle->Allocate();
        pair.second->Execute();
    }
}



void LoadedNetwork::AllocateAndExecuteConstantWorkloadsAsync()
{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadNetwork_AllocateAndExecuteConstants");
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();
    for (auto&& layer : order)
    {
        if (layer->GetType() == LayerType::Constant)
        {
            const auto& outSlot = layer->GetOutputSlots()[0];
            const auto factoryId = outSlot.GetTensorHandleFactoryId();
            ARMNN_ASSERT(factoryId != ITensorHandleFactory::LegacyFactoryId);
            auto& workloadFactory = GetWorkloadFactory(*layer);

            layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory);
            ITensorHandle* tensorHandle = outSlot.GetOutputHandler().GetData();

            m_ConstantTensorHandles[layer->GetGuid()] = tensorHandle;
            tensorHandle->Allocate();

            WorkingMemDescriptor memDesc;
            memDesc.m_Outputs.push_back(tensorHandle);
            m_ConstantWorkloads[layer->GetGuid()]->ExecuteAsync(memDesc);
        }
    }
}

void LoadedNetwork::SendNetworkStructure()
{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "LoadNetwork_SendNetworkStructure");
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
    ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();

    std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                        TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);

    timelineUtils->CreateTypedEntity(networkGuid, LabelsAndEventClasses::NETWORK_GUID);

    for (auto&& layer : order)
    {
        // Add layer to the post-optimisation network structure
        AddLayerStructure(timelineUtils, *layer, networkGuid);
        switch (layer->GetType())
        {
        case LayerType::Input:
        case LayerType::Output:
        {
            // Inputs and outputs are treated in a special way - see EnqueueInput() and EnqueueOutput().
            break;
        }
        default:
            {
            for (auto& workload : m_WorkloadQueue)
            {
                // Add workload to the post-optimisation network structure
                AddWorkloadStructure(timelineUtils, workload, *layer);
            }
            break;
            }
        }
    }
    // Commit to send the post-optimisation network structure
    timelineUtils->Commit();
}

ProfilingGuid LoadedNetwork::GetNetworkGuid()
{
    return m_OptimizedNetwork->GetGuid();
}

TensorInfo LoadedNetwork::GetInputTensorInfo(LayerBindingId layerId) const
{
    for (auto&& inputLayer : m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetInputLayers())
    {
        ARMNN_ASSERT_MSG(inputLayer->GetNumOutputSlots() == 1, "Input layer should have exactly 1 output slot");
        if (inputLayer->GetBindingId() == layerId)
        {
            return inputLayer->GetOutputSlot(0).GetTensorInfo();
        }
    }

    throw InvalidArgumentException(fmt::format("No input layer is associated with id {}", layerId));
}

TensorInfo LoadedNetwork::GetOutputTensorInfo(LayerBindingId layerId) const
{
    for (auto&& outputLayer : m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetOutputLayers())
    {
        ARMNN_ASSERT_MSG(outputLayer->GetNumInputSlots() == 1, "Output layer should have exactly 1 input slot");
        ARMNN_ASSERT_MSG(outputLayer->GetInputSlot(0).GetConnection(), "Input slot on Output layer must be connected");
        if (outputLayer->GetBindingId() == layerId)
        {
            return outputLayer->GetInputSlot(0).GetConnection()->GetTensorInfo();
        }
    }

    throw InvalidArgumentException(fmt::format("No output layer is associated with id {}", layerId));
}

const IWorkloadFactory& LoadedNetwork::GetWorkloadFactory(const Layer& layer) const
{
    const IWorkloadFactory* workloadFactory = nullptr;

    auto it = m_WorkloadFactories.find(layer.GetBackendId());
    if (it ==  m_WorkloadFactories.end())
    {
        throw RuntimeException(fmt::format("No workload factory for {0} to be used for layer: {1}",
                                           layer.GetBackendId().Get(),
                                           layer.GetNameStr()),
                                           CHECK_LOCATION());
    }

    workloadFactory = it->second.get();

    ARMNN_ASSERT_MSG(workloadFactory, "No workload factory");

    std::string reasonIfUnsupported;
    ARMNN_ASSERT_MSG(IWorkloadFactory::IsLayerSupported(layer,
                                                        {},
                                                        reasonIfUnsupported,
                                                        m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions()),
        "Factory does not support layer");
    IgnoreUnused(reasonIfUnsupported);
    return *workloadFactory;
}

namespace {

// Non-copyable class owning accelerator-specific tensor data.
class TensorPin
{
public:
    TensorPin(std::unique_ptr<ITensorHandle> handle, const TensorInfo& info, LayerBindingId id)
        : m_TensorHandle(std::move(handle))
        , m_TensorInfo(info)
        , m_Id(id)
    {
    }

    ITensorHandle* GetTensorHandle() const { return m_TensorHandle.get(); }
    const TensorInfo& GetTensorInfo() const { return m_TensorInfo; }
    LayerBindingId GetBindingId() const { return m_Id; }

private:
    std::unique_ptr<ITensorHandle> m_TensorHandle;
    TensorInfo m_TensorInfo;
    LayerBindingId m_Id;
};

static const TensorPin& GetTensorPin(LayerBindingId id,
    const std::vector<TensorPin>& pins,
    char const* bindingPointDesc)
{
    auto it = std::find_if(pins.begin(), pins.end(),
        [id](const TensorPin& pin)
    {
        return pin.GetBindingId() == id;
    });

    if (it != pins.end())
    {
        return *it;
    }
    else
    {
        throw InvalidArgumentException(fmt::format("No tensor supplied for {0} {1}", bindingPointDesc, id));
    }
}

// Stores data that needs to be kept accessible for the entire execution of a workload.
class WorkloadData
{
public:
    WorkloadData(const InputTensors& inputTensors, const OutputTensors& outputTensors)
    {
        m_InputTensorPins.reserve(inputTensors.size());
        m_OutputTensorPins.reserve(outputTensors.size());

        for (auto inputTensorPair : inputTensors)
        {
            auto inputTensor = inputTensorPair.second;

            std::unique_ptr<ITensorHandle> tensorHandle =
                std::make_unique<ConstPassthroughTensorHandle>(inputTensor.GetInfo(),inputTensor.GetMemoryArea());
            LayerBindingId layerId = inputTensorPair.first;

            m_InputTensorPins.emplace_back(std::move(tensorHandle), inputTensor.GetInfo(), layerId);
        }

        for (auto outputTensorPair : outputTensors)
        {
            auto outputTensor = outputTensorPair.second;

            std::unique_ptr<ITensorHandle> tensorHandle =
                std::make_unique<PassthroughTensorHandle>(outputTensor.GetInfo(), outputTensor.GetMemoryArea());
            LayerBindingId layerId = outputTensorPair.first;

            m_OutputTensorPins.emplace_back(std::move(tensorHandle), outputTensor.GetInfo(), layerId);
        }
    }

    const TensorPin& GetInputTensorPin(LayerBindingId id) const
    {
        return GetTensorPin(id, m_InputTensorPins, "input");
    }

    const TensorPin& GetOutputTensorPin(LayerBindingId id) const
    {
        return GetTensorPin(id, m_OutputTensorPins, "output");
    }

private:

    std::vector<TensorPin> m_InputTensorPins;
    std::vector<TensorPin> m_OutputTensorPins;
};

}

Status LoadedNetwork::EnqueueWorkload(const InputTensors& inputTensors,
                                      const OutputTensors& outputTensors,
                                      std::vector<ImportedInputId> preImportedInputIds,
                                      std::vector<ImportedOutputId> preImportedOutputIds)
{
    const Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();

    // Walk graph to determine the order of execution.
    if (graph.GetNumLayers() < 2)
    {
        ARMNN_LOG(warning) << "IRuntime::EnqueueWorkload()::Less than two nodes in graph";
        return Status::Failure;
    }

    // Data that must be kept alive for the entire execution of the workload.
    WorkloadData workloadData(inputTensors, outputTensors);

    if (graph.GetNumInputs() != inputTensors.size())
    {
        throw InvalidArgumentException("Number of inputs provided does not match network.");
    }

    // For each input to the network, call EnqueueInput with the data passed by the user.
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
        m_InputQueue.clear();
        m_InputQueue.reserve(graph.GetNumInputs());

        if (preImportedInputIds.size() > graph.GetNumInputs())
        {
            throw InvalidArgumentException("Invalid number of preImportedInputIds");
        }

        unsigned int inputIndex = 0;
        unsigned int importedInputIdIndex = 0;
        std::sort(preImportedInputIds.begin(), preImportedInputIds.end());
        for (const BindableLayer* inputLayer : graph.GetInputLayers())
        {
            if (importedInputIdIndex < preImportedInputIds.size() &&
                inputIndex == preImportedInputIds[importedInputIdIndex])
            {
                // Only replace tensorhandles if they have not already been replaced
                if (!m_IsInputImported[inputIndex])
                {
                    auto outputTensorHandle = m_PreImportedInputHandles[inputIndex].m_TensorHandle.get();

                    for (const auto& workloadInfo: m_InputWorkloadSlotPairs[inputLayer->GetBindingId()])
                    {
                        auto workload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                        workload->ReplaceInputTensorHandle(outputTensorHandle, workloadInfo.m_SlotIndex);
                    }
                    m_IsInputImported[inputIndex] = true;
                }
                importedInputIdIndex++;
            }
            else
            {
                if (m_IsInputImported[inputIndex])
                {
                    OutputHandler& handler = const_cast<OutputHandler&>(inputLayer->GetOutputHandler(0));

                    for (const auto& workloadInfo: m_InputWorkloadSlotPairs[inputLayer->GetBindingId()])
                    {
                        auto workload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                        workload->ReplaceInputTensorHandle(handler.GetData(), workloadInfo.m_SlotIndex);
                    }

                    m_IsInputImported[inputIndex] = false;
                }

                // InputTensorHandle is not imported yet, process to enqueue input
                const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId());
                EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
            }
            inputIndex++;
        }
    }
    // For each output to the network, call EnqueueOutput with the data passed by the user.
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
        m_OutputQueue.clear();
        m_OutputQueue.reserve(graph.GetNumOutputs());

        if (preImportedOutputIds.size() > graph.GetNumOutputs())
        {
            throw InvalidArgumentException("Invalid number of preImportedOutputIds");
        }

        unsigned int outputIndex = 0;
        unsigned int importedOutputIdIndex = 0;
        std::sort(preImportedOutputIds.begin(), preImportedOutputIds.end());
        for (const BindableLayer* outputLayer : graph.GetOutputLayers())
        {
            if (importedOutputIdIndex < preImportedOutputIds.size() &&
                outputIndex == preImportedOutputIds[importedOutputIdIndex])
            {
                // Only replace tensorhandles if they have not already been replaced
                ITensorHandle* inputTensorHandle = m_PreImportedOutputHandles[outputIndex].m_TensorHandle.get();

                if (!m_IsOutputImported[outputIndex])
                {
                    const auto bindingId = outputLayer->GetBindingId();
                    const auto& indices = m_OutputWorkloadSlotPairs[bindingId];

                    auto outputWorkload = m_WorkloadQueue[indices.m_OutputSlotIndices.m_WorkloadIndex].get();

                    outputWorkload->ReplaceOutputTensorHandle(inputTensorHandle,
                                                              indices.m_OutputSlotIndices.m_SlotIndex);

                    for (const auto& workloadInfo: indices.m_InputSlotIndices)
                    {
                        auto inputWorkload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                        inputWorkload->ReplaceInputTensorHandle(inputTensorHandle, workloadInfo.m_SlotIndex);
                    }
                    m_IsOutputImported[outputIndex] = true;
                }

                ARMNN_ASSERT_MSG(inputTensorHandle != nullptr, "Data should have been allocated.");
                MemSyncQueueDescriptor syncDesc;
                syncDesc.m_Inputs.push_back(inputTensorHandle);
                WorkloadInfo info;
                info.m_InputTensorInfos.push_back(
                        outputLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetTensorInfo());
                auto syncWorkload = std::make_unique<SyncMemGenericWorkload>(syncDesc, info);
                ARMNN_ASSERT_MSG(syncWorkload, "No sync workload created");
                m_OutputQueue.push_back(move(syncWorkload));
                importedOutputIdIndex++;
            }
            else
            {
                if (m_IsOutputImported[outputIndex])
                {
                    const auto bindingId = outputLayer->GetBindingId();
                    const auto& indices = m_OutputWorkloadSlotPairs[bindingId];

                    auto outputWorkload = m_WorkloadQueue[indices.m_OutputSlotIndices.m_WorkloadIndex].get();
                    const OutputHandler& outputHandler =
                            outputLayer->GetInputSlot(0).GetConnectedOutputSlot()->GetOutputHandler();

                    outputWorkload->ReplaceOutputTensorHandle(
                            outputHandler.GetData(), indices.m_OutputSlotIndices.m_SlotIndex);

                    for (const auto& workloadInfo: indices.m_InputSlotIndices)
                    {
                        auto inputWorkload = m_WorkloadQueue[workloadInfo.m_WorkloadIndex].get();
                        inputWorkload->ReplaceInputTensorHandle(outputHandler.GetData(), workloadInfo.m_SlotIndex);
                    }
                    m_IsOutputImported[outputIndex] = false;
                }

                const TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId());
                // OutputTensorHandle is not imported yet, process to enqueue Output
                EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo());
            }
            outputIndex++;
        }
    }

    std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                        TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
    ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();
    if (timelineUtils)
    {
        // Add inference timeline trace if profiling is enabled.
        ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
        timelineUtils->CreateTypedEntity(inferenceGuid, LabelsAndEventClasses::INFERENCE_GUID);
        timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink,
                                          networkGuid,
                                          inferenceGuid,
                                          LabelsAndEventClasses::EXECUTION_OF_GUID);
        timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);
    }

    bool executionSucceeded = true;

    {
        if (m_ProfilingService.IsProfilingEnabled())
        {
            m_ProfilingService.IncrementCounterValue(INFERENCES_RUN);
        }
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Execute");
        ARMNN_SCOPED_HEAP_PROFILING("Executing");
        executionSucceeded = Execute(timelineUtils, inferenceGuid);
    }

    if (timelineUtils)
    {
        // Add end of life of the inference timeline if profiling is enabled.
        timelineUtils->RecordEvent(inferenceGuid, LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);
        timelineUtils->Commit();
    }

    return executionSucceeded ? Status::Success : Status::Failure;
}

void LoadedNetwork::EnqueueInput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo)
{
    if (layer.GetType() != LayerType::Input)
    {
        throw InvalidArgumentException("EnqueueInput: given layer not an InputLayer");
    }

    if (tensorHandle == nullptr)
    {
        throw InvalidArgumentException("EnqueueInput: tensorHandle must not be NULL");
    }

    InputQueueDescriptor inputQueueDescriptor;
    WorkloadInfo info;

    inputQueueDescriptor.m_Inputs.push_back(tensorHandle);
    info.m_InputTensorInfos.push_back(tensorInfo);

    ARMNN_ASSERT_MSG(layer.GetNumOutputSlots() == 1, "Can only handle Input Layer with one output");
    const OutputHandler& handler = layer.GetOutputHandler();
    const TensorInfo& outputTensorInfo = handler.GetTensorInfo();
    ITensorHandle* outputTensorHandle = handler.GetData();
    ARMNN_ASSERT_MSG(outputTensorHandle != nullptr,
                     "Data should have been allocated.");
    inputQueueDescriptor.m_Outputs.push_back(outputTensorHandle);
    info.m_OutputTensorInfos.push_back(outputTensorInfo);

    MemorySourceFlags importFlags = outputTensorHandle->GetImportFlags();
    bool needMemCopy = true;
    if (m_NetworkProperties.m_ImportEnabled)  // Try import the input tensor
    {
        if(CheckFlag(importFlags, m_NetworkProperties.m_InputSource))
        {
            needMemCopy = false;
            // This assumes a CPU Tensor handle
            void* mem = tensorHandle->Map(false);
            if (outputTensorHandle->Import(mem, m_NetworkProperties.m_InputSource))
            {
                tensorHandle->Unmap();
                return; // No need for a workload since the import has been done.
            }
            tensorHandle->Unmap();
            throw MemoryImportException("EnqueueInput: Memory Import failed");
        }
    }
    if (needMemCopy)
    {
        // Create a mem copy workload for input since we did not import
        std::unique_ptr<IWorkload> inputWorkload = std::make_unique<CopyMemGenericWorkload>(inputQueueDescriptor, info);

        ARMNN_ASSERT_MSG(inputWorkload, "No input workload created");

        std::unique_ptr<TimelineUtilityMethods> timelineUtils =
                            TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
        if (timelineUtils)
        {
            // Add Input Workload to the post-optimisation network structure
            AddWorkloadStructure(timelineUtils, inputWorkload, layer);
            timelineUtils->Commit();
        }

        m_InputQueue.push_back(move(inputWorkload));
    }
}

void LoadedNetwork::EnqueueOutput(const BindableLayer& layer, ITensorHandle* tensorHandle, const TensorInfo& tensorInfo)
{
    if (layer.GetType() != LayerType::Output)
    {
        throw InvalidArgumentException("EnqueueOutput: given layer not an OutputLayer");
    }

    if (tensorHandle == nullptr)
    {
        throw InvalidArgumentException("EnqueueOutput: tensorHandle must not be NULL");
    }

    OutputQueueDescriptor outputQueueDescriptor;
    WorkloadInfo info;

    outputQueueDescriptor.m_Outputs.push_back(tensorHandle);
    info.m_OutputTensorInfos.push_back(tensorInfo);

    ARMNN_ASSERT_MSG(layer.GetNumInputSlots() == 1, "Output Layer should have exactly one input.");

    // Gets the output handler from the previous node.
    const OutputHandler& outputHandler = layer.GetInputSlots()[0].GetConnectedOutputSlot()->GetOutputHandler();

    const TensorInfo& inputTensorInfo = outputHandler.GetTensorInfo();
    ITensorHandle* inputTensorHandle = outputHandler.GetData();
    ARMNN_ASSERT_MSG(inputTensorHandle != nullptr, "Data should have been allocated.");

    // Try import the output tensor.
    // Note: We can only import the output pointer if all of the following  hold true:
    // a) The imported pointer is aligned sufficiently
    // b) The tensor has zero padding
    // c) There is only one connection to the OutputSlot and it is to an OutputLayer.
    // d) The output pointer is allocated via malloc. (Other types will be supported in a later release)
    // e) m_IsExportEnabled must be set to true
    bool needMemCopy = true;
    if (m_NetworkProperties.m_ExportEnabled &&
        (layer.GetInputSlots()[0].GetConnectedOutputSlot()->GetNumConnections() == 1))
    {
        if(layer.GetInputSlots()[0].GetConnectedOutputSlot()->GetOwningLayer().GetType() != LayerType::Input)
        {
            MemorySourceFlags importFlags = inputTensorHandle->GetImportFlags();
            if (CheckFlag(importFlags, m_NetworkProperties.m_OutputSource))
            {
                needMemCopy = false;
                void *mem = tensorHandle->Map(false);
                bool importOk = inputTensorHandle->Import(mem, m_NetworkProperties.m_OutputSource);
                tensorHandle->Unmap();

                if (importOk)
                {
                    // Insert synchronization workload
                    MemSyncQueueDescriptor syncDesc;
                    syncDesc.m_Inputs.push_back(inputTensorHandle);
                    info.m_InputTensorInfos.push_back(inputTensorInfo);
                    auto syncWorkload = std::make_unique<SyncMemGenericWorkload>(syncDesc, info);
                    ARMNN_ASSERT_MSG(syncWorkload, "No sync workload created");
                    m_OutputQueue.push_back(move(syncWorkload));
                }
                else
                {
                    throw MemoryExportException("EnqueueOutput: Memory Export failed");
                }
            }
        }
    }
    if (needMemCopy)
    {
        // If we got here then we didn't export the memory, so add an output workload which performs a memcopy.
        outputQueueDescriptor.m_Inputs.push_back(inputTensorHandle);
        info.m_InputTensorInfos.push_back(inputTensorInfo);

        std::unique_ptr<IWorkload> outputWorkload =
            std::make_unique<CopyMemGenericWorkload>(outputQueueDescriptor, info);
        ARMNN_ASSERT_MSG(outputWorkload, "No output workload created");

        std::unique_ptr<TimelineUtilityMethods> timelineUtils =
            TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
        if (timelineUtils)
        {
            // Add Output Workload to the post-optimisation network structure
            AddWorkloadStructure(timelineUtils, outputWorkload, layer);
            timelineUtils->Commit();
        }

        m_OutputQueue.push_back(move(outputWorkload));
    }
}

void LoadedNetwork::AllocateWorkingMemory(std::lock_guard<std::mutex>& lock)
{
    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Working Memory Allocation");

    // this unused parameter makes sure we can only call this function with a valid lock
    IgnoreUnused(lock);

    if (m_IsWorkingMemAllocated)
    {
        return;
    }

    if (m_ExternalMemoryManager)
    {
        m_ExternalMemoryManager->Allocate();

        for (unsigned int i = 0; i < m_TensorMemory.size(); ++i)
        {
            m_Tensorhandles[i]->Import(m_TensorMemory[i].first->m_Data, m_TensorMemory[i].second);
        }
    }

    for (auto&& memoryManager : m_BackendMemoryMangers)
    {
        if (memoryManager)
        {
            memoryManager->Acquire();
        }
    }
    m_TensorHandleFactoryRegistry.AquireMemory();
    m_IsWorkingMemAllocated = true;
}

void LoadedNetwork::FreeWorkingMemory()
{
    std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);

    if (!m_IsWorkingMemAllocated)
    {
        return;
    }

    if (m_ExternalMemoryManager)
    {
        m_ExternalMemoryManager->Deallocate();
    }

    // Informs the memory managers to release memory in its respective memory group
    for (auto&& memoryManager : m_BackendMemoryMangers)
    {
        if (memoryManager)
        {
            memoryManager->Release();
        }
    }
    m_TensorHandleFactoryRegistry.ReleaseMemory();
    m_IsWorkingMemAllocated = false;
}

bool LoadedNetwork::Execute(std::unique_ptr<TimelineUtilityMethods>& timelineUtils,
                           ProfilingGuid inferenceGuid)
{
    bool success = true;

    auto Fail = [&](const std::exception& error)
    {
        ARMNN_LOG(error) << "An error occurred attempting to execute a workload: " << error.what();
        success = false;
    };

    try
    {
        std::lock_guard<std::mutex> lockGuard(m_WorkingMemMutex);
        AllocateWorkingMemory(lockGuard);

        ProfilingDynamicGuid workloadInferenceID(0);
        auto ExecuteQueue = [&timelineUtils, &workloadInferenceID, &inferenceGuid](WorkloadQueue& queue)
        {
            for (auto& workload : queue)
            {
                if(timelineUtils)
                {
                    workloadInferenceID = timelineUtils->RecordWorkloadInferenceAndStartOfLifeEvent(workload->GetGuid(),
                                                                                                    inferenceGuid);
                }
                workload->Execute();
                if(timelineUtils)
                {
                    timelineUtils->RecordEndOfLifeEvent(workloadInferenceID);
                }
            }
        };

        ExecuteQueue(m_InputQueue);
        ExecuteQueue(m_WorkloadQueue);
        ExecuteQueue(m_OutputQueue);
    }
    catch (const RuntimeException& error)
    {
        Fail(error);
    }
    catch (const std::runtime_error& error)
    {
        Fail(error);
    }

    return success;
}

void LoadedNetwork::EnqueueInput(const ConstTensor& inputTensor, ITensorHandle* inputTensorHandle)
{
    if (m_NetworkProperties.m_ImportEnabled)  // Try import the input tensor
    {
        MemorySourceFlags importFlags = inputTensorHandle->GetImportFlags();
        if (CheckFlag(importFlags, m_NetworkProperties.m_InputSource) )
        {
            std::unique_ptr<ITensorHandle> tensorHandle =
                    std::make_unique<ConstPassthroughTensorHandle>(inputTensor.GetInfo(),
                                                                   inputTensor.GetMemoryArea());
            void* mem = tensorHandle->Map(false);

            if (inputTensorHandle->Import(mem, m_NetworkProperties.m_InputSource))
            {
                tensorHandle->Unmap();
                return;
            }
            tensorHandle->Unmap();
            throw MemoryImportException("EnqueueInput: Memory Import failed");
        }
        else
        {
            throw MemoryImportException("EnqueueInput: Memory Import failed, backend does not support Import");
        }
    }
    else
    {
        std::unique_ptr<ITensorHandle> tensorHandle =
                std::make_unique<ConstPassthroughTensorHandle>(inputTensor.GetInfo(), inputTensor.GetMemoryArea());

        auto copyFunc = [](void* dst, const void* src, size_t size)
        {
            memcpy(dst, src, size);
        };

        CopyTensorContentsGeneric(tensorHandle.get(), inputTensorHandle, copyFunc);
    }
}

// Note: We can only import the output pointer if all of the following  hold true:
// a) The imported pointer is aligned sufficiently
// b) The tensor has zero padding
// c) There is only one connection to the OutputSlot and it is to an OutputLayer.
// d) The output pointer is allocated via malloc. (Other types will be supported in a later release)
// e) m_IsExportEnabled must be set to true
void LoadedNetwork::ImportOutputTensor(const Tensor& outputTensor, ITensorHandle* outputTensorHandle)
{
    ARMNN_ASSERT_MSG(outputTensorHandle != nullptr, "Data should have been allocated.");
    MemorySourceFlags importFlags = outputTensorHandle->GetImportFlags();
    if (CheckFlag(importFlags, m_NetworkProperties.m_OutputSource))
    {
        std::unique_ptr<ITensorHandle> tensorHandle =
                std::make_unique<PassthroughTensorHandle>(outputTensor.GetInfo(),
                                                          outputTensor.GetMemoryArea());

        void* mem = tensorHandle->Map(false);
        bool importOk = outputTensorHandle->Import(mem, m_NetworkProperties.m_OutputSource);
        tensorHandle->Unmap();

        if (!importOk)
        {
            throw MemoryExportException("ImportOutputTensor: Memory Export failed");
        }
    }
    else
    {
        throw MemoryExportException("ImportOutputTensor: Memory Export failed, attempting to export Input Layer");
    }

}

void CopyToOutputTensor(const Tensor& outputTensor, ITensorHandle* outputTensorHandle)
{
    auto copyFunc = [](void* dst, const void* src, size_t size)
    {
        memcpy(dst, src, size);
    };

    std::unique_ptr<ITensorHandle> tensorHandle =
            std::make_unique<PassthroughTensorHandle>(outputTensor.GetInfo(),
                                                      outputTensor.GetMemoryArea());

    CopyTensorContentsGeneric(outputTensorHandle, tensorHandle.get(), copyFunc);
}


const armnn::ConstTensor GetInputTensor(const LayerBindingId layerId, const InputTensors& inputTensors)
{
    for (auto inputTensorPair : inputTensors)
    {
        LayerBindingId id = inputTensorPair.first;
        if (id == layerId)
        {
            return inputTensorPair.second;
        }
    }
    throw InvalidArgumentException("Input does not exist.");
}

const armnn::Tensor GetOutputTensor(const LayerBindingId layerId, const OutputTensors& outputTensors)
{
    for (auto outputTensorPair : outputTensors)
    {
        LayerBindingId id = outputTensorPair.first;
        if (id == layerId)
        {
            return outputTensorPair.second;
        }
    }
    throw InvalidArgumentException("Output does not exist.");
}

std::vector<ImportedInputId> LoadedNetwork::ImportInputs(const InputTensors& inputTensors,
                                                         MemorySource forceImportMemorySource)
{
    if (!m_NetworkProperties.m_AsyncEnabled)
    {
        // Cannot import if import is not enabled and forceImportMemorySource is undefined
        if (forceImportMemorySource == MemorySource::Undefined)
        {
            throw MemoryImportException("ImportInputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
        }
        if (inputTensors.size() != m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumInputs())
        {
            throw MemoryImportException("ImportInputs: Force Import failed, incorrect number of tensors");
        }

        std::vector<ImportedInputId> importedInputs;
        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
        unsigned int inputIndex = 0;
        for (const BindableLayer* inputLayer : graph.GetInputLayers())
        {
            auto outputTensorHandle = m_PreImportedInputHandles[inputIndex].m_TensorHandle.get();

            if (!outputTensorHandle)
            {
                inputIndex++;
                continue;
            }

            auto layerBindingId = inputLayer->GetBindingId();
            auto it = std::find_if(inputTensors.begin(), inputTensors.end(), [=](const auto& inputTensor)
            {
                return inputTensor.first == layerBindingId;
            });

            if (it == inputTensors.end())
            {
                inputIndex++;
                continue;
            }

            const auto& inputTensor = *it;
            std::unique_ptr<ITensorHandle> passThroughTensorHandle =
                    std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
                                                                   inputTensor.second.GetMemoryArea());

            if (outputTensorHandle->CanBeImported(passThroughTensorHandle->Map(), forceImportMemorySource)
                && (outputTensorHandle->Import(passThroughTensorHandle->Map(), forceImportMemorySource)))
            {
                importedInputs.push_back(inputIndex);
            }
            passThroughTensorHandle->Unmap();

            inputIndex++;
        }

        return importedInputs;
    }
    else
    {
        // Import when the import of network properties is enabled
        std::vector<ImportedInputId> importedInputs;
        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

        for (auto inputTensor : inputTensors)
        {
            auto layerBindingId = inputTensor.first;
            auto it = std::find_if(graph.GetInputLayers().begin(), graph.GetInputLayers().end(), [=](auto* layer)
            {
                return layer->GetBindingId() == layerBindingId;
            });

            if (it == graph.GetInputLayers().end())
            {
                throw MemoryImportException(fmt::format(
                    "ImportInputs: Memory Import failed, unknown LayerBindingId: {}", layerBindingId));
            }

            const Layer* layer = *it;
            if (layer->GetType() != LayerType::Input)
            {
                throw InvalidArgumentException("ImportInputs: given layer not an InputLayer");
            }

            auto& backend = m_Backends.at(layer->GetBackendId());
            if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
            {
                std::string er = backend->GetId();
                er += " does not have PreImportIOTensors capability";
                throw BackendCapabilityException(er);
            }

            const OutputSlot& outputSlot = layer->GetOutputSlots()[0];

            ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
            const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();

            ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
            ARMNN_ASSERT(handleFactory);

            ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
                                                            handleFactory->CreateTensorHandle(tensorInfo, false)};

            ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();

            if (!CheckFlag(tensorHandle->GetImportFlags(), m_NetworkProperties.m_InputSource))
            {
                throw MemoryImportException(
                    fmt::format("ImportInputs: Memory Import failed, backend: "
                                "{} does not support importing from source {}"
                                , factoryId, m_NetworkProperties.m_InputSource));
            }

            std::unique_ptr<ITensorHandle> passThroughTensorHandle =
                    std::make_unique<ConstPassthroughTensorHandle>(inputTensor.second.GetInfo(),
                                                                   inputTensor.second.GetMemoryArea());

            if (tensorHandle->Import(passThroughTensorHandle->Map(), m_NetworkProperties.m_InputSource))
            {
                importedInputs.push_back(m_CurImportedInputId++);
                passThroughTensorHandle->Unmap();
            }
            else
            {
                passThroughTensorHandle->Unmap();
                throw MemoryImportException("ImportInputs: Memory Import failed");
            }

            m_PreImportedInputHandles.push_back(std::move(importedTensorHandlePin));
        }
        return importedInputs;
    }
}

std::vector<ImportedOutputId> LoadedNetwork::ImportOutputs(const OutputTensors& outputTensors,
                                                           MemorySource forceImportMemorySource)
{
    if (!m_NetworkProperties.m_AsyncEnabled)
    {
        // Cannot import if import is not enabled and forceImportMemorySource is undefined
        if (forceImportMemorySource == MemorySource::Undefined)
        {
            throw MemoryImportException("ImportOutputs: Memory Import failed, NetworkProperties.m_ImportEnabled");
        }
        // If forceImportMemorySource is defined, try import if memory is aligned
        if (outputTensors.size() != m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().GetNumOutputs())
        {
            throw MemoryImportException("ImportOutputs: Force Import failed, incorrect number of tensors");
        }
        std::vector<ImportedInputId> importedOutputs;
        Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

        unsigned int outputIndex = 0;
        for (const BindableLayer* const outputLayer : graph.GetOutputLayers())
        {
            auto inputTensorHandle = m_PreImportedOutputHandles[outputIndex].m_TensorHandle.get();

            if (!inputTensorHandle)
            {
                outputIndex++;
                continue;
            }

            auto layerBindingId = outputLayer->GetBindingId();
            auto it = std::find_if(outputTensors.begin(), outputTensors.end(), [=] (const auto& outputTensor)
            {
                return outputTensor.first == layerBindingId;
            });

            if (it == outputTensors.end())
            {
                outputIndex++;
                continue;
            }

            const auto outputTensor = *it;
            // Check if the output memory can be imported
            if (inputTensorHandle->CanBeImported(outputTensor.second.GetMemoryArea(), forceImportMemorySource)
                && inputTensorHandle->Import(outputTensor.second.GetMemoryArea(), forceImportMemorySource))
            {
                importedOutputs.push_back(outputIndex);
            }
            outputIndex++;
        }
        return importedOutputs;
    }

    std::vector<ImportedOutputId> importedOutputs;
    Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

    for (const auto& outputTensor : outputTensors)
    {
        auto layerBindingId = outputTensor.first;
        auto it = std::find_if(graph.GetOutputLayers().begin(), graph.GetOutputLayers().end(), [=](auto* layer)
        {
            return layer->GetBindingId() == layerBindingId;
        });

        if (it == graph.GetOutputLayers().end())
        {
            throw MemoryImportException(fmt::format("ImportOutputs: Memory Import failed, unknown LayerBindingId: {}",
                                                     layerBindingId));
        }

        const Layer* layer = *it;
        if (layer->GetType() != LayerType::Output)
        {
            throw InvalidArgumentException("ImportOutputs: given layer not an OutputLayer");
        }

        auto& backend = m_Backends.at(layer->GetBackendId());
        if (!HasCapability(BackendOptions::BackendOption{"PreImportIOTensors", true}, backend->GetCapabilities()))
        {
            std::string er = backend->GetId();
            er += " does not have PreImportIOTensors capability";
            throw BackendCapabilityException(er);
        }

        const InputSlot& inputSlot = layer->GetInputSlots()[0];
        ITensorHandleFactory::FactoryId factoryId = inputSlot.GetConnectedOutputSlot()->GetTensorHandleFactoryId();
        const TensorInfo& tensorInfo = inputSlot.GetConnectedOutputSlot()->GetTensorInfo();

        ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
        ARMNN_ASSERT(handleFactory);

        ImportedTensorHandlePin importedTensorHandlePin{layerBindingId,
                                                        handleFactory->CreateTensorHandle(tensorInfo, false)};

        ITensorHandle* tensorHandle = importedTensorHandlePin.m_TensorHandle.get();

        if (!CheckFlag(tensorHandle->GetImportFlags(), m_NetworkProperties.m_OutputSource))
        {
            throw MemoryImportException(fmt::format("ImportInputs: Memory Import failed, backend: "
                                                    "{} does not support importing from source {}"
                                                    , factoryId, m_NetworkProperties.m_OutputSource));
        }

        if (tensorHandle->Import(outputTensor.second.GetMemoryArea(), m_NetworkProperties.m_OutputSource))
        {
            importedOutputs.push_back(m_CurImportedOutputId++);
        }
        else
        {
            throw MemoryImportException("ImportInputs: Memory Import failed");
        }

        m_PreImportedOutputHandles.push_back(std::move(importedTensorHandlePin));
    }

    return importedOutputs;
}

void LoadedNetwork::ClearImportedInputs(const std::vector<ImportedInputId> inputIds)
{
    for (auto id : inputIds)
    {
        if (id > m_PreImportedInputHandles.size())
        {
            throw InvalidArgumentException(fmt::format("ClearImportedInputs::Unknown ImportedInputId: {}", id));
        }

        auto& importedTensorHandle = m_PreImportedInputHandles[id].m_TensorHandle;
        if (!importedTensorHandle)
        {
            throw InvalidArgumentException(
                    fmt::format("ClearImportedInputs::ImportedInput with id: {} has already been deleted", id));
        }
        // Call Unimport then destroy the tensorHandle
        importedTensorHandle->Unimport();
        importedTensorHandle = {};
    }
}

void LoadedNetwork::ClearImportedOutputs(const std::vector<ImportedOutputId> outputIds)
{
    for (auto id : outputIds)
    {
        if (id > m_PreImportedOutputHandles.size())
        {
            throw InvalidArgumentException(fmt::format("ClearImportedOutputs::Unknown ImportedOutputId: {}", id));
        }

       auto& importedTensorHandle = m_PreImportedOutputHandles[id].m_TensorHandle;
       if (!importedTensorHandle)
       {
           throw InvalidArgumentException(
                   fmt::format("ClearImportedOutputs::ImportedOutput with id: {} has already been deleted", id));
       }
       // Call Unimport then destroy the tensorHandle
       importedTensorHandle->Unimport();
       importedTensorHandle = {};
    }
}

Status LoadedNetwork::Execute(const InputTensors& inputTensors,
                              const OutputTensors& outputTensors,
                              IWorkingMemHandle& iWorkingMemHandle,
                              std::vector<ImportedInputId> preImportedInputs,
                              std::vector<ImportedOutputId> preImportedOutputs)
{
    const Graph& graph = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();

    if (inputTensors.size() + preImportedInputs.size() != graph.GetNumInputs())
    {
        if (preImportedInputs.empty())
        {
            throw InvalidArgumentException("LoadedNetwork::Execute: Number of inputs provided does not match network.");
        }
        else
        {
            throw InvalidArgumentException("LoadedNetwork::Execute: "
                                           "Number of inputs + preImportedInputs provided does not match network.");
        }
    }

    if (outputTensors.size() + preImportedOutputs.size() != graph.GetNumOutputs())
    {
        if (preImportedOutputs.empty())
        {
            throw InvalidArgumentException("LoadedNetwork::Execute: "
                                           "Number of outputs provided does not match network.");
        }
        else
        {
            throw InvalidArgumentException("LoadedNetwork::Execute: "
                                           "Number of outputs + preImportedOutputs provided does not match network.");
        }
    }

    WorkingMemHandle& workingMemHandle = dynamic_cast<WorkingMemHandle&>(iWorkingMemHandle);
    // Collect all the given LayerBindingIds and check them for duplicates and unknowns.
    std::vector<LayerBindingId>& bindingIds = workingMemHandle.GetBindingIdVector();
    unsigned int index = 0;
    for (auto pair : inputTensors)
    {
        bindingIds[index++] = pair.first;
    }
    for (ImportedInputId id : preImportedInputs)
    {
        bindingIds[index++] = ValidateImportedInputID(id);
    }
    for (auto pair : outputTensors)
    {
        bindingIds[index++] = pair.first;
    }
    for (ImportedOutputId id : preImportedOutputs)
    {
        bindingIds[index++] = ValidateImportedOutputID(id);
    }

    workingMemHandle.ValidateBindingIds();

    auto resetMemHandle = [&]()
    {
        for (ImportedInputId id: preImportedInputs)
        {
            const LayerBindingId layerBindingId = m_PreImportedInputHandles[id].m_LayerBindingId;

            auto inputHandle = workingMemHandle.GetInputHandle(layerBindingId);
            auto inputConnections = workingMemHandle.GetInputConnections(layerBindingId);
            for (auto it : inputConnections)
            {
                *it = inputHandle;
            }
        }

        for (ImportedOutputId id: preImportedOutputs)
        {
            const LayerBindingId layerBindingId = m_PreImportedOutputHandles[id].m_LayerBindingId;

            auto outputHandle = workingMemHandle.GetOutputHandle(layerBindingId);
            auto outputConnections = workingMemHandle.GetOutputConnection(layerBindingId);

            for (auto it : outputConnections)
            {
                *it = outputHandle;
            }
        }
    };

    std::unique_ptr<TimelineUtilityMethods> timelineUtils =
           TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
   ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();
    if (timelineUtils)
    {
        // Add inference timeline trace if profiling is enabled.
       ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
        timelineUtils->CreateTypedEntity(inferenceGuid,LabelsAndEventClasses::INFERENCE_GUID);
        timelineUtils->CreateRelationship(ProfilingRelationshipType::RetentionLink,
                                          networkGuid,
                                          inferenceGuid,
                                         LabelsAndEventClasses::EXECUTION_OF_GUID);
        timelineUtils->RecordEvent(inferenceGuid,LabelsAndEventClasses::ARMNN_PROFILING_SOL_EVENT_CLASS);
    }

    bool executionSucceeded = true;

    if (timelineUtils)
    {
        // Add end of life of the inference timeline if profiling is enabled.
        timelineUtils->RecordEvent(inferenceGuid,LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);
        timelineUtils->Commit();
    }

    if (!workingMemHandle.IsAllocated())
    {
        workingMemHandle.Allocate();
    }

    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
        for (auto pair : inputTensors)
        {
            EnqueueInput(pair.second, workingMemHandle.GetInputHandle(pair.first));
        }

        // Swap in the pre-imported inputs if any
        for (ImportedInputId id : preImportedInputs)
        {
            const ImportedTensorHandlePin& importedInputPin = m_PreImportedInputHandles[id];
            const LayerBindingId layerBindingId = m_PreImportedInputHandles[id].m_LayerBindingId;
            const auto& preimportedHandle = importedInputPin.m_TensorHandle;

            auto inputConnections = workingMemHandle.GetInputConnections(layerBindingId);
            for (auto it : inputConnections)
            {
                *it = preimportedHandle.get();
            }
        }
    }
    {
        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
        if (m_NetworkProperties.m_ExportEnabled)
        {
            for (auto pair: outputTensors)
            {
                ImportOutputTensor(pair.second, workingMemHandle.GetOutputHandle(pair.first));
            }
        }

        for (ImportedOutputId id : preImportedOutputs)
        {
            const ImportedTensorHandlePin& importedOutputPin = m_PreImportedOutputHandles[id];
            const LayerBindingId layerBindingId = m_PreImportedOutputHandles[id].m_LayerBindingId;
            const auto& preimportedHandle = importedOutputPin.m_TensorHandle;

            auto outputConnections = workingMemHandle.GetOutputConnection(layerBindingId);

            for (auto it : outputConnections)
            {
                *it = preimportedHandle.get();
            }
        }
    }

    auto Fail = [&](const std::exception& error)
    {
        ARMNN_LOG(error) << "An error occurred attempting to execute a workload: " << error.what();
        executionSucceeded = false;
    };
   ProfilingDynamicGuid workloadInferenceID(0);

    try
    {
        for (unsigned int i = 0; i < m_WorkloadQueue.size(); ++i)
        {
            auto& workload = m_WorkloadQueue[i];
            if (timelineUtils)
            {
                workloadInferenceID = timelineUtils->RecordWorkloadInferenceAndStartOfLifeEvent(workload->GetGuid(),
                                                                                                inferenceGuid);
            }
            workload->ExecuteAsync(workingMemHandle.GetWorkingMemDescriptorAt(i));

            if (timelineUtils)
            {
                timelineUtils->RecordEndOfLifeEvent(workloadInferenceID);
            }
        }
    }
    catch (const RuntimeException& error)
    {
        resetMemHandle();
        Fail(error);
    }
    catch (const std::runtime_error& error)
    {
        resetMemHandle();
        Fail(error);
    }
    catch (...)
    {
        resetMemHandle();
        throw;
    }

    if (!m_NetworkProperties.m_ExportEnabled)
    {
        for (auto pair: outputTensors)
        {
            CopyToOutputTensor(pair.second, workingMemHandle.GetOutputHandle(pair.first));
        }
    }
    else
    {
       ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "SyncMemGeneric_Execute");
       workingMemHandle.MemSyncOutputs();
    }

    resetMemHandle();

    return executionSucceeded ? Status::Success : Status::Failure;
}

/// Create a new unique WorkingMemHandle object. Create multiple handles if you wish to have
/// overlapped Execution by calling this function from different threads.
std::unique_ptr<IWorkingMemHandle> LoadedNetwork::CreateWorkingMemHandle(NetworkId networkId)
{
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();

    // Tensors that will need to be allocated internally within armnn
    std::vector<std::unique_ptr<ITensorHandle>> managedTensorHandles;
    // Tensors that will be allocated externally by the user
    std::vector<std::unique_ptr<ITensorHandle>> unmanagedTensorHandles;

    std::vector<WorkingMemDescriptor> workingMemDescriptors;
    std::unordered_map<LayerGuid, WorkingMemDescriptor> workingMemDescriptorMap;

    auto GetTensorHandle = [&](Layer* layer, const OutputSlot& outputSlot)
    {
        ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId();
        const TensorInfo& tensorInfo = outputSlot.GetTensorInfo();

        if (factoryId == ITensorHandleFactory::LegacyFactoryId)
        {
            BackendId id = layer->GetBackendId();
            ARMNN_NO_DEPRECATE_WARN_BEGIN
            return m_WorkloadFactories.at(id)->CreateTensorHandle(tensorInfo, false);
            ARMNN_NO_DEPRECATE_WARN_END
        }
        else
        {
            ITensorHandleFactory* handleFactory = m_TensorHandleFactoryRegistry.GetFactory(factoryId);
            ARMNN_ASSERT(handleFactory);
            return handleFactory->CreateTensorHandle(tensorInfo, false);
        }
    };

    struct HandleInfo
    {
        ITensorHandle* m_TensorHandle;

        bool m_IsInputLayerHandle = false;
        bool m_IsOutputLayerHandle = false;

        WorkingMemHandle::InputMemDescriptorCoords m_InputMemDescriptorCoords;
        WorkingMemHandle::OutputMemDescriptorCoords m_OutputMemDescriptorCoords;
    };

    std::unordered_map<const OutputSlot*, HandleInfo> outputToHandleInfoMap;

    unsigned int layerIndex = 0;
    for (auto&& layer : order)
    {
        // Constant layers execution and management is handled during loaded network construction
        if (layer->GetType() == LayerType::Constant)
        {
            continue;
        }

        WorkingMemDescriptor workingMemDescriptor;

        bool isMemoryManaged = true;
        bool isInputLayer = false;
        bool isOutputLayer = false;
        bool isConnectedToOutputLayer = false;

        if (layer->GetType() == LayerType::Input || layer->GetType() == LayerType::MemImport)
        {
            // Input layers/workloads will not be executed so the descriptor is not added to workingMemDescriptors
            // However we will still need to manage the tensorHandle
            isInputLayer = true;
            isMemoryManaged = !m_NetworkProperties.m_ImportEnabled;
        }
        else if (layer->GetType() == LayerType::Output)
        {
            isOutputLayer = true;
        }

        unsigned int slotIndex = 0;
        // Create a tensor handle for each output slot of a layer
        // Once we create it, we start managing its lifetime
        for (auto& slot : layer->GetOutputSlots())
        {
            for (unsigned int i = 0; i < slot.GetNumConnections(); ++i)
            {
                if ((slot.GetConnection(i)->GetOwningLayer().GetType() == LayerType::Output))
                {
                    if (!isConnectedToOutputLayer)
                    {
                        isConnectedToOutputLayer = true;
                        // If Export is enabled disable memory management, so we can export, otherwise we do a copy
                        isMemoryManaged = !m_NetworkProperties.m_ExportEnabled;
                    }
                    else
                    {
                        // Importing in this case would likely cause unexpected behaviour, so we disallow it.
                        ARMNN_LOG(warning) <<
                           fmt::format("Layer name: '{0}' guid: '{1}' has two or more OutputLayers connected to it. "
                                       "This will prevent importing on the connected OutputLayers.",
                                        layer->GetName(), layer->GetGuid());
                        isMemoryManaged = true;
                    }
                }
            }

            ITensorHandle* tensorHandle;
            if (isMemoryManaged)
            {
                managedTensorHandles.emplace_back(GetTensorHandle(layer, slot));
                tensorHandle = managedTensorHandles.back().get();
            }
            else
            {
                unmanagedTensorHandles.emplace_back(GetTensorHandle(layer, slot));
                tensorHandle = unmanagedTensorHandles.back().get();
            }

            workingMemDescriptor.m_Outputs.push_back(tensorHandle);

            HandleInfo& handleInfo = outputToHandleInfoMap[&slot];
            handleInfo.m_TensorHandle = tensorHandle;

            // Store the coordinates of the current layer's OutputSlot that is connected to the OutputLayer
            if (isConnectedToOutputLayer)
            {
                handleInfo.m_IsOutputLayerHandle = true;
                handleInfo.m_OutputMemDescriptorCoords.m_OutputSlotCoords = {layerIndex, slotIndex};
            }
            // Store the LayerBindingId of the InputLayer
            if (isInputLayer)
            {
                handleInfo.m_IsInputLayerHandle = true;
                LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();
                handleInfo.m_InputMemDescriptorCoords.m_LayerBindingId = bindingId;
            }
            slotIndex++;
        }
        // Loop through the input slots in the same layer and decrement the reference counter associated
        // to each tensor handle we encounter.
        // Once it reaches zero, the lifetime of the tensor handle has ended, and we mark its memory as available
        // so that the next tensor handle with a non overlapping lifetime can share its memory.
        for (auto& slot : layer->GetInputSlots())
        {
            ARMNN_ASSERT(slot.GetConnection());
            auto outputSlot = slot.GetConnectedOutputSlot();
            auto key = outputSlot->GetOwningLayer().GetGuid();

            // Constant layers execution and management is handled during loaded network construction
            auto found = m_ConstantTensorHandles.find(key);
            if (found != m_ConstantTensorHandles.end())
            {
                ITensorHandle* tensorHandle = found->second;
                workingMemDescriptor.m_Inputs.push_back(tensorHandle);

                // Odd case where a constant layer is connected to an output layer
                // We will need to create a HandleInfo to track it
                if (isOutputLayer)
                {
                    LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();

                    HandleInfo& handleInfo = outputToHandleInfoMap[outputSlot];
                    handleInfo.m_TensorHandle = tensorHandle;
                    handleInfo.m_IsOutputLayerHandle = true;
                    handleInfo.m_OutputMemDescriptorCoords.m_LayerBindingIds.push_back(bindingId);
                    handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, 0});
                }
                continue;
            }

            HandleInfo& handleInfo = outputToHandleInfoMap.at(outputSlot);

            ITensorHandle* inputTensorHandle = handleInfo.m_TensorHandle;
            workingMemDescriptor.m_Inputs.push_back(inputTensorHandle);

            // Store the LayerBindingId of the OutputLayer
            if (isOutputLayer)
            {
                LayerBindingId bindingId = static_cast<BindableLayer*>(layer)->GetBindingId();
                handleInfo.m_OutputMemDescriptorCoords.m_LayerBindingIds.push_back(bindingId);
                handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, 0});
            }
            // In this case the layer is not an Output Layer but shares its input tensorhandle with an OutputLayer
            // It will need to be updated as well, if we swap out the tensorhandle
            else if (handleInfo.m_IsOutputLayerHandle)
            {
                handleInfo.m_OutputMemDescriptorCoords.m_InputSlotCoords.push_back({layerIndex, slot.GetSlotIndex()});
            }

            // Store the coordinates of the InputSlots connected to the InputLayer
            // There can be more than one InputSlot connected to an InputLayer, so we use a vector
            if (handleInfo.m_IsInputLayerHandle)
            {
                std::pair<LayerGuid, unsigned int> connectionLocation{layerIndex, slot.GetSlotIndex()};
                handleInfo.m_InputMemDescriptorCoords.m_InputSlotCoords.emplace_back(connectionLocation);
            }
        }
        workingMemDescriptorMap.insert({layer->GetGuid(), workingMemDescriptor});

        // Input/Output layers/workloads will not be executed, so the descriptor is not added to workingMemDescriptors
        // However we will still need to manage the tensorHandle
        if (!isInputLayer)
        {
            workingMemDescriptors.push_back(workingMemDescriptor);
            layerIndex++;
        }
    }

    std::vector<std::pair<std::shared_ptr<TensorMemory>, MemorySource>> tensorMemory;

    auto externalMemoryManager = CreateExternalMemoryManger(tensorMemory);

    // Sort m_TensorMemory, so it's order matches the outputSlot order
    std::sort(tensorMemory.begin(), tensorMemory.end(),
              [](const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& lhs,
                 const std::pair<std::shared_ptr<TensorMemory>, MemorySource>& rhs)
              {
                  return lhs.first->m_OutputSlotId < rhs.first->m_OutputSlotId;
              });

    std::vector<WorkingMemHandle::InputMemDescriptorCoords> inputConnectionsInfo;
    std::vector<WorkingMemHandle::OutputMemDescriptorCoords> outputConnectionsInfo;

    for (const auto& handleInfo: outputToHandleInfoMap)
    {
        if (handleInfo.second.m_IsOutputLayerHandle)
        {
            outputConnectionsInfo.emplace_back(handleInfo.second.m_OutputMemDescriptorCoords);
        }

        if (handleInfo.second.m_IsInputLayerHandle)
        {
            inputConnectionsInfo.emplace_back(handleInfo.second.m_InputMemDescriptorCoords);
        }
    }

    return std::make_unique<WorkingMemHandle>(networkId,
                                              inputConnectionsInfo,
                                              outputConnectionsInfo,
                                              workingMemDescriptors,
                                              workingMemDescriptorMap,
                                              std::move(externalMemoryManager),
                                              std::move(tensorMemory),
                                              std::move(managedTensorHandles),
                                              std::move(unmanagedTensorHandles));
}

void LoadedNetwork::RegisterDebugCallback(const DebugCallbackFunction& func)
{
    for (auto&& workloadPtr: m_WorkloadQueue)
    {
        workloadPtr.get()->RegisterDebugCallback(func);
    }
}


void LoadedNetwork::CreateMemoryProfileAsync()
{
    struct PartialBlock
    {
        unsigned int m_StartOfLife;
        unsigned int m_Lifetime;

        size_t m_MemSize;
        unsigned int m_Index;

        BackendId m_BackendId;
    };

    auto align = [](size_t numToAlign)
    {
        const size_t alignment = sizeof(float);
        return ((numToAlign + alignment - 1) / alignment) * alignment;
    };

    std::unordered_map<const OutputSlot*, PartialBlock> memBlockTrackerMap;

    const bool inputImportingEnabled = m_NetworkProperties.m_InputSource != MemorySource::Undefined;
    const bool outputImportingEnabled = m_NetworkProperties.m_OutputSource != MemorySource::Undefined;

    unsigned int timestep = 0;
    unsigned int outputIndex = 0;
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

    for (auto&& layer : order)
    {
        const LayerType& layerType = layer->GetType();
        // Don't manage memory if importing.
        if (layerType == LayerType::Input && inputImportingEnabled)
        {
            continue;
        }
        // Don't manage memory if importing.
        if (layerType == LayerType::Output && outputImportingEnabled
            && layer->GetInputSlot(0).GetConnectedOutputSlot()->GetNumConnections() == 1)
        {
            continue;
        }
        // Because Constant Layer memory can not be shared, the memory must persist for the lifetime of execution,
        // management is done separately.
        if (layerType == LayerType::Constant)
        {
            continue;
        }

        BackendId backendId = layer->GetBackendId();
        for (auto& outputSlot : layer->GetOutputSlots())
        {
            if (!m_SupportsExternallyManagedMemory[backendId])
            {
                continue;
            }

            PartialBlock partialBlock;

            partialBlock.m_StartOfLife = timestep;

            size_t alignedSize = align(outputSlot.GetOutputHandler().GetTensorInfo().GetNumBytes());
            partialBlock.m_MemSize = alignedSize;
            partialBlock.m_Index = outputIndex++;
            partialBlock.m_Lifetime = outputSlot.GetNumConnections();
            partialBlock.m_BackendId = backendId;

            if (partialBlock.m_Lifetime == 0)
            {
                m_MemBlockMap[partialBlock.m_BackendId].emplace_back(partialBlock.m_StartOfLife,
                                                                     partialBlock.m_StartOfLife,
                                                                     partialBlock.m_MemSize,
                                                                     0,
                                                                     partialBlock.m_Index);
            }
            else
            {
                memBlockTrackerMap[&outputSlot] = partialBlock;
            }
        }

        for (auto& inputSlot : layer->GetInputSlots())
        {
            const Layer& connectedInputLayer = inputSlot.GetConnectedOutputSlot()->GetOwningLayer();
            const LayerType& owningLayerType = connectedInputLayer.GetType();

            if (owningLayerType == LayerType::Constant)
            {
                continue;
            }
            if (inputImportingEnabled && owningLayerType == LayerType::Input)
            {
                continue;
            }

            auto outputSlot = inputSlot.GetConnectedOutputSlot();

            PartialBlock& partialBlock = memBlockTrackerMap.at(outputSlot);

            auto& lifetime = partialBlock.m_Lifetime;
            --lifetime;

            if (lifetime == 0)
            {
                m_MemBlockMap[partialBlock.m_BackendId].emplace_back(partialBlock.m_StartOfLife,
                                                                     timestep,
                                                                     partialBlock.m_MemSize,
                                                                     0,
                                                                     partialBlock.m_Index);
            }
        }
        ++timestep;
    }
}

void LoadedNetwork::CreateMemoryProfile()
{
    // Finds the first TensorHandle ancestor of a SubTensorHandle. If the ITensorHandle provided
    // is a TensorHandle, the function just returns it
    auto TraceSubTensorHandleAncestry = [](ITensorHandle* const subTensorHandle)
    {
        ITensorHandle* ancestor = subTensorHandle;
        while (ancestor && ancestor->GetParent())
        {
            ancestor = ancestor->GetParent();
        }
        return ancestor;
    };

    struct PartialBlock
    {
        unsigned int m_StartOfLife;
        unsigned int m_Lifetime;

        size_t m_MemSize;
        unsigned int m_Index;

        BackendId m_BackendId;
    };

    auto align = [](size_t numToAlign)
    {
        const size_t alignment = sizeof(float);
        return ((numToAlign + alignment - 1) / alignment) * alignment;
    };

    std::unordered_map<ITensorHandle*, PartialBlock> memBlockTrackerMap;

    const bool inputImportingEnabled = m_NetworkProperties.m_InputSource != MemorySource::Undefined;
    const bool outputImportingEnabled = m_NetworkProperties.m_OutputSource != MemorySource::Undefined;

    unsigned int timestep = 0;
    unsigned int outputIndex = 0;
    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();

    for (auto&& layer : order)
    {
        const LayerType& layerType = layer->GetType();
        // Don't manage memory if importing.
        if (layerType == LayerType::Input && inputImportingEnabled)
        {
            continue;
        }
        // Don't manage memory if importing.
        if (layerType == LayerType::Output && outputImportingEnabled
            && layer->GetInputSlot(0).GetConnectedOutputSlot()->GetNumConnections() == 1)
        {
            continue;
        }
        // Because Constant Layer memory can not be shared, the memory must persist for the lifetime of execution,
        // management is done separately.
        if (layerType == LayerType::Constant)
        {
            continue;
        }

        BackendId backendId = layer->GetBackendId();
        for (auto& outputSlot : layer->GetOutputSlots())
        {
            if (!m_SupportsExternallyManagedMemory[backendId])
            {
                continue;
            }

            ITensorHandle* tensorHandle = outputSlot.GetOutputHandler().GetData();
            tensorHandle = TraceSubTensorHandleAncestry(tensorHandle);

            if (memBlockTrackerMap.find(tensorHandle) == memBlockTrackerMap.end())
            {
                PartialBlock partialBlock;

                partialBlock.m_StartOfLife = timestep;

                size_t alignedSize = align(outputSlot.GetOutputHandler().GetTensorInfo().GetNumBytes());
                partialBlock.m_MemSize = alignedSize;
                partialBlock.m_Index = outputIndex++;
                partialBlock.m_Lifetime = outputSlot.GetNumConnections();
                partialBlock.m_BackendId = backendId;

                if (partialBlock.m_Lifetime == 0)
                {
                    m_MemBlockMap[partialBlock.m_BackendId].emplace_back(partialBlock.m_StartOfLife,
                                                                         partialBlock.m_StartOfLife,
                                                                         partialBlock.m_MemSize,
                                                                         0,
                                                                         partialBlock.m_Index);
                }
                else
                {
                    memBlockTrackerMap[tensorHandle] = partialBlock;
                }
                m_Tensorhandles.push_back(tensorHandle);

            }
            else
            {
                memBlockTrackerMap.at(tensorHandle).m_Lifetime += outputSlot.GetNumConnections();
            }
        }

        for (auto& inputSlot : layer->GetInputSlots())
        {
            const Layer& connectedInputLayer = inputSlot.GetConnectedOutputSlot()->GetOwningLayer();
            const LayerType& owningLayerType = connectedInputLayer.GetType();

            if (owningLayerType == LayerType::Constant)
            {
                continue;
            }
            if (inputImportingEnabled && owningLayerType == LayerType::Input)
            {
                continue;
            }
            if (!m_SupportsExternallyManagedMemory[connectedInputLayer.GetBackendId()])
            {
                continue;
            }

            auto outputSlot = inputSlot.GetConnectedOutputSlot();

            ITensorHandle* tensorHandle = outputSlot->GetOutputHandler().GetData();
            tensorHandle = TraceSubTensorHandleAncestry(tensorHandle);

            PartialBlock& partialBlock = memBlockTrackerMap.at(tensorHandle);

            auto& lifetime = partialBlock.m_Lifetime;
            --lifetime;

            if (lifetime == 0)
            {
                m_MemBlockMap[partialBlock.m_BackendId].emplace_back(partialBlock.m_StartOfLife,
                                                                     timestep,
                                                                     partialBlock.m_MemSize,
                                                                     0,
                                                                     partialBlock.m_Index);
            }
        }
        ++timestep;
    }

}

std::unique_ptr<MemoryManager> LoadedNetwork::CreateExternalMemoryManger(
        std::vector<std::pair<std::shared_ptr<TensorMemory>, MemorySource>>& tensorMemoryVec)
{
    std::unique_ptr<MemoryManager> memoryManager = std::make_unique<MemoryManager>();
    auto allocatorMap = BackendRegistryInstance().GetAllocators();

    for (auto& backend : m_MemBinMap)
    {
        std::vector<BufferStorage> bufferStorageVec;

        std::shared_ptr<ICustomAllocator> backendAllocator;
        if (allocatorMap.find(backend.first) != allocatorMap.end())
        {
            backendAllocator = allocatorMap[backend.first];
        }
        else
        {
            backendAllocator = m_Backends[backend.first]->GetDefaultAllocator();
        }

        for (auto& memBin : backend.second)
        {
            BufferStorage bufferStorage;
            bufferStorage.m_BufferSize = memBin.m_MemSize;
            bufferStorage.m_TensorMemoryVector.reserve(memBin.m_MemBlocks.size());

            for (auto& memBlock : memBin.m_MemBlocks)
            {
                auto tensorMemory = std::make_shared<TensorMemory>(TensorMemory{memBlock.m_Offset, memBlock.m_Index});

                tensorMemoryVec.emplace_back(tensorMemory, backendAllocator->GetMemorySourceType());
                bufferStorage.m_TensorMemoryVector.emplace_back(tensorMemory);
            }

            bufferStorageVec.emplace_back(std::move(bufferStorage));
        }

        memoryManager->StoreMemToAllocate(bufferStorageVec, backendAllocator, 4);
    }

    return memoryManager;
}

LayerBindingId LoadedNetwork::ValidateImportedInputID(ImportedInputId id)
{
    try
    {
        const auto& importedTensorHandlePin = m_PreImportedInputHandles.at(id);
        if (!importedTensorHandlePin.m_TensorHandle)
        {
            throw InvalidArgumentException(fmt::format("LoadedNetwork::Execute:"
                                                       "PreImportedInput: {} has been deleted", id));
        }
        return importedTensorHandlePin.m_LayerBindingId;
    }
    catch (const std::out_of_range&)
    {
        throw InvalidArgumentException(fmt::format("LoadedNetwork::Execute: Unknown ImportedInputId: {}", id));
    }
}

LayerBindingId LoadedNetwork::ValidateImportedOutputID(ImportedOutputId id)
{
    try
    {
        const auto& importedTensorHandlePin = m_PreImportedOutputHandles.at(id);
        if (!importedTensorHandlePin.m_TensorHandle)
        {
            throw InvalidArgumentException(fmt::format("LoadedNetwork::Execute: "
                                                       "PreImportedOutput: {} has been deleted", id));
        }
        return importedTensorHandlePin.m_LayerBindingId;
    }
    catch (const std::out_of_range&)
    {
        throw InvalidArgumentException(fmt::format("LoadedNetwork::Execute: Unknown ImportedOutputId: {}", id));
    }
}

}