IVGCVSW-5823 Refactor Async Network API

 * Moved IAsyncNetwork into IRuntime.
 * All LoadedNetworks can be executed Asynchronously.

Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: Ibbc901ab9110dc2f881425b75489bccf9ad54169

1 files changed, 0 insertions, 665 deletions

diff --git a/src/armnn/AsyncNetwork.cpp b/src/armnn/AsyncNetwork.cpp
deleted file mode 100644
index 230346a0c3..0000000000
--- a/src/armnn/AsyncNetwork.cpp
+++ /dev/null
@@ -1,665 +0,0 @@
-//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "AsyncNetwork.hpp"
-#include "Graph.hpp"
-#include "Layer.hpp"
-#include "Profiling.hpp"
-
-#include <armnn/BackendHelper.hpp>
-#include <armnn/BackendRegistry.hpp>
-#include <armnn/Logging.hpp>
-#include <armnn/utility/Assert.hpp>
-
-#include <armnn/backends/IMemoryManager.hpp>
-#include <backendsCommon/CpuTensorHandle.hpp>
-#include <backendsCommon/WorkloadData.hpp>
-#include <backendsCommon/MemCopyWorkload.hpp>
-#include <LabelsAndEventClasses.hpp>
-
-#include <fmt/format.h>
-
-namespace armnn
-{
-
-namespace experimental
-{
-
-IAsyncNetwork::IAsyncNetwork(std::unique_ptr<IOptimizedNetwork> net,
-                             const INetworkProperties& networkProperties,
-                             profiling::ProfilingService& profilingService)
-       : pAsyncNetworkImpl( new AsyncNetworkImpl(std::move(net), networkProperties, profilingService)) {};
-
-IAsyncNetwork::~IAsyncNetwork() = default;
-
-TensorInfo IAsyncNetwork::GetInputTensorInfo(LayerBindingId layerId) const
-{
-    return pAsyncNetworkImpl->GetInputTensorInfo(layerId);
-}
-
-TensorInfo IAsyncNetwork::GetOutputTensorInfo(LayerBindingId layerId) const
-{
-    return pAsyncNetworkImpl->GetOutputTensorInfo(layerId);
-}
-
-Status IAsyncNetwork::Execute(const InputTensors& inputTensors,
-                              const OutputTensors& outputTensors,
-                              IWorkingMemHandle& workingMemHandle)
-{
-    return pAsyncNetworkImpl->Execute(inputTensors, outputTensors, workingMemHandle);
-}
-
-std::unique_ptr<IWorkingMemHandle> IAsyncNetwork::CreateWorkingMemHandle()
-{
-    return pAsyncNetworkImpl->CreateWorkingMemHandle();
-}
-
-std::shared_ptr<IProfiler> IAsyncNetwork::GetProfiler() const
-{
-    return pAsyncNetworkImpl->GetProfiler();
-}
-
-void IAsyncNetwork::RegisterDebugCallback(const DebugCallbackFunction& func)
-{
-    pAsyncNetworkImpl->RegisterDebugCallback(func);
-}
-
-void AddLayerStructure(std::unique_ptr<profiling::TimelineUtilityMethods>& timelineUtils,
-                       const Layer& layer,
-                       profiling::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,
-                                               profiling::LabelsAndEventClasses::LAYER_GUID);
-    for (auto&& input : layer.GetInputSlots())
-    {
-        const IOutputSlot* source = input.GetConnectedOutputSlot();
-        ARMNN_ASSERT(source != NULL);
-        timelineUtils->CreateConnectionRelationship(profiling::ProfilingRelationshipType::RetentionLink,
-                                                    source->GetOwningLayerGuid(),
-                                                    layer.GetGuid());
-    }
-}
-
-void AddWorkloadStructure(std::unique_ptr<profiling::TimelineUtilityMethods>& timelineUtils,
-                          std::unique_ptr<IWorkload>& workload,
-                          const Layer& layer)
-{
-    // Add workload to the post-optimisation network structure
-    timelineUtils->CreateTypedEntity(workload->GetGuid(), profiling::LabelsAndEventClasses::WORKLOAD_GUID);
-    timelineUtils->MarkEntityWithLabel(workload->GetGuid(),
-                                       layer.GetBackendId().Get(),
-                                       profiling::LabelsAndEventClasses::BACKENDID_GUID);
-
-    // Link the workload to the layer
-    timelineUtils->CreateRelationship(profiling::ProfilingRelationshipType::RetentionLink,
-                                      layer.GetGuid(),
-                                      workload->GetGuid(),
-                                      profiling::LabelsAndEventClasses::CHILD_GUID);
-}
-
-TensorInfo AsyncNetworkImpl::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 {0}}", layerId));
-}
-
-TensorInfo AsyncNetworkImpl::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 {0}}", layerId));
-}
-
-// Need something like the collectors to get the correct tensors for the inputs
-void AsyncNetworkImpl::CollectInputTensorHandles(
-        std::unordered_map<LayerGuid, std::vector<ITensorHandle*> >& tensorHandles,
-        std::vector<ITensorHandle*>& inputs,
-        const armnn::Layer* layer,
-        const TensorHandleFactoryRegistry& registry,
-        const bool isMemoryManaged)
-{
-    for (auto&& inputSlot : layer->GetInputSlots())
-    {
-        // The graph must be well-formed at this point.
-        ARMNN_ASSERT(inputSlot.GetConnection());
-        auto outputSlot = inputSlot.GetConnectedOutputSlot();
-        auto key = outputSlot->GetOwningLayer().GetGuid();
-        auto search = tensorHandles.find(key);
-
-        if (search == tensorHandles.end())
-        {
-            ITensorHandleFactory::FactoryId factoryId = outputSlot->GetTensorHandleFactoryId();
-            const TensorInfo& tensorInfo = outputSlot->GetTensorInfo();
-
-            ARMNN_ASSERT(factoryId != ITensorHandleFactory::LegacyFactoryId);
-            ITensorHandleFactory* handleFactory = registry.GetFactory(factoryId);
-            ARMNN_ASSERT(handleFactory);
-            std::unique_ptr<ITensorHandle> tensor = handleFactory->CreateTensorHandle(tensorInfo, isMemoryManaged);
-            ITensorHandle* tensorPtr = tensor.release();
-            inputs.push_back(tensorPtr);
-        }
-        else
-        {
-            unsigned int index = outputSlot->CalculateIndexOnOwner();
-            inputs.push_back(search->second[index]);
-        }
-    }
-}
-
-void AsyncNetworkImpl::CreateOutputTensorHandles(
-        std::unordered_map<LayerGuid, std::vector<ITensorHandle*> >& tensorHandles,
-        std::vector<ITensorHandle*>& outputs,
-        const armnn::Layer* layer,
-        const TensorHandleFactoryRegistry& registry,
-        const bool isMemoryManaged)
-{
-    auto guid = layer->GetGuid();
-    std::vector<ITensorHandle*> tensorHandleVectors;
-    tensorHandleVectors.reserve(layer->GetNumOutputSlots());
-
-    for (unsigned int idx=0; idx < layer->GetNumOutputSlots(); idx++)
-    {
-        const OutputSlot& slot = layer->GetOutputSlot(idx);
-        ITensorHandleFactory::FactoryId factoryId = slot.GetTensorHandleFactoryId();
-        const TensorInfo& tensorInfo = slot.GetTensorInfo();
-
-        ARMNN_ASSERT(factoryId != ITensorHandleFactory::LegacyFactoryId);
-        ITensorHandleFactory* handleFactory = registry.GetFactory(factoryId);
-        ARMNN_ASSERT(handleFactory);
-        std::unique_ptr<ITensorHandle> tensor = handleFactory->CreateTensorHandle(tensorInfo, isMemoryManaged);
-        ITensorHandle* tensorPtr = tensor.release();
-        outputs.push_back(tensorPtr);
-        tensorHandleVectors.push_back(tensorPtr);
-    }
-    tensorHandles.insert({guid, tensorHandleVectors});
-}
-
-const IWorkloadFactory& AsyncNetworkImpl::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.first.get();
-
-    ARMNN_ASSERT_MSG(workloadFactory, "No workload factory");
-
-    std::string reasonIfUnsupported;
-    ARMNN_ASSERT_MSG(IWorkloadFactory::IsLayerSupported(layer, {}, reasonIfUnsupported),
-                     "Factory does not support layer");
-    IgnoreUnused(reasonIfUnsupported);
-    return *workloadFactory;
-}
-
-void AsyncNetworkImpl::EnqueueInput(const BindableLayer& layer,
-                                    const ConstTensor& inputTensor,
-                                    WorkingMemHandle& context)
-{
-    if (layer.GetType() != LayerType::Input)
-    {
-        throw InvalidArgumentException("EnqueueInput: given layer not an InputLayer");
-    }
-    LayerGuid id = layer.GetOutputSlot(0).GetConnection(0)->GetOwningLayer().GetGuid();
-    WorkingMemDescriptor descriptor = context.GetWorkingMemDescriptor(id);
-    ARMNN_ASSERT_MSG(descriptor.m_Outputs.size() == 1, "Can only handle Input Layer with one output");
-
-    MemorySourceFlags importFlags = descriptor.m_Outputs[0]->GetImportFlags();
-    if (m_NetworkProperties.m_ImportEnabled)  // Try import the input tensor
-    {
-        if (CheckFlag(importFlags, MemorySource::Malloc) )
-        {
-            // This assumes a CPU Tensor handle
-            std::unique_ptr<ITensorHandle> tensorHandle =
-                    std::make_unique<ConstPassthroughCpuTensorHandle>(inputTensor.GetInfo(),
-                                                                      inputTensor.GetMemoryArea());
-
-            void* mem = tensorHandle->Map(false);
-            if (descriptor.m_Outputs[0]->Import(mem, MemorySource::Malloc))
-            {
-                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<ConstPassthroughCpuTensorHandle>(inputTensor.GetInfo(), inputTensor.GetMemoryArea());
-
-        auto copyFunc = [](void* dst, const void* src, size_t size)
-        {
-            memcpy(dst, src, size);
-        };
-
-        for (const auto& input : descriptor.m_Inputs)
-        {
-            CopyTensorContentsGeneric(tensorHandle.get(), input, copyFunc);
-        }
-    }
-}
-
-void AsyncNetworkImpl::EnqueueOutput(const BindableLayer& layer, const Tensor& outputTensor, WorkingMemHandle& handle)
-{
-    if (layer.GetType() != LayerType::Output)
-    {
-        throw InvalidArgumentException("EnqueueOutput: given layer not an OutputLayer");
-    }
-    ARMNN_ASSERT_MSG(layer.GetNumInputSlots() == 1, "Output Layer should have exactly one input.");
-
-    LayerGuid id = layer.GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayerGuid();
-    WorkingMemDescriptor descriptor = handle.GetWorkingMemDescriptor(id);
-
-    ITensorHandle* inputTensorHandle = descriptor.m_Inputs[0];
-    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
-    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, MemorySource::Malloc))
-            {
-                std::unique_ptr<ITensorHandle> tensorHandle =
-                        std::make_unique<PassthroughCpuTensorHandle>(outputTensor.GetInfo(),
-                                                                     outputTensor.GetMemoryArea());
-
-                void* mem = tensorHandle->Map(false);
-                bool importOk = inputTensorHandle->Import(mem, MemorySource::Malloc);
-                tensorHandle->Unmap();
-
-                if (importOk)
-                {
-                    ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "SyncMemGeneric_Execute");
-                    descriptor.m_Inputs[0]->Map(true);
-                    descriptor.m_Inputs[0]->Unmap();
-                }
-                else
-                {
-                    throw MemoryExportException("EnqueueOutput: Memory Export failed");
-                }
-            }
-            else
-            {
-                throw MemoryExportException("EnqueueOutput: Memory Export failed, backend does not support Export");
-            }
-        }
-        else
-        {
-            throw MemoryExportException("EnqueueOutput: Memory Export failed, attempting to export Input Layer");
-        }
-    }
-    else
-    {
-        auto copyFunc = [](void* dst, const void* src, size_t size)
-        {
-            memcpy(dst, src, size);
-        };
-
-        std::unique_ptr<ITensorHandle> tensorHandle =
-                std::make_unique<PassthroughCpuTensorHandle>(outputTensor.GetInfo(), outputTensor.GetMemoryArea());
-
-        CopyTensorContentsGeneric(descriptor.m_Outputs[0], tensorHandle.get(), copyFunc);
-    }
-}
-
-AsyncNetworkImpl::AsyncNetworkImpl(std::unique_ptr<IOptimizedNetwork> net,
-                           const INetworkProperties& networkProperties,
-                           profiling::ProfilingService& profilingService) :
-    m_OptimizedNetwork(std::move(net)),
-    m_NetworkProperties(networkProperties),
-    m_ProfilingService(profilingService)
-{
-    // Create a profiler and register it for the current thread.
-    m_Profiler = std::make_shared<IProfiler>();
-    ProfilerManager::GetInstance().RegisterProfiler(m_Profiler.get());
-
-    Graph &order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().TopologicalSort();
-
-    //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).
-    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 (backend->SupportsTensorAllocatorAPI())
-            {
-                backend->RegisterTensorHandleFactories(m_TensorHandleFactoryRegistry);
-
-                auto workloadFactory = backend->CreateWorkloadFactory(m_TensorHandleFactoryRegistry);
-                m_WorkloadFactories.emplace(
-                        std::make_pair(backendId, std::make_pair(std::move(workloadFactory), nullptr)));
-            }
-            else
-            {
-                IBackendInternal::IMemoryManagerSharedPtr memoryManager = backend->CreateMemoryManager();
-                auto workloadFactory = backend->CreateWorkloadFactory(memoryManager);
-
-                m_WorkloadFactories.emplace(
-                        std::make_pair(backendId, std::make_pair(std::move(workloadFactory), memoryManager)));
-            }
-        }
-    }
-
-    // Check backends support BackendCapability::AsyncExecution
-    for (auto const& backend : m_Backends)
-    {
-        if (!IsCapabilitySupported(backend.first, BackendCapability::AsyncExecution))
-        {
-            ARMNN_LOG(warning) << fmt::format("AsyncNetworkImpl() Backend: '{0}' does not support Async Execution. "
-                                              "Will fall back to default implementation.",
-                                              backend.first.Get());
-        }
-
-    }
-
-    profiling::ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
-    std::unique_ptr<profiling::TimelineUtilityMethods> timelineUtils =
-            profiling::TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
-    if (timelineUtils)
-    {
-        timelineUtils->CreateTypedEntity(networkGuid, profiling::LabelsAndEventClasses::NETWORK_GUID);
-    }
-
-    //Then create workloads.
-    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);
-                }
-
-                m_WorkloadQueue.push_back(move(workload));
-                // release the constant data in the layer..
-                layer->ReleaseConstantData();
-                break;
-            }
-        }
-    }
-
-    if (timelineUtils)
-    {
-        // Commit to send the post-optimisation network structure
-        timelineUtils->Commit();
-    }
-
-    // Now that the intermediate tensor memory has been set-up, do any post allocation configuration for each workload.
-    // PostAllocationConfiguure will now need to be handled in the ExecuteOn(WorkingMemDescriptor)
-    for (auto &workload : m_WorkloadQueue)
-    {
-        workload->PostAllocationConfigure();
-    }
-}
-
-Status AsyncNetworkImpl::Execute(const InputTensors& inputTensors,
-                             const OutputTensors& outputTensors,
-                             IWorkingMemHandle& iWorkingMemHandle)
-{
-    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;
-    }
-
-    if (graph.GetNumInputs() != inputTensors.size())
-    {
-        throw InvalidArgumentException("Number of inputs provided does not match network.");
-    }
-
-    std::unique_ptr<profiling::TimelineUtilityMethods> timelineUtils =
-            profiling::TimelineUtilityMethods::GetTimelineUtils(m_ProfilingService);
-    profiling::ProfilingGuid inferenceGuid = m_ProfilingService.GetNextGuid();
-    if (timelineUtils)
-    {
-        // Add inference timeline trace if profiling is enabled.
-        profiling::ProfilingGuid networkGuid = m_OptimizedNetwork->GetGuid();
-        timelineUtils->CreateTypedEntity(inferenceGuid, profiling::LabelsAndEventClasses::INFERENCE_GUID);
-        timelineUtils->CreateRelationship(profiling::ProfilingRelationshipType::RetentionLink,
-                                          networkGuid,
-                                          inferenceGuid,
-                                          profiling::LabelsAndEventClasses::EXECUTION_OF_GUID);
-        timelineUtils->RecordEvent(inferenceGuid, profiling::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, profiling::LabelsAndEventClasses::ARMNN_PROFILING_EOL_EVENT_CLASS);
-        timelineUtils->Commit();
-    }
-    WorkingMemHandle& workingMemHandle = dynamic_cast<WorkingMemHandle&>(iWorkingMemHandle);
-    std::lock_guard<std::mutex> lockGuard(workingMemHandle.GetMutex());
-
-    if (!workingMemHandle.IsAllocated())
-    {
-        workingMemHandle.Allocate();
-    }
-
-    {
-        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs");
-        unsigned int i = 0;
-
-        for (const BindableLayer* inputLayer : graph.GetInputLayers())
-        {
-            EnqueueInput(*inputLayer, inputTensors[i].second, workingMemHandle);
-            ++i;
-        }
-    }
-
-    auto Fail = [&](const std::exception& error)
-    {
-        ARMNN_LOG(error) << "An error occurred attempting to execute a workload: " << error.what();
-        executionSucceeded = false;
-    };
-    profiling::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)
-    {
-        Fail(error);
-    }
-    catch (const std::runtime_error& error)
-    {
-        Fail(error);
-    }
-    // For each output to the network, call EnqueueOutput with the data passed by the user.
-    {
-        ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareOutputs");
-        unsigned int i = static_cast<unsigned int>(m_WorkloadQueue.size() - graph.GetNumOutputs());
-
-        for (const BindableLayer* outputLayer : graph.GetOutputLayers())
-        {
-            EnqueueOutput(*outputLayer, outputTensors[i].second, workingMemHandle);
-            ++i;
-        }
-    }
-    return executionSucceeded ? Status::Success : Status::Failure;
-}
-
-/// Get the profiler used for this network
-std::shared_ptr<IProfiler> AsyncNetworkImpl::GetProfiler() const
-{
-    return m_Profiler;
-}
-
-void AsyncNetworkImpl::RegisterDebugCallback(const DebugCallbackFunction& func)
-{
-    for (auto&& workloadPtr: m_WorkloadQueue)
-    {
-        workloadPtr.get()->RegisterDebugCallback(func);
-    }
-}
-
-/// 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> AsyncNetworkImpl::CreateWorkingMemHandle()
-{
-    Graph& order = m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph();
-    std::unordered_map<LayerGuid, std::vector<ITensorHandle*> > tensorHandles;
-    std::vector<WorkingMemDescriptor> workingMemDescriptors;
-    std::unordered_map<LayerGuid, WorkingMemDescriptor> workingMemDescriptorMap;
-
-    for (auto&& layer : order)
-    {
-        if (layer->GetType() == LayerType::Input || layer->GetType() == LayerType::Output)
-        {
-            continue;
-        }
-        WorkingMemDescriptor workingMemDescriptor;
-        // Look for the 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))
-        {
-            CollectInputTensorHandles(tensorHandles,
-                                      workingMemDescriptor.m_Inputs,
-                                      layer,
-                                      m_TensorHandleFactoryRegistry,
-                                      !m_NetworkProperties.m_ExportEnabled);
-            CreateOutputTensorHandles(tensorHandles,
-                                      workingMemDescriptor.m_Outputs,
-                                      layer,
-                                      m_TensorHandleFactoryRegistry,
-                                      !m_NetworkProperties.m_ExportEnabled);
-        }
-        else
-        {
-            CollectInputTensorHandles(tensorHandles,
-                                      workingMemDescriptor.m_Inputs,
-                                      layer,
-                                      m_TensorHandleFactoryRegistry);
-            CreateOutputTensorHandles(tensorHandles,
-                                      workingMemDescriptor.m_Outputs,
-                                      layer,
-                                      m_TensorHandleFactoryRegistry);
-        }
-        workingMemDescriptorMap.insert({layer->GetGuid(), workingMemDescriptor});
-        workingMemDescriptors.push_back(workingMemDescriptor);
-    }
-    return std::make_unique<WorkingMemHandle>(workingMemDescriptors, workingMemDescriptorMap);
-}
-
-void AsyncNetworkImpl::FreeWorkingMemory()
-{
-    // Informs the memory managers to release memory in it's respective memory group
-    for (auto&& workloadFactory : m_WorkloadFactories)
-    {
-        IBackendInternal::IMemoryManagerSharedPtr memoryManager = workloadFactory.second.second;
-        if (memoryManager)
-        {
-            memoryManager->Release();
-        }
-    }
-    m_TensorHandleFactoryRegistry.ReleaseMemory();
-}
-
-} // end experimental namespace
-
-} // end armnn namespace