| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "LoadedNetwork.hpp" |
| #include "Layer.hpp" |
| #include "Graph.hpp" |
| #include "Network.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 <backendsCommon/TensorHandle.hpp> |
| #include <armnn/backends/IMemoryManager.hpp> |
| #include <backendsCommon/MemCopyWorkload.hpp> |
| #include <backendsCommon/MemSyncWorkload.hpp> |
| |
| #include <LabelsAndEventClasses.hpp> |
| |
| #include <fmt/format.h> |
| #include <armnn/utility/Timer.hpp> |
| |
| namespace armnn |
| { |
| |
| using namespace std; |
| using namespace armnn::profiling; |
| |
| 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, |
| profiling::ProfilingService& profilingService, |
| const NetworkId networkIdOut) |
| { |
| 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, networkIdOut)); |
| } |
| 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, |
| profiling::ProfilingService& profilingService, |
| const NetworkId networkId) : |
| m_OptimizedNetwork(std::move(net)), |
| m_NetworkProperties(networkProperties), |
| m_NetworkId(networkId), |
| m_TensorHandleFactoryRegistry(), |
| 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()) |
| { |
| auto workloadFactory = backend->CreateWorkloadFactory( |
| m_TensorHandleFactoryRegistry, m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions()); |
| 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_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions()); |
| |
| m_WorkloadFactories.emplace( |
| std::make_pair(backendId, std::make_pair(std::move(workloadFactory), memoryManager))); |
| } |
| } |
| } |
| |
| // Create the thread pool which will have working memory handles assigned to each thread |
| // Should occur after factories are registered so thet the WorkingMemHandles can be created |
| if (m_NetworkProperties.m_NumThreads > 0 && networkProperties.m_AsyncEnabled) |
| { |
| CreateThreadPool(m_NetworkProperties.m_NumThreads); |
| } |
| |
| if (!networkProperties.m_AsyncEnabled) |
| { |
| for (auto &&layer : order) |
| { |
| auto &workloadFactory = GetWorkloadFactory(*layer); |
| |
| 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, |
| !m_NetworkProperties.m_ImportEnabled, |
| m_NetworkProperties.m_InputSource); |
| 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, |
| !m_NetworkProperties.m_ExportEnabled, |
| m_NetworkProperties.m_OutputSource); |
| } |
| else |
| { |
| layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory); |
| } |
| } |
| } |
| } |
| } |
| |
| 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. |
| 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 && layer->GetType() == LayerType::Constant) |
| { |
| m_ConstantWorkloads[layer->GetGuid()] = std::move(workload); |
| } |
| else |
| { |
| m_WorkloadQueue.push_back(move(workload)); |
| } |
| |
| // release the constant data in the layer.. |
| layer->ReleaseConstantData(); |
| break; |
| } |
| } |
| } |
| |
| for (auto&& workloadFactory : m_WorkloadFactories) |
| { |
| workloadFactory.second.first->AfterWorkloadsCreated(); |
| } |
| |
| if (timelineUtils) |
| { |
| // Commit to send the post-optimisation network structure |
| timelineUtils->Commit(); |
| } |
| |
| if (!networkProperties.m_AsyncEnabled) |
| { |
| // Set up memory. |
| m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().AllocateDynamicBuffers(); |
| |
| // Now that the intermediate tensor memory has been set-up, |
| // do any post allocation configuration for each workload. |
| for (auto &workload : m_WorkloadQueue) |
| { |
| workload->PostAllocationConfigure(); |
| } |
| } |
| else |
| { |
| AllocateAndExecuteConstantWorkloads(); |
| } |
| } |
| |
| void LoadedNetwork::AllocateAndExecuteConstantWorkloads() |
| { |
| 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() |
| { |
| 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(); |
| } |
| |
| profiling::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.first.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) |
| { |
| 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()); |
| for (const BindableLayer* inputLayer : graph.GetInputLayers()) |
| { |
| const TensorPin& pin = workloadData.GetInputTensorPin(inputLayer->GetBindingId()); |
| EnqueueInput(*inputLayer, pin.GetTensorHandle(), pin.GetTensorInfo()); |
| } |
| } |
| |
| // 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()); |
| for (const BindableLayer* outputLayer : graph.GetOutputLayers()) |
| { |
| const TensorPin& pin = workloadData.GetOutputTensorPin(outputLayer->GetBindingId()); |
| EnqueueOutput(*outputLayer, pin.GetTensorHandle(), pin.GetTensorInfo()); |
| } |
| } |
| |
| 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(armnn::profiling::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, MemorySource::Malloc) ) |
| { |
| needMemCopy = false; |
| // This assumes a CPU Tensor handle |
| void* mem = tensorHandle->Map(false); |
| if (outputTensorHandle->Import(mem, MemorySource::Malloc)) |
| { |
| 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, MemorySource::Malloc)) |
| { |
| needMemCopy = false; |
| void *mem = tensorHandle->Map(false); |
| bool importOk = inputTensorHandle->Import(mem, MemorySource::Malloc); |
| 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; |
| } |
| for (auto&& workloadFactory : m_WorkloadFactories) |
| { |
| IBackendInternal::IMemoryManagerSharedPtr memoryManager = workloadFactory.second.second; |
| 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; |
| } |
| // 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(); |
| m_IsWorkingMemAllocated = false; |
| } |
| |
| bool LoadedNetwork::Execute(std::unique_ptr<TimelineUtilityMethods>& timelineUtils, |
| profiling::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::CreateThreadPool(std::size_t numThreads) |
| { |
| |
| for (auto i = 0u; i < numThreads; ++i) |
| { |
| std::unique_ptr<IWorkingMemHandle> workingMemHandle = CreateWorkingMemHandle(m_NetworkId); |
| m_Threads.emplace_back( |
| std::make_unique<std::thread>( |
| &LoadedNetwork::ProcessExecPriorities, |
| this, |
| std::move(workingMemHandle) |
| ) |
| ); |
| } |
| } |
| |
| void LoadedNetwork::TerminateThreadPool() noexcept |
| { |
| { |
| std::unique_lock<std::mutex> threadPoolLock(m_ThreadPoolMutex); |
| m_TerminatePool = true; |
| } |
| |
| m_ThreadPoolEvent.notify_all(); |
| |
| for (auto &thread : m_Threads) |
| { |
| thread->join(); |
| } |
| } |
| |
| void LoadedNetwork::Schedule(const InputTensors& inputTensors, |
| const OutputTensors& outputTensors, |
| const QosExecPriority priority, |
| std::shared_ptr<IAsyncExecutionCallback> cb) |
| { |
| // Group execution parameters so that they can be easily added to the queue |
| ExecutionTuple groupExecParams = std::make_tuple(inputTensors, outputTensors, cb); |
| std::shared_ptr<ExecutionTuple> operation = make_shared<ExecutionTuple>(groupExecParams); |
| |
| // Add a message to the queue and notify the request thread |
| std::unique_lock<std::mutex> lock(m_ThreadPoolMutex); |
| switch (priority) { |
| case QosExecPriority::High: |
| m_HighPriorityQueue.push(operation); |
| break; |
| case QosExecPriority::Low: |
| m_LowPriorityQueue.push(operation); |
| break; |
| case QosExecPriority::Medium: |
| default: |
| m_MediumPriorityQueue.push(operation); |
| } |
| m_ThreadPoolEvent.notify_one(); |
| } |
| |
| void LoadedNetwork::ProcessExecPriorities(std::unique_ptr<IWorkingMemHandle> workingMemHandle) |
| { |
| int expireRate = EXPIRE_RATE; |
| int highPriorityCount = 0; |
| int mediumPriorityCount = 0; |
| |
| IWorkingMemHandle& workingMemHandleRef = *workingMemHandle.get(); |
| |
| while (true) |
| { |
| std::shared_ptr<ExecutionTuple> currentExecInProgress(nullptr); |
| { |
| // Wait for a message to be added to the queue |
| // This is in a separate scope to minimise the lifetime of the lock |
| std::unique_lock<std::mutex> lock(m_ThreadPoolMutex); |
| |
| m_ThreadPoolEvent.wait(lock, |
| [=] { |
| return m_TerminatePool || !m_HighPriorityQueue.empty() || |
| !m_MediumPriorityQueue.empty() || !m_LowPriorityQueue.empty(); |
| }); |
| |
| if (m_TerminatePool && m_HighPriorityQueue.empty() && m_MediumPriorityQueue.empty() && |
| m_LowPriorityQueue.empty()) |
| { |
| break; |
| } |
| |
| // Get the message to process from the front of each queue based on priority from high to low |
| // Get high priority first if it does not exceed the expire rate |
| if (!m_HighPriorityQueue.empty() && highPriorityCount < expireRate) |
| { |
| currentExecInProgress = m_HighPriorityQueue.front(); |
| m_HighPriorityQueue.pop(); |
| highPriorityCount += 1; |
| } |
| // If high priority queue is empty or the count exceeds the expire rate, get medium priority message |
| else if (!m_MediumPriorityQueue.empty() && mediumPriorityCount < expireRate) |
| { |
| currentExecInProgress = m_MediumPriorityQueue.front(); |
| m_MediumPriorityQueue.pop(); |
| mediumPriorityCount += 1; |
| // Reset high priority count |
| highPriorityCount = 0; |
| } |
| // If medium priority queue is empty or the count exceeds the expire rate, get low priority message |
| else if (!m_LowPriorityQueue.empty()) |
| { |
| currentExecInProgress = m_LowPriorityQueue.front(); |
| m_LowPriorityQueue.pop(); |
| // Reset high and medium priority count |
| highPriorityCount = 0; |
| mediumPriorityCount = 0; |
| } |
| else |
| { |
| // Reset high and medium priority count |
| highPriorityCount = 0; |
| mediumPriorityCount = 0; |
| continue; |
| } |
| } |
| |
| // invoke the asynchronous execution method |
| auto inputTensors = std::get<0>(*currentExecInProgress); |
| auto outputTensors = std::get<1>(*currentExecInProgress); |
| auto cb = std::get<2>(*currentExecInProgress); |
| |
| // Get time at start of inference |
| HighResolutionClock startTime = armnn::GetTimeNow(); |
| |
| try // executing the inference |
| { |
| // Execute and populate the time at end of inference in the callback |
| Execute(inputTensors, outputTensors, workingMemHandleRef) == Status::Success ? |
| cb->Notify(Status::Success, std::make_pair(startTime, armnn::GetTimeNow())) : |
| cb->Notify(Status::Failure, std::make_pair(startTime, armnn::GetTimeNow())); |
| } |
| catch (const RuntimeException& error) |
| { |
| cb->Notify(Status::Failure, std::make_pair(startTime, armnn::GetTimeNow())); |
| } |
| } |
| } |
| |
| void LoadedNetwork::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.GetGuid(); |
| WorkingMemDescriptor descriptor = context.GetWorkingMemDescriptor(id); |
| |
| 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<ConstPassthroughTensorHandle>(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<ConstPassthroughTensorHandle>(inputTensor.GetInfo(), inputTensor.GetMemoryArea()); |
| |
| auto copyFunc = [](void* dst, const void* src, size_t size) |
| { |
| memcpy(dst, src, size); |
| }; |
| |
| for (const auto& input : descriptor.m_Outputs) |
| { |
| CopyTensorContentsGeneric(tensorHandle.get(), input, copyFunc); |
| } |
| } |
| } |
| |
| void LoadedNetwork::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.GetGuid(); |
| 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<PassthroughTensorHandle>(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"); |
| inputTensorHandle->Map(true); |
| inputTensorHandle->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<PassthroughTensorHandle>(outputTensor.GetInfo(), |
| outputTensor.GetMemoryArea()); |
| |
| CopyTensorContentsGeneric(inputTensorHandle, 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."); |
| } |
| |
| Status LoadedNetwork::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"); |
| for (const BindableLayer* inputLayer : graph.GetInputLayers()) |
| { |
| EnqueueInput(*inputLayer, GetInputTensor(inputLayer->GetBindingId(), inputTensors), workingMemHandle); |
| } |
| } |
| |
| 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"); |
| for (const BindableLayer *outputLayer : graph.GetOutputLayers()) |
| { |
| EnqueueOutput(*outputLayer, GetOutputTensor(outputLayer->GetBindingId(), outputTensors), workingMemHandle); |
| } |
| } |
| |
| 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(); |
| std::unordered_map<LayerGuid, std::vector<std::unique_ptr<ITensorHandle> > > tensorHandleMap; |
| std::vector<WorkingMemDescriptor> workingMemDescriptors; |
| std::unordered_map<LayerGuid, WorkingMemDescriptor> workingMemDescriptorMap; |
| TensorHandleFactoryRegistry tensorHandleFactoryRegistry; |
| WorkloadFactoryMap workloadFactoryMap; |
| |
| std::vector<std::shared_ptr<IMemoryManager>> memoryManagers; |
| |
| for (auto const& backend : m_Backends) |
| { |
| if (backend.second->SupportsTensorAllocatorAPI()) |
| { |
| backend.second->RegisterTensorHandleFactories(tensorHandleFactoryRegistry); |
| memoryManagers.emplace_back(tensorHandleFactoryRegistry.GetMemoryManagers().back()); |
| } |
| else |
| { |
| std::shared_ptr<IMemoryManager> memoryManager = backend.second->CreateMemoryManager(); |
| auto workloadFactory = backend.second->CreateWorkloadFactory( |
| memoryManager, m_OptimizedNetwork->pOptimizedNetworkImpl->GetModelOptions()); |
| |
| workloadFactoryMap.emplace( |
| std::make_pair(backend.first, std::make_pair(std::move(workloadFactory), memoryManager))); |
| memoryManagers.emplace_back(memoryManager); |
| } |
| } |
| |
| auto GetTensorHandle = [&](Layer* layer, const OutputSlot& outputSlot, bool isMemoryManaged) |
| { |
| ITensorHandleFactory::FactoryId factoryId = outputSlot.GetTensorHandleFactoryId(); |
| const TensorInfo& tensorInfo = outputSlot.GetTensorInfo(); |
| |
| if (factoryId == ITensorHandleFactory::LegacyFactoryId) |
| { |
| BackendId id = layer->GetBackendId(); |
| ARMNN_NO_DEPRECATE_WARN_BEGIN |
| return workloadFactoryMap.at(id).first->CreateTensorHandle(tensorInfo, isMemoryManaged); |
| ARMNN_NO_DEPRECATE_WARN_END |
| } |
| else |
| { |
| ITensorHandleFactory* handleFactory = tensorHandleFactoryRegistry.GetFactory(factoryId); |
| ARMNN_ASSERT(handleFactory); |
| return handleFactory->CreateTensorHandle(tensorInfo, isMemoryManaged); |
| } |
| }; |
| |
| std::unordered_map<const ITensorHandle*, unsigned int> handleReferenceCounts; |
| for (auto&& layer : order) |
| { |
| WorkingMemDescriptor workingMemDescriptor; |
| |
| // Constant layers execution and management is handled during loaded network construction |
| if (layer->GetType() == LayerType::Constant) |
| { |
| continue; |
| } |
| bool isMemoryManaged = true; |
| bool isInputLayer = true; |
| // 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)) |
| { |
| isMemoryManaged = !m_NetworkProperties.m_ExportEnabled; |
| } |
| else 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 = false; |
| isMemoryManaged = !m_NetworkProperties.m_ExportEnabled; |
| } |
| |
| // 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()) |
| { |
| tensorHandleMap[layer->GetGuid()].emplace_back(GetTensorHandle(layer, slot, isMemoryManaged)); |
| ITensorHandle* tensorHandle = tensorHandleMap[layer->GetGuid()].back().get(); |
| |
| workingMemDescriptor.m_Outputs.push_back(tensorHandle); |
| tensorHandle->Manage(); |
| unsigned int numConnections = slot.GetNumConnections(); |
| ARMNN_ASSERT(numConnections != 0); |
| |
| handleReferenceCounts[tensorHandle] = numConnections; |
| } |
| // 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 it's memory as available |
| // so that the next tensor handle with a non overlapping lifetime can share it's 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()) |
| { |
| workingMemDescriptor.m_Inputs.push_back(found->second); |
| continue; |
| } |
| |
| auto search = tensorHandleMap.find(key); |
| unsigned int index = outputSlot->CalculateIndexOnOwner(); |
| ITensorHandle* inputTensorHandle = search->second[index].get(); |
| workingMemDescriptor.m_Inputs.push_back(inputTensorHandle); |
| --handleReferenceCounts.at(inputTensorHandle); |
| if (handleReferenceCounts.at(inputTensorHandle) == 0u) |
| { |
| // Stop managing lifetime of tensor handle |
| inputTensorHandle->Allocate(); |
| handleReferenceCounts.erase(inputTensorHandle); |
| } |
| } |
| workingMemDescriptorMap.insert({layer->GetGuid(), workingMemDescriptor}); |
| |
| // Input 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); |
| } |
| } |
| |
| return std::make_unique<WorkingMemHandle>(networkId, |
| workingMemDescriptors, |
| workingMemDescriptorMap, |
| memoryManagers, |
| std::move(tensorHandleMap)); |
| } |
| |
| void LoadedNetwork::RegisterDebugCallback(const DebugCallbackFunction& func) |
| { |
| for (auto&& workloadPtr: m_WorkloadQueue) |
| { |
| workloadPtr.get()->RegisterDebugCallback(func); |
| } |
| } |
| |
| } |