diff options
Diffstat (limited to 'src/armnn/LoadedNetwork.cpp')
| -rw-r--r-- | src/armnn/LoadedNetwork.cpp | 358 |
1 files changed, 226 insertions, 132 deletions
diff --git a/src/armnn/LoadedNetwork.cpp b/src/armnn/LoadedNetwork.cpp index d75a2021b2..85451cb0d8 100644 --- a/src/armnn/LoadedNetwork.cpp +++ b/src/armnn/LoadedNetwork.cpp @@ -161,35 +161,38 @@ LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net, } } } - - for (auto&& layer : order) + if (!networkProperties.m_AsyncEnabled) { - auto& workloadFactory = GetWorkloadFactory(*layer); - - switch (layer->GetType()) + for (auto &&layer : order) { - 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); - break; - } - default: + auto &workloadFactory = GetWorkloadFactory(*layer); + + switch (layer->GetType()) { - // 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)) + 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_ExportEnabled); + !m_NetworkProperties.m_ImportEnabled); + break; } - else + default: { - layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory); + // 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); + } + else + { + layer->CreateTensorHandles(m_TensorHandleFactoryRegistry, workloadFactory); + } } } } @@ -249,7 +252,17 @@ LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net, AddWorkloadStructure(timelineUtils, workload, *layer); } - m_WorkloadQueue.push_back(move(workload)); + // 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; @@ -268,16 +281,50 @@ LoadedNetwork::LoadedNetwork(std::unique_ptr<IOptimizedNetwork> net, timelineUtils->Commit(); } - // Set up memory. - m_OptimizedNetwork->pOptimizedNetworkImpl->GetGraph().AllocateDynamicBuffers(); + 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) + // 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 { - workload->PostAllocationConfigure(); + 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(); @@ -803,9 +850,8 @@ void LoadedNetwork::EnqueueInput(const BindableLayer& layer, { throw InvalidArgumentException("EnqueueInput: given layer not an InputLayer"); } - LayerGuid id = layer.GetOutputSlot(0).GetConnection(0)->GetOwningLayer().GetGuid(); + LayerGuid id = layer.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 @@ -841,7 +887,7 @@ void LoadedNetwork::EnqueueInput(const BindableLayer& layer, memcpy(dst, src, size); }; - for (const auto& input : descriptor.m_Inputs) + for (const auto& input : descriptor.m_Outputs) { CopyTensorContentsGeneric(tensorHandle.get(), input, copyFunc); } @@ -856,7 +902,7 @@ void LoadedNetwork::EnqueueOutput(const BindableLayer& layer, const Tensor& outp } ARMNN_ASSERT_MSG(layer.GetNumInputSlots() == 1, "Output Layer should have exactly one input."); - LayerGuid id = layer.GetInputSlot(0).GetConnectedOutputSlot()->GetOwningLayerGuid(); + LayerGuid id = layer.GetGuid(); WorkingMemDescriptor descriptor = handle.GetWorkingMemDescriptor(id); ITensorHandle* inputTensorHandle = descriptor.m_Inputs[0]; @@ -888,8 +934,8 @@ void LoadedNetwork::EnqueueOutput(const BindableLayer& layer, const Tensor& outp if (importOk) { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "SyncMemGeneric_Execute"); - descriptor.m_Inputs[0]->Map(true); - descriptor.m_Inputs[0]->Unmap(); + inputTensorHandle->Map(true); + inputTensorHandle->Unmap(); } else { @@ -914,10 +960,38 @@ void LoadedNetwork::EnqueueOutput(const BindableLayer& layer, const Tensor& outp }; std::unique_ptr<ITensorHandle> tensorHandle = - std::make_unique<PassthroughCpuTensorHandle>(outputTensor.GetInfo(), outputTensor.GetMemoryArea()); + std::make_unique<PassthroughCpuTensorHandle>(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."); +} - CopyTensorContentsGeneric(descriptor.m_Outputs[0], tensorHandle.get(), copyFunc); +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, @@ -971,12 +1045,9 @@ Status LoadedNetwork::Execute(const InputTensors& inputTensors, { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "PrepareInputs"); - unsigned int i = 0; - for (const BindableLayer* inputLayer : graph.GetInputLayers()) { - EnqueueInput(*inputLayer, inputTensors[i].second, workingMemHandle); - ++i; + EnqueueInput(*inputLayer, GetInputTensor(inputLayer->GetBindingId(), inputTensors), workingMemHandle); } } @@ -1016,130 +1087,153 @@ Status LoadedNetwork::Execute(const InputTensors& inputTensors, // 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()) + for (const BindableLayer *outputLayer : graph.GetOutputLayers()) { - EnqueueOutput(*outputLayer, outputTensors[i].second, workingMemHandle); - ++i; + EnqueueOutput(*outputLayer, GetOutputTensor(outputLayer->GetBindingId(), outputTensors), workingMemHandle); } } return executionSucceeded ? Status::Success : Status::Failure; } -// Need something like the collectors to get the correct tensors for the inputs -void LoadedNetwork::CollectInputTensorHandles( - std::unordered_map<LayerGuid, std::vector<ITensorHandle*> >& tensorHandles, - std::vector<ITensorHandle*>& inputs, - const armnn::Layer* layer, - const TensorHandleFactoryRegistry& registry, - const bool isMemoryManaged) + +/// 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) { - 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); + 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; - if (search == tensorHandles.end()) - { - ITensorHandleFactory::FactoryId factoryId = outputSlot->GetTensorHandleFactoryId(); - const TensorInfo& tensorInfo = outputSlot->GetTensorInfo(); + std::vector<std::shared_ptr<IMemoryManager>> memoryManagers; - 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); + for (auto const& backend : m_Backends) + { + if (backend.second->SupportsTensorAllocatorAPI()) + { + backend.second->RegisterTensorHandleFactories(tensorHandleFactoryRegistry); + memoryManagers.emplace_back(tensorHandleFactoryRegistry.GetMemoryManagers().back()); } else { - unsigned int index = outputSlot->CalculateIndexOnOwner(); - inputs.push_back(search->second[index]); + 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); } } -} - -void LoadedNetwork::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++) + auto GetTensorHandle = [&](Layer* layer, const OutputSlot& outputSlot, bool isMemoryManaged) { - const OutputSlot& slot = layer->GetOutputSlot(idx); - ITensorHandleFactory::FactoryId factoryId = slot.GetTensorHandleFactoryId(); - const TensorInfo& tensorInfo = slot.GetTensorInfo(); + 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(); - outputs.push_back(tensorPtr); - tensorHandleVectors.push_back(tensorPtr); - } - tensorHandles.insert({guid, tensorHandleVectors}); -} - -/// 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<ITensorHandle*> > tensorHandles; - std::vector<WorkingMemDescriptor> workingMemDescriptors; - std::unordered_map<LayerGuid, WorkingMemDescriptor> workingMemDescriptorMap; + 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) { - if (layer->GetType() == LayerType::Input || layer->GetType() == LayerType::Output) + WorkingMemDescriptor workingMemDescriptor; + + // Constant layers execution and management is handled during loaded network construction + if (layer->GetType() == LayerType::Constant) { continue; } - WorkingMemDescriptor workingMemDescriptor; + 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)) + 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); + isMemoryManaged = !m_NetworkProperties.m_ExportEnabled; } - else + 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()) { - CollectInputTensorHandles(tensorHandles, - workingMemDescriptor.m_Inputs, - layer, - m_TensorHandleFactoryRegistry); - CreateOutputTensorHandles(tensorHandles, - workingMemDescriptor.m_Outputs, - layer, - m_TensorHandleFactoryRegistry); + 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}); - workingMemDescriptors.push_back(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); + workingMemDescriptorMap, + memoryManagers, + std::move(tensorHandleMap)); } void LoadedNetwork::RegisterDebugCallback(const DebugCallbackFunction& func) |