Graph.cpp

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

#include "Graph.hpp"
#include "SubgraphView.hpp"
#include "LayersFwd.hpp"

#include <armnn/backends/IBackendInternal.hpp>

#include <armnn/BackendId.hpp>
#include <armnn/Logging.hpp>
#include <armnn/TypesUtils.hpp>
#include <armnn/Utils.hpp>
#include <armnn/utility/Assert.hpp>

#include <boost/format.hpp>

#include <unordered_map>
#include <DotSerializer.hpp>
#include <sstream>

namespace armnn
{

Graph::Graph(const Graph& other)
:   m_LayersInOrder(other.m_LayersInOrder)
{
    std::unordered_map<const Layer*, Layer*> otherToClonedMap;

    for (auto&& otherLayer : other.m_Layers)
    {
        Layer* const layer = otherLayer->Clone(*this);
        otherToClonedMap.emplace(otherLayer, layer);
    }

    // Copies slot connections.
    for (auto&& otherLayer : other.m_Layers)
    {
        Layer* const thisLayer = otherToClonedMap[otherLayer];

        auto outputSlot = thisLayer->BeginOutputSlots();
        for (auto&& otherOutputSlot : otherLayer->GetOutputSlots())
        {
            for (auto&& otherInputSlot : otherOutputSlot.GetConnections())
            {
                const Layer& otherTgtLayer = otherInputSlot->GetOwningLayer();
                Layer* const thisTgtLayer = otherToClonedMap[&otherTgtLayer];

                InputSlot& inputSlot = thisTgtLayer->GetInputSlot(otherInputSlot->GetSlotIndex());
                outputSlot->Connect(inputSlot);
            }
            outputSlot->SetTensorInfo(otherOutputSlot.GetTensorInfo());
            ++outputSlot;
        }
    }
}

Status Graph::Print() const
{
    if (m_Layers.empty())
    {
        ARMNN_LOG(info) << "\n Graph is empty.\n";
        return Status::Success;
    }
    ARMNN_LOG(info) << "\n";
    ARMNN_LOG(info) << "Walking Pattern: \n";

    for (auto&& it : TopologicalSort())
    {
        ARMNN_LOG(info) << it->GetName() << ":" << GetLayerTypeAsCString(it->GetType())
                                << ":" << it->GetBackendId().Get();
    }
    ARMNN_LOG(info) << "\n\n";

    return Status::Success;
}

Status Graph::SerializeToDot(std::ostream& stream)
{
    {
        DotGraph graph(stream, "Optimized");

        {
            // Default node attributes:
            DotDefaults nodes(stream, "node");
            nodes.GetAttributeSet()
                .AddAttribute("shape", "record");
        }

        {
            // Default edge attributes:
            DotDefaults edges(stream, "edge");
            edges.GetAttributeSet()
                .AddAttribute("fontsize", 8)
                .AddAttribute("fontcolor", "blue")
                .AddAttribute("fontname", "arial-bold");
        }

        // First declares the nodes.
        for (auto&& layer : m_Layers)
        {
            DotNode node(stream, layer->GetGuid(), GetLayerTypeAsCString(layer->GetType()));
            // Extracts the layer parameters.
            ParameterStringifyFunction extractParams = [&node](const std::string & name, const std::string & value){
                node.GetContents().AddContent(name + " : " + value);
            };
            layer->SerializeLayerParameters(extractParams);
        }

        // Second declares the edges.
        for (auto&& layer : m_Layers)
        {
            LayerGuid toId = layer->GetGuid();

            for (unsigned int i=0;i<layer->GetNumInputSlots(); i++)
            {
                OutputSlot* outputSlot = static_cast<OutputSlot*>(layer->GetInputSlot(i).GetConnection());
                LayerGuid fromId = outputSlot->GetOwningLayer().GetGuid();
                DotEdge edge(stream, fromId, toId);

                // Now print the tensor shape on the edge.
                {
                    // Constructs the label attribute with HTML markup.
                    std::stringstream ss;
                    ss << "< " << outputSlot->GetTensorInfo().GetShape() << " >";
                    edge.GetAttributeSet().AddAttribute("label", ss);
                }
            }
        }
    }

    if (stream.bad())
    {
        return Status::Failure;
    }
    return Status::Success;
}

Status Graph::AllocateDynamicBuffers()
{
    // Layers must be sorted in topological order
    ARMNN_ASSERT(m_LayersInOrder);

    std::unordered_set<const ITensorHandle*> preallocatedTensors;
    std::unordered_map<const ITensorHandle*, unsigned int> handleReferenceCounts;

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

    // Checks whether a TensorHandle has been pre-allocated
    auto IsPreallocated = [&](ITensorHandle* const tensorHandle)
    {
        return tensorHandle && preallocatedTensors.find(tensorHandle) != preallocatedTensors.end();
    };

    // Constant tensor handles need to last from the beginning of execution till the end,
    // therefore we pre-allocate them upfront
    for (auto&& layer : m_Layers)
    {
        if (layer->GetType() == LayerType::Constant)
        {
            for (auto&& slot = layer->BeginOutputSlots(); slot != layer->EndOutputSlots(); ++slot)
            {
                ITensorHandle *tensorHandle = TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData());

                if (tensorHandle && !IsPreallocated(tensorHandle))
                {
                    tensorHandle->Allocate();
                    preallocatedTensors.insert(tensorHandle);
                }
            }
        }
    }

    // Iterate over the network in topological order
    for (auto&& layer : m_Layers)
    {
        // Count the amount of times each output slot references a certain buffer (ITensorHandle).
        // The first time we encounter a new tensor handle, we start managing its lifetime.
        for (auto&& slot = layer->BeginOutputSlots(); slot != layer->EndOutputSlots(); ++slot)
        {
            ITensorHandle *tensorHandle = TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData());

            if (tensorHandle && !IsPreallocated(tensorHandle))
            {
                unsigned int numConnections = slot->GetNumConnections();
                if (handleReferenceCounts.find(tensorHandle) == handleReferenceCounts.end())
                {
                    handleReferenceCounts[tensorHandle] = numConnections;
                    tensorHandle->Manage();
                    if (handleReferenceCounts[tensorHandle] == 0u)
                    {
                          // if nobody consumes this tensor we call Allocate()
                          tensorHandle->Allocate();
                    }
                }
                else
                {
                    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, we end the lifetime of the tensor handle
        for (auto&& slot = layer->BeginInputSlots(); slot != layer->EndInputSlots(); ++slot)
        {
            ITensorHandle *tensorHandle = TraceSubTensorHandleAncestry(
                slot->GetConnectedOutputSlot()->GetOutputHandler().GetData());

            if (tensorHandle && !IsPreallocated(tensorHandle))
            {
                --handleReferenceCounts[tensorHandle];

                if (handleReferenceCounts[tensorHandle] == 0u)
                {
                    // Stop managing lifetime of tensor handle
                    tensorHandle->Allocate();
                    handleReferenceCounts.erase(tensorHandle);
                }
            }
        }
    }

    return Status::Success;
}

const Graph& Graph::TopologicalSort() const
{
    if (!m_LayersInOrder)
    {
        // Resets layer order.
        for (auto&& it : m_Layers)
        {
            it->ResetPriority();
        }

        auto compareLayerPriority = [](const LayerList::value_type& layerA, const LayerList::value_type& layerB)
            {
                return layerA->GetPriority() < layerB->GetPriority();
            };

        m_Layers.sort(compareLayerPriority);

        m_LayersInOrder = true;
    }

    return *this;
}

void Graph::AddCompatibilityLayers(std::map<BackendId, std::unique_ptr<IBackendInternal>>& backends,
                                   TensorHandleFactoryRegistry& registry)
{
    // Returns true if the given layer could potentially need an intermediate copy/import layer (depending on its
    // connections to other layers).
    auto MayNeedCompatibilityLayer = [](const Layer& layer)
    {
        // All layers should have been associated with a valid compute device at this point.
        ARMNN_ASSERT(layer.GetBackendId() != Compute::Undefined);
        // Does not need another compatibility layer if a copy or import layer is already present.
        return layer.GetType() != LayerType::MemCopy &&
               layer.GetType() != LayerType::MemImport;
    };

    auto IsCompatibilityStrategy = [](EdgeStrategy strategy)
    {
        return strategy == EdgeStrategy::CopyToTarget ||
               strategy == EdgeStrategy::ExportToTarget;
    };

    ForEachLayer([this, &backends, &registry, MayNeedCompatibilityLayer, IsCompatibilityStrategy](Layer* srcLayer)
    {
        ARMNN_ASSERT(srcLayer);

        if (!MayNeedCompatibilityLayer(*srcLayer))
        {
            // The current layer does not need copy layers, move to the next one
            return;
        }

        const std::vector<OutputSlot>& srcOutputSlots = srcLayer->GetOutputSlots();
        for (unsigned int srcOutputIndex = 0; srcOutputIndex < srcOutputSlots.size(); srcOutputIndex++)
        {
            OutputSlot& srcOutputSlot = srcLayer->GetOutputSlot(srcOutputIndex);
            const std::vector<InputSlot*> srcConnections = srcOutputSlot.GetConnections();
            const std::vector<EdgeStrategy> srcEdgeStrategies = srcOutputSlot.GetEdgeStrategies();
            for (unsigned int srcConnectionIndex = 0; srcConnectionIndex < srcConnections.size(); srcConnectionIndex++)
            {
                InputSlot* dstInputSlot = srcConnections[srcConnectionIndex];
                ARMNN_ASSERT(dstInputSlot);

                EdgeStrategy strategy = srcEdgeStrategies[srcConnectionIndex];
                ARMNN_ASSERT_MSG(strategy != EdgeStrategy::Undefined,
                                 "Undefined memory strategy found while adding copy layers for compatibility");

                const Layer& dstLayer = dstInputSlot->GetOwningLayer();
                if (MayNeedCompatibilityLayer(dstLayer) &&
                    IsCompatibilityStrategy(strategy))
                {
                    // A copy layer is needed in between the source and destination layers.
                    // Record the operation rather than attempting to modify the graph as we go.
                    // (invalidating iterators)
                    const std::string compLayerName = boost::str(boost::format("[ %1% (%2%) -> %3% (%4%) ]")
                                                                 % srcLayer->GetName()
                                                                 % srcOutputIndex
                                                                 % dstLayer.GetName()
                                                                 % dstInputSlot->GetSlotIndex());

                    Layer* compLayer = nullptr;
                    if (strategy == EdgeStrategy::CopyToTarget)
                    {
                        compLayer = InsertNewLayer<MemCopyLayer>(*dstInputSlot, compLayerName.c_str());
                    }
                    else
                    {
                        ARMNN_ASSERT_MSG(strategy == EdgeStrategy::ExportToTarget, "Invalid edge strategy found.");
                        compLayer = InsertNewLayer<MemImportLayer>(*dstInputSlot, compLayerName.c_str());
                    }

                    compLayer->SetBackendId(dstLayer.GetBackendId());

                    OutputSlot& compOutputSlot = compLayer->GetOutputSlot(0);
                    auto backendIt = backends.find(dstLayer.GetBackendId());
                    if (backendIt != backends.end() &&
                        backendIt->second &&
                        backendIt->second->SupportsTensorAllocatorAPI())
                    {
                        auto backend = backendIt->second.get();
                        auto tensorHandleFactoryIds = backend->GetHandleFactoryPreferences();
                        bool found = false;

                        for (auto preference : tensorHandleFactoryIds)
                        {
                            auto factory = registry.GetFactory(preference);
                            if (factory)
                            {
                                auto srcPref = srcOutputSlot.GetTensorHandleFactoryId();
                                auto srcFactory = registry.GetFactory(srcPref);

                                if (srcFactory)
                                {
                                    bool canExportImport =
                                        (factory->GetImportFlags() & srcFactory->GetExportFlags()) != 0;

                                    if (factory->SupportsMapUnmap() || canExportImport)
                                    {
                                        compOutputSlot.SetTensorHandleFactory(preference);
                                        found = true;
                                        break;
                                    }
                                }
                            }
                        }

                        if (!found)
                        {
                            compOutputSlot.SetTensorHandleFactory(ITensorHandleFactory::LegacyFactoryId);
                        }
                    }
                    else
                    {
                        compOutputSlot.SetTensorHandleFactory(ITensorHandleFactory::LegacyFactoryId);
                    }

                    // The output strategy of a compatibility layer is always DirectCompatibility.
                    compOutputSlot.SetEdgeStrategy(0, EdgeStrategy::DirectCompatibility);

                    // Recalculate the connection index on the previous layer as we have just inserted into it.
                    const std::vector<InputSlot*>& newSourceConnections = srcOutputSlot.GetConnections();
                    auto newSrcConnectionIndex = std::distance(newSourceConnections.begin(),
                                                               std::find(newSourceConnections.begin(),
                                                                         newSourceConnections.end(),
                                                                         &compLayer->GetInputSlot(0)));

                    // The input strategy of a compatibility layer is always DirectCompatibilty.
                    srcOutputSlot.SetEdgeStrategy(boost::numeric_cast<unsigned int>(newSrcConnectionIndex),
                                                    EdgeStrategy::DirectCompatibility);
                }
            }
        }
    });
}

void Graph::SubstituteSubgraph(SubgraphView& subgraph, IConnectableLayer* substituteLayer)
{
    ARMNN_ASSERT(substituteLayer != nullptr);

    ReplaceSubgraphConnections(subgraph, substituteLayer);
    EraseSubgraphLayers(subgraph);
}

void Graph::SubstituteSubgraph(SubgraphView& subgraph, const SubgraphView& substituteSubgraph)
{
    // Look through each layer in the new subgraph and add any that are not already a member of this graph
    substituteSubgraph.ForEachLayer([this](Layer* layer)
    {
        if (std::find(std::begin(m_Layers), std::end(m_Layers), layer) == std::end(m_Layers))
        {
            layer->Reparent(*this, m_Layers.end());
            m_LayersInOrder = false;
        }
    });

    ReplaceSubgraphConnections(subgraph, substituteSubgraph);
    EraseSubgraphLayers(subgraph);
    TopologicalSort();
}

void Graph::ReplaceSubgraphConnections(const SubgraphView& subgraph, IConnectableLayer* substituteLayer)
{
    ARMNN_ASSERT(substituteLayer != nullptr);

    // Create a new sub-graph with only the given layer, using
    // the given sub-graph as a reference of which parent graph to use
    SubgraphView substituteSubgraph(substituteLayer);
    ReplaceSubgraphConnections(subgraph, substituteSubgraph);
}

void Graph::ReplaceSubgraphConnections(const SubgraphView& subgraph, const SubgraphView& substituteSubgraph)
{
    ARMNN_ASSERT_MSG(!substituteSubgraph.GetLayers().empty(), "New sub-graph used for substitution must not be empty");

    const SubgraphView::Layers& substituteSubgraphLayers = substituteSubgraph.GetLayers();
    std::for_each(substituteSubgraphLayers.begin(), substituteSubgraphLayers.end(), [&](Layer* layer)
    {
        IgnoreUnused(layer);
        ARMNN_ASSERT_MSG(std::find(m_Layers.begin(), m_Layers.end(), layer) != m_Layers.end(),
                         "Substitute layer is not a member of graph");
    });

    const SubgraphView::InputSlots& subgraphInputSlots = subgraph.GetInputSlots();
    const SubgraphView::OutputSlots& subgraphOutputSlots = subgraph.GetOutputSlots();

    unsigned int subgraphNumInputSlots = boost::numeric_cast<unsigned int>(subgraphInputSlots.size());
    unsigned int subgraphNumOutputSlots = boost::numeric_cast<unsigned int>(subgraphOutputSlots.size());

    const SubgraphView::InputSlots& substituteSubgraphInputSlots = substituteSubgraph.GetInputSlots();
    const SubgraphView::OutputSlots& substituteSubgraphOutputSlots = substituteSubgraph.GetOutputSlots();

    ARMNN_ASSERT(subgraphNumInputSlots == substituteSubgraphInputSlots.size());
    ARMNN_ASSERT(subgraphNumOutputSlots == substituteSubgraphOutputSlots.size());

    // Disconnect the sub-graph and replace it with the substitute sub-graph

    // Step 1: process input slots
    for (unsigned int inputSlotIdx = 0; inputSlotIdx < subgraphNumInputSlots; ++inputSlotIdx)
    {
        InputSlot* subgraphInputSlot = subgraphInputSlots.at(inputSlotIdx);
        ARMNN_ASSERT(subgraphInputSlot);

        IOutputSlot* connectedOutputSlot = subgraphInputSlot->GetConnection();
        ARMNN_ASSERT(connectedOutputSlot);
        connectedOutputSlot->Disconnect(*subgraphInputSlot);

        IInputSlot* substituteInputSlot = substituteSubgraphInputSlots.at(inputSlotIdx);
        ARMNN_ASSERT(substituteInputSlot);
        connectedOutputSlot->Connect(*substituteInputSlot);
    }

    // Step 2: process output slots
    for(unsigned int outputSlotIdx = 0; outputSlotIdx < subgraphNumOutputSlots; ++outputSlotIdx)
    {
        OutputSlot* subgraphOutputSlot = subgraphOutputSlots.at(outputSlotIdx);
        ARMNN_ASSERT(subgraphOutputSlot);

        OutputSlot* substituteOutputSlot = substituteSubgraphOutputSlots.at(outputSlotIdx);
        ARMNN_ASSERT(substituteOutputSlot);
        subgraphOutputSlot->MoveAllConnections(*substituteOutputSlot);
    }
}

void Graph::EraseSubgraphLayers(SubgraphView &subgraph)
{
    for (auto layer : subgraph.GetLayers())
    {
        EraseLayer(layer);
    }
    subgraph.Clear();
}

void Graph::InferTensorInfos()
{
    for (auto&& layer : TopologicalSort())
    {
        for (auto&& input : layer->GetInputSlots())
        {
            const IOutputSlot* source = input.GetConnectedOutputSlot();
            if (source == NULL)
            {
                std::ostringstream message;
                message << "Input not connected on "
                        << GetLayerTypeAsCString(layer->GetType())
                        << " layer \""
                        << layer->GetName()
                        << "\"";
                throw LayerValidationException(message.str());
            }

            if (!source->IsTensorInfoSet())
            {
                throw LayerValidationException("All inputs must have the TensorInfo set at this point.");
            }

            if (layer->m_ShapeInferenceMethod == ShapeInferenceMethod::ValidateOnly)
            {
                layer->ValidateTensorShapesFromInputs();
            }
        }
    }
}

} // namespace armnn