// change the node associated with nodeName to newNode; used in the KL-reg based adaptation to reduce feature copy
// need to update all the mappings as well childrens
void ComputationNetwork::ChangeNode(wstring nodeName, ComputationNodeBasePtr newNode)
{
InvalidateCompiledNetwork();
ComputationNodeBasePtr oldNode = GetNodeFromName(nodeName);
if (oldNode->OperationName() != newNode->OperationName())
InvalidArgument("newNode must have the same type as the old node.");
// change children
for (auto nodeIter = m_nameToNodeMap.begin(); nodeIter != m_nameToNodeMap.end(); nodeIter++)
{
ComputationNodeBasePtr node = nodeIter->second;
for (int i = 0; i < node->GetNumInputs(); i++)
if (node->GetInputs()[i] == oldNode)
node->SetInput(i, newNode);
}
// change name map
m_nameToNodeMap[nodeName] = newNode;
for (int i = 0; i < oldNode->GetNumInputs(); i++)
newNode->SetInput(i, oldNode->GetInputs()[i]);
// change other maps
for (auto groupIter : GetAllNodeGroups())
{
auto& group = *groupIter;
for (int i = 0; i < group.size(); i++)
if (group[i] == oldNode)
group[i] = newNode;
}
}
// sets m_learningRateMultiplier in all LearnableParameters feeding into the passed rootNode
// Called from MEL
void ComputationNetwork::SetLearnableNodesBelowLearningRateMultiplier(const float learningRateMultiplier, const ComputationNodeBasePtr& rootNode)
{
// find nodes from all available nodes
if (rootNode == nullptr)
{
for (auto nodeIter = m_nameToNodeMap.begin(); nodeIter != m_nameToNodeMap.end(); nodeIter++)
{
ComputationNodeBasePtr node = nodeIter->second;
if (node->OperationName() == OperationNameOf(LearnableParameter))
node->SetLearningRateMultiplier(learningRateMultiplier);
}
}
else
{
// for calculating a specific node
if (!EvalOrderExists(rootNode))
const_cast<ComputationNetwork&>(*this).FormEvalOrder(rootNode);
for (const auto& node : GetAllNodesForRoot(rootNode))
{
if (node->OperationName() == OperationNameOf(LearnableParameter))
node->SetLearningRateMultiplier(learningRateMultiplier);
}
}
}
// sets m_parameterUpdateRequired in all LearnableParameters feeding into the passed rootNode
// Called from MEL --TODO: correct?
void ComputationNetwork::SetLearnableNodesBelowNeedGradient(const bool needGradient, const ComputationNodeBasePtr& rootNode)
{
// find nodes from all available nodes
if (rootNode == nullptr)
{
for (auto nodeIter = m_nameToNodeMap.begin(); nodeIter != m_nameToNodeMap.end(); nodeIter++)
{
ComputationNodeBasePtr node = nodeIter->second;
if (node->OperationName() == OperationNameOf(LearnableParameter))
node->SetParameterUpdateRequired(needGradient);
}
}
else
{
// for calculating a specific node
for (const auto& node : GetEvalOrder(rootNode))
{
if (node->OperationName() == OperationNameOf(LearnableParameter))
node->SetParameterUpdateRequired(needGradient);
}
}
}
// recovers the processing order within a recurrent loop
// TODO: Once we only use the nested network for recurrent traversal, this will be no longer necessary.
void ComputationNetwork::DetermineLoopForwardOrder(unordered_set<ComputationNodeBasePtr>& visited,
unordered_set<ComputationNodeBasePtr>& recStack,
list<ComputationNodeBasePtr>& nodesStack,
ComputationNodeBasePtr cur)
{
if (visited.find(cur) == visited.end())
{
visited.insert(cur);
recStack.insert(cur);
if (GetRecurrenceSteppingDirection(cur) == 0) // recurrence stops at delay nodes
{
for (size_t i = 0; i < cur->GetNumInputs(); i++)
if (cur->Input(i)->m_loopId == cur->m_loopId)
DetermineLoopForwardOrder(visited, recStack, nodesStack, cur->Input(i));
}
recStack.erase(cur);
nodesStack.push_back(cur);
}
else if (recStack.find(cur) != recStack.end())
LogicError("%ls %ls operation is part of an infinite loop that cannot be unrolled.", cur->NodeName().c_str(), cur->OperationName().c_str());
}
