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changed MinibatchPackingFlags from unsigned char to char, so we can use a Matrix<char> for that in the future; 

updated GetMinibatchCopy() to use MBLayout;
added a runtime check to Evaluate() in order to verify invariants of MBLayout;
renamed SetActualNbrSlicesInEachRecIter() to SetActualNbrSlicesInEachRecurentIteration() for consistency
This commit is contained in:
Frank Seide 2015-09-17 20:44:07 -07:00
Родитель 0c1111bb01
Коммит 9575edc2c4
9 изменённых файлов: 39 добавлений и 51 удалений
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10
Common/DataReader.cpp
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@ -259,12 +259,11 @@ template<class ElemType>
bool DataReader<ElemType>::GetMinibatchCopy(
std::vector<std::vector<std::pair<wstring, size_t>>>& uttInfo,
std::map<std::wstring, Matrix<ElemType>*>& matrices,
Matrix<float>& sentenceBegin,
std::vector<MinibatchPackingFlags>& minibatchPackingFlags)
MBLayoutPtr pMBLayout)
{
bool ans = false;
for (size_t i = 0; i < m_ioNames.size(); i++)
ans = (m_dataReader[m_ioNames[i]]->GetMinibatchCopy(uttInfo, matrices, sentenceBegin, minibatchPackingFlags) || ans);
ans = (m_dataReader[m_ioNames[i]]->GetMinibatchCopy(uttInfo, matrices, pMBLayout) || ans);
return ans;
}
@ -272,12 +271,11 @@ template<class ElemType>
bool DataReader<ElemType>::SetNetOutput(
const std::vector<std::vector<std::pair<wstring, size_t>>>& uttInfo,
const Matrix<ElemType>& outputs,
const Matrix<float>& sentenceBegin,
const std::vector<MinibatchPackingFlags>& minibatchPackingFlags)
const MBLayoutPtr pMBLayout)
{
bool ans = false;
for (size_t i = 0; i < m_ioNames.size(); i++)
ans = (m_dataReader[m_ioNames[i]]->SetNetOutput(uttInfo, outputs, sentenceBegin, minibatchPackingFlags) || ans);
ans = (m_dataReader[m_ioNames[i]]->SetNetOutput(uttInfo, outputs, pMBLayout) || ans);
return ans;
}

14
Common/Include/DataReader.h
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@ -102,22 +102,22 @@ public:
// Gets a copy of the minibatch for the forward computation. This can be
// useful if some of the computation has to happen in the reader.
// TODO: No, there should be no computation in the reader.
virtual bool GetMinibatchCopy(
std::vector<std::vector<std::pair<wstring, size_t>>>& /*uttInfo*/,
std::map<std::wstring, Matrix<ElemType>*>& /*matrices*/,
Matrix<float>& /*sentenceBegin*/,
std::vector<MinibatchPackingFlags>& /*minibatchPackingFlags*/)
MBLayoutPtr /*data copied here*/)
{
return false;
}
// Sets the neural network output to the reader. This can be useful if some
// of the computation has to happen in the reader.
// TODO: No, there should be no computation in the reader.
virtual bool SetNetOutput(
const std::vector<std::vector<std::pair<wstring, size_t>>>& /*uttInfo*/,
const Matrix<ElemType>& /*outputs*/,
const Matrix<float>& /*sentenceBegin*/,
const std::vector<MinibatchPackingFlags>& /*minibatchPackingFlags*/)
const MBLayoutPtr)
{
return false;
}
@ -227,16 +227,14 @@ public:
virtual bool GetMinibatchCopy(
std::vector<std::vector<std::pair<wstring, size_t>>>& uttInfo,
std::map<std::wstring, Matrix<ElemType>*>& matrices,
Matrix<float>& sentenceBegin,
std::vector<MinibatchPackingFlags>& minibatchPackingFlags);
MBLayoutPtr);
// Sets the neural network output to the reader. This can be useful if some
// of the computation has to happen in the reader.
virtual bool SetNetOutput(
const std::vector<std::vector<std::pair<wstring, size_t>>>& uttInfo,
const Matrix<ElemType>& outputs,
const Matrix<float>& sentenceBegin,
const std::vector<MinibatchPackingFlags>& minibatchPackingFlags);
const MBLayoutPtr);
void CopyMBLayoutTo(MBLayoutPtr pMBLayout);

15
Common/Include/basetypes.h
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@ -1052,20 +1052,9 @@ public:
#define EPSILON 1e-5
#define ISCLOSE(a, b, threshold) (abs(a - b) < threshold)?true:false
// why are the following in basetypes.h?
/**
These macros are used for sentence segmentation information.
TODO: get rid of this, no need
*/
//#define ((int) MinibatchPackingFlags::SequenceStart) ((int) MinibatchPackingFlags::SequenceStart)
//#define ((int) MinibatchPackingFlags::None) ((int) MinibatchPackingFlags::None)
//#define ((int) MinibatchPackingFlags::SequenceEnd) ((int) MinibatchPackingFlags::SequenceEnd)
//#define ((int) MinibatchPackingFlags::NoInput) ((int) MinibatchPackingFlags::NoInput)
//#define ((int) MinibatchPackingFlags::NoFeature) ((int) MinibatchPackingFlags::NoFeature)
//#define ((int) MinibatchPackingFlags::NoLabel) ((int) MinibatchPackingFlags::NoLabel)
// why is this in basetypes.h?
// boundary flags for a frame
enum class MinibatchPackingFlags : unsigned char
enum class MinibatchPackingFlags : char // (note: not using unsigned char because these go into a matrix, and we use Matrix<char>, since we use it as a data holder)
{
None = 0,
SequenceStart = 1 << 0, // binary 0001 frame is first of an utterance

9
MachineLearning/CNTKComputationNetworkLib/ComputationNetwork.h
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@ -594,6 +594,13 @@ public:
// TODO: rename to ForwardProp()? To make it very clear?
void Evaluate(const ComputationNodeBasePtr rootNode)
{
// checks that will disappear once we complete the refactoring. If this passes for a while, we will eliminate one
// If this fails, comment this out (it is safe) and tell fseide@microsoft.com.
if (m_pMBLayout && m_nbrSlicesInEachRecurrentIteration != m_pMBLayout->GetNumStreams())
LogicError("Evaluate: detected that m_nbrSlicesInEachRecurrentIteration != m_pMBLayout->GetNumStreams()");
if (m_pMBLayout && m_pMBLayout->GetNumFrames() != m_pMBLayout->GetSize())
LogicError("Evaluate: detected that m_pMBLayout->GetNumFrames() != m_pMBLayout->GetSize()");
// prepare to compute with the subnetwork that this rootNode depends on, including
// - auto-detecting recurrent loops
// - calling Validate() on all nodes, which, a.o, resizes the matrices
@ -673,7 +680,7 @@ public:
// returns the result from SetActualMiniBatchSize(). Note GetActualMBSize() also exists but returns a value derived from the inputs dimensions
size_t GetMaxMBSize() { return m_actMiniBSize; }
void SetActualNbrSlicesInEachRecIter(const size_t aSize)
void SetActualNbrSlicesInEachRecurentIteration(const size_t aSize)
{
m_nbrSlicesInEachRecurrentIteration = aSize;
}

4
MachineLearning/CNTKSGDLib/MultiNetworksSGD.h
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@ -1160,7 +1160,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t actualMBSize = encoderNet->GetActualMBSize();
encoderNet->SetActualMiniBatchSize(actualMBSize);
encoderNet->SetActualNbrSlicesInEachRecIter(encoderTrainSetDataReader->NumberSlicesInEachRecurrentIter());
encoderNet->SetActualNbrSlicesInEachRecurentIteration(encoderTrainSetDataReader->NumberSlicesInEachRecurrentIter());
encoderTrainSetDataReader->CopyMBLayoutTo(encoderNet->GetMBLayoutPtr());
encoderNet->Evaluate(encoderEvaluationNodes[0]);
@ -1168,7 +1168,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
actualMBSize = decoderNet->GetActualMBSize();
decoderNet->SetActualMiniBatchSize(actualMBSize);
decoderNet->SetActualNbrSlicesInEachRecIter(decoderTrainSetDataReader->NumberSlicesInEachRecurrentIter());
decoderNet->SetActualNbrSlicesInEachRecurentIteration(decoderTrainSetDataReader->NumberSlicesInEachRecurrentIter());
/// not the sentence begining, because the initial hidden layer activity is from the encoder network
decoderTrainSetDataReader->CopyMBLayoutTo(decoderNet->GetMBLayoutPtr());

18
MachineLearning/CNTKSGDLib/SGD.cpp
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@ -1320,7 +1320,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t actualMBSize = net.GetActualMBSize();
net.SetActualMiniBatchSize(actualMBSize);
net.SetActualNbrSlicesInEachRecIter(trainSetDataReader->NumberSlicesInEachRecurrentIter());
net.SetActualNbrSlicesInEachRecurentIteration(trainSetDataReader->NumberSlicesInEachRecurrentIter());
trainSetDataReader->CopyMBLayoutTo(net.GetMBLayoutPtr());
// TODO: Exactly this loop should be INSIDE ComputationNetwork--pass the nodes array instead!
@ -1769,11 +1769,8 @@ namespace Microsoft { namespace MSR { namespace CNTK {
// whole utterance.
assert(trainSetDataReader != NULL);
std::vector<std::vector<std::pair<wstring, size_t>>> uttInfo;
Matrix<float> sentenceBoundary;
std::vector<MinibatchPackingFlags> minibatchPackingFlags;
while (trainSetDataReader->GetMinibatchCopy(uttInfo, *inputMatrices,
sentenceBoundary,
minibatchPackingFlags))
auto pMBLayout = make_shared<MBLayout>();
while (trainSetDataReader->GetMinibatchCopy(uttInfo, *inputMatrices, pMBLayout))
{
ComputationNetwork::UpdateEvalTimeStamps(featureNodes);
@ -1783,13 +1780,12 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t actualMBSize = net.GetActualMBSize();
net.SetActualMiniBatchSize(actualMBSize);
net.SetActualNbrSlicesInEachRecIter(trainSetDataReader->NumberSlicesInEachRecurrentIter());
net.SetActualNbrSlicesInEachRecurentIteration(trainSetDataReader->NumberSlicesInEachRecurrentIter());
trainSetDataReader->CopyMBLayoutTo(net.GetMBLayoutPtr());
net.Evaluate(outputNodes[0]); // Only evaluate the first output
trainSetDataReader->SetNetOutput(uttInfo,
dynamic_pointer_cast<ComputationNode<ElemType>>(outputNodes[0])->FunctionValues(),
sentenceBoundary,
minibatchPackingFlags);
pMBLayout);
}
}
@ -1964,7 +1960,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
{
nSamplesSinceLastModelSync += actualMBSize;
net.SetActualMiniBatchSize(actualMBSize);
net.SetActualNbrSlicesInEachRecIter(nSlices);
net.SetActualNbrSlicesInEachRecurentIteration(nSlices);
if (!useDistributedMBReading && useParallelTrain && trainSetDataReader->RequireSentenceSeg())
{
@ -1987,7 +1983,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
if (m_needAdaptRegularization && m_adaptationRegType == AdaptationRegType::KL && refNode != nullptr)
{
refNet.SetActualMiniBatchSize(actualMBSize);
refNet.SetActualNbrSlicesInEachRecIter(trainSetDataReader->NumberSlicesInEachRecurrentIter());
refNet.SetActualNbrSlicesInEachRecurentIteration(trainSetDataReader->NumberSlicesInEachRecurrentIter());
refNet.Evaluate(refNode);
Matrix<ElemType>::ScaleAndAdd((ElemType)m_adaptationRegWeight,
dynamic_pointer_cast<ComputationNode<ElemType>>(refNode)->FunctionValues(),

14
MachineLearning/CNTKSGDLib/SimpleEvaluator.h
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@ -129,7 +129,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
actualMBSize = m_net.GetActualMBSize();
m_net.SetActualMiniBatchSize(actualMBSize);
m_net.SetActualNbrSlicesInEachRecIter(dataReader->NumberSlicesInEachRecurrentIter());
m_net.SetActualNbrSlicesInEachRecurentIteration(dataReader->NumberSlicesInEachRecurrentIter());
dataReader->CopyMBLayoutTo(m_net.GetMBLayoutPtr());
//for now since we share the same label masking flag we call this on one node only
@ -450,7 +450,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
LogicError("decoderTrainSetDataReader read data but encoderNet reports no data read");
(*ptr)->SetActualMiniBatchSize(actualMBSize);
(*ptr)->SetActualNbrSlicesInEachRecIter((*preader)->NumberSlicesInEachRecurrentIter());
(*ptr)->SetActualNbrSlicesInEachRecurentIteration((*preader)->NumberSlicesInEachRecurrentIter());
(*preader)->CopyMBLayoutTo((*ptr)->GetMBLayoutPtr());
const auto & pairs = (*ptr)->PairNodes();
@ -464,7 +464,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
decoderNet->SetActualMiniBatchSize(actualMBSize);
if (actualMBSize == 0)
LogicError("decoderTrainSetDataReader read data but decoderNet reports no data read");
decoderNet->SetActualNbrSlicesInEachRecIter(decoderDataReader->NumberSlicesInEachRecurrentIter());
decoderNet->SetActualNbrSlicesInEachRecurentIteration(decoderDataReader->NumberSlicesInEachRecurrentIter());
decoderDataReader->CopyMBLayoutTo(decoderNet->GetMBLayoutPtr());
size_t i = 0;
@ -667,7 +667,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
(*ptr)->SetActualMiniBatchSize(actualMBSize);
mNutt = (*ptrreader)->NumberSlicesInEachRecurrentIter();
(*ptr)->SetActualNbrSlicesInEachRecIter(mNutt);
(*ptr)->SetActualNbrSlicesInEachRecurentIteration(mNutt);
(*ptrreader)->CopyMBLayoutTo((*ptr)->GetMBLayoutPtr());
const auto & pairs = (*ptr)->PairNodes();
@ -679,7 +679,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
/// not the sentence begining, because the initial hidden layer activity is from the encoder network
decoderNet->SetActualMiniBatchSize(actualMBSize);
decoderNet->SetActualNbrSlicesInEachRecIter(mNutt);
decoderNet->SetActualNbrSlicesInEachRecurentIteration(mNutt);
encoderDataReader->CopyMBLayoutTo(decoderNet->GetMBLayoutPtr());
FindBestPathWithVariableLength(decoderNet, actualMBSize, decoderDataReader, dataWriter, outputNodes, writeNodes, decoderFeatureNodes, beam, &decoderInputMatrices, best_path);
@ -910,7 +910,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
/// use reader to initialize evalnet's sentence start information to let it know that this
/// is the begining of sentence
evalnet->SetActualMiniBatchSize(mbSize);
evalnet->SetActualNbrSlicesInEachRecIter(dataReader->NumberSlicesInEachRecurrentIter());
evalnet->SetActualNbrSlicesInEachRecurentIteration(dataReader->NumberSlicesInEachRecurrentIter());
dataReader->CopyMBLayoutTo(evalnet->GetMBLayoutPtr());
clock_t start, now;
@ -1069,7 +1069,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
/// use reader to initialize evalnet's sentence start information to let it know that this
/// is the begining of sentence
evalnet->SetActualMiniBatchSize(mbSize);
evalnet->SetActualNbrSlicesInEachRecIter(dataReader->NumberSlicesInEachRecurrentIter());
evalnet->SetActualNbrSlicesInEachRecurentIteration(dataReader->NumberSlicesInEachRecurrentIter());
clock_t start, now;
start = clock();

2
MachineLearning/CNTKSGDLib/SimpleOutputWriter.h
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@ -74,7 +74,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
size_t actualMBSize = m_net.GetActualMBSize();
m_net.SetActualMiniBatchSize(actualMBSize);
m_net.SetActualNbrSlicesInEachRecIter(dataReader.NumberSlicesInEachRecurrentIter());
m_net.SetActualNbrSlicesInEachRecurentIteration(dataReader.NumberSlicesInEachRecurrentIter());
dataReader.CopyMBLayoutTo(m_net.GetMBLayoutPtr());
for (int i=0; i<outputNodes.size(); i++)

4
Math/Math/Matrix.h
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@ -523,7 +523,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
MBLayout() : m_sentenceBoundaryFlags(CPUDEVICE) { }
Matrix<float> m_sentenceBoundaryFlags; // (t,stream)
// ^^ float -> MinibatchPackingFlags, right?
// ^^ float -> MinibatchPackingFlags, right? Or unsigned char; or change that to 'char' because Matrix<char> already exists
// This matrix ^^ is always in CPU memory --TODO: should rather be a matrix of some int
/// conditionally point to either a pointer to that provided by network, or point to
/// an individual sentence boundary info, which happens if timeStep > 1 is required for PastValue node
@ -538,7 +538,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
#if 0 // we have this pattern often:
// TODO: mbSize and #slices must also move into MBLayout
evalnet->SetActualMiniBatchSize(mbSize);
evalnet->SetActualNbrSlicesInEachRecIter(dataReader->NumberSlicesInEachRecurrentIter());
evalnet->SetActualNbrSlicesInEachRecurentIteration(dataReader->NumberSlicesInEachRecurrentIter());
dataReader->CopyMBLayoutTo(evalnet->GetMBLayoutPtr());
#endif
#if 0 // a VERY TELLING piece of code