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https://github.com/moses-smt/mosesdecoder.git
synced 2024-07-14 14:50:41 +03:00
some refactoring of VW, towards customizable loss calculation in training
This commit is contained in:
Ales Tamchyna
parent
f9ec387a5b
commit
7601618477
220
moses/FF/VW/VW.h
220
moses/FF/VW/VW.h
@ -59,6 +59,37 @@ private:
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int m_min, m_max;
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};
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/**
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* Calculation of training loss.
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*/
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class TrainingLoss
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{
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public:
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virtual float operator()(const TargetPhrase &candidate, const TargetPhrase &correct, bool isCorrect) const = 0;
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};
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/**
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* Basic 1/0 training loss.
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*/
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class TrainingLossBasic : public TrainingLoss
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{
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public:
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virtual float operator()(const TargetPhrase &candidate, const TargetPhrase &correct, bool isCorrect) const {
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return isCorrect ? 0.0 : 1.0;
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}
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};
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/**
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* BLEU2+1 training loss.
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*/
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class TrainingLossBLEU : public TrainingLoss
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{
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public:
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virtual float operator()(const TargetPhrase &candidate, const TargetPhrase &correct, bool isCorrect) const {
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return isCorrect ? 0.0 : 1.0;
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}
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};
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/**
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* VW thread-specific data about target sentence.
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*/
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@ -96,6 +127,7 @@ struct VWTargetSentence {
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};
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typedef ThreadLocalByFeatureStorage<Discriminative::Classifier, Discriminative::ClassifierFactory &> TLSClassifier;
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typedef ThreadLocalByFeatureStorage<VWTargetSentence> TLSTargetSentence;
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class VW : public StatelessFeatureFunction, public TLSTargetSentence
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@ -116,6 +148,10 @@ public:
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VERBOSE(1, "VW :: No loss function specified, assuming logistic loss.\n");
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m_normalizer = (Discriminative::Normalizer *) new Discriminative::LogisticLossNormalizer();
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}
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if (! m_trainingLoss) {
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m_trainingLoss = (TrainingLoss *) new TrainingLossBasic();
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}
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}
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virtual ~VW() {
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@ -150,76 +186,107 @@ public:
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VERBOSE(2, "VW :: Evaluating translation options\n");
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const std::vector<VWFeatureBase*>& sourceFeatures = VWFeatureBase::GetSourceFeatures(GetScoreProducerDescription());
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// which feature functions do we use (on the source and target side)
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const std::vector<VWFeatureBase*>& sourceFeatures =
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VWFeatureBase::GetSourceFeatures(GetScoreProducerDescription());
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const std::vector<VWFeatureBase*>& targetFeatures =
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VWFeatureBase::GetTargetFeatures(GetScoreProducerDescription());
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const WordsRange &sourceRange = translationOptionList.Get(0)->GetSourceWordsRange();
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const InputPath &inputPath = translationOptionList.Get(0)->GetInputPath();
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// optionally update translation options using leave-one-out
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std::vector<bool> keep = (m_leaveOneOut.size() > 0)
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? LeaveOneOut(translationOptionList)
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: std::vector<bool>(translationOptionList.size(), true);
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std::vector<float> losses(translationOptionList.size());
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std::vector<float>::iterator iterLoss;
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TranslationOptionList::const_iterator iterTransOpt;
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std::vector<bool>::const_iterator iterKeep;
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if (m_train) {
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// check which translation options are correct in advance
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bool seenCorrect = false;
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for(iterTransOpt = translationOptionList.begin(), iterLoss = losses.begin(), iterKeep = keep.begin() ;
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iterTransOpt != translationOptionList.end() ; ++iterTransOpt, ++iterLoss, ++iterKeep) {
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bool isCorrect = IsCorrectTranslationOption(**iterTransOpt);
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*iterLoss = isCorrect ? 0.0 : 1.0;
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if (isCorrect && *iterKeep) seenCorrect = true;
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//
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// extract features for training the classifier (only call this when using vwtrainer, not in Moses!)
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//
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// find which topts are correct
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std::vector<bool> correct(translationOptionList.size());
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for (size_t i = 0; i < translationOptionList.size(); i++)
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correct[i] = IsCorrectTranslationOption(* translationOptionList.Get(i));
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// optionally update translation options using leave-one-out
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std::vector<bool> keep = (m_leaveOneOut.size() > 0)
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? LeaveOneOut(translationOptionList, correct)
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: std::vector<bool>(translationOptionList.size(), true);
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// check whether we (still) have some correct translation
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int firstCorrect = -1;
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for (size_t i = 0; i < translationOptionList.size(); i++) {
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if (keep[i] && correct[i]) {
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firstCorrect = i;
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break;
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}
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}
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// do not train if there are no positive examples
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if (! seenCorrect) {
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if (firstCorrect == -1) {
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VERBOSE(2, "VW :: skipping topt collection, no correct translation for span\n");
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return;
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}
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}
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for(size_t i = 0; i < sourceFeatures.size(); ++i)
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(*sourceFeatures[i])(input, inputPath, sourceRange, classifier);
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// the first correct topt can be used by some loss functions
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const TargetPhrase &correctPhrase = translationOptionList.Get(firstCorrect)->GetTargetPhrase();
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const std::vector<VWFeatureBase*>& targetFeatures = VWFeatureBase::GetTargetFeatures(GetScoreProducerDescription());
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// extract source side features
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for(size_t i = 0; i < sourceFeatures.size(); ++i)
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(*sourceFeatures[i])(input, inputPath, sourceRange, classifier);
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for(iterTransOpt = translationOptionList.begin(), iterLoss = losses.begin(), iterKeep = keep.begin() ;
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iterTransOpt != translationOptionList.end() ; ++iterTransOpt, ++iterLoss) {
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// go over topts, extract target side features and train the classifier
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for (size_t toptIdx = 0; toptIdx < translationOptionList.size(); toptIdx++) {
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if (! *iterKeep)
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continue;
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// this topt was discarded by leaving one out
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if (! keep[toptIdx])
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continue;
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const TargetPhrase &targetPhrase = (*iterTransOpt)->GetTargetPhrase();
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for(size_t i = 0; i < targetFeatures.size(); ++i)
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(*targetFeatures[i])(input, inputPath, targetPhrase, classifier);
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// extract target-side features for each topt
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const TargetPhrase &targetPhrase = translationOptionList.Get(toptIdx)->GetTargetPhrase();
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for(size_t i = 0; i < targetFeatures.size(); ++i)
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(*targetFeatures[i])(input, inputPath, targetPhrase, classifier);
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if (! m_train) {
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*iterLoss = classifier.Predict(MakeTargetLabel(targetPhrase));
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} else {
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classifier.Train(MakeTargetLabel(targetPhrase), *iterLoss);
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float loss = (*m_trainingLoss)(targetPhrase, correctPhrase, correct[toptIdx]);
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// train classifier on current example
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classifier.Train(MakeTargetLabel(targetPhrase), loss);
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}
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}
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} else {
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//
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// predict using a trained classifier, use this in decoding (=at test time)
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//
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std::vector<float> losses(translationOptionList.size());
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(*m_normalizer)(losses);
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// extract source side features
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for(size_t i = 0; i < sourceFeatures.size(); ++i)
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(*sourceFeatures[i])(input, inputPath, sourceRange, classifier);
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for(iterTransOpt = translationOptionList.begin(), iterLoss = losses.begin(), iterKeep = keep.begin() ;
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iterTransOpt != translationOptionList.end() ; ++iterTransOpt, ++iterLoss) {
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if (! *iterKeep)
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continue;
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for (size_t toptIdx = 0; toptIdx < translationOptionList.size(); toptIdx++) {
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const TranslationOption *topt = translationOptionList.Get(toptIdx);
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const TargetPhrase &targetPhrase = topt->GetTargetPhrase();
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TranslationOption &transOpt = **iterTransOpt;
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// extract target-side features for each topt
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for(size_t i = 0; i < targetFeatures.size(); ++i)
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(*targetFeatures[i])(input, inputPath, targetPhrase, classifier);
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std::vector<float> newScores(m_numScoreComponents);
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newScores[0] = FloorScore(TransformScore(*iterLoss));
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// get classifier score
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losses[toptIdx] = classifier.Predict(MakeTargetLabel(targetPhrase));
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}
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ScoreComponentCollection &scoreBreakDown = transOpt.GetScoreBreakdown();
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scoreBreakDown.PlusEquals(this, newScores);
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// normalize classifier scores to get a probability distribution
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(*m_normalizer)(losses);
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transOpt.UpdateScore();
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// update scores of topts
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for (size_t toptIdx = 0; toptIdx < translationOptionList.size(); toptIdx++) {
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TranslationOption *topt = *(translationOptionList.begin() + toptIdx);
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std::vector<float> newScores(m_numScoreComponents);
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newScores[0] = FloorScore(TransformScore(losses[toptIdx]));
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ScoreComponentCollection &scoreBreakDown = topt->GetScoreBreakdown();
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scoreBreakDown.PlusEquals(this, newScores);
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topt->UpdateScore();
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}
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}
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}
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@ -231,7 +298,6 @@ public:
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ScoreComponentCollection* accumulator) const {
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}
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void SetParameter(const std::string& key, const std::string& value) {
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if (key == "train") {
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m_train = Scan<bool>(value);
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@ -241,10 +307,25 @@ public:
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m_vwOptions = value;
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} else if (key == "leave-one-out-from") {
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m_leaveOneOut = value;
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} else if (key == "training-loss") {
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// which type of loss to use for training
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if (value == "basic") {
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m_trainingLoss = (TrainingLoss *) new TrainingLossBasic();
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} else if (value == "bleu") {
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m_trainingLoss = (TrainingLoss *) new TrainingLossBLEU();
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} else {
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UTIL_THROW2("Unknown training loss type:" << value);
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}
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} else if (key == "loss") {
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m_normalizer = value == "logistic"
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? (Discriminative::Normalizer *) new Discriminative::LogisticLossNormalizer()
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: (Discriminative::Normalizer *) new Discriminative::SquaredLossNormalizer();
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// which normalizer to use (theoretically depends on the loss function used for training the
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// classifier (squared/logistic/hinge/...), hence the name "loss"
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if (value == "logistic") {
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m_normalizer = (Discriminative::Normalizer *) new Discriminative::LogisticLossNormalizer();
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} else if (value == "squared") {
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m_normalizer = (Discriminative::Normalizer *) new Discriminative::SquaredLossNormalizer();
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} else {
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UTIL_THROW2("Unknown loss type:" << value);
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}
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} else {
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StatelessFeatureFunction::SetParameter(key, value);
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}
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@ -255,12 +336,13 @@ public:
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if (! m_train)
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return;
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UTIL_THROW_IF2(source.GetType() != TabbedSentenceInput, "This feature function requires the TabbedSentence input type");
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UTIL_THROW_IF2(source.GetType() != TabbedSentenceInput,
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"This feature function requires the TabbedSentence input type");
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const TabbedSentence& tabbedSentence = static_cast<const TabbedSentence&>(source);
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UTIL_THROW_IF2(tabbedSentence.GetColumns().size() < 2, "TabbedSentence must contain target<tab>alignment");
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UTIL_THROW_IF2(tabbedSentence.GetColumns().size() < 2,
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"TabbedSentence must contain target<tab>alignment");
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// target sentence represented as a phrase
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Phrase *target = new Phrase();
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target->CreateFromString(
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@ -279,11 +361,6 @@ public:
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targetSent.m_sentence = target;
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targetSent.m_alignment = alignment;
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//std::cerr << static_cast<const Phrase&>(tabbedSentence) << std::endl;
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//std::cerr << *target << std::endl;
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//std::cerr << *alignment << std::endl;
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// pre-compute max- and min- aligned points for faster translation option checking
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targetSent.SetConstraints(source.GetSize());
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}
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@ -328,7 +405,9 @@ private:
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targetStart2 = i;
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int targetEnd2 = targetEnd;
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for(int i = targetEnd2; i < targetSentence.m_sentence->GetSize() && !targetSentence.m_targetConstraints[i].IsSet(); ++i)
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for(int i = targetEnd2;
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i < targetSentence.m_sentence->GetSize() && !targetSentence.m_targetConstraints[i].IsSet();
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++i)
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targetEnd2 = i;
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//std::cerr << "Longer: " << targetStart2 << " " << targetEnd2 << std::endl;
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@ -364,7 +443,7 @@ private:
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return false;
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}
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std::vector<bool> LeaveOneOut(const TranslationOptionList &topts) const {
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std::vector<bool> LeaveOneOut(const TranslationOptionList &topts, const std::vector<bool> &correct) const {
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UTIL_THROW_IF2(m_leaveOneOut.size() == 0 || ! m_train, "LeaveOneOut called in wrong setting!");
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float sourceRawCount = 0.0;
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@ -372,12 +451,14 @@ private:
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std::vector<bool> keepOpt;
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TranslationOptionList::const_iterator iterTransOpt;
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for(iterTransOpt = topts.begin(); iterTransOpt != topts.end(); ++iterTransOpt) {
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const TargetPhrase &targetPhrase = (*iterTransOpt)->GetTargetPhrase();
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for (size_t i = 0; i < topts.size(); i++) {
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TranslationOption *topt = *(topts.begin() + i);
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const TargetPhrase &targetPhrase = topt->GetTargetPhrase();
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// extract raw counts from phrase-table property
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const CountsPhraseProperty *property = static_cast<const CountsPhraseProperty *>(targetPhrase.GetProperty("Counts"));
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const CountsPhraseProperty *property =
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static_cast<const CountsPhraseProperty *>(targetPhrase.GetProperty("Counts"));
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if (! property) {
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VERBOSE(1, "VW :: Counts not found for topt! Is this an OOV?\n");
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// keep all translation opts without updating, this is either OOV or bad usage...
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@ -394,7 +475,7 @@ private:
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}
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}
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float discount = IsCorrectTranslationOption(**iterTransOpt) ? ONE : 0.0;
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float discount = correct[i] ? ONE : 0.0;
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float target = property->GetTargetMarginal() - discount;
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float joint = property->GetJointCount() - discount;
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if (discount != 0.0) VERBOSE(2, "VW :: leaving one out!\n");
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@ -407,9 +488,9 @@ private:
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scores[0] = TransformScore(joint / target); // P(f|e)
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scores[2] = TransformScore(joint / sourceRawCount); // P(e|f)
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ScoreComponentCollection &scoreBreakDown = (*iterTransOpt)->GetScoreBreakdown();
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ScoreComponentCollection &scoreBreakDown = topt->GetScoreBreakdown();
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scoreBreakDown.Assign(feature, scores);
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(*iterTransOpt)->UpdateScore();
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topt->UpdateScore();
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keepOpt.push_back(true);
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} else {
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// they only occurred together once, discard topt
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@ -425,6 +506,9 @@ private:
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std::string m_modelPath;
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std::string m_vwOptions;
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// calculator of training loss
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TrainingLoss *m_trainingLoss = NULL;
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// optionally contains feature name of a phrase table where we recompute scores with leaving one out
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std::string m_leaveOneOut;
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@ -52,6 +52,7 @@ public:
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const TranslationOption *Get(size_t ind) const {
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return m_coll.at(ind);
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}
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void Remove( size_t ind ) {
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UTIL_THROW_IF2(ind >= m_coll.size(),
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"Out of bound index " << ind);
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