mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-29 06:52:34 +03:00
789a2e2bc3
Mostly signed/unsigned comparisons and reordered member initializations; also a few unused variables. There are more, but if I chip away at them for a while, who knows, it may catch on and warnings may eventually become socially stigmatizing. :)
468 lines
16 KiB
C++
468 lines
16 KiB
C++
#include "PDTAimp.h"
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namespace Moses
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{
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PDTAimp::PDTAimp(PhraseDictionaryTreeAdaptor *p)
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: m_dict(0),
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m_obj(p),
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useCache(1),
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totalE(0),
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distinctE(0)
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{
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m_numInputScores = 0;
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m_inputFeature = &InputFeature::Instance();
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if (m_inputFeature) {
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const PhraseDictionary *firstPt = PhraseDictionary::GetColl()[0];
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if (firstPt == m_obj) {
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m_numInputScores = m_inputFeature->GetNumScoreComponents();
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}
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}
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}
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PDTAimp::~PDTAimp()
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{
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CleanUp();
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delete m_dict;
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if (StaticData::Instance().GetVerboseLevel() >= 2) {
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TRACE_ERR("tgt candidates stats: total="<<totalE<<"; distinct="
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<<distinctE<<" ("<<distinctE/(0.01*totalE)<<"); duplicates="
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<<totalE-distinctE<<" ("<<(totalE-distinctE)/(0.01*totalE)
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<<")\n");
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TRACE_ERR("\npath statistics\n");
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if(path1Best.size()) {
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TRACE_ERR("1-best: ");
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std::copy(path1Best.begin()+1,path1Best.end(),
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std::ostream_iterator<size_t>(std::cerr," \t"));
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TRACE_ERR("\n");
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}
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if(pathCN.size()) {
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TRACE_ERR("CN (full): ");
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std::transform(pathCN.begin()+1
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,pathCN.end()
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,std::ostream_iterator<double>(std::cerr," \t")
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,Exp);
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TRACE_ERR("\n");
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}
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if(pathExplored.size()) {
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TRACE_ERR("CN (explored): ");
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std::copy(pathExplored.begin()+1,pathExplored.end(),
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std::ostream_iterator<size_t>(std::cerr," \t"));
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TRACE_ERR("\n");
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}
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}
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}
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void PDTAimp::CleanUp()
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{
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assert(m_dict);
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m_dict->FreeMemory();
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for(size_t i=0; i<m_tgtColls.size(); ++i) delete m_tgtColls[i];
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m_tgtColls.clear();
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m_cache.clear();
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m_rangeCache.clear();
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uniqSrcPhr.clear();
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}
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TargetPhraseCollectionWithSourcePhrase const*
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PDTAimp::GetTargetPhraseCollection(Phrase const &src) const
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{
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assert(m_dict);
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if(src.GetSize()==0) return 0;
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std::pair<MapSrc2Tgt::iterator,bool> piter;
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if(useCache) {
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piter=m_cache.insert(std::make_pair(src,static_cast<TargetPhraseCollectionWithSourcePhrase const*>(0)));
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if(!piter.second) return piter.first->second;
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} else if (m_cache.size()) {
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MapSrc2Tgt::const_iterator i=m_cache.find(src);
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return (i!=m_cache.end() ? i->second : 0);
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}
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std::vector<std::string> srcString(src.GetSize());
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// convert source Phrase into vector of strings
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for(size_t i=0; i<srcString.size(); ++i) {
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Factors2String(src.GetWord(i),srcString[i]);
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}
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// get target phrases in string representation
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std::vector<StringTgtCand> cands;
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std::vector<std::string> wacands;
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m_dict->GetTargetCandidates(srcString,cands,wacands);
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if(cands.empty()) {
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return 0;
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}
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//TODO: Multiple models broken here
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std::vector<float> weights = StaticData::Instance().GetWeights(m_obj);
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std::vector<TargetPhrase> tCands;
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tCands.reserve(cands.size());
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std::vector<std::pair<float,size_t> > costs;
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costs.reserve(cands.size());
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std::vector<Phrase> sourcePhrases;
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sourcePhrases.reserve(cands.size());
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// convert into TargetPhrases
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for(size_t i=0; i<cands.size(); ++i) {
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TargetPhrase targetPhrase(m_obj);
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StringTgtCand::Tokens const& factorStrings=cands[i].tokens;
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Scores const& probVector=cands[i].scores;
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std::vector<float> scoreVector(probVector.size());
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std::transform(probVector.begin(),probVector.end(),scoreVector.begin(),
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TransformScore);
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std::transform(scoreVector.begin(),scoreVector.end(),scoreVector.begin(),
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FloorScore);
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//sparse features.
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//These are already in log-space
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for (size_t j = 0; j < cands[i].fnames.size(); ++j) {
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targetPhrase.GetScoreBreakdown().Assign(m_obj, *cands[i].fnames[j], cands[i].fvalues[j]);
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}
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CreateTargetPhrase(targetPhrase,factorStrings,scoreVector, Scores(0), &wacands[i], &src);
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costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
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tCands.push_back(targetPhrase);
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sourcePhrases.push_back(src);
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}
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TargetPhraseCollectionWithSourcePhrase *rv;
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rv=PruneTargetCandidates(tCands,costs, sourcePhrases);
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if(rv->IsEmpty()) {
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delete rv;
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return 0;
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} else {
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if(useCache) piter.first->second=rv;
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m_tgtColls.push_back(rv);
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return rv;
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}
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}
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void PDTAimp::Create(const std::vector<FactorType> &input
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, const std::vector<FactorType> &output
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, const std::string &filePath
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, const std::vector<float> &weight
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)
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{
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// set my members
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m_dict=new PhraseDictionaryTree();
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m_input=input;
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m_output=output;
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const StaticData &staticData = StaticData::Instance();
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m_dict->NeedAlignmentInfo(staticData.NeedAlignmentInfo());
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std::string binFname=filePath+".binphr.idx";
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if(!FileExists(binFname.c_str())) {
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UTIL_THROW2( "bin ttable does not exist");
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//TRACE_ERR( "bin ttable does not exist -> create it\n");
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//InputFileStream in(filePath);
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//m_dict->Create(in,filePath);
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}
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VERBOSE(1,"reading bin ttable\n");
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// m_dict->Read(filePath);
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bool res=m_dict->Read(filePath);
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if (!res) {
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std::cerr << "bin ttable was read in a wrong way\n";
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exit(1);
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}
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}
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void PDTAimp::CacheSource(ConfusionNet const& src)
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{
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assert(m_dict);
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const size_t srcSize=src.GetSize();
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std::vector<size_t> exploredPaths(srcSize+1,0);
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std::vector<double> exPathsD(srcSize+1,-1.0);
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// collect some statistics
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std::vector<size_t> cnDepths(srcSize,0);
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for(size_t i=0; i<srcSize; ++i) cnDepths[i]=src[i].size();
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for(size_t len=1; len<=srcSize; ++len)
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for(size_t i=0; i<=srcSize-len; ++i) {
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double pd=0.0;
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for(size_t k=i; k<i+len; ++k) pd+=log(1.0*cnDepths[k]);
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exPathsD[len]=(exPathsD[len]>=0.0 ? addLogScale(pd,exPathsD[len]) : pd);
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}
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// update global statistics
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if(pathCN.size()<=srcSize) pathCN.resize(srcSize+1,-1.0);
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for(size_t len=1; len<=srcSize; ++len)
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pathCN[len]=pathCN[len]>=0.0 ? addLogScale(pathCN[len],exPathsD[len]) : exPathsD[len];
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if(path1Best.size()<=srcSize) path1Best.resize(srcSize+1,0);
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for(size_t len=1; len<=srcSize; ++len) path1Best[len]+=srcSize-len+1;
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if (StaticData::Instance().GetVerboseLevel() >= 2 && exPathsD.size()) {
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TRACE_ERR("path stats for current CN: \nCN (full): ");
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std::transform(exPathsD.begin()+1
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,exPathsD.end()
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,std::ostream_iterator<double>(std::cerr," ")
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,Exp);
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TRACE_ERR("\n");
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}
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typedef std::map<StringTgtCand::Tokens,TScores> E2Costs;
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std::map<Range,E2Costs> cov2cand;
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std::vector<State> stack;
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for(Position i=0 ; i < srcSize ; ++i)
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stack.push_back(State(i, i, m_dict->GetRoot(), std::vector<float>(m_numInputScores,0.0)));
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std::vector<float> weightTrans = StaticData::Instance().GetWeights(m_obj);
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std::vector<float> weightInput = StaticData::Instance().GetWeights(m_inputFeature);
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float weightWP = StaticData::Instance().GetWeightWordPenalty();
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while(!stack.empty()) {
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State curr(stack.back());
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stack.pop_back();
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UTIL_THROW_IF2(curr.end() >= srcSize, "Error");
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const ConfusionNet::Column &currCol=src[curr.end()];
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// in a given column, loop over all possibilities
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for(size_t colidx=0; colidx<currCol.size(); ++colidx) {
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const Word& w=currCol[colidx].first; // w=the i^th possibility in column colidx
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std::string s;
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Factors2String(w,s);
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bool isEpsilon=(s=="" || s==EPSILON);
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//assert that we have the right number of link params in this CN option
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UTIL_THROW_IF2(currCol[colidx].second.denseScores.size() < m_numInputScores,
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"Incorrect number of input scores");
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// do not start with epsilon (except at first position)
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if(isEpsilon && curr.begin()==curr.end() && curr.begin()>0) continue;
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// At a given node in the prefix tree, look to see if w defines an edge to
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// another node (Extend). Stay at the same node if w==EPSILON
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PPtr nextP = (isEpsilon ? curr.ptr : m_dict->Extend(curr.ptr,s));
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if(nextP) { // w is a word that should be considered
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Range newRange(curr.begin(),curr.end()+src.GetColumnIncrement(curr.end(),colidx));
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//add together the link scores from the current state and the new arc
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float inputScoreSum = 0;
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std::vector<float> newInputScores(m_numInputScores,0.0);
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if (m_numInputScores) {
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std::transform(currCol[colidx].second.denseScores.begin(), currCol[colidx].second.denseScores.end(),
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curr.GetScores().begin(),
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newInputScores.begin(),
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std::plus<float>());
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//we need to sum up link weights (excluding realWordCount, which isn't in numLinkParams)
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//if the sum is too low, then we won't expand this.
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//TODO: dodgy! shouldn't we consider weights here? what about zero-weight params?
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inputScoreSum = std::accumulate(newInputScores.begin(),newInputScores.begin()+m_numInputScores,0.0);
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}
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Phrase newSrc(curr.src);
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if(!isEpsilon) newSrc.AddWord(w);
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if(newRange.second<srcSize && inputScoreSum>LOWEST_SCORE) {
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// if there is more room to grow, add a new state onto the queue
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// to be explored that represents [begin, curEnd+)
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stack.push_back(State(newRange,nextP,newInputScores));
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stack.back().src=newSrc;
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}
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std::vector<StringTgtCand> tcands;
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// now, look up the target candidates (aprx. TargetPhraseCollection) for
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// the current path through the CN
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m_dict->GetTargetCandidates(nextP,tcands);
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if(newRange.second>=exploredPaths.size()+newRange.first)
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exploredPaths.resize(newRange.second-newRange.first+1,0);
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++exploredPaths[newRange.second-newRange.first];
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totalE+=tcands.size();
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if(tcands.size()) {
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E2Costs& e2costs=cov2cand[newRange];
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Phrase const* srcPtr=uniqSrcPhr(newSrc);
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for(size_t i=0; i<tcands.size(); ++i) {
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//put input scores in first - already logged, just drop in directly
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std::vector<float> transcores(m_obj->GetNumScoreComponents());
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UTIL_THROW_IF2(transcores.size() != weightTrans.size(),
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"Incorrect number of translation scores");
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//put in phrase table scores, logging as we insert
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std::transform(tcands[i].scores.begin()
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,tcands[i].scores.end()
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,transcores.begin()
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,TransformScore);
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//tally up
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float score=std::inner_product(transcores.begin(), transcores.end(), weightTrans.begin(), 0.0f);
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// input feature
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score +=std::inner_product(newInputScores.begin(), newInputScores.end(), weightInput.begin(), 0.0f);
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//count word penalty
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score-=tcands[i].tokens.size() * weightWP;
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std::pair<E2Costs::iterator,bool> p=e2costs.insert(std::make_pair(tcands[i].tokens,TScores()));
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if(p.second) ++distinctE;
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TScores & scores=p.first->second;
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if(p.second || scores.total<score) {
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scores.total=score;
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scores.transScore=transcores;
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scores.inputScores=newInputScores;
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scores.src=srcPtr;
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}
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}
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}
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}
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}
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} // end while(!stack.empty())
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if (StaticData::Instance().GetVerboseLevel() >= 2 && exploredPaths.size()) {
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TRACE_ERR("CN (explored): ");
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std::copy(exploredPaths.begin()+1,exploredPaths.end(),
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std::ostream_iterator<size_t>(std::cerr," "));
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TRACE_ERR("\n");
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}
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if(pathExplored.size()<exploredPaths.size())
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pathExplored.resize(exploredPaths.size(),0);
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for(size_t len=1; len<=srcSize; ++len)
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pathExplored[len]+=exploredPaths[len];
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m_rangeCache.resize(src.GetSize(),vTPC(src.GetSize(),0));
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for(std::map<Range,E2Costs>::const_iterator i=cov2cand.begin(); i!=cov2cand.end(); ++i) {
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assert(i->first.first<m_rangeCache.size());
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assert(i->first.second>0);
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assert(static_cast<size_t>(i->first.second-1)<m_rangeCache[i->first.first].size());
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assert(m_rangeCache[i->first.first][i->first.second-1]==0);
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std::vector<TargetPhrase> tCands;
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tCands.reserve(i->second.size());
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std::vector<std::pair<float,size_t> > costs;
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costs.reserve(i->second.size());
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std::vector<Phrase> sourcePhrases;
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sourcePhrases.reserve(i->second.size());
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for(E2Costs::const_iterator j=i->second.begin(); j!=i->second.end(); ++j) {
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TScores const & scores=j->second;
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TargetPhrase targetPhrase(m_obj);
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CreateTargetPhrase(targetPhrase
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, j ->first
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, scores.transScore
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, scores.inputScores
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, NULL
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, scores.src);
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costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
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tCands.push_back(targetPhrase);
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sourcePhrases.push_back(*scores.src);
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//std::cerr << i->first.first << "-" << i->first.second << ": " << targetPhrase << std::endl;
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}
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TargetPhraseCollectionWithSourcePhrase *rv=PruneTargetCandidates(tCands, costs, sourcePhrases);
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if(rv->IsEmpty())
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delete rv;
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else {
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m_rangeCache[i->first.first][i->first.second-1]=rv;
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m_tgtColls.push_back(rv);
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}
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}
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// free memory
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m_dict->FreeMemory();
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}
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void PDTAimp::CreateTargetPhrase(TargetPhrase& targetPhrase,
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StringTgtCand::Tokens const& factorStrings,
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Scores const& transVector,
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Scores const& inputVector,
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const std::string *alignmentString,
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Phrase const* srcPtr) const
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{
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FactorCollection &factorCollection = FactorCollection::Instance();
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for(size_t k=0; k<factorStrings.size(); ++k) {
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util::TokenIter<util::MultiCharacter, false> word(*factorStrings[k], StaticData::Instance().GetFactorDelimiter());
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Word& w=targetPhrase.AddWord();
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for(size_t l=0; l<m_output.size(); ++l, ++word) {
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w[m_output[l]]= factorCollection.AddFactor(*word);
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}
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}
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if (alignmentString) {
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targetPhrase.SetAlignmentInfo(*alignmentString);
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}
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if (m_numInputScores) {
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targetPhrase.GetScoreBreakdown().Assign(m_inputFeature, inputVector);
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}
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targetPhrase.GetScoreBreakdown().Assign(m_obj, transVector);
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targetPhrase.EvaluateInIsolation(*srcPtr, m_obj->GetFeaturesToApply());
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}
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TargetPhraseCollectionWithSourcePhrase* PDTAimp::PruneTargetCandidates
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(const std::vector<TargetPhrase> & tCands,
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std::vector<std::pair<float,size_t> >& costs,
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const std::vector<Phrase> &sourcePhrases) const
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{
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// convert into TargetPhraseCollection
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UTIL_THROW_IF2(tCands.size() != sourcePhrases.size(),
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"Number of target phrases must equal number of source phrases");
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TargetPhraseCollectionWithSourcePhrase *rv=new TargetPhraseCollectionWithSourcePhrase;
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// set limit to tableLimit or actual size, whatever is smaller
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std::vector<std::pair<float,size_t> >::iterator nth =
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costs.begin() + ((m_obj->m_tableLimit>0 && // 0 indicates no limit
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m_obj->m_tableLimit < costs.size()) ?
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m_obj->m_tableLimit : costs.size());
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// find the nth phrase according to future cost
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NTH_ELEMENT3(costs.begin(),nth ,costs.end());
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// add n top phrases to the return list
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for(std::vector<std::pair<float,size_t> >::iterator
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it = costs.begin(); it != nth; ++it) {
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size_t ind = it->second;
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TargetPhrase *targetPhrase = new TargetPhrase(tCands[ind]);
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const Phrase &sourcePhrase = sourcePhrases[ind];
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rv->Add(targetPhrase, sourcePhrase);
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}
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return rv;
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}
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}
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