path: root/moses/src/PDTAimp.h

// $Id$
// vim:tabstop=2

#ifndef moses_PDTAimp_h
#define moses_PDTAimp_h

#include "StaticData.h"  // needed for factor splitter
#include "PhraseDictionaryTree.h"
#include "UniqueObject.h"
#include "InputFileStream.h"
#include "PhraseDictionaryTreeAdaptor.h"
#include "SparsePhraseDictionaryFeature.h"
#include "Util.h"

namespace Moses
{

inline double addLogScale(double x,double y)
{
  if(x>y) return addLogScale(y,x);
  else return x+log(1.0+exp(y-x));
}

inline double Exp(double x)
{
  return exp(x);
}

class PDTAimp
{
  // only these classes are allowed to instantiate this class
  friend class PhraseDictionaryTreeAdaptor;

protected:
  PDTAimp(PhraseDictionaryTreeAdaptor *p,unsigned nis)
    : m_languageModels(0),m_dict(0),
      m_obj(p),useCache(1),m_numInputScores(nis),totalE(0),distinctE(0) {}

 public:
  LMList const* m_languageModels;
  std::vector<FactorType> m_input,m_output;
  PhraseDictionaryTree *m_dict;
  typedef std::vector<TargetPhraseCollection const*> vTPC;
  mutable vTPC m_tgtColls;

  typedef std::map<Phrase,TargetPhraseCollection const*> MapSrc2Tgt;
  mutable MapSrc2Tgt m_cache;
  PhraseDictionaryTreeAdaptor *m_obj;
  int useCache;

  std::vector<vTPC> m_rangeCache;
  unsigned m_numInputScores;

  UniqueObjectManager<Phrase> uniqSrcPhr;

  size_t totalE,distinctE;
  std::vector<size_t> path1Best,pathExplored;
  std::vector<double> pathCN;

  ~PDTAimp() {
    CleanUp();
    delete m_dict;

    if (StaticData::Instance().GetVerboseLevel() >= 2) {

      TRACE_ERR("tgt candidates stats:  total="<<totalE<<";  distinct="
                <<distinctE<<" ("<<distinctE/(0.01*totalE)<<");  duplicates="
                <<totalE-distinctE<<" ("<<(totalE-distinctE)/(0.01*totalE)
                <<")\n");

      TRACE_ERR("\npath statistics\n");

      if(path1Best.size()) {
        TRACE_ERR("1-best:        ");
        std::copy(path1Best.begin()+1,path1Best.end(),
                  std::ostream_iterator<size_t>(std::cerr," \t"));
        TRACE_ERR("\n");
      }
      if(pathCN.size()) {
        TRACE_ERR("CN (full):     ");
        std::transform(pathCN.begin()+1
                       ,pathCN.end()
                       ,std::ostream_iterator<double>(std::cerr," \t")
                       ,Exp);
        TRACE_ERR("\n");
      }
      if(pathExplored.size()) {
        TRACE_ERR("CN (explored): ");
        std::copy(pathExplored.begin()+1,pathExplored.end(),
                  std::ostream_iterator<size_t>(std::cerr," \t"));
        TRACE_ERR("\n");
      }
    }

  }

  void Factors2String(Word const& w,std::string& s) const {
    s=w.GetString(m_input,false);
  }

  void CleanUp() {
    CHECK(m_dict);
    m_dict->FreeMemory();
    for(size_t i=0; i<m_tgtColls.size(); ++i) delete m_tgtColls[i];
    m_tgtColls.clear();
    m_cache.clear();
    m_rangeCache.clear();
    uniqSrcPhr.clear();
  }

  TargetPhraseCollection const*
  GetTargetPhraseCollection(Phrase const &src) const {

    CHECK(m_dict);
    if(src.GetSize()==0) return 0;

    std::pair<MapSrc2Tgt::iterator,bool> piter;
    if(useCache) {
      piter=m_cache.insert(std::make_pair(src,static_cast<TargetPhraseCollection const*>(0)));
      if(!piter.second) return piter.first->second;
    } else if (m_cache.size()) {
      MapSrc2Tgt::const_iterator i=m_cache.find(src);
      return (i!=m_cache.end() ? i->second : 0);
    }

    std::vector<std::string> srcString(src.GetSize());
    // convert source Phrase into vector of strings
    for(size_t i=0; i<srcString.size(); ++i) {
      Factors2String(src.GetWord(i),srcString[i]);
    }

    // get target phrases in string representation
    std::vector<StringTgtCand> cands;
    std::vector<std::string> wacands;
    m_dict->GetTargetCandidates(srcString,cands,wacands);
    if(cands.empty()) {
      return 0;
    }

    const TranslationSystem& system =  StaticData::Instance().GetTranslationSystem(TranslationSystem::DEFAULT);
    std::vector<float> weights = system.GetTranslationWeights(m_obj->GetDictIndex());
    float weightWP = system.GetWeightWordPenalty();

    std::vector<TargetPhrase> tCands;
    tCands.reserve(cands.size());
    std::vector<std::pair<float,size_t> > costs;
    costs.reserve(cands.size());

    // convert into TargetPhrases
    for(size_t i=0; i<cands.size(); ++i) {
      TargetPhrase targetPhrase(Output);

      StringTgtCand::Tokens const& factorStrings=cands[i].tokens;
      Scores const& probVector=cands[i].scores;

      std::vector<float> scoreVector(probVector.size());
      std::transform(probVector.begin(),probVector.end(),scoreVector.begin(),
                     TransformScore);
      std::transform(scoreVector.begin(),scoreVector.end(),scoreVector.begin(),
                     FloorScore);
      //sparse features.
      //These are already in log-space
      ScoreComponentCollection sparseFeatures;
      if (m_obj->GetFeature()->GetSparsePhraseDictionaryFeature()) {
        for (size_t j = 0; j < cands[i].fnames.size(); ++j) {
          sparseFeatures.Assign(m_obj->GetFeature()->GetSparsePhraseDictionaryFeature(),
            *(cands[i].fnames[j]), cands[i].fvalues[j]);
        } 
      }
      CreateTargetPhrase(targetPhrase,factorStrings,scoreVector, sparseFeatures, wacands[i], weights, weightWP, &src);
      costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
      tCands.push_back(targetPhrase);
    }

    TargetPhraseCollection *rv;
    rv=PruneTargetCandidates(tCands,costs);
    if(rv->IsEmpty()) {
      delete rv;
      return 0;
    } else {
      if(useCache) piter.first->second=rv;
      m_tgtColls.push_back(rv);
      return rv;
    }

  }



  void Create(const std::vector<FactorType> &input
              , const std::vector<FactorType> &output
              , const std::string &filePath
	      , const std::vector<float> &weight
              , const LMList &languageModels
             ) {

    // set my members
    m_dict=new PhraseDictionaryTree(weight.size()-m_numInputScores);
    m_input=input;
    m_output=output;
    m_languageModels=&languageModels;

    const StaticData &staticData = StaticData::Instance();
    m_dict->UseWordAlignment(staticData.UseAlignmentInfo());

    std::string binFname=filePath+".binphr.idx";
    if(!FileExists(binFname.c_str())) {
      TRACE_ERR( "bin ttable does not exist -> create it\n");
      InputFileStream in(filePath);
      m_dict->Create(in,filePath);
    }
    TRACE_ERR( "reading bin ttable\n");
//		m_dict->Read(filePath);
    bool res=m_dict->Read(filePath);
    if (!res) {
      std::stringstream strme;
      strme << "bin ttable was read in a wrong way\n";
      UserMessage::Add(strme.str());
      exit(1);
    }
  }

  typedef PhraseDictionaryTree::PrefixPtr PPtr;
  typedef unsigned short Position;
  typedef std::pair<Position,Position> Range;
  struct State {
    PPtr ptr;
    Range range;
    std::vector<float> scores;
    Phrase src;

    State() : range(0,0),scores(0),src(ARRAY_SIZE_INCR) {}
    State(Position b,Position e,const PPtr& v,const std::vector<float>& sv=std::vector<float>(0))
      : ptr(v),range(b,e),scores(sv),src(ARRAY_SIZE_INCR) {}
    State(Range const& r,const PPtr& v,const std::vector<float>& sv=std::vector<float>(0))
      : ptr(v),range(r),scores(sv),src(ARRAY_SIZE_INCR) {}

    Position begin() const {
      return range.first;
    }
    Position end() const {
      return range.second;
    }
    std::vector<float> GetScores() const {
      return scores;
    }

    friend std::ostream& operator<<(std::ostream& out,State const& s) {
      out<<" R=("<<s.begin()<<","<<s.end()<<"),";
      for(std::vector<float>::const_iterator scoreIterator = s.GetScores().begin(); scoreIterator<s.GetScores().end(); scoreIterator++) {
        out<<", "<<*scoreIterator;
      }
      out<<")";
      return out;
    }

  };

  void CreateTargetPhrase(TargetPhrase& targetPhrase,
                          StringTgtCand::Tokens const& factorStrings,
                          Scores const& scoreVector,
                          const ScoreComponentCollection& sparseFeatures,
                          const std::string& alignmentString,
                          std::vector<float> &weights,
                          float weightWP,
                          Phrase const* srcPtr=0) const {
    CreateTargetPhrase(targetPhrase, factorStrings, scoreVector, sparseFeatures, weights, weightWP, srcPtr);
    targetPhrase.SetAlignmentInfo(alignmentString);
  }


  void CreateTargetPhrase(TargetPhrase& targetPhrase,
                          StringTgtCand::Tokens const& factorStrings,
                          Scores const& scoreVector,
                          const ScoreComponentCollection& sparseFeatures,
			  std::vector<float> &weights,
			  float weightWP,
                          Phrase const* srcPtr=0) const {
    FactorCollection &factorCollection = FactorCollection::Instance();

    for(size_t k=0; k<factorStrings.size(); ++k) {
      std::vector<std::string> factors=TokenizeMultiCharSeparator(*factorStrings[k],StaticData::Instance().GetFactorDelimiter());
      CHECK(factors.size()==m_output.size());
      Word& w=targetPhrase.AddWord();
      for(size_t l=0; l<m_output.size(); ++l) {
        w[m_output[l]]= factorCollection.AddFactor(Output, m_output[l], factors[l]);
      }
    }

    targetPhrase.SetScore(m_obj->GetFeature(), scoreVector, sparseFeatures, weights, weightWP, *m_languageModels);
    targetPhrase.SetSourcePhrase(*srcPtr);
  }




  TargetPhraseCollection* PruneTargetCandidates(std::vector<TargetPhrase> const & tCands,
      std::vector<std::pair<float,size_t> >& costs) const {
    // convert into TargetPhraseCollection
    TargetPhraseCollection *rv=new TargetPhraseCollection;

    // set limit to tableLimit or actual size, whatever is smaller
    std::vector<std::pair<float,size_t> >::iterator nth =
      costs.begin() + ((m_obj->m_tableLimit>0 && // 0 indicates no limit
                        m_obj->m_tableLimit < costs.size()) ?
                       m_obj->m_tableLimit : costs.size());

    // find the nth phrase according to future cost
    std::nth_element(costs.begin(),nth ,costs.end());

    // add n top phrases to the return list
    for(std::vector<std::pair<float,size_t> >::iterator
        it = costs.begin(); it != nth; ++it)
      rv->Add(new TargetPhrase(tCands[it->second]));

    return rv;
  }

  // POD for target phrase scores
  struct TScores {
    float total;
    Scores trans;
    Phrase const* src;

    TScores() : total(0.0),src(0) {}
  };

  void CacheSource(ConfusionNet const& src) {
    CHECK(m_dict);
    const size_t srcSize=src.GetSize();

    std::vector<size_t> exploredPaths(srcSize+1,0);
    std::vector<double> exPathsD(srcSize+1,-1.0);

    // collect some statistics
    std::vector<size_t> cnDepths(srcSize,0);
    for(size_t i=0; i<srcSize; ++i) cnDepths[i]=src[i].size();

    for(size_t len=1; len<=srcSize; ++len)
      for(size_t i=0; i<=srcSize-len; ++i) {
        double pd=0.0;
        for(size_t k=i; k<i+len; ++k)	pd+=log(1.0*cnDepths[k]);
        exPathsD[len]=(exPathsD[len]>=0.0 ? addLogScale(pd,exPathsD[len]) : pd);
      }

    // update global statistics
    if(pathCN.size()<=srcSize) pathCN.resize(srcSize+1,-1.0);
    for(size_t len=1; len<=srcSize; ++len)
      pathCN[len]=pathCN[len]>=0.0 ? addLogScale(pathCN[len],exPathsD[len]) : exPathsD[len];

    if(path1Best.size()<=srcSize) path1Best.resize(srcSize+1,0);
    for(size_t len=1; len<=srcSize; ++len) path1Best[len]+=srcSize-len+1;


    if (StaticData::Instance().GetVerboseLevel() >= 2 && exPathsD.size()) {
      TRACE_ERR("path stats for current CN: \nCN (full):     ");
      std::transform(exPathsD.begin()+1
                     ,exPathsD.end()
                     ,std::ostream_iterator<double>(std::cerr," ")
                     ,Exp);
      TRACE_ERR("\n");
    }

    typedef StringTgtCand::Tokens sPhrase;
    typedef std::map<StringTgtCand::Tokens,TScores> E2Costs;

    std::map<Range,E2Costs> cov2cand;
    std::vector<State> stack;
    for(Position i=0 ; i < srcSize ; ++i)
      stack.push_back(State(i, i, m_dict->GetRoot(), std::vector<float>(m_numInputScores,0.0)));

    const TranslationSystem& system =  StaticData::Instance().GetTranslationSystem(TranslationSystem::DEFAULT);
    std::vector<float> weightT = system.GetTranslationWeights(m_obj->GetDictIndex());
    float weightWP = system.GetWeightWordPenalty();

    while(!stack.empty()) {
      State curr(stack.back());
      stack.pop_back();

      CHECK(curr.end()<srcSize);
      const ConfusionNet::Column &currCol=src[curr.end()];
      // in a given column, loop over all possibilities
      for(size_t colidx=0; colidx<currCol.size(); ++colidx) {
        const Word& w=currCol[colidx].first; // w=the i^th possibility in column colidx
        std::string s;
        Factors2String(w,s);
        bool isEpsilon=(s=="" || s==EPSILON);

        //assert that we have the right number of link params in this CN option
        CHECK(currCol[colidx].second.size() >= m_numInputScores);

        // do not start with epsilon (except at first position)
        if(isEpsilon && curr.begin()==curr.end() && curr.begin()>0) continue;

        // At a given node in the prefix tree, look to see if w defines an edge to
        // another node (Extend).  Stay at the same node if w==EPSILON
        PPtr nextP = (isEpsilon ? curr.ptr : m_dict->Extend(curr.ptr,s));

        if(nextP) { // w is a word that should be considered
          Range newRange(curr.begin(),curr.end()+src.GetColumnIncrement(curr.end(),colidx));

          //add together the link scores from the current state and the new arc
          float inputScoreSum = 0;
          std::vector<float> newInputScores(m_numInputScores,0.0);
          if (m_numInputScores) {
            std::transform(currCol[colidx].second.begin(), currCol[colidx].second.end(),
                           curr.GetScores().begin(),
                           newInputScores.begin(),
                           std::plus<float>());


            //we need to sum up link weights (excluding realWordCount, which isn't in numLinkParams)
            //if the sum is too low, then we won't expand this.
            //TODO: dodgy! shouldn't we consider weights here? what about zero-weight params?
            inputScoreSum = std::accumulate(newInputScores.begin(),newInputScores.begin()+m_numInputScores,0.0);
          }

          Phrase newSrc(curr.src);
          if(!isEpsilon) newSrc.AddWord(w);
          if(newRange.second<srcSize && inputScoreSum>LOWEST_SCORE) {
            // if there is more room to grow, add a new state onto the queue
            // to be explored that represents [begin, curEnd+)
            stack.push_back(State(newRange,nextP,newInputScores));
            stack.back().src=newSrc;
          }

          std::vector<StringTgtCand> tcands;
          // now, look up the target candidates (aprx. TargetPhraseCollection) for
          // the current path through the CN
          m_dict->GetTargetCandidates(nextP,tcands);

          if(newRange.second>=exploredPaths.size()+newRange.first)
            exploredPaths.resize(newRange.second-newRange.first+1,0);
          ++exploredPaths[newRange.second-newRange.first];

          totalE+=tcands.size();

          if(tcands.size()) {
            E2Costs& e2costs=cov2cand[newRange];
            Phrase const* srcPtr=uniqSrcPhr(newSrc);
            for(size_t i=0; i<tcands.size(); ++i) {
              //put input scores in first - already logged, just drop in directly
              std::vector<float> nscores(newInputScores);

              //resize to include phrase table scores
              nscores.resize(m_numInputScores+tcands[i].scores.size(),0.0f);

              //put in phrase table scores, logging as we insert
              std::transform(tcands[i].scores.begin(),tcands[i].scores.end(),nscores.begin() + m_numInputScores,TransformScore);
              
	      CHECK(nscores.size()==weightT.size());

              //tally up
              float score=std::inner_product(nscores.begin(), nscores.end(), weightT.begin(), 0.0f);

              //count word penalty
              score-=tcands[i].tokens.size() * weightWP;

              std::pair<E2Costs::iterator,bool> p=e2costs.insert(std::make_pair(tcands[i].tokens,TScores()));

              if(p.second) ++distinctE;

              TScores & scores=p.first->second;
              if(p.second || scores.total<score) {
                scores.total=score;
                scores.trans=nscores;
                scores.src=srcPtr;
              }
            }
          }
        }
      }
    } // end while(!stack.empty())


    if (StaticData::Instance().GetVerboseLevel() >= 2 && exploredPaths.size()) {
      TRACE_ERR("CN (explored): ");
      std::copy(exploredPaths.begin()+1,exploredPaths.end(),
                std::ostream_iterator<size_t>(std::cerr," "));
      TRACE_ERR("\n");
    }

    if(pathExplored.size()<exploredPaths.size())
      pathExplored.resize(exploredPaths.size(),0);
    for(size_t len=1; len<=srcSize; ++len)
      pathExplored[len]+=exploredPaths[len];


    m_rangeCache.resize(src.GetSize(),vTPC(src.GetSize(),0));

    for(std::map<Range,E2Costs>::const_iterator i=cov2cand.begin(); i!=cov2cand.end(); ++i) {
      CHECK(i->first.first<m_rangeCache.size());
      CHECK(i->first.second>0);
      CHECK(static_cast<size_t>(i->first.second-1)<m_rangeCache[i->first.first].size());
      CHECK(m_rangeCache[i->first.first][i->first.second-1]==0);

      std::vector<TargetPhrase> tCands;
      tCands.reserve(i->second.size());
      std::vector<std::pair<float,size_t> > costs;
      costs.reserve(i->second.size());

      for(E2Costs::const_iterator j=i->second.begin(); j!=i->second.end(); ++j) {
        TScores const & scores=j->second;
        TargetPhrase targetPhrase(Output);
        CreateTargetPhrase(targetPhrase,j->first,scores.trans,ScoreComponentCollection(),weightT,weightWP,scores.src);
        costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
        tCands.push_back(targetPhrase);
        //std::cerr << i->first.first << "-" << i->first.second << ": " << targetPhrase << std::endl;
      }

      TargetPhraseCollection *rv=PruneTargetCandidates(tCands,costs);

      if(rv->IsEmpty())
        delete rv;
      else {
        m_rangeCache[i->first.first][i->first.second-1]=rv;
        m_tgtColls.push_back(rv);
      }
    }
    // free memory
    m_dict->FreeMemory();
  }


  size_t GetNumInputScores() const {
    return m_numInputScores;
  }
};

}
#endif