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mosesdecoder/moses/PDTAimp.h
Hieu Hoang 233062a468 add DIMwid /Robin Kurtz
2013-10-02 11:33:10 +01:00

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// -*- c++ -*-
// $Id$
// vim:tabstop=2
#ifndef moses_PDTAimp_h
#define moses_PDTAimp_h
#include "StaticData.h" // needed for factor splitter
#include "moses/TranslationModel/PhraseDictionaryTree.h"
#include "UniqueObject.h"
#include "InputFileStream.h"
#include "moses/TranslationModel/PhraseDictionaryTreeAdaptor.h"
#include "Util.h"
#include "util/tokenize_piece.hh"
#include "moses/FF/InputFeature.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);
}
/** implementation of the binary phrase table for the phrase-based decoder. Used by PhraseDictionaryTreeAdaptor
*/
class PDTAimp
{
// only these classes are allowed to instantiate this class
friend class PhraseDictionaryTreeAdaptor;
protected:
PDTAimp(PhraseDictionaryTreeAdaptor *p)
: m_dict(0),
m_obj(p),
useCache(1),
totalE(0),
distinctE(0) {
m_numInputScores = 0;
const StaticData &staticData = StaticData::Instance();
m_inputFeature = staticData.GetInputFeature();
if (m_inputFeature) {
const PhraseDictionary *firstPt = staticData.GetPhraseDictionaries()[0];
if (firstPt == m_obj) {
m_numInputScores = m_inputFeature->GetNumScoreComponents();
}
}
}
public:
std::vector<FactorType> m_input,m_output;
PhraseDictionaryTree *m_dict;
const InputFeature *m_inputFeature;
typedef std::vector<TargetPhraseCollectionWithSourcePhrase const*> vTPC;
mutable vTPC m_tgtColls;
typedef std::map<Phrase,TargetPhraseCollectionWithSourcePhrase 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();
}
TargetPhraseCollectionWithSourcePhrase 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<TargetPhraseCollectionWithSourcePhrase 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;
}
//TODO: Multiple models broken here
std::vector<float> weights = StaticData::Instance().GetWeights(m_obj);
std::vector<TargetPhrase> tCands;
tCands.reserve(cands.size());
std::vector<std::pair<float,size_t> > costs;
costs.reserve(cands.size());
std::vector<Phrase> sourcePhrases;
sourcePhrases.reserve(cands.size());
// convert into TargetPhrases
for(size_t i=0; i<cands.size(); ++i) {
TargetPhrase targetPhrase;
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
for (size_t j = 0; j < cands[i].fnames.size(); ++j) {
targetPhrase.GetScoreBreakdown().Assign(m_obj, *cands[i].fnames[j], cands[i].fvalues[j]);
}
CreateTargetPhrase(targetPhrase,factorStrings,scoreVector, Scores(0), &wacands[i], &src);
costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
tCands.push_back(targetPhrase);
sourcePhrases.push_back(src);
}
TargetPhraseCollectionWithSourcePhrase *rv;
rv=PruneTargetCandidates(tCands,costs, sourcePhrases);
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
) {
// set my members
m_dict=new PhraseDictionaryTree();
m_input=input;
m_output=output;
const StaticData &staticData = StaticData::Instance();
m_dict->NeedAlignmentInfo(staticData.NeedAlignmentInfo());
std::string binFname=filePath+".binphr.idx";
if(!FileExists(binFname.c_str())) {
UserMessage::Add( "bin ttable does not exist\n");
abort();
//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& transVector,
Scores const& inputVector,
const std::string *alignmentString,
Phrase const* srcPtr=0) const {
FactorCollection &factorCollection = FactorCollection::Instance();
for(size_t k=0; k<factorStrings.size(); ++k) {
util::TokenIter<util::MultiCharacter, false> word(*factorStrings[k], StaticData::Instance().GetFactorDelimiter());
Word& w=targetPhrase.AddWord();
for(size_t l=0; l<m_output.size(); ++l, ++word) {
w[m_output[l]]= factorCollection.AddFactor(*word);
}
}
if (alignmentString) {
targetPhrase.SetAlignmentInfo(*alignmentString);
}
if (m_numInputScores) {
targetPhrase.GetScoreBreakdown().Assign(m_inputFeature, inputVector);
}
targetPhrase.GetScoreBreakdown().Assign(m_obj, transVector);
targetPhrase.Evaluate(*srcPtr, m_obj->GetFeaturesToApply());
}
TargetPhraseCollectionWithSourcePhrase* PruneTargetCandidates
(const std::vector<TargetPhrase> & tCands,
std::vector<std::pair<float,size_t> >& costs,
const std::vector<Phrase> &sourcePhrases) const {
// convert into TargetPhraseCollection
CHECK(tCands.size() == sourcePhrases.size());
TargetPhraseCollectionWithSourcePhrase *rv=new TargetPhraseCollectionWithSourcePhrase;
// 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) {
size_t ind = it->second;
TargetPhrase *targetPhrase = new TargetPhrase(tCands[ind]);
const Phrase &sourcePhrase = sourcePhrases[ind];
rv->Add(targetPhrase, sourcePhrase);
}
return rv;
}
// POD for target phrase scores
struct TScores {
float total;
Scores transScore, inputScores;
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)));
std::vector<float> weightTrans = StaticData::Instance().GetWeights(m_obj);
std::vector<float> weightInput = StaticData::Instance().GetWeights(m_inputFeature);
float weightWP = StaticData::Instance().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.denseScores.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.denseScores.begin(), currCol[colidx].second.denseScores.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::cerr << "newSrc=" << newSrc << std::endl;
std::vector<StringTgtCand> tcands;
// now, look up the target candidates (aprx. TargetPhraseCollection) for
// the current path through the CN
m_dict->GetTargetCandidates(nextP,tcands);
std::cerr << "tcands=" << tcands.size() << std::endl;
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> transcores(m_obj->GetNumScoreComponents());
CHECK(transcores.size()==weightTrans.size());
//put in phrase table scores, logging as we insert
std::transform(tcands[i].scores.begin()
,tcands[i].scores.end()
,transcores.begin()
,TransformScore);
//tally up
float score=std::inner_product(transcores.begin(), transcores.end(), weightTrans.begin(), 0.0f);
// input feature
score +=std::inner_product(newInputScores.begin(), newInputScores.end(), weightInput.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.transScore=transcores;
scores.inputScores=newInputScores;
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());
std::vector<Phrase> sourcePhrases;
sourcePhrases.reserve(i->second.size());
for(E2Costs::const_iterator j=i->second.begin(); j!=i->second.end(); ++j) {
TScores const & scores=j->second;
TargetPhrase targetPhrase;
CreateTargetPhrase(targetPhrase
, j ->first
, scores.transScore
, scores.inputScores
, NULL
, scores.src);
costs.push_back(std::make_pair(-targetPhrase.GetFutureScore(),tCands.size()));
tCands.push_back(targetPhrase);
sourcePhrases.push_back(*scores.src);
//std::cerr << i->first.first << "-" << i->first.second << ": " << targetPhrase << std::endl;
}
TargetPhraseCollectionWithSourcePhrase *rv=PruneTargetCandidates(tCands, costs, sourcePhrases);
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
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