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// $Id$
// vim:tabstop=2
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2006 University of Edinburgh
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
***********************************************************************/
#include <cassert>
#include <iostream>
#include <limits>
#include <vector>
#include <algorithm>
#include "TranslationOption.h"
#include "TranslationOptionCollection.h"
#include "DummyScoreProducers.h"
#include "Hypothesis.h"
#include "Util.h"
#include "SquareMatrix.h"
#include "LexicalReordering.h"
#include "StaticData.h"
#include "InputType.h"
#include "LMList.h"
#include "hash.h"
using namespace std;
unsigned int Hypothesis::s_HypothesesCreated = 0;
#ifdef USE_HYPO_POOL
ObjectPool<Hypothesis> Hypothesis::s_objectPool("Hypothesis", 300000);
#endif
Hypothesis::Hypothesis(InputType const& source, const TargetPhrase &emptyTarget)
: m_prevHypo(NULL)
, m_transOpt(NULL)
, m_targetPhrase(emptyTarget)
, m_sourcePhrase(0)
, m_sourceCompleted(source.GetSize())
, m_sourceInput(source)
, m_currSourceWordsRange(NOT_FOUND, NOT_FOUND)
, m_currTargetWordsRange(NOT_FOUND, NOT_FOUND)
, m_wordDeleted(false)
, m_languageModelStates(StaticData::Instance().GetLMSize(), LanguageModelSingleFactor::UnknownState)
, m_arcList(NULL)
, m_id(0)
, m_lmstats(NULL)
{ // used for initial seeding of trans process
// initialize scores
//_hash_computed = false;
s_HypothesesCreated = 1;
ResetScore();
}
/***
* continue prevHypo by appending the phrases in transOpt
*/
Hypothesis::Hypothesis(const Hypothesis &prevHypo, const TranslationOption &transOpt)
: m_prevHypo(&prevHypo)
, m_transOpt(&transOpt)
, m_targetPhrase(transOpt.GetTargetPhrase())
, m_sourcePhrase(transOpt.GetSourcePhrase())
, m_sourceCompleted (prevHypo.m_sourceCompleted )
, m_sourceInput (prevHypo.m_sourceInput)
, m_currSourceWordsRange (transOpt.GetSourceWordsRange())
, m_currTargetWordsRange ( prevHypo.m_currTargetWordsRange.GetEndPos() + 1
,prevHypo.m_currTargetWordsRange.GetEndPos() + transOpt.GetTargetPhrase().GetSize())
, m_wordDeleted(false)
, m_totalScore(0.0f)
, m_futureScore(0.0f)
, m_scoreBreakdown (prevHypo.m_scoreBreakdown)
, m_languageModelStates(prevHypo.m_languageModelStates)
, m_arcList(NULL)
, m_id(s_HypothesesCreated++)
, m_lmstats(NULL)
{
// assert that we are not extending our hypothesis by retranslating something
// that this hypothesis has already translated!
// cerr << m_sourceCompleted << " " << m_currSourceWordsRange << endl;
assert(!m_sourceCompleted.Overlap(m_currSourceWordsRange));
//_hash_computed = false;
m_sourceCompleted.SetValue(m_currSourceWordsRange.GetStartPos(), m_currSourceWordsRange.GetEndPos(), true);
m_wordDeleted = transOpt.IsDeletionOption();
m_scoreBreakdown.PlusEquals(transOpt.GetScoreBreakdown());
}
Hypothesis::~Hypothesis()
{
if (m_arcList)
{
ArcList::iterator iter;
for (iter = m_arcList->begin() ; iter != m_arcList->end() ; ++iter)
{
FREEHYPO(*iter);
}
m_arcList->clear();
delete m_arcList;
m_arcList = NULL;
delete m_lmstats; m_lmstats = NULL;
}
}
void Hypothesis::AddArc(Hypothesis *loserHypo)
{
if (!m_arcList) {
if (loserHypo->m_arcList) // we don't have an arcList, but loser does
{
this->m_arcList = loserHypo->m_arcList; // take ownership, we'll delete
loserHypo->m_arcList = 0; // prevent a double deletion
}
else
{ this->m_arcList = new ArcList(); }
} else {
if (loserHypo->m_arcList) { // both have an arc list: merge. delete loser
size_t my_size = m_arcList->size();
size_t add_size = loserHypo->m_arcList->size();
this->m_arcList->resize(my_size + add_size, 0);
std::memcpy(&(*m_arcList)[0] + my_size, &(*m_arcList)[0], add_size * sizeof(Hypothesis *));
delete loserHypo->m_arcList;
loserHypo->m_arcList = 0;
} else { // loserHypo doesn't have any arcs
// DO NOTHING
}
}
m_arcList->push_back(loserHypo);
}
/***
* return the subclass of Hypothesis most appropriate to the given translation option
*/
Hypothesis* Hypothesis::CreateNext(const TranslationOption &transOpt) const
{
return Create(*this, transOpt);
}
/***
* return the subclass of Hypothesis most appropriate to the given translation option
*/
Hypothesis* Hypothesis::Create(const Hypothesis &prevHypo, const TranslationOption &transOpt)
{
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(prevHypo, transOpt);
#else
return new Hypothesis(prevHypo, transOpt);
#endif
}
/***
* return the subclass of Hypothesis most appropriate to the given target phrase
*/
Hypothesis* Hypothesis::Create(InputType const& m_source, const TargetPhrase &emptyTarget)
{
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(m_source, emptyTarget);
#else
return new Hypothesis(m_source, emptyTarget);
#endif
}
/** check, if two hypothesis can be recombined.
this is actually a sorting function that allows us to
keep an ordered list of hypotheses. This makes recombination
much quicker.
*/
int Hypothesis::NGramCompare(const Hypothesis &compare) const
{ // -1 = this < compare
// +1 = this > compare
// 0 = this ==compare
if (m_languageModelStates < compare.m_languageModelStates) return -1;
if (m_languageModelStates > compare.m_languageModelStates) return 1;
int compareBitmap = m_sourceCompleted.Compare(compare.m_sourceCompleted);
if (compareBitmap != 0)
return compareBitmap;
if (m_currSourceWordsRange.GetEndPos() < compare.m_currSourceWordsRange.GetEndPos()) return -1;
if (m_currSourceWordsRange.GetEndPos() > compare.m_currSourceWordsRange.GetEndPos()) return 1;
if (! StaticData::Instance().GetSourceStartPosMattersForRecombination()) return 0;
if (m_currSourceWordsRange.GetStartPos() < compare.m_currSourceWordsRange.GetStartPos()) return -1;
if (m_currSourceWordsRange.GetStartPos() > compare.m_currSourceWordsRange.GetStartPos()) return 1;
return 0;
}
/** Calculates the overall language model score by combining the scores
* of language models generated for each of the factors. Because the factors
* represent a variety of tag sets, and because factors with smaller tag sets
* (such as POS instead of words) allow us to calculate richer statistics, we
* allow a different length of n-gram to be specified for each factor.
* /param lmListInitial todo - describe this parameter
* /param lmListEnd todo - describe this parameter
*/
void Hypothesis::CalcLMScore(const LMList &languageModels)
{
const size_t startPos = m_currTargetWordsRange.GetStartPos();
LMList::const_iterator iterLM;
// will be null if LM stats collection is disabled
if (StaticData::Instance().IsComputeLMBackoffStats()) {
m_lmstats = new vector<vector<unsigned int> >(languageModels.size(), vector<unsigned int>(0));
}
size_t lmIdx = 0;
// already have LM scores from previous and trigram score of poss trans.
// just need trigram score of the words of the start of current phrase
for (iterLM = languageModels.begin() ; iterLM != languageModels.end() ; ++iterLM,++lmIdx)
{
const LanguageModel &languageModel = **iterLM;
size_t nGramOrder = languageModel.GetNGramOrder();
size_t currEndPos = m_currTargetWordsRange.GetEndPos();
float lmScore;
size_t nLmCallCount = 0;
if(m_currTargetWordsRange.GetNumWordsCovered() == 0) {
lmScore = 0; //the score associated with dropping source words is not part of the language model
} else { //non-empty target phrase
if (m_lmstats)
(*m_lmstats)[lmIdx].resize(m_currTargetWordsRange.GetNumWordsCovered(), 0);
// 1st n-gram
vector<const Word*> contextFactor(nGramOrder);
size_t index = 0;
for (int currPos = (int) startPos - (int) nGramOrder + 1 ; currPos <= (int) startPos ; currPos++)
{
if (currPos >= 0)
contextFactor[index++] = &GetWord(currPos);
else
contextFactor[index++] = &languageModel.GetSentenceStartArray();
}
lmScore = languageModel.GetValue(contextFactor);
if (m_lmstats) { languageModel.GetState(contextFactor, &(*m_lmstats)[lmIdx][nLmCallCount++]); }
//cout<<"context factor: "<<languageModel.GetValue(contextFactor)<<endl;
// main loop
size_t endPos = std::min(startPos + nGramOrder - 2
, currEndPos);
for (size_t currPos = startPos + 1 ; currPos <= endPos ; currPos++)
{
// shift all args down 1 place
for (size_t i = 0 ; i < nGramOrder - 1 ; i++)
contextFactor[i] = contextFactor[i + 1];
// add last factor
contextFactor.back() = &GetWord(currPos);
lmScore += languageModel.GetValue(contextFactor);
if (m_lmstats)
languageModel.GetState(contextFactor, &(*m_lmstats)[lmIdx][nLmCallCount++]);
//cout<<"context factor: "<<languageModel.GetValue(contextFactor)<<endl;
}
// end of sentence
if (m_sourceCompleted.IsComplete())
{
const size_t size = GetSize();
contextFactor.back() = &languageModel.GetSentenceEndArray();
for (size_t i = 0 ; i < nGramOrder - 1 ; i ++)
{
int currPos = (int)(size - nGramOrder + i + 1);
if (currPos < 0)
contextFactor[i] = &languageModel.GetSentenceStartArray();
else
contextFactor[i] = &GetWord((size_t)currPos);
}
if (m_lmstats) {
(*m_lmstats)[lmIdx].resize((*m_lmstats)[lmIdx].size() + 1); // extra space for the last call
lmScore += languageModel.GetValue(contextFactor, &m_languageModelStates[lmIdx], &(*m_lmstats)[lmIdx][nLmCallCount++]);
} else
lmScore += languageModel.GetValue(contextFactor, &m_languageModelStates[lmIdx]);
} else {
for (size_t currPos = endPos+1; currPos <= currEndPos; currPos++) {
for (size_t i = 0 ; i < nGramOrder - 1 ; i++)
contextFactor[i] = contextFactor[i + 1];
contextFactor.back() = &GetWord(currPos);
if (m_lmstats)
languageModel.GetState(contextFactor, &(*m_lmstats)[lmIdx][nLmCallCount++]);
}
m_languageModelStates[lmIdx]=languageModel.GetState(contextFactor);
}
}
m_scoreBreakdown.PlusEquals(&languageModel, lmScore);
}
}
void Hypothesis::CalcDistortionScore()
{
const DistortionScoreProducer *dsp = StaticData::Instance().GetDistortionScoreProducer();
float distortionScore = dsp->CalculateDistortionScore(
m_prevHypo->GetCurrSourceWordsRange(),
this->GetCurrSourceWordsRange()
);
m_scoreBreakdown.PlusEquals(dsp, distortionScore);
}
void Hypothesis::ResetScore()
{
m_scoreBreakdown.ZeroAll();
m_futureScore = m_totalScore = 0.0f;
}
/***
* calculate the logarithm of our total translation score (sum up components)
*/
void Hypothesis::CalcScore(const SquareMatrix &futureScore)
{
const StaticData &staticData = StaticData::Instance();
// DISTORTION COST
CalcDistortionScore();
// LANGUAGE MODEL COST
CalcLMScore(staticData.GetAllLM());
// WORD PENALTY
m_scoreBreakdown.PlusEquals(staticData.GetWordPenaltyProducer(), - (float) m_currTargetWordsRange.GetNumWordsCovered());
// FUTURE COST
CalcFutureScore(futureScore);
//LEXICAL REORDERING COST
std::vector<LexicalReordering*> m_reorderModels = staticData.GetReorderModels();
for(unsigned int i = 0; i < m_reorderModels.size(); i++)
{
m_scoreBreakdown.PlusEquals(m_reorderModels[i], m_reorderModels[i]->CalcScore(this));
}
// TOTAL
m_totalScore = m_scoreBreakdown.InnerProduct(staticData.GetAllWeights()) + m_futureScore;
}
void Hypothesis::CalcFutureScore(const SquareMatrix &futureScore)
{
const size_t maxSize= numeric_limits<size_t>::max();
size_t start = maxSize;
m_futureScore = 0.0f;
for(size_t currPos = 0 ; currPos < m_sourceCompleted.GetSize() ; currPos++)
{
if(m_sourceCompleted.GetValue(currPos) == 0 && start == maxSize)
{
start = currPos;
}
if(m_sourceCompleted.GetValue(currPos) == 1 && start != maxSize)
{
// m_score[ScoreType::FutureScoreEnum] += futureScore[start][currPos - 1];
m_futureScore += futureScore.GetScore(start, currPos - 1);
start = maxSize;
}
}
if (start != maxSize)
{
// m_score[ScoreType::FutureScoreEnum] += futureScore[start][m_sourceCompleted.GetSize() - 1];
m_futureScore += futureScore.GetScore(start, m_sourceCompleted.GetSize() - 1);
}
// add future costs for distortion model
if(StaticData::Instance().UseDistortionFutureCosts())
m_futureScore += m_sourceCompleted.GetFutureCosts( (int)m_currSourceWordsRange.GetEndPos() )
* StaticData::Instance().GetWeightDistortion();
}
const Hypothesis* Hypothesis::GetPrevHypo()const{
return m_prevHypo;
}
/**
* print hypothesis information for pharaoh-style logging
*/
void Hypothesis::PrintHypothesis(const InputType &source, float /*weightDistortion*/, float /*weightWordPenalty*/) const
{
if (!m_prevHypo) { TRACE_ERR(endl << "NULL hypo" << endl); return; }
TRACE_ERR(endl << "creating hypothesis "<< m_id <<" from "<< m_prevHypo->m_id<<" ( ");
int end = (int)(m_prevHypo->m_targetPhrase.GetSize()-1);
int start = end-1;
if ( start < 0 ) start = 0;
if ( m_prevHypo->m_currTargetWordsRange.GetStartPos() == NOT_FOUND ) {
TRACE_ERR( "<s> ");
}
else {
TRACE_ERR( "... ");
}
if (end>=0) {
WordsRange range(start, end);
TRACE_ERR( m_prevHypo->m_targetPhrase.GetSubString(range) << " ");
}
TRACE_ERR( ")"<<endl);
TRACE_ERR( "\tbase score "<< (m_prevHypo->m_totalScore - m_prevHypo->m_futureScore) <<endl);
TRACE_ERR( "\tcovering "<<m_currSourceWordsRange.GetStartPos()<<"-"<<m_currSourceWordsRange.GetEndPos()<<": "
<< *m_sourcePhrase <<endl);
TRACE_ERR( "\ttranslated as: "<<m_targetPhrase<<endl); // <<" => translation cost "<<m_score[ScoreType::PhraseTrans];
if (m_wordDeleted) TRACE_ERR( "\tword deleted"<<endl);
// TRACE_ERR( "\tdistance: "<<GetCurrSourceWordsRange().CalcDistortion(m_prevHypo->GetCurrSourceWordsRange())); // << " => distortion cost "<<(m_score[ScoreType::Distortion]*weightDistortion)<<endl;
// TRACE_ERR( "\tlanguage model cost "); // <<m_score[ScoreType::LanguageModelScore]<<endl;
// TRACE_ERR( "\tword penalty "); // <<(m_score[ScoreType::WordPenalty]*weightWordPenalty)<<endl;
TRACE_ERR( "\tscore "<<m_totalScore - m_futureScore<<" + future cost "<<m_futureScore<<" = "<<m_totalScore<<endl);
TRACE_ERR( "\tunweighted feature scores: " << m_scoreBreakdown << endl);
//PrintLMScores();
}
void Hypothesis::CleanupArcList()
{
// point this hypo's main hypo to itself
SetWinningHypo(this);
if (!m_arcList) return;
/* keep only number of arcs we need to create all n-best paths.
* However, may not be enough if only unique candidates are needed,
* so we'll keep all of arc list if nedd distinct n-best list
*/
const StaticData &staticData = StaticData::Instance();
size_t nBestSize = staticData.GetNBestSize();
bool distinctNBest = staticData.GetDistinctNBest();
if (!distinctNBest && m_arcList->size() > nBestSize * 5)
{ // prune arc list only if there too many arcs
nth_element(m_arcList->begin()
, m_arcList->begin() + nBestSize - 1
, m_arcList->end()
, CompareHypothesisTotalScore());
// delete bad ones
ArcList::iterator iter;
for (iter = m_arcList->begin() + nBestSize ; iter != m_arcList->end() ; ++iter)
{
Hypothesis *arc = *iter;
FREEHYPO(arc);
}
m_arcList->erase(m_arcList->begin() + nBestSize
, m_arcList->end());
}
// set all arc's main hypo variable to this hypo
ArcList::iterator iter = m_arcList->begin();
for (; iter != m_arcList->end() ; ++iter)
{
Hypothesis *arc = *iter;
arc->SetWinningHypo(this);
}
}
TO_STRING_BODY(Hypothesis)
// friend
ostream& operator<<(ostream& out, const Hypothesis& hypothesis)
{
hypothesis.ToStream(out);
// words bitmap
out << "[" << hypothesis.m_sourceCompleted << "] ";
// scores
out << " [total=" << hypothesis.GetTotalScore() << "]";
out << " " << hypothesis.GetScoreBreakdown();
return out;
}
std::string Hypothesis::GetSourcePhraseStringRep(const vector<FactorType> factorsToPrint) const
{
if (!m_prevHypo) { return ""; }
return m_sourcePhrase->GetStringRep(factorsToPrint);
#if 0
if(m_sourcePhrase)
{
return m_sourcePhrase->GetSubString(m_currSourceWordsRange).GetStringRep(factorsToPrint);
}
else
{
return m_sourceInput.GetSubString(m_currSourceWordsRange).GetStringRep(factorsToPrint);
}
#endif
}
std::string Hypothesis::GetTargetPhraseStringRep(const vector<FactorType> factorsToPrint) const
{
if (!m_prevHypo) { return ""; }
return m_targetPhrase.GetStringRep(factorsToPrint);
}
std::string Hypothesis::GetSourcePhraseStringRep() const
{
vector<FactorType> allFactors;
const size_t maxSourceFactors = StaticData::Instance().GetMaxNumFactors(Input);
for(size_t i=0; i < maxSourceFactors; i++)
{
allFactors.push_back(i);
}
return GetSourcePhraseStringRep(allFactors);
}
std::string Hypothesis::GetTargetPhraseStringRep() const
{
vector<FactorType> allFactors;
const size_t maxTargetFactors = StaticData::Instance().GetMaxNumFactors(Output);
for(size_t i=0; i < maxTargetFactors; i++)
{
allFactors.push_back(i);
}
return GetTargetPhraseStringRep(allFactors);
}
const ScoreComponentCollection &Hypothesis::GetCachedReorderingScore() const
{
return m_transOpt->GetReorderingScore();
}
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