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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 "util/check.hh"
#include <iostream>
#include <limits>
#include <vector>
#include <algorithm>
#include "FFState.h"
#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 "Manager.h"
#include "hash.h"
using namespace std;
namespace Moses
{
#ifdef USE_HYPO_POOL
ObjectPool<Hypothesis> Hypothesis::s_objectPool("Hypothesis", 300000);
#endif
Hypothesis::Hypothesis(Manager& manager, InputType const& source, const TargetPhrase &emptyTarget)
: m_prevHypo(NULL)
, m_targetPhrase(emptyTarget)
, m_sourcePhrase(0)
, m_sourceCompleted(source.GetSize(), manager.m_source.m_sourceCompleted)
, m_sourceInput(source)
, m_currSourceWordsRange(
m_sourceCompleted.GetFirstGapPos()>0 ? 0 : NOT_FOUND,
m_sourceCompleted.GetFirstGapPos()>0 ? m_sourceCompleted.GetFirstGapPos()-1 : NOT_FOUND)
, m_currTargetWordsRange(0, emptyTarget.GetSize()-1)
, m_wordDeleted(false)
, m_ffStates(manager.GetTranslationSystem()->GetStatefulFeatureFunctions().size())
, m_arcList(NULL)
, m_transOpt(NULL)
, m_manager(manager)
, m_id(m_manager.GetNextHypoId())
{
// used for initial seeding of trans process
// initialize scores
//_hash_computed = false;
//s_HypothesesCreated = 1;
ResetScore();
const vector<const StatefulFeatureFunction*>& ffs = m_manager.GetTranslationSystem()->GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i)
m_ffStates[i] = ffs[i]->EmptyHypothesisState(source);
m_manager.GetSentenceStats().AddCreated();
}
/***
* continue prevHypo by appending the phrases in transOpt
*/
Hypothesis::Hypothesis(const Hypothesis &prevHypo, const TranslationOption &transOpt)
: m_prevHypo(&prevHypo)
, 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_ffStates(prevHypo.m_ffStates.size())
, m_arcList(NULL)
, m_transOpt(&transOpt)
, m_manager(prevHypo.GetManager())
, m_id(m_manager.GetNextHypoId())
{
// assert that we are not extending our hypothesis by retranslating something
// that this hypothesis has already translated!
CHECK(!m_sourceCompleted.Overlap(m_currSourceWordsRange));
//_hash_computed = false;
m_sourceCompleted.SetValue(m_currSourceWordsRange.GetStartPos(), m_currSourceWordsRange.GetEndPos(), true);
m_wordDeleted = transOpt.IsDeletionOption();
m_manager.GetSentenceStats().AddCreated();
}
Hypothesis::~Hypothesis()
{
for (unsigned i = 0; i < m_ffStates.size(); ++i)
delete m_ffStates[i];
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;
}
}
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, &(*loserHypo->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 Phrase* constraint) const
{
return Create(*this, transOpt, constraint);
}
/***
* return the subclass of Hypothesis most appropriate to the given translation option
*/
Hypothesis* Hypothesis::Create(const Hypothesis &prevHypo, const TranslationOption &transOpt, const Phrase* constrainingPhrase)
{
// This method includes code for constraint decoding
bool createHypothesis = true;
if (constrainingPhrase != NULL) {
size_t constraintSize = constrainingPhrase->GetSize();
size_t start = 1 + prevHypo.GetCurrTargetWordsRange().GetEndPos();
const Phrase &transOptPhrase = transOpt.GetTargetPhrase();
size_t transOptSize = transOptPhrase.GetSize();
size_t endpoint = start + transOptSize - 1;
if (endpoint < constraintSize) {
WordsRange range(start, endpoint);
Phrase relevantConstraint = constrainingPhrase->GetSubString(range);
if ( ! relevantConstraint.IsCompatible(transOptPhrase) ) {
createHypothesis = false;
}
} else {
createHypothesis = false;
}
}
if (createHypothesis) {
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(prevHypo, transOpt);
#else
return new Hypothesis(prevHypo, transOpt);
#endif
} else {
// If the previous hypothesis plus the proposed translation option
// fail to match the provided constraint,
// return a null hypothesis.
return NULL;
}
}
/***
* return the subclass of Hypothesis most appropriate to the given target phrase
*/
Hypothesis* Hypothesis::Create(Manager& manager, InputType const& m_source, const TargetPhrase &emptyTarget)
{
#ifdef USE_HYPO_POOL
Hypothesis *ptr = s_objectPool.getPtr();
return new(ptr) Hypothesis(manager, m_source, emptyTarget);
#else
return new Hypothesis(manager, 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::RecombineCompare(const Hypothesis &compare) const
{
// -1 = this < compare
// +1 = this > compare
// 0 = this ==compare
int comp = m_sourceCompleted.Compare(compare.m_sourceCompleted);
if (comp != 0)
return comp;
for (unsigned i = 0; i < m_ffStates.size(); ++i) {
if (m_ffStates[i] == NULL || compare.m_ffStates[i] == NULL) {
comp = m_ffStates[i] - compare.m_ffStates[i];
} else {
comp = m_ffStates[i]->Compare(*compare.m_ffStates[i]);
}
if (comp != 0) return comp;
}
return 0;
}
void Hypothesis::ResetScore()
{
m_currScoreBreakdown.ZeroAll();
m_scoreBreakdown.reset(0);
m_futureScore = m_totalScore = 0.0f;
}
void Hypothesis::IncorporateTransOptScores() {
m_currScoreBreakdown.PlusEquals(m_transOpt->GetScoreBreakdown());
}
void Hypothesis::EvaluateWith(StatefulFeatureFunction* sfff,
int state_idx) {
m_ffStates[state_idx] = sfff->Evaluate(
*this,
m_prevHypo ? m_prevHypo->m_ffStates[state_idx] : NULL,
&m_currScoreBreakdown);
}
void Hypothesis::EvaluateWith(const StatelessFeatureFunction* slff) {
slff->Evaluate(PhraseBasedFeatureContext(this), &m_currScoreBreakdown);
}
void Hypothesis::CalculateFutureScore(const SquareMatrix& futureScore) {
m_futureScore = futureScore.CalcFutureScore( m_sourceCompleted );
}
void Hypothesis::CalculateFinalScore() {
m_totalScore = GetScoreBreakdown().InnerProduct(
StaticData::Instance().GetAllWeights()) + m_futureScore;
}
/***
* calculate the logarithm of our total translation score (sum up components)
*/
void Hypothesis::CalcScore(const SquareMatrix &futureScore)
{
// some stateless score producers cache their values in the translation
// option: add these here
// language model scores for n-grams completely contained within a target
// phrase are also included here
m_currScoreBreakdown = m_transOpt->GetScoreBreakdown();
// other stateless features have their scores cached in the
// TranslationOptionsCollection
m_manager.getSntTranslationOptions()->InsertPreCalculatedScores
(*m_transOpt, &m_currScoreBreakdown);
const StaticData &staticData = StaticData::Instance();
clock_t t=0; // used to track time
// compute values of stateless feature functions that were not
// cached in the translation option
const vector<const StatelessFeatureFunction*>& sfs =
m_manager.GetTranslationSystem()->GetStatelessFeatureFunctions();
for (unsigned i = 0; i < sfs.size(); ++i) {
if (!sfs[i]->ComputeValueInTranslationOption()) {
EvaluateWith(sfs[i]);
}
}
const vector<const StatefulFeatureFunction*>& ffs =
m_manager.GetTranslationSystem()->GetStatefulFeatureFunctions();
for (unsigned i = 0; i < ffs.size(); ++i) {
m_ffStates[i] = ffs[i]->Evaluate(
*this,
m_prevHypo ? m_prevHypo->m_ffStates[i] : NULL,
&m_currScoreBreakdown);
}
IFVERBOSE(2) {
t = clock(); // track time excluding LM
}
// FUTURE COST
m_futureScore = futureScore.CalcFutureScore( m_sourceCompleted );
// Apply sparse producer weights
ScoreComponentCollection tempScoreBreakdown = m_currScoreBreakdown;
const vector<const FeatureFunction*>& sparseProducers = m_manager.GetTranslationSystem()->GetSparseProducers();
for (unsigned i = 0; i < sparseProducers.size(); ++i) {
float weight = sparseProducers[i]->GetSparseProducerWeight();
tempScoreBreakdown.MultiplyEquals(sparseProducers[i], weight);
}
// TOTAL
m_totalScore = tempScoreBreakdown.InnerProduct(staticData.GetAllWeights()) + m_futureScore;
if (m_prevHypo) {
m_totalScore += m_prevHypo->m_totalScore - m_prevHypo->m_futureScore;
}
IFVERBOSE(2) {
m_manager.GetSentenceStats().AddTimeOtherScore( clock()-t );
}
}
/** Calculates the expected score of extending this hypothesis with the
* specified translation option. Includes actual costs for everything
* except for expensive actual language model score.
* This function is used by early discarding.
* /param transOpt - translation option being considered
*/
float Hypothesis::CalcExpectedScore( const SquareMatrix &futureScore )
{
const StaticData &staticData = StaticData::Instance();
clock_t t=0;
IFVERBOSE(2) {
t = clock(); // track time excluding LM
}
CHECK(!"Need to add code to get the distortion scores");
//CalcDistortionScore();
// LANGUAGE MODEL ESTIMATE (includes word penalty cost)
float estimatedLMScore = m_transOpt->GetFutureScore() - m_transOpt->GetScoreBreakdown().InnerProduct(staticData.GetAllWeights());
// FUTURE COST
m_futureScore = futureScore.CalcFutureScore( m_sourceCompleted );
// TOTAL
float total = m_totalScore + estimatedLMScore;
IFVERBOSE(2) {
m_manager.GetSentenceStats().AddTimeEstimateScore( clock()-t );
}
return total;
}
void Hypothesis::CalcRemainingScore()
{
const StaticData &staticData = StaticData::Instance();
clock_t t=0; // used to track time
// LANGUAGE MODEL COST
CHECK(!"Need to add code to get the LM score(s)");
//CalcLMScore(staticData.GetAllLM());
IFVERBOSE(2) {
t = clock(); // track time excluding LM
}
// WORD PENALTY
m_currScoreBreakdown.PlusEquals(m_manager.GetTranslationSystem()->GetWordPenaltyProducer()
, - (float)m_currTargetWordsRange.GetNumWordsCovered());
// TOTAL
m_totalScore = m_currScoreBreakdown.InnerProduct(staticData.GetAllWeights()) + m_futureScore;
if (m_prevHypo) {
m_totalScore += m_prevHypo->m_totalScore - m_prevHypo->m_futureScore;
}
IFVERBOSE(2) {
m_manager.GetSentenceStats().AddTimeOtherScore( clock()-t );
}
}
const Hypothesis* Hypothesis::GetPrevHypo()const
{
return m_prevHypo;
}
/**
* print hypothesis information for pharaoh-style logging
*/
void Hypothesis::PrintHypothesis() 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: "<<(Phrase&) 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_currScoreBreakdown << 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() || staticData.UseMBR() || staticData.GetOutputSearchGraph() || staticData.UseLatticeMBR() ;
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& hypo)
{
hypo.ToStream(out);
// words bitmap
out << "[" << hypo.m_sourceCompleted << "] ";
// scores
out << " [total=" << hypo.GetTotalScore() << "]";
out << " " << hypo.GetScoreBreakdown();
// alignment
out << " " << hypo.GetCurrTargetPhrase().GetAlignmentInfo();
/*
const Hypothesis *prevHypo = hypo.GetPrevHypo();
if (prevHypo)
out << endl << *prevHypo;
*/
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);
}
}
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