mosesdecoder/moses/LM/ParallelBackoff.cpp
2013-02-21 11:30:54 +00:00

357 lines
9.3 KiB
C++

// $Id$
/***********************************************************************
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 "ParallelBackoff.h"
#include <vector>
#include <string>
#include <sstream>
#include <fstream>
#include "MultiFactor.h"
#include "moses/Word.h"
#include "moses/Factor.h"
#include "moses/FactorTypeSet.h"
#include "moses/FactorCollection.h"
#include "moses/Phrase.h"
#include "moses/TypeDef.h"
#include "moses/Util.h"
#include "FNgramSpecs.h"
#include "FNgramStats.h"
#include "FactoredVocab.h"
#include "FNgram.h"
#include "wmatrix.h"
#include "Vocab.h"
#include "File.h"
using namespace std;
namespace Moses
{
namespace
{
class LanguageModelParallelBackoff : public LanguageModelMultiFactor
{
private:
std::vector<FactorType> m_factorTypesOrdered;
FactoredVocab *m_srilmVocab;
FNgram *m_srilmModel;
VocabIndex m_unknownId;
VocabIndex m_wtid;
VocabIndex m_wtbid;
VocabIndex m_wteid;
FNgramSpecs<FNgramCount>* fnSpecs;
//std::vector<VocabIndex> m_lmIdLookup;
std::map<size_t, VocabIndex>* lmIdMap;
std::fstream* debugStream;
WidMatrix *widMatrix;
public:
LanguageModelParallelBackoff(const std::string &line)
:LanguageModelMultiFactor("ParallelBackoffLM", line)
{}
~LanguageModelParallelBackoff();
bool Load(const std::string &filePath, const std::vector<FactorType> &factorTypes, size_t nGramOrder);
VocabIndex GetLmID( const std::string &str ) const;
VocabIndex GetLmID( const Factor *factor, FactorType ft ) const;
void CreateFactors();
LMResult GetValueForgotState(const std::vector<const Word*> &contextFactor, FFState &outState) const;
const FFState *GetNullContextState() const;
const FFState *GetBeginSentenceState() const;
FFState *NewState(const FFState *from) const;
};
LanguageModelParallelBackoff::~LanguageModelParallelBackoff()
{
///
}
bool LanguageModelParallelBackoff::Load(const std::string &filePath, const std::vector<FactorType> &factorTypes, size_t nGramOrder)
{
cerr << "Loading Language Model Parallel Backoff!!!\n";
widMatrix = new ::WidMatrix();
m_factorTypes = FactorMask(factorTypes);
m_srilmVocab = new ::FactoredVocab();
//assert(m_srilmVocab != 0);
fnSpecs = 0;
File f(filePath.c_str(),"r");
fnSpecs = new ::FNgramSpecs<FNgramCount>(f,*m_srilmVocab, 0/*debug*/);
cerr << "Loaded fnSpecs!\n";
m_srilmVocab->unkIsWord() = true;
m_srilmVocab->nullIsWord() = true;
m_srilmVocab->toLower() = false;
FNgramStats *factoredStats = new FNgramStats(*m_srilmVocab, *fnSpecs);
factoredStats->debugme(2);
cerr << "Factored stats\n";
FNgram* fngramLM = new FNgram(*m_srilmVocab,*fnSpecs);
cerr << "FNgram object created\n";
fngramLM->skipOOVs = false;
if (!factoredStats->read()) {
cerr << "error reading in counts in factor file\n";
exit(1);
}
cerr << "Factored stats read!\n";
factoredStats->estimateDiscounts();
factoredStats->computeCardinalityFunctions();
factoredStats->sumCounts();
cerr << "Another three operations made!\n";
if (!fngramLM->read()) {
cerr << "format error in lm file\n";
exit(1);
}
cerr << "fngramLM reads!\n";
m_filePath = filePath;
m_nGramOrder= nGramOrder;
m_factorTypesOrdered= factorTypes;
m_unknownId = m_srilmVocab->unkIndex();
cerr << "m_unknowdId = " << m_unknownId << endl;
m_srilmModel = fngramLM;
cerr << "Create factors...\n";
CreateFactors();
cerr << "Factors created! \n";
//FactorCollection &factorCollection = FactorCollection::Instance();
/*for (size_t index = 0 ; index < m_factorTypesOrdered.size() ; ++index)
{
FactorType factorType = m_factorTypesOrdered[index];
m_sentenceStartArray[factorType] = factorCollection.AddFactor(Output, factorType, BOS_);
m_sentenceEndArray[factorType] = factorCollection.AddFactor(Output, factorType, EOS_);
//factorIdStart = m_sentenceStartArray[factorType]->GetId();
//factorIdEnd = m_sentenceEndArray[factorType]->GetId();
for (size_t i = 0; i < 10; i++)
{
lmIdMap[factorIdStart * 10 + i] = GetLmID(BOS_);
lmIdMap[factorIdEnd * 10 + i] = GetLmID(EOS_);
}
//(*lmIdMap)[factorIdStart * 10 + index] = GetLmID(BOS_);
//(*lmIdMap)[factorIdEnd * 10 + index] = GetLmID(EOS_);
}*/
return true;
}
VocabIndex LanguageModelParallelBackoff::GetLmID( const std::string &str ) const
{
return m_srilmVocab->getIndex( str.c_str(), m_unknownId );
}
VocabIndex LanguageModelParallelBackoff::GetLmID( const Factor *factor, size_t ft ) const
{
size_t factorId = factor->GetId();
if ( lmIdMap->find( factorId * 10 + ft ) != lmIdMap->end() ) {
return lmIdMap->find( factorId * 10 + ft )->second;
} else {
return m_unknownId;
}
}
void LanguageModelParallelBackoff::CreateFactors()
{
// add factors which have srilm id
FactorCollection &factorCollection = FactorCollection::Instance();
lmIdMap = new std::map<size_t, VocabIndex>();
VocabString str;
VocabIter iter(*m_srilmVocab);
iter.init();
size_t pomFactorTypeNum = 0;
while ( (str = iter.next()) != NULL) {
if ((str[0] < 'a' || str[0] > 'k') && str[0] != 'W') {
continue;
}
VocabIndex lmId = GetLmID(str);
pomFactorTypeNum = str[0] - 'a';
size_t factorId = factorCollection.AddFactor(Output, m_factorTypesOrdered[pomFactorTypeNum], &(str[2]) )->GetId();
(*lmIdMap)[factorId * 10 + pomFactorTypeNum] = lmId;
}
size_t factorIdStart;
size_t factorIdEnd;
// sentence markers
for (size_t index = 0 ; index < m_factorTypesOrdered.size() ; ++index) {
FactorType factorType = m_factorTypesOrdered[index];
m_sentenceStartWord[index] = factorCollection.AddFactor(Output, factorType, BOS_);
m_sentenceEndWord[index] = factorCollection.AddFactor(Output, factorType, EOS_);
factorIdStart = m_sentenceStartWord[index]->GetId();
factorIdEnd = m_sentenceEndWord[index]->GetId();
/*for (size_t i = 0; i < 10; i++)
{
lmIdMap[factorIdStart * 10 + i] = GetLmID(BOS_);
lmIdMap[factorIdEnd * 10 + i] = GetLmID(EOS_);
}*/
(*lmIdMap)[factorIdStart * 10 + index] = GetLmID(BOS_);
(*lmIdMap)[factorIdEnd * 10 + index] = GetLmID(EOS_);
cerr << "BOS_:" << GetLmID(BOS_) << ", EOS_:" << GetLmID(EOS_) << endl;
}
m_wtid = GetLmID("W-<unk>");
m_wtbid = GetLmID("W-<s>");
m_wteid = GetLmID("W-</s>");
cerr << "W-<unk> index: " << m_wtid << endl;
cerr << "W-<s> index: " << m_wtbid << endl;
cerr << "W-</s> index: " << m_wteid << endl;
}
LMResult LanguageModelParallelBackoff::GetValueForgotState(const std::vector<const Word*> &contextFactor, FFState & /*outState */) const
{
static WidMatrix widMatrix;
for (int i=0; i<contextFactor.size(); i++)
::memset(widMatrix[i],0,(m_factorTypesOrdered.size() + 1)*sizeof(VocabIndex));
for (size_t i = 0; i < contextFactor.size(); i++) {
const Word &word = *contextFactor[i];
for (size_t j = 0; j < m_factorTypesOrdered.size(); j++) {
const Factor *factor = word[ m_factorTypesOrdered[j] ];
if (factor == NULL)
widMatrix[i][j + 1] = 0;
else
widMatrix[i][j + 1] = GetLmID(factor, j);
}
if (widMatrix[i][1] == GetLmID(m_sentenceStartWord[0], 0) ) {
widMatrix[i][0] = m_wtbid;
} else if (widMatrix[i][1] == GetLmID(m_sentenceEndWord[0], 0 )) {
widMatrix[i][0] = m_wteid;
} else {
widMatrix[i][0] = m_wtid;
}
}
LMResult ret;
ret.score = m_srilmModel->wordProb( widMatrix, contextFactor.size() - 1, contextFactor.size() );
ret.score = FloorScore(TransformLMScore(ret.score));
ret.unknown = !contextFactor.empty() && (widMatrix[contextFactor.size() - 1][0] == m_unknownId);
return ret;
/*if (contextFactor.size() == 0)
{
return 0;
}
for (size_t currPos = 0 ; currPos < m_nGramOrder ; ++currPos )
{
const Word &word = *contextFactor[currPos];
for (size_t index = 0 ; index < m_factorTypesOrdered.size() ; ++index)
{
FactorType factorType = m_factorTypesOrdered[index];
const Factor *factor = word[factorType];
(*widMatrix)[currPos][index] = GetLmID(factor, index);
}
}
float p = m_srilmModel->wordProb( (*widMatrix), m_nGramOrder - 1, m_nGramOrder );
return FloorScore(TransformLMScore(p)); */
}
// The old version did not initialize finalState like it should. Technically that makes the behavior undefined, so it's not clear what else to do here.
FFState *LanguageModelParallelBackoff::NewState(const FFState * /*from*/) const
{
return NULL;
}
const FFState *LanguageModelParallelBackoff::GetNullContextState() const
{
return NULL;
}
const FFState *LanguageModelParallelBackoff::GetBeginSentenceState() const
{
return NULL;
}
}
}