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Adding unit tests to Backward LM

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This commit is contained in:
Lane Schwartz 2013-04-12 13:58:47 -04:00
parent 95e3a37469
commit aacc936204
9 changed files with 1135 additions and 22 deletions
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2
Jamroot
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@ -115,7 +115,7 @@ project : requirements
;
#Add directories here if you want their incidental targets too (i.e. tests).
build-projects lm util phrase-extract search moses mert moses-cmd moses-chart-cmd mira scripts regression-testing ;
build-projects lm util phrase-extract search moses moses/LM mert moses-cmd moses-chart-cmd mira scripts regression-testing ;
alias programs : lm//programs moses-chart-cmd//moses_chart moses-cmd//programs OnDiskPt//CreateOnDiskPt OnDiskPt//queryOnDiskPt mert//programs misc//programs symal phrase-extract phrase-extract//lexical-reordering phrase-extract//extract-ghkm phrase-extract//pcfg-extract phrase-extract//pcfg-score biconcor mira//mira contrib/server//mosesserver ;

193
moses/LM/Backward.cpp
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@ -25,33 +25,35 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#include "lm/model.hh"
#include "moses/FFState.h"
#include "moses/Hypothesis.h"
#include "moses/Phrase.h"
#include "moses/LM/Ken.h"
#include "moses/LM/Backward.h"
//#include <iostream>
namespace Moses {
// By placing BackwardLMState inside an anonymous namespace,
// it is visible *only* within this file
namespace {
struct BackwardLMState : public FFState {
lm::ngram::ChartState state;
int Compare(const FFState &o) const {
const BackwardLMState &other = static_cast<const BackwardLMState &>(o);
return state.left.Compare(other.state.left);
}
};
}
/** Constructs a new backward language model. */
template <class Model> BackwardLanguageModel<Model>::BackwardLanguageModel(const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(file,factorType,lazy) {
//
// This space intentionally left blank
//
}
/**
* Constructs an empty backward language model state.
*
* This state will correspond with a translation hypothesis
* where no source words have been translated.
*
* In a forward language model, the language model state of an empty hypothesis
* would store the beginning of sentence marker <s>.
*
* Because this is a backward language model, the language model state returned by this method
* instead stores the end of sentence marker </s>.
*/
template <class Model> const FFState *BackwardLanguageModel<Model>::EmptyHypothesisState(const InputType &/*input*/) const {
BackwardLMState *ret = new BackwardLMState();
lm::ngram::RuleScore<Model> ruleScore(*m_ngram, ret->state);
@ -59,7 +61,56 @@ namespace Moses {
ruleScore.Finish();
return ret;
}
/*
template <class Model> double BackwardLanguageModel<Model>::Score(FFState *ffState) {
BackwardLMState *lmState = static_cast< BackwardLMState* >(ffState);
lm::ngram::ChartState &state = lmState->state;
lm::ngram::RuleScore<Model> ruleScore(*m_ngram, lmState);
return ruleScore.Finish();
}
*/
/**
* Pre-calculate the n-gram probabilities for the words in the specified phrase.
*
* Note that when this method is called, we do not have access to the context
* in which this phrase will eventually be applied.
*
* In other words, we know what words are in this phrase,
* but we do not know what words will come before or after this phrase.
*
* The parameters fullScore, ngramScore, and oovCount are all output parameters.
*
* The value stored in oovCount is the number of words in the phrase
* that are not in the language model's vocabulary.
*
* The sum of the ngram scores for all words in this phrase are stored in fullScore.
*
* The value stored in ngramScore is similar, but only full-order ngram scores are included.
*
* This is best shown by example:
*
* Assume a trigram backward language model and a phrase "a b c d e f g"
*
* fullScore would represent the sum of the logprob scores for the following values:
*
* p(g)
* p(f | g)
* p(e | g f)
* p(d | f e)
* p(c | e d)
* p(b | d c)
* p(a | c b)
*
* ngramScore would represent the sum of the logprob scores for the following values:
*
* p(g)
* p(f | g)
* p(e | g f)
* p(d | f e)
* p(c | e d)
* p(b | d c)
* p(a | c b)
*/
template <class Model> void BackwardLanguageModel<Model>::CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const {
fullScore = 0;
ngramScore = 0;
@ -77,7 +128,35 @@ namespace Moses {
);
float before_boundary = 0.0f;
for (size_t position = phrase.GetSize() - 1,
int lastWord = phrase.GetSize() - 1;
int ngramBoundary = m_ngram->Order() - 1;
int boundary = ( lastWord < ngramBoundary ) ? 0 : ngramBoundary;
int position;
for (position = lastWord; position >= 0; position-=1) {
const Word &word = phrase.GetWord(position);
UTIL_THROW_IF(
(word.IsNonTerminal()),
util::Exception,
"BackwardLanguageModel does not currently support rules that include non-terminals "
);
lm::WordIndex index = TranslateID(word);
scorer.Terminal(index);
if (!index) ++oovCount;
if (position==boundary) {
before_boundary = scorer.Finish();
}
}
/*
before_boundary = scorer.Finish();
for (int position = phrase.GetSize() - 1,
ngramBoundary = m_ngram->Order() - 1; position >= 0; position-=1) {
const Word &word = phrase.GetWord(position);
@ -85,7 +164,7 @@ namespace Moses {
UTIL_THROW_IF(
(word.IsNonTerminal()),
util::Exception,
"BackwardLanguageModel does not currently support rules that include non-terminals"
"BackwardLanguageModel does not currently support rules that include non-terminals "
);
lm::WordIndex index = TranslateID(word);
@ -97,14 +176,90 @@ namespace Moses {
}
}
fullScore += scorer.Finish();
*/
fullScore = scorer.Finish();
ngramScore = TransformLMScore(fullScore - before_boundary);
fullScore = TransformLMScore(fullScore);
}
/**
* Calculate the ngram probabilities for the words at the beginning
* (and under some circumstances, also at the end)
* of the phrase represented by the provided hypothesis.
*
* Additionally, calculate a new language model state.
*
* This is best shown by example:
*
* Assume a trigram language model.
*
* Assume the previous phrase was "a b c d e f g",
* which means the previous language model state is "g f".
*
* When the phrase corresponding to "a b c d e f g" was previously processed by CalcScore
* the following full-order ngrams would have been calculated:
*
* p(a | c b)
* p(b | d c)
* p(c | e d)
* p(d | f e)
* p(e | g f)
*
* The following less-than-full-order ngrams would also have been calculated by CalcScore:
*
* p(f | g)
* p(g)
*
* In this method, we now have access to additional context which may allow
* us to compute the full-order ngrams for f and g.
*
* Assume the new provided hypothesis contains the new phrase "h i j k"
*
* Given these assumptions, this method is responsible
* for calculating the scores for the following:
*
* p(f | h g)
* p(g | i h)
*
* This method must also calculate and return a new language model state.
*
* In this example, the returned language model state would be "k j"
*
* If the provided hypothesis represents the end of a completed translation
* (all source words have been translated)
* then this method is additionally responsible for calculating the following:
*
* p(j | <s> k)
* p(k | <s>)
*
*/
template <class Model> FFState *BackwardLanguageModel<Model>::Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const {
const lm::ngram::ChartState &in_state = static_cast<const BackwardLMState&>(*ps).state;
std::auto_ptr<BackwardLMState> ret(new BackwardLMState());
// If the current hypothesis contains zero target words
if (!hypo.GetCurrTargetLength()) {
// reuse and return the previous state
ret->state = in_state;
return ret.release();
}
UTIL_THROW_IF(
(1==1),
util::Exception,
"This method (BackwardLanguageModel<Model>::Evaluate) is not yet fully implemented"
);
return ret.release();
}
LanguageModel *ConstructBackwardLM(const std::string &file, FactorType factorType, bool lazy) {
try {
lm::ngram::ModelType model_type;

24
moses/LM/Backward.h
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@ -25,12 +25,19 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#include <string>
#include "moses/LM/Ken.h"
#include "moses/LM/BackwardLMState.h"
#include "lm/state.hh"
namespace Moses {
//! This will also load. Returns a templated backward LM.
LanguageModel *ConstructBackwardLM(const std::string &file, FactorType factorType, bool lazy);
class FFState;
// template<typename M> class BackwardLanguageModelTest;
class BackwardLanguageModelTest;
/*
* An implementation of single factor backward LM using Kenneth's code.
*/
@ -42,6 +49,8 @@ template <class Model> class BackwardLanguageModel : public LanguageModelKen<Mod
virtual void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
virtual FFState *Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const;
private:
// These lines are required to make the parent class's protected members visible to this class
@ -50,6 +59,21 @@ template <class Model> class BackwardLanguageModel : public LanguageModelKen<Mod
using LanguageModelKen<Model>::m_factorType;
using LanguageModelKen<Model>::TranslateID;
// friend class Moses::BackwardLanguageModelTest<Model>;
friend class Moses::BackwardLanguageModelTest;
/*
lm::ngram::ChartState* GetState(FFState *ffState) {
return NULL;
}
*/
/*
double Score(FFState *ffState) {
BackwardLMState *lmState = static_cast< BackwardLMState* >(ffState);
lm::ngram::ChartState &state = lmState->state;
lm::ngram::RuleScore<Model> ruleScore(*m_ngram, lmState);
return ruleScore.Finish();
}
*/
};
} // namespace Moses

32
moses/LM/BackwardLMState.cpp Normal file
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@ -0,0 +1,32 @@
// $Id$
/***********************************************************************
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 "moses/LM/BackwardLMState.h"
#include "lm/state.hh"
namespace Moses {
int BackwardLMState::Compare(const FFState &o) const {
const BackwardLMState &other = static_cast<const BackwardLMState &>(o);
return state.left.Compare(other.state.left);
}
}

69
moses/LM/BackwardLMState.h Normal file
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@ -0,0 +1,69 @@
// $Id$
/***********************************************************************
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
***********************************************************************/
#ifndef moses_BackwardLMState_h
#define moses_BackwardLMState_h
#include "moses/FFState.h"
#include "moses/LM/Backward.h"
#include "lm/state.hh"
/*
namespace lm {
namespace ngram {
class ChartState;
}
}
*/
//#include "lm/state.hh"
namespace Moses {
//template<typename M>
class BackwardLanguageModelTest;
class BackwardLMState : public FFState {
public:
/*
int Compare(const FFState &o) const {
const BackwardLMState &other = static_cast<const BackwardLMState &>(o);
return state.left.Compare(other.state.left);
}
*/
int Compare(const FFState &o) const;
// Allow BackwardLanguageModel to access the private members of this class
template <class Model> friend class BackwardLanguageModel;
// template <class Model> friend class Moses::BackwardLanguageModelTest;
friend class Moses::BackwardLanguageModelTest;
private:
lm::ngram::ChartState state;
};
}
#endif

190
moses/LM/BackwardTest.cpp Normal file
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@ -0,0 +1,190 @@
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (C) 2010 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
***********************************************************************/
#define BOOST_TEST_MODULE BackwardTest
#include <boost/test/unit_test.hpp>
#include "lm/config.hh"
#include "lm/left.hh"
#include "lm/model.hh"
#include "lm/state.hh"
#include "moses/Sentence.h"
#include "moses/TypeDef.h"
#include "moses/StaticData.h"
//#include "BackwardLMState.h"
#include "moses/LM/Backward.h"
#include "moses/LM/BackwardLMState.h"
#include "moses/Util.h"
#include "lm/state.hh"
#include "lm/left.hh"
#include <vector>
using namespace Moses;
//using namespace std;
/*
template <class M> void Foo() {
Moses::BackwardLanguageModel<M> *backwardLM;
// = new Moses::BackwardLanguageModel<M>( filename, factorType, lazy );
}
template <class M> void Everything() {
// Foo<M>();
}
*/
namespace Moses {
// Apparently some Boost versions use templates and are pretty strict about types matching.
#define SLOPPY_CHECK_CLOSE(ref, value, tol) BOOST_CHECK_CLOSE(static_cast<double>(ref), static_cast<double>(value), static_cast<double>(tol));
class BackwardLanguageModelTest {
public:
BackwardLanguageModelTest() :
dummyInput(new Sentence()),
backwardLM(
// BackwardLanguageModel
// new Moses::BackwardLanguageModel<Model>(
ConstructBackwardLM(
boost::unit_test::framework::master_test_suite().argv[1],
0,
false)
)
{
// This space intentionally left blank
}
~BackwardLanguageModelTest() {
delete dummyInput;
delete backwardLM;
}
void testEmptyHypothesis() {
FFState *ffState = const_cast< FFState * >(backwardLM->EmptyHypothesisState( *dummyInput ));
BOOST_CHECK( ffState != NULL );
/*
// lm::ngram::ChartState &state = static_cast< const BackwardLMState >(*ffState).state;
BackwardLMState *lmState = static_cast< BackwardLMState* >(ffState);
//const lm::ngram::ChartState &state = static_cast< const BackwardLMState* >(ffState)->state;
//lm::ngram::ChartState &state = lmState->state;
// BOOST_CHECK( state.left.length == 1 );
// BOOST_CHECK( state.right.Length() == 0 );
BackwardLanguageModel<lm::ngram::ProbingModel> *lm = static_cast< BackwardLanguageModel<lm::ngram::ProbingModel> *>(backwardLM);
lm::ngram::ChartState &state = lmState->state;
lm::ngram::RuleScore<lm::ngram::ProbingModel> ruleScore(*(lm->m_ngram), state);
double score = ruleScore.Finish();
SLOPPY_CHECK_CLOSE(-1.457693, score, 0.001);
*/
delete ffState;
}
void testCalcScore() {
//std::vector<WordIndex> words
Phrase phrase;
BOOST_CHECK( phrase.GetSize() == 0 );
std::vector<FactorType> outputFactorOrder;
outputFactorOrder.push_back(0);
phrase.CreateFromString(
//StaticData::Instance().GetOutputFactorOrder(),
outputFactorOrder,
"the",
StaticData::Instance().GetFactorDelimiter());
BOOST_CHECK( phrase.GetSize() == 1 );
// BackwardLanguageModel<lm::ngram::ProbingModel> *lm = static_cast< BackwardLanguageModel<lm::ngram::ProbingModel> *>(backwardLM);
//Word &word = phrase.GetWord(0);
//Word
// BOOST_CHECK( word == lm->m_ngram->GetVocabulary().Index("the") );
float fullScore;
float ngramScore;
size_t oovCount;
backwardLM->CalcScore(phrase, fullScore, ngramScore, oovCount);
BOOST_CHECK( oovCount == 0 );
SLOPPY_CHECK_CLOSE( TransformLMScore(-1.383059), fullScore, 0.01);
SLOPPY_CHECK_CLOSE( TransformLMScore( 0.0 ), ngramScore, 0.01);
}
private:
const Sentence *dummyInput;
// BackwardLanguageModel<Model> *backwardLM;
LanguageModel *backwardLM;
/*
void LookupVocab(const StringPiece &str, std::vector<WordIndex *> &out) {
out.clear();
for (util::TokenIter<util::SingleCharacter, true> i(str, ' '); i; ++i) {
out.push_back(lm->m_ngram.GetVocabulary().Index(*i));
}
}
*/
};
}
const char *FileLocation() {
if (boost::unit_test::framework::master_test_suite().argc < 2) {
BOOST_FAIL("Jamfile must specify arpa file for this test, but did not");
}
return boost::unit_test::framework::master_test_suite().argv[1];
}
BOOST_AUTO_TEST_CASE(ProbingAll) {
// Everything<lm::ngram::Model>();
/*
const std::string filename( boost::unit_test::framework::master_test_suite().argv[1] );
size_t factorType = 0;
bool lazy = false;
LanguageModel *backwardLM = ConstructBackwardLM( filename, factorType, lazy );
const Sentence *dummyInput = new Sentence();
const FFState *ffState = backwardLM->EmptyHypothesisState( *dummyInput );
//new BackwardLanguageModel<lm::ngram::Model>( filename, factorType, lazy );
delete dummyInput;
delete backwardLM;
*/
//BackwardLanguageModelTest<lm::ngram::TrieModel> test;
BackwardLanguageModelTest test;
test.testEmptyHypothesis();
test.testCalcScore();
// test->testEmptyHypothesis();
}

8
moses/LM/Jamfile
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@ -88,11 +88,15 @@ if $(with-ldhtlm) {
obj ORLM.o : ORLM.cpp ..//headers ../TranslationModel/DynSAInclude//dynsa : : : <include>../TranslationModel/DynSAInclude ;
#The factory needs the macros LM_IRST etc to know which ones to use.
obj Factory.o : Factory.cpp ..//headers $(dependencies) : <include>../DynSAInclude <dependency>$(LM-LOG) ;
obj Factory.o : Factory.cpp ..//headers $(dependencies) ../../lm//kenlm : <include>../DynSAInclude <dependency>$(LM-LOG) ;
#Top-level LM library. If you've added a file that doesn't depend on external
#libraries, put it here.
alias LM : Backward.cpp Base.cpp Factory.o Implementation.cpp Joint.cpp Ken.cpp MultiFactor.cpp Remote.cpp SingleFactor.cpp ORLM.o
alias LM : Backward.cpp BackwardLMState.cpp Base.cpp Factory.o Implementation.cpp Joint.cpp Ken.cpp MultiFactor.cpp Remote.cpp SingleFactor.cpp ORLM.o
../../lm//kenlm ..//headers $(dependencies) ;
import testing ;
#unit-test moses_lm_test : [ glob *Test.cpp ] ..//moses LM /top//boost_unit_test_framework ;
run BackwardTest.cpp ..//moses LM ../../lm//kenlm /top//boost_unit_test_framework : : backward.arpa ;

73
moses/LM/Ken.cpp
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@ -160,6 +160,46 @@ template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageM
m_lmIdLookup(copy_from.m_lmIdLookup) {
}
/**
* Pre-calculate the n-gram probabilities for the words in the specified phrase.
*
* Note that when this method is called, we do not have access to the context
* in which this phrase will eventually be applied.
*
* In other words, we know what words are in this phrase,
* but we do not know what words will come before or after this phrase.
*
* The parameters fullScore, ngramScore, and oovCount are all output parameters.
*
* The value stored in oovCount is the number of words in the phrase
* that are not in the language model's vocabulary.
*
* The sum of the ngram scores for all words in this phrase are stored in fullScore.
*
* The value stored in ngramScore is similar, but only full-order ngram scores are included.
*
* This is best shown by example:
*
* Assume a trigram language model and a phrase "a b c d e f g"
*
* fullScore would represent the sum of the logprob scores for the following values:
*
* p(a)
* p(b | a)
* p(c | a b)
* p(d | b c)
* p(e | c d)
* p(f | d e)
* p(g | e f)
*
* ngramScore would represent the sum of the logprob scores for the following values:
*
* p(c | a b)
* p(d | b c)
* p(e | c d)
* p(f | d e)
* p(g | e f)
*/
template <class Model> void LanguageModelKen<Model>::CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const {
fullScore = 0;
ngramScore = 0;
@ -210,6 +250,39 @@ template <class Model> void LanguageModelKen<Model>::CalcScore(const Phrase &phr
fullScore = TransformLMScore(fullScore);
}
/**
* Calculate the ngram probabilities for the words at the beginning
* (and under some circumstances, also at the end)
* of the phrase represented by the provided hypothesis.
*
* Additionally, calculate a new language model state.
*
* This is best shown by example:
*
* Assume a trigram language model.
*
* Assume the previous phrase was "w x y z",
* which means the previous language model state is "y z".
*
* Assume the provided hypothesis contains the new phrase "a b c d e f g"
*
* Given these assumptions, this method is responsible
* for calculating the scores for the following:
*
* p(a | y z)
* p(b | z a)
*
* This method must also calculate and return a new language model state.
*
* In this example, the returned language model state would be "f g"
*
* If the provided hypothesis represents the end of a completed translation
* (all source words have been translated)
* then this method is additionally responsible for calculating the following:
*
* p(</s> | f g)
*
*/
template <class Model> FFState *LanguageModelKen<Model>::Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const {
const lm::ngram::State &in_state = static_cast<const KenLMState&>(*ps).state;

566
moses/LM/backward.arpa Normal file
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@ -0,0 +1,566 @@
\data\
ngram 1=167
ngram 2=361
ngram 3=25
\1-grams:
-2.059753 &apos; -0.1382608
-2.184691 ( -0.08246645
-2.184691 ) -0.1039416
-1.281601 , -0.07104895
-1.457693 . -0.07898764
-2.661813 / -0.09528423
-2.661813 1 -0.1039416
-2.661813 2 -0.1039416
-2.360783 : -0.09722306
-2.661813 ; -0.09625472
-1.457693 </s>
-99 <s> -1.21504
-2.059753 a -0.08046046
-2.360783 all -0.09431157
-2.661813 allowed -0.1039416
-2.661813 also -0.1058422
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-2.661813 copyleft -0.08346596
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