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/***********************************************************************
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 <cstring>
#include <iostream>
#include <memory>
#include <cstdlib>
#include <boost/shared_ptr.hpp>
#include <boost/lexical_cast.hpp>
#include "lm/binary_format.hh"
#include "lm/enumerate_vocab.hh"
#include "lm/left.hh"
#include "lm/model.hh"
#include "util/exception.hh"
#include "util/tokenize_piece.hh"
#include "util/string_stream.hh"
#include "Ken.h"
#include "Base.h"
#include "moses/FF/FFState.h"
#include "moses/TypeDef.h"
#include "moses/Util.h"
#include "moses/FactorCollection.h"
#include "moses/Phrase.h"
#include "moses/InputFileStream.h"
#include "moses/StaticData.h"
#include "moses/ChartHypothesis.h"
#include "moses/Incremental.h"
#include "moses/Syntax/SHyperedge.h"
#include "moses/Syntax/SVertex.h"
using namespace std;
namespace Moses
{
namespace
{
struct KenLMState : public FFState {
lm::ngram::State state;
virtual size_t hash() const {
size_t ret = hash_value(state);
return ret;
}
virtual bool operator==(const FFState& o) const {
const KenLMState &other = static_cast<const KenLMState &>(o);
bool ret = state == other.state;
return ret;
}
};
class MappingBuilder : public lm::EnumerateVocab
{
public:
MappingBuilder(FactorCollection &factorCollection, std::vector<lm::WordIndex> &mapping)
: m_factorCollection(factorCollection), m_mapping(mapping) {}
void Add(lm::WordIndex index, const StringPiece &str) {
std::size_t factorId = m_factorCollection.AddFactor(str)->GetId();
if (m_mapping.size() <= factorId) {
// 0 is <unk> :-)
m_mapping.resize(factorId + 1);
}
m_mapping[factorId] = index;
}
private:
FactorCollection &m_factorCollection;
std::vector<lm::WordIndex> &m_mapping;
};
} // namespace
template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string &file, util::LoadMethod load_method)
{
m_lmIdLookup.clear();
lm::ngram::Config config;
if(this->m_verbosity >= 1) {
config.messages = &std::cerr;
} else {
config.messages = NULL;
}
FactorCollection &collection = FactorCollection::Instance();
MappingBuilder builder(collection, m_lmIdLookup);
config.enumerate_vocab = &builder;
config.load_method = load_method;
m_ngram.reset(new Model(file.c_str(), config));
VERBOSE(2, "LanguageModelKen " << m_description << " reset to " << file << "\n");
}
template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
:LanguageModel(line)
,m_beginSentenceFactor(FactorCollection::Instance().AddFactor(BOS_))
,m_factorType(factorType)
{
ReadParameters();
LoadModel(file, load_method);
}
template <class Model> LanguageModelKen<Model>::LanguageModelKen()
:LanguageModel("KENLM")
,m_beginSentenceFactor(FactorCollection::Instance().AddFactor(BOS_))
,m_factorType(0)
{
ReadParameters();
}
template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageModelKen<Model> ©_from)
:LanguageModel(copy_from.GetArgLine()),
m_ngram(copy_from.m_ngram),
// TODO: don't copy this.
m_beginSentenceFactor(copy_from.m_beginSentenceFactor),
m_factorType(copy_from.m_factorType),
m_lmIdLookup(copy_from.m_lmIdLookup)
{
}
template <class Model> const FFState * LanguageModelKen<Model>::EmptyHypothesisState(const InputType &/*input*/) const
{
KenLMState *ret = new KenLMState();
ret->state = m_ngram->BeginSentenceState();
return ret;
}
template <class Model> void LanguageModelKen<Model>::CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const
{
fullScore = 0;
ngramScore = 0;
oovCount = 0;
if (!phrase.GetSize()) return;
lm::ngram::ChartState discarded_sadly;
lm::ngram::RuleScore<Model> scorer(*m_ngram, discarded_sadly);
size_t position;
if (m_beginSentenceFactor == phrase.GetWord(0).GetFactor(m_factorType)) {
scorer.BeginSentence();
position = 1;
} else {
position = 0;
}
size_t ngramBoundary = m_ngram->Order() - 1;
size_t end_loop = std::min(ngramBoundary, phrase.GetSize());
for (; position < end_loop; ++position) {
const Word &word = phrase.GetWord(position);
if (word.IsNonTerminal()) {
fullScore += scorer.Finish();
scorer.Reset();
} else {
lm::WordIndex index = TranslateID(word);
scorer.Terminal(index);
if (!index) ++oovCount;
}
}
float before_boundary = fullScore + scorer.Finish();
for (; position < phrase.GetSize(); ++position) {
const Word &word = phrase.GetWord(position);
if (word.IsNonTerminal()) {
fullScore += scorer.Finish();
scorer.Reset();
} else {
lm::WordIndex index = TranslateID(word);
scorer.Terminal(index);
if (!index) ++oovCount;
}
}
fullScore += scorer.Finish();
ngramScore = TransformLMScore(fullScore - before_boundary);
fullScore = TransformLMScore(fullScore);
}
template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const
{
const lm::ngram::State &in_state = static_cast<const KenLMState&>(*ps).state;
std::auto_ptr<KenLMState> ret(new KenLMState());
if (!hypo.GetCurrTargetLength()) {
ret->state = in_state;
return ret.release();
}
const std::size_t begin = hypo.GetCurrTargetWordsRange().GetStartPos();
//[begin, end) in STL-like fashion.
const std::size_t end = hypo.GetCurrTargetWordsRange().GetEndPos() + 1;
const std::size_t adjust_end = std::min(end, begin + m_ngram->Order() - 1);
std::size_t position = begin;
typename Model::State aux_state;
typename Model::State *state0 = &ret->state, *state1 = &aux_state;
float score = m_ngram->Score(in_state, TranslateID(hypo.GetWord(position)), *state0);
++position;
for (; position < adjust_end; ++position) {
score += m_ngram->Score(*state0, TranslateID(hypo.GetWord(position)), *state1);
std::swap(state0, state1);
}
if (hypo.IsSourceCompleted()) {
// Score end of sentence.
std::vector<lm::WordIndex> indices(m_ngram->Order() - 1);
const lm::WordIndex *last = LastIDs(hypo, &indices.front());
score += m_ngram->FullScoreForgotState(&indices.front(), last, m_ngram->GetVocabulary().EndSentence(), ret->state).prob;
} else if (adjust_end < end) {
// Get state after adding a long phrase.
std::vector<lm::WordIndex> indices(m_ngram->Order() - 1);
const lm::WordIndex *last = LastIDs(hypo, &indices.front());
m_ngram->GetState(&indices.front(), last, ret->state);
} else if (state0 != &ret->state) {
// Short enough phrase that we can just reuse the state.
ret->state = *state0;
}
score = TransformLMScore(score);
if (OOVFeatureEnabled()) {
std::vector<float> scores(2);
scores[0] = score;
scores[1] = 0.0;
out->PlusEquals(this, scores);
} else {
out->PlusEquals(this, score);
}
return ret.release();
}
class LanguageModelChartStateKenLM : public FFState
{
public:
LanguageModelChartStateKenLM() {}
const lm::ngram::ChartState &GetChartState() const {
return m_state;
}
lm::ngram::ChartState &GetChartState() {
return m_state;
}
size_t hash() const {
size_t ret = hash_value(m_state);
return ret;
}
virtual bool operator==(const FFState& o) const {
const LanguageModelChartStateKenLM &other = static_cast<const LanguageModelChartStateKenLM &>(o);
bool ret = m_state == other.m_state;
return ret;
}
private:
lm::ngram::ChartState m_state;
};
template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const ChartHypothesis& hypo, int featureID, ScoreComponentCollection *accumulator) const
{
LanguageModelChartStateKenLM *newState = new LanguageModelChartStateKenLM();
lm::ngram::RuleScore<Model> ruleScore(*m_ngram, newState->GetChartState());
const TargetPhrase &target = hypo.GetCurrTargetPhrase();
const AlignmentInfo::NonTermIndexMap &nonTermIndexMap =
target.GetAlignNonTerm().GetNonTermIndexMap();
const size_t size = hypo.GetCurrTargetPhrase().GetSize();
size_t phrasePos = 0;
// Special cases for first word.
if (size) {
const Word &word = hypo.GetCurrTargetPhrase().GetWord(0);
if (word.GetFactor(m_factorType) == m_beginSentenceFactor) {
// Begin of sentence
ruleScore.BeginSentence();
phrasePos++;
} else if (word.IsNonTerminal()) {
// Non-terminal is first so we can copy instead of rescoring.
const ChartHypothesis *prevHypo = hypo.GetPrevHypo(nonTermIndexMap[phrasePos]);
const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(prevHypo->GetFFState(featureID))->GetChartState();
ruleScore.BeginNonTerminal(prevState);
phrasePos++;
}
}
for (; phrasePos < size; phrasePos++) {
const Word &word = hypo.GetCurrTargetPhrase().GetWord(phrasePos);
if (word.IsNonTerminal()) {
const ChartHypothesis *prevHypo = hypo.GetPrevHypo(nonTermIndexMap[phrasePos]);
const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(prevHypo->GetFFState(featureID))->GetChartState();
ruleScore.NonTerminal(prevState);
} else {
ruleScore.Terminal(TranslateID(word));
}
}
float score = ruleScore.Finish();
score = TransformLMScore(score);
score -= hypo.GetTranslationOption().GetScores().GetScoresForProducer(this)[0];
if (OOVFeatureEnabled()) {
std::vector<float> scores(2);
scores[0] = score;
scores[1] = 0.0;
accumulator->PlusEquals(this, scores);
} else {
accumulator->PlusEquals(this, score);
}
return newState;
}
template <class Model> FFState *LanguageModelKen<Model>::EvaluateWhenApplied(const Syntax::SHyperedge& hyperedge, int featureID, ScoreComponentCollection *accumulator) const
{
LanguageModelChartStateKenLM *newState = new LanguageModelChartStateKenLM();
lm::ngram::RuleScore<Model> ruleScore(*m_ngram, newState->GetChartState());
const TargetPhrase &target = *hyperedge.label.translation;
const AlignmentInfo::NonTermIndexMap &nonTermIndexMap =
target.GetAlignNonTerm().GetNonTermIndexMap2();
const size_t size = target.GetSize();
size_t phrasePos = 0;
// Special cases for first word.
if (size) {
const Word &word = target.GetWord(0);
if (word.GetFactor(m_factorType) == m_beginSentenceFactor) {
// Begin of sentence
ruleScore.BeginSentence();
phrasePos++;
} else if (word.IsNonTerminal()) {
// Non-terminal is first so we can copy instead of rescoring.
const Syntax::SVertex *pred = hyperedge.tail[nonTermIndexMap[phrasePos]];
const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(pred->states[featureID])->GetChartState();
ruleScore.BeginNonTerminal(prevState);
phrasePos++;
}
}
for (; phrasePos < size; phrasePos++) {
const Word &word = target.GetWord(phrasePos);
if (word.IsNonTerminal()) {
const Syntax::SVertex *pred = hyperedge.tail[nonTermIndexMap[phrasePos]];
const lm::ngram::ChartState &prevState = static_cast<const LanguageModelChartStateKenLM*>(pred->states[featureID])->GetChartState();
ruleScore.NonTerminal(prevState);
} else {
ruleScore.Terminal(TranslateID(word));
}
}
float score = ruleScore.Finish();
score = TransformLMScore(score);
score -= target.GetScoreBreakdown().GetScoresForProducer(this)[0];
if (OOVFeatureEnabled()) {
std::vector<float> scores(2);
scores[0] = score;
scores[1] = 0.0;
accumulator->PlusEquals(this, scores);
} else {
accumulator->PlusEquals(this, score);
}
return newState;
}
template <class Model> void LanguageModelKen<Model>::IncrementalCallback(Incremental::Manager &manager) const
{
manager.LMCallback(*m_ngram, m_lmIdLookup);
}
template <class Model> void LanguageModelKen<Model>::ReportHistoryOrder(std::ostream &out, const Phrase &phrase) const
{
out << "|lm=(";
if (!phrase.GetSize()) return;
typename Model::State aux_state;
typename Model::State start_of_sentence_state = m_ngram->BeginSentenceState();
typename Model::State *state0 = &start_of_sentence_state;
typename Model::State *state1 = &aux_state;
for (std::size_t position=0; position<phrase.GetSize(); position++) {
const lm::WordIndex idx = TranslateID(phrase.GetWord(position));
lm::FullScoreReturn ret(m_ngram->FullScore(*state0, idx, *state1));
if (position) out << ",";
out << (int) ret.ngram_length << ":" << TransformLMScore(ret.prob);
if (idx == 0) out << ":unk";
std::swap(state0, state1);
}
out << ")| ";
}
template <class Model>
bool LanguageModelKen<Model>::IsUseable(const FactorMask &mask) const
{
bool ret = mask[m_factorType];
return ret;
}
/* Instantiate LanguageModelKen here. Tells the compiler to generate code
* for the instantiations' non-inline member functions in this file.
* Otherwise, depending on the compiler, those functions may not be present
* at link time.
*/
template class LanguageModelKen<lm::ngram::ProbingModel>;
template class LanguageModelKen<lm::ngram::RestProbingModel>;
template class LanguageModelKen<lm::ngram::TrieModel>;
template class LanguageModelKen<lm::ngram::ArrayTrieModel>;
template class LanguageModelKen<lm::ngram::QuantTrieModel>;
template class LanguageModelKen<lm::ngram::QuantArrayTrieModel>;
LanguageModel *ConstructKenLM(const std::string &lineOrig)
{
FactorType factorType = 0;
string filePath;
util::LoadMethod load_method = util::POPULATE_OR_READ;
util::TokenIter<util::SingleCharacter, true> argument(lineOrig, ' ');
++argument; // KENLM
util::StringStream line;
line << "KENLM";
for (; argument; ++argument) {
const char *equals = std::find(argument->data(), argument->data() + argument->size(), '=');
UTIL_THROW_IF2(equals == argument->data() + argument->size(),
"Expected = in KenLM argument " << *argument);
StringPiece name(argument->data(), equals - argument->data());
StringPiece value(equals + 1, argument->data() + argument->size() - equals - 1);
if (name == "factor") {
factorType = boost::lexical_cast<FactorType>(value);
} else if (name == "order") {
// Ignored
} else if (name == "path") {
filePath.assign(value.data(), value.size());
} else if (name == "lazyken") {
// deprecated: use load instead.
if (value == "0" || value == "false") {
load_method = util::POPULATE_OR_READ;
} else if (value == "1" || value == "true") {
load_method = util::LAZY;
} else {
UTIL_THROW2("Can't parse lazyken argument " << value << ". Also, lazyken is deprecated. Use load with one of the arguments lazy, populate_or_lazy, populate_or_read, read, or parallel_read.");
}
} else if (name == "load") {
if (value == "lazy") {
load_method = util::LAZY;
} else if (value == "populate_or_lazy") {
load_method = util::POPULATE_OR_LAZY;
} else if (value == "populate_or_read" || value == "populate") {
load_method = util::POPULATE_OR_READ;
} else if (value == "read") {
load_method = util::READ;
} else if (value == "parallel_read") {
load_method = util::PARALLEL_READ;
} else {
UTIL_THROW2("Unknown KenLM load method " << value);
}
} else {
// pass to base class to interpret
line << " " << name << "=" << value;
}
}
return ConstructKenLM(line.str(), filePath, factorType, load_method);
}
LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
{
lm::ngram::ModelType model_type;
if (lm::ngram::RecognizeBinary(file.c_str(), model_type)) {
switch(model_type) {
case lm::ngram::PROBING:
return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
case lm::ngram::REST_PROBING:
return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, load_method);
case lm::ngram::TRIE:
return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, load_method);
case lm::ngram::QUANT_TRIE:
return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, load_method);
case lm::ngram::ARRAY_TRIE:
return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, load_method);
case lm::ngram::QUANT_ARRAY_TRIE:
return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, load_method);
default:
UTIL_THROW2("Unrecognized kenlm model type " << model_type);
}
} else {
return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
}
}
}
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