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load= option to KenLM exposing more load_method options

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Kenneth Heafield 2016-02-20 00:07:48 +00:00
parent 5f06e3310f
commit 7a1baeecda
4 changed files with 40 additions and 79 deletions
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3
moses/LM/Backward.cpp
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@ -40,7 +40,8 @@ namespace Moses
{ {
/** Constructs a new backward language model. */ /** Constructs a new backward language model. */
template <class Model> BackwardLanguageModel<Model>::BackwardLanguageModel(const std::string &line, const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(line,file,factorType,lazy) // TODO(lane): load_method instead of lazy bool
template <class Model> BackwardLanguageModel<Model>::BackwardLanguageModel(const std::string &line, const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(line,file,factorType, lazy ? util::LAZY : util::POPULATE_OR_READ)
{ {
// //
// This space intentionally left blank // This space intentionally left blank

102
moses/LM/Ken.cpp
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@ -69,63 +69,6 @@ struct KenLMState : public FFState {
}; };
///*
// * An implementation of single factor LM using Ken's code.
// */
//template <class Model> class LanguageModelKen : public LanguageModel
//{
//public:
// LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy);
//
// const FFState *EmptyHypothesisState(const InputType &/*input*/) const {
// KenLMState *ret = new KenLMState();
// ret->state = m_ngram->BeginSentenceState();
// return ret;
// }
//
// void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
//
// FFState *Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const;
//
// FFState *EvaluateWhenApplied(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection *accumulator) const;
//
// void IncrementalCallback(Incremental::Manager &manager) const {
// manager.LMCallback(*m_ngram, m_lmIdLookup);
// }
//
// bool IsUseable(const FactorMask &mask) const;
//private:
// LanguageModelKen(const LanguageModelKen<Model> &copy_from);
//
// lm::WordIndex TranslateID(const Word &word) const {
// std::size_t factor = word.GetFactor(m_factorType)->GetId();
// return (factor >= m_lmIdLookup.size() ? 0 : m_lmIdLookup[factor]);
// }
//
// // Convert last words of hypothesis into vocab ids, returning an end pointer.
// lm::WordIndex *LastIDs(const Hypothesis &hypo, lm::WordIndex *indices) const {
// lm::WordIndex *index = indices;
// lm::WordIndex *end = indices + m_ngram->Order() - 1;
// int position = hypo.GetCurrTargetWordsRange().GetEndPos();
// for (; ; ++index, --position) {
// if (index == end) return index;
// if (position == -1) {
// *index = m_ngram->GetVocabulary().BeginSentence();
// return index + 1;
// }
// *index = TranslateID(hypo.GetWord(position));
// }
// }
//
// boost::shared_ptr<Model> m_ngram;
//
// std::vector<lm::WordIndex> m_lmIdLookup;
//
// FactorType m_factorType;
//
// const Factor *m_beginSentenceFactor;
//};
class MappingBuilder : public lm::EnumerateVocab class MappingBuilder : public lm::EnumerateVocab
{ {
public: public:
@ -148,7 +91,7 @@ private:
} // namespace } // namespace
template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string &file, bool lazy) template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string &file, util::LoadMethod load_method)
{ {
lm::ngram::Config config; lm::ngram::Config config;
if(this->m_verbosity >= 1) { if(this->m_verbosity >= 1) {
@ -159,19 +102,19 @@ template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string
FactorCollection &collection = FactorCollection::Instance(); FactorCollection &collection = FactorCollection::Instance();
MappingBuilder builder(collection, m_lmIdLookup); MappingBuilder builder(collection, m_lmIdLookup);
config.enumerate_vocab = &builder; config.enumerate_vocab = &builder;
config.load_method = lazy ? util::LAZY : util::POPULATE_OR_READ; config.load_method = load_method;
m_ngram.reset(new Model(file.c_str(), config)); m_ngram.reset(new Model(file.c_str(), config));
m_beginSentenceFactor = collection.AddFactor(BOS_); m_beginSentenceFactor = collection.AddFactor(BOS_);
} }
template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy) template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
:LanguageModel(line) :LanguageModel(line)
,m_factorType(factorType) ,m_factorType(factorType)
{ {
ReadParameters(); ReadParameters();
LoadModel(file, lazy); LoadModel(file, load_method);
} }
template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageModelKen<Model> &copy_from) template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageModelKen<Model> &copy_from)
@ -479,7 +422,7 @@ LanguageModel *ConstructKenLM(const std::string &lineOrig)
{ {
FactorType factorType = 0; FactorType factorType = 0;
string filePath; string filePath;
bool lazy = false; util::LoadMethod load_method = util::POPULATE_OR_READ;
util::TokenIter<util::SingleCharacter, true> argument(lineOrig, ' '); util::TokenIter<util::SingleCharacter, true> argument(lineOrig, ' ');
++argument; // KENLM ++argument; // KENLM
@ -500,38 +443,53 @@ LanguageModel *ConstructKenLM(const std::string &lineOrig)
} else if (name == "path") { } else if (name == "path") {
filePath.assign(value.data(), value.size()); filePath.assign(value.data(), value.size());
} else if (name == "lazyken") { } else if (name == "lazyken") {
lazy = boost::lexical_cast<bool>(value); // deprecated: use load instead.
load_method = boost::lexical_cast<bool>(value) ? util::LAZY : util::POPULATE_OR_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 { } else {
// pass to base class to interpret // pass to base class to interpret
line << " " << name << "=" << value; line << " " << name << "=" << value;
} }
} }
return ConstructKenLM(line.str(), filePath, factorType, lazy); return ConstructKenLM(line.str(), filePath, factorType, load_method);
} }
LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, bool lazy) LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
{ {
lm::ngram::ModelType model_type; lm::ngram::ModelType model_type;
if (lm::ngram::RecognizeBinary(file.c_str(), model_type)) { if (lm::ngram::RecognizeBinary(file.c_str(), model_type)) {
switch(model_type) { switch(model_type) {
case lm::ngram::PROBING: case lm::ngram::PROBING:
return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
case lm::ngram::REST_PROBING: case lm::ngram::REST_PROBING:
return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, load_method);
case lm::ngram::TRIE: case lm::ngram::TRIE:
return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, load_method);
case lm::ngram::QUANT_TRIE: case lm::ngram::QUANT_TRIE:
return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, load_method);
case lm::ngram::ARRAY_TRIE: case lm::ngram::ARRAY_TRIE:
return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, load_method);
case lm::ngram::QUANT_ARRAY_TRIE: case lm::ngram::QUANT_ARRAY_TRIE:
return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, load_method);
default: default:
UTIL_THROW2("Unrecognized kenlm model type " << model_type); UTIL_THROW2("Unrecognized kenlm model type " << model_type);
} }
} else { } else {
return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy); return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
} }
} }

7
moses/LM/Ken.h
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@ -26,6 +26,7 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#include <boost/shared_ptr.hpp> #include <boost/shared_ptr.hpp>
#include "lm/word_index.hh" #include "lm/word_index.hh"
#include "util/mmap.hh"
#include "moses/LM/Base.h" #include "moses/LM/Base.h"
#include "moses/Hypothesis.h" #include "moses/Hypothesis.h"
@ -41,7 +42,7 @@ class FFState;
LanguageModel *ConstructKenLM(const std::string &line); LanguageModel *ConstructKenLM(const std::string &line);
//! This will also load. Returns a templated KenLM class //! This will also load. Returns a templated KenLM class
LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, bool lazy); LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
/* /*
* An implementation of single factor LM using Kenneth's code. * An implementation of single factor LM using Kenneth's code.
@ -49,7 +50,7 @@ LanguageModel *ConstructKenLM(const std::string &line, const std::string &file,
template <class Model> class LanguageModelKen : public LanguageModel template <class Model> class LanguageModelKen : public LanguageModel
{ {
public: public:
LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy); LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
virtual const FFState *EmptyHypothesisState(const InputType &/*input*/) const; virtual const FFState *EmptyHypothesisState(const InputType &/*input*/) const;
@ -73,7 +74,7 @@ protected:
FactorType m_factorType; FactorType m_factorType;
void LoadModel(const std::string &file, bool lazy); void LoadModel(const std::string &file, util::LoadMethod load_method);
lm::WordIndex TranslateID(const Word &word) const { lm::WordIndex TranslateID(const Word &word) const {
std::size_t factor = word.GetFactor(m_factorType)->GetId(); std::size_t factor = word.GetFactor(m_factorType)->GetId();

7
moses/LM/Reloading.h
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@ -64,8 +64,8 @@ private:
template <class Model> class ReloadingLanguageModel : public LanguageModelKen<Model> template <class Model> class ReloadingLanguageModel : public LanguageModelKen<Model>
{ {
public: public:
// TODO(Lane) copy less code, update to load_method
ReloadingLanguageModel(const std::string &line, const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(line, file, factorType, lazy), m_file(file), m_lazy(lazy) { ReloadingLanguageModel(const std::string &line, const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(line, file, factorType, lazy ? util::LAZY : util::POPULATE_OR_READ), m_file(file), m_lazy(lazy) {
std::cerr << "ReloadingLM constructor: " << m_file << std::endl; std::cerr << "ReloadingLM constructor: " << m_file << std::endl;
// std::cerr << std::string(line).replace(0,11,"KENLM") << std::endl; // std::cerr << std::string(line).replace(0,11,"KENLM") << std::endl;
@ -74,7 +74,8 @@ public:
virtual void InitializeForInput(ttasksptr const& ttask) { virtual void InitializeForInput(ttasksptr const& ttask) {
std::cerr << "ReloadingLM InitializeForInput" << std::endl; std::cerr << "ReloadingLM InitializeForInput" << std::endl;
LanguageModelKen<Model>::LoadModel(m_file, m_lazy); // TODO(lane): load_method
LanguageModelKen<Model>::LoadModel(m_file, m_lazy ? util::LAZY : util::POPULATE_OR_READ);
/* /*
lm::ngram::Config config; lm::ngram::Config config;
if(this->m_verbosity >= 1) { if(this->m_verbosity >= 1) {