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https://github.com/moses-smt/mosesdecoder.git
synced 2024-09-11 11:25:40 +03:00
load= option to KenLM exposing more load_method options
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@ -40,7 +40,8 @@ namespace Moses
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{
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/** Constructs a new backward language model. */
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template <class Model> BackwardLanguageModel<Model>::BackwardLanguageModel(const std::string &line, const std::string &file, FactorType factorType, bool lazy) : LanguageModelKen<Model>(line,file,factorType,lazy)
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// TODO(lane): load_method instead of lazy bool
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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)
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{
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//
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// This space intentionally left blank
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102
moses/LM/Ken.cpp
102
moses/LM/Ken.cpp
@ -69,63 +69,6 @@ struct KenLMState : public FFState {
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};
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///*
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// * An implementation of single factor LM using Ken's code.
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// */
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//template <class Model> class LanguageModelKen : public LanguageModel
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//{
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//public:
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// LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy);
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//
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// const FFState *EmptyHypothesisState(const InputType &/*input*/) const {
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// KenLMState *ret = new KenLMState();
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// ret->state = m_ngram->BeginSentenceState();
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// return ret;
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// }
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//
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// void CalcScore(const Phrase &phrase, float &fullScore, float &ngramScore, size_t &oovCount) const;
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//
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// FFState *Evaluate(const Hypothesis &hypo, const FFState *ps, ScoreComponentCollection *out) const;
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//
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// FFState *EvaluateWhenApplied(const ChartHypothesis& cur_hypo, int featureID, ScoreComponentCollection *accumulator) const;
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//
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// void IncrementalCallback(Incremental::Manager &manager) const {
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// manager.LMCallback(*m_ngram, m_lmIdLookup);
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// }
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//
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// bool IsUseable(const FactorMask &mask) const;
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//private:
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// LanguageModelKen(const LanguageModelKen<Model> ©_from);
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//
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// lm::WordIndex TranslateID(const Word &word) const {
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// std::size_t factor = word.GetFactor(m_factorType)->GetId();
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// return (factor >= m_lmIdLookup.size() ? 0 : m_lmIdLookup[factor]);
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// }
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//
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// // Convert last words of hypothesis into vocab ids, returning an end pointer.
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// lm::WordIndex *LastIDs(const Hypothesis &hypo, lm::WordIndex *indices) const {
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// lm::WordIndex *index = indices;
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// lm::WordIndex *end = indices + m_ngram->Order() - 1;
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// int position = hypo.GetCurrTargetWordsRange().GetEndPos();
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// for (; ; ++index, --position) {
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// if (index == end) return index;
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// if (position == -1) {
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// *index = m_ngram->GetVocabulary().BeginSentence();
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// return index + 1;
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// }
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// *index = TranslateID(hypo.GetWord(position));
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// }
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// }
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//
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// boost::shared_ptr<Model> m_ngram;
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//
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// std::vector<lm::WordIndex> m_lmIdLookup;
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//
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// FactorType m_factorType;
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//
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// const Factor *m_beginSentenceFactor;
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//};
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class MappingBuilder : public lm::EnumerateVocab
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{
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public:
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@ -148,7 +91,7 @@ private:
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} // namespace
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template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string &file, bool lazy)
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template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string &file, util::LoadMethod load_method)
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{
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lm::ngram::Config config;
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if(this->m_verbosity >= 1) {
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@ -159,19 +102,19 @@ template <class Model> void LanguageModelKen<Model>::LoadModel(const std::string
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FactorCollection &collection = FactorCollection::Instance();
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MappingBuilder builder(collection, m_lmIdLookup);
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config.enumerate_vocab = &builder;
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config.load_method = lazy ? util::LAZY : util::POPULATE_OR_READ;
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config.load_method = load_method;
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m_ngram.reset(new Model(file.c_str(), config));
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m_beginSentenceFactor = collection.AddFactor(BOS_);
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}
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template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy)
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template <class Model> LanguageModelKen<Model>::LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
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:LanguageModel(line)
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,m_factorType(factorType)
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{
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ReadParameters();
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LoadModel(file, lazy);
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LoadModel(file, load_method);
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}
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template <class Model> LanguageModelKen<Model>::LanguageModelKen(const LanguageModelKen<Model> ©_from)
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@ -479,7 +422,7 @@ LanguageModel *ConstructKenLM(const std::string &lineOrig)
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{
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FactorType factorType = 0;
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string filePath;
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bool lazy = false;
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util::LoadMethod load_method = util::POPULATE_OR_READ;
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util::TokenIter<util::SingleCharacter, true> argument(lineOrig, ' ');
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++argument; // KENLM
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@ -500,38 +443,53 @@ LanguageModel *ConstructKenLM(const std::string &lineOrig)
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} else if (name == "path") {
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filePath.assign(value.data(), value.size());
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} else if (name == "lazyken") {
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lazy = boost::lexical_cast<bool>(value);
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// deprecated: use load instead.
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load_method = boost::lexical_cast<bool>(value) ? util::LAZY : util::POPULATE_OR_READ;
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} else if (name == "load") {
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if (value == "lazy") {
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load_method = util::LAZY;
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} else if (value == "populate_or_lazy") {
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load_method = util::POPULATE_OR_LAZY;
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} else if (value == "populate_or_read" || value == "populate") {
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load_method = util::POPULATE_OR_READ;
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} else if (value == "read") {
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load_method = util::READ;
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} else if (value == "parallel_read") {
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load_method = util::PARALLEL_READ;
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} else {
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UTIL_THROW2("Unknown KenLM load method " << value);
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}
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} else {
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// pass to base class to interpret
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line << " " << name << "=" << value;
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}
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}
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return ConstructKenLM(line.str(), filePath, factorType, lazy);
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return ConstructKenLM(line.str(), filePath, factorType, load_method);
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}
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LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, bool lazy)
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LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method)
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{
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lm::ngram::ModelType model_type;
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if (lm::ngram::RecognizeBinary(file.c_str(), model_type)) {
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switch(model_type) {
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case lm::ngram::PROBING:
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return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
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case lm::ngram::REST_PROBING:
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return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::RestProbingModel>(line, file, factorType, load_method);
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case lm::ngram::TRIE:
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return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::TrieModel>(line, file, factorType, load_method);
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case lm::ngram::QUANT_TRIE:
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return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::QuantTrieModel>(line, file, factorType, load_method);
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case lm::ngram::ARRAY_TRIE:
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return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::ArrayTrieModel>(line, file, factorType, load_method);
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case lm::ngram::QUANT_ARRAY_TRIE:
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return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::QuantArrayTrieModel>(line, file, factorType, load_method);
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default:
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UTIL_THROW2("Unrecognized kenlm model type " << model_type);
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}
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} else {
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return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, lazy);
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return new LanguageModelKen<lm::ngram::ProbingModel>(line, file, factorType, load_method);
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}
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}
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@ -26,6 +26,7 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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#include <boost/shared_ptr.hpp>
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#include "lm/word_index.hh"
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#include "util/mmap.hh"
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#include "moses/LM/Base.h"
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#include "moses/Hypothesis.h"
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@ -41,7 +42,7 @@ class FFState;
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LanguageModel *ConstructKenLM(const std::string &line);
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//! This will also load. Returns a templated KenLM class
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LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, bool lazy);
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LanguageModel *ConstructKenLM(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
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/*
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* An implementation of single factor LM using Kenneth's code.
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@ -49,7 +50,7 @@ LanguageModel *ConstructKenLM(const std::string &line, const std::string &file,
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template <class Model> class LanguageModelKen : public LanguageModel
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{
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public:
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LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, bool lazy);
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LanguageModelKen(const std::string &line, const std::string &file, FactorType factorType, util::LoadMethod load_method);
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virtual const FFState *EmptyHypothesisState(const InputType &/*input*/) const;
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@ -73,7 +74,7 @@ protected:
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FactorType m_factorType;
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void LoadModel(const std::string &file, bool lazy);
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void LoadModel(const std::string &file, util::LoadMethod load_method);
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lm::WordIndex TranslateID(const Word &word) const {
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std::size_t factor = word.GetFactor(m_factorType)->GetId();
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@ -64,8 +64,8 @@ private:
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template <class Model> class ReloadingLanguageModel : public LanguageModelKen<Model>
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{
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public:
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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) {
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// TODO(Lane) copy less code, update to load_method
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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) {
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std::cerr << "ReloadingLM constructor: " << m_file << std::endl;
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// std::cerr << std::string(line).replace(0,11,"KENLM") << std::endl;
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@ -74,7 +74,8 @@ public:
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virtual void InitializeForInput(ttasksptr const& ttask) {
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std::cerr << "ReloadingLM InitializeForInput" << std::endl;
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LanguageModelKen<Model>::LoadModel(m_file, m_lazy);
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// TODO(lane): load_method
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LanguageModelKen<Model>::LoadModel(m_file, m_lazy ? util::LAZY : util::POPULATE_OR_READ);
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/*
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lm::ngram::Config config;
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if(this->m_verbosity >= 1) {
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