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Merge branch 'master' of github.com:moses-smt/mosesdecoder

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This commit is contained in:
Hieu Hoang 2012-11-16 16:44:56 +00:00
commit 90b251a50f
36 changed files with 1024 additions and 836 deletions
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259
moses-chart-cmd/IOWrapper.cpp
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@ -40,6 +40,7 @@ POSSIBILITY OF SUCH DAMAGE.
#include "moses/StaticData.h"
#include "moses/DummyScoreProducers.h"
#include "moses/InputFileStream.h"
#include "moses/Incremental.h"
#include "moses/PhraseDictionary.h"
#include "moses/ChartTrellisPathList.h"
#include "moses/ChartTrellisPath.h"
@ -377,8 +378,136 @@ void IOWrapper::OutputBestHypo(const ChartHypothesis *hypo, long translationId)
m_singleBestOutputCollector->Write(translationId, out.str());
}
void IOWrapper::OutputNBestList(const ChartTrellisPathList &nBestList, const ChartHypothesis *bestHypo, const TranslationSystem* system, long translationId)
{
void IOWrapper::OutputBestHypo(search::Applied applied, long translationId) {
if (!m_singleBestOutputCollector) return;
std::ostringstream out;
IOWrapper::FixPrecision(out);
if (StaticData::Instance().GetOutputHypoScore()) {
out << applied.GetScore() << ' ';
}
Phrase outPhrase;
Incremental::ToPhrase(applied, outPhrase);
// delete 1st & last
CHECK(outPhrase.GetSize() >= 2);
outPhrase.RemoveWord(0);
outPhrase.RemoveWord(outPhrase.GetSize() - 1);
out << outPhrase.GetStringRep(StaticData::Instance().GetOutputFactorOrder());
out << '\n';
m_singleBestOutputCollector->Write(translationId, out.str());
}
void IOWrapper::OutputBestNone(long translationId) {
if (!m_singleBestOutputCollector) return;
if (StaticData::Instance().GetOutputHypoScore()) {
m_singleBestOutputCollector->Write(translationId, "0 \n");
} else {
m_singleBestOutputCollector->Write(translationId, "\n");
}
}
namespace {
void OutputSparseFeatureScores(std::ostream& out, const ScoreComponentCollection &features, const FeatureFunction *ff, std::string &lastName) {
const StaticData &staticData = StaticData::Instance();
bool labeledOutput = staticData.IsLabeledNBestList();
const FVector scores = features.GetVectorForProducer( ff );
// report weighted aggregate
if (! ff->GetSparseFeatureReporting()) {
const FVector &weights = staticData.GetAllWeights().GetScoresVector();
if (labeledOutput && !boost::contains(ff->GetScoreProducerDescription(), ":"))
out << " " << ff->GetScoreProducerWeightShortName() << ":";
out << " " << scores.inner_product(weights);
}
// report each feature
else {
for(FVector::FNVmap::const_iterator i = scores.cbegin(); i != scores.cend(); i++) {
if (i->second != 0) { // do not report zero-valued features
if (labeledOutput)
out << " " << i->first << ":";
out << " " << i->second;
}
}
}
}
void WriteFeatures(const TranslationSystem &system, const ScoreComponentCollection &features, std::ostream &out) {
bool labeledOutput = StaticData::Instance().IsLabeledNBestList();
// lm
const LMList& lml = system.GetLanguageModels();
if (lml.size() > 0) {
if (labeledOutput)
out << "lm:";
LMList::const_iterator lmi = lml.begin();
for (; lmi != lml.end(); ++lmi) {
out << " " << features.GetScoreForProducer(*lmi);
}
}
std::string lastName = "";
// output stateful sparse features
const vector<const StatefulFeatureFunction*>& sff = system.GetStatefulFeatureFunctions();
for( size_t i=0; i<sff.size(); i++ )
if (sff[i]->GetNumScoreComponents() == ScoreProducer::unlimited)
OutputSparseFeatureScores(out, features, sff[i], lastName);
// translation components
const vector<PhraseDictionaryFeature*>& pds = system.GetPhraseDictionaries();
if (pds.size() > 0) {
for( size_t i=0; i<pds.size(); i++ ) {
size_t pd_numinputscore = pds[i]->GetNumInputScores();
vector<float> scores = features.GetScoresForProducer( pds[i] );
for (size_t j = 0; j<scores.size(); ++j){
if (labeledOutput && (i == 0) ){
if ((j == 0) || (j == pd_numinputscore)){
lastName = pds[i]->GetScoreProducerWeightShortName(j);
out << " " << lastName << ":";
}
}
out << " " << scores[j];
}
}
}
// word penalty
if (labeledOutput)
out << " w:";
out << " " << features.GetScoreForProducer(system.GetWordPenaltyProducer());
// generation
const vector<GenerationDictionary*>& gds = system.GetGenerationDictionaries();
if (gds.size() > 0) {
for( size_t i=0; i<gds.size(); i++ ) {
size_t pd_numinputscore = gds[i]->GetNumInputScores();
vector<float> scores = features.GetScoresForProducer( gds[i] );
for (size_t j = 0; j<scores.size(); ++j){
if (labeledOutput && (i == 0) ){
if ((j == 0) || (j == pd_numinputscore)){
lastName = gds[i]->GetScoreProducerWeightShortName(j);
out << " " << lastName << ":";
}
}
out << " " << scores[j];
}
}
}
// output stateless sparse features
lastName = "";
const vector<const StatelessFeatureFunction*>& slf = system.GetStatelessFeatureFunctions();
for( size_t i=0; i<slf.size(); i++ ) {
if (slf[i]->GetNumScoreComponents() == ScoreProducer::unlimited) {
OutputSparseFeatureScores(out, features, slf[i], lastName);
}
}
}
} // namespace
void IOWrapper::OutputNBestList(const ChartTrellisPathList &nBestList, const TranslationSystem* system, long translationId) {
std::ostringstream out;
// Check if we're writing to std::cout.
@ -387,17 +516,10 @@ void IOWrapper::OutputNBestList(const ChartTrellisPathList &nBestList, const Cha
// preserve existing behaviour, but should probably be done either way.
IOWrapper::FixPrecision(out);
// The output from -output-hypo-score is always written to std::cout.
if (StaticData::Instance().GetOutputHypoScore()) {
if (bestHypo != NULL) {
out << bestHypo->GetTotalScore() << " ";
} else {
out << "0 ";
}
}
// Used to check StaticData's GetOutputHypoScore(), but it makes no sense with nbest output.
}
bool labeledOutput = StaticData::Instance().IsLabeledNBestList();
//bool includeAlignment = StaticData::Instance().NBestIncludesAlignment();
bool includeWordAlignment = StaticData::Instance().PrintAlignmentInfoInNbest();
ChartTrellisPathList::const_iterator iter;
@ -421,75 +543,7 @@ void IOWrapper::OutputNBestList(const ChartTrellisPathList &nBestList, const Cha
// before each model type, the corresponding command-line-like name must be emitted
// MERT script relies on this
// lm
const LMList& lml = system->GetLanguageModels();
if (lml.size() > 0) {
if (labeledOutput)
out << "lm:";
LMList::const_iterator lmi = lml.begin();
for (; lmi != lml.end(); ++lmi) {
out << " " << path.GetScoreBreakdown().GetScoreForProducer(*lmi);
}
}
std::string lastName = "";
// output stateful sparse features
const vector<const StatefulFeatureFunction*>& sff = system->GetStatefulFeatureFunctions();
for( size_t i=0; i<sff.size(); i++ )
if (sff[i]->GetNumScoreComponents() == ScoreProducer::unlimited)
OutputSparseFeatureScores( out, path, sff[i], lastName );
// translation components
const vector<PhraseDictionaryFeature*>& pds = system->GetPhraseDictionaries();
if (pds.size() > 0) {
for( size_t i=0; i<pds.size(); i++ ) {
size_t pd_numinputscore = pds[i]->GetNumInputScores();
vector<float> scores = path.GetScoreBreakdown().GetScoresForProducer( pds[i] );
for (size_t j = 0; j<scores.size(); ++j){
if (labeledOutput && (i == 0) ){
if ((j == 0) || (j == pd_numinputscore)){
lastName = pds[i]->GetScoreProducerWeightShortName(j);
out << " " << lastName << ":";
}
}
out << " " << scores[j];
}
}
}
// word penalty
if (labeledOutput)
out << " w:";
out << " " << path.GetScoreBreakdown().GetScoreForProducer(system->GetWordPenaltyProducer());
// generation
const vector<GenerationDictionary*>& gds = system->GetGenerationDictionaries();
if (gds.size() > 0) {
for( size_t i=0; i<gds.size(); i++ ) {
size_t pd_numinputscore = gds[i]->GetNumInputScores();
vector<float> scores = path.GetScoreBreakdown().GetScoresForProducer( gds[i] );
for (size_t j = 0; j<scores.size(); ++j){
if (labeledOutput && (i == 0) ){
if ((j == 0) || (j == pd_numinputscore)){
lastName = gds[i]->GetScoreProducerWeightShortName(j);
out << " " << lastName << ":";
}
}
out << " " << scores[j];
}
}
}
// output stateless sparse features
lastName = "";
const vector<const StatelessFeatureFunction*>& slf = system->GetStatelessFeatureFunctions();
for( size_t i=0; i<slf.size(); i++ ) {
if (slf[i]->GetNumScoreComponents() == ScoreProducer::unlimited) {
OutputSparseFeatureScores( out, path, slf[i], lastName );
}
}
WriteFeatures(*system, path.GetScoreBreakdown(), out);
// total
out << " ||| " << path.GetTotalScore();
@ -528,34 +582,33 @@ void IOWrapper::OutputNBestList(const ChartTrellisPathList &nBestList, const Cha
out <<std::flush;
CHECK(m_nBestOutputCollector);
assert(m_nBestOutputCollector);
m_nBestOutputCollector->Write(translationId, out.str());
}
void IOWrapper::OutputSparseFeatureScores( std::ostream& out, const ChartTrellisPath &path, const FeatureFunction *ff, std::string &lastName )
{
const StaticData &staticData = StaticData::Instance();
bool labeledOutput = staticData.IsLabeledNBestList();
const FVector scores = path.GetScoreBreakdown().GetVectorForProducer( ff );
// report weighted aggregate
if (! ff->GetSparseFeatureReporting()) {
const FVector &weights = staticData.GetAllWeights().GetScoresVector();
if (labeledOutput && !boost::contains(ff->GetScoreProducerDescription(), ":"))
out << " " << ff->GetScoreProducerWeightShortName() << ":";
out << " " << scores.inner_product(weights);
void IOWrapper::OutputNBestList(const std::vector<search::Applied> &nbest, const TranslationSystem &system, long translationId) {
std::ostringstream out;
// wtf? copied from the original OutputNBestList
if (m_nBestOutputCollector->OutputIsCout()) {
IOWrapper::FixPrecision(out);
}
// report each feature
else {
for(FVector::FNVmap::const_iterator i = scores.cbegin(); i != scores.cend(); i++) {
if (i->second != 0) { // do not report zero-valued features
if (labeledOutput)
out << " " << i->first << ":";
out << " " << i->second;
}
}
Phrase outputPhrase;
ScoreComponentCollection features;
for (std::vector<search::Applied>::const_iterator i = nbest.begin(); i != nbest.end(); ++i) {
Incremental::PhraseAndFeatures(system, *i, outputPhrase, features);
// <s> and </s>
CHECK(outputPhrase.GetSize() >= 2);
outputPhrase.RemoveWord(0);
outputPhrase.RemoveWord(outputPhrase.GetSize() - 1);
out << translationId << " ||| ";
OutputSurface(out, outputPhrase, m_outputFactorOrder, false);
out << " ||| ";
WriteFeatures(system, features, out);
out << " ||| " << i->GetScore() << '\n';
}
out << std::flush;
assert(m_nBestOutputCollector);
m_nBestOutputCollector->Write(translationId, out.str());
}
void IOWrapper::FixPrecision(std::ostream &stream, size_t size)

9
moses-chart-cmd/IOWrapper.h
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@ -45,6 +45,7 @@ POSSIBILITY OF SUCH DAMAGE.
#include "moses/OutputCollector.h"
#include "moses/ChartHypothesis.h"
#include "moses/ChartTrellisPath.h"
#include "search/applied.hh"
namespace Moses
{
@ -94,14 +95,14 @@ public:
Moses::InputType* GetInput(Moses::InputType *inputType);
void OutputBestHypo(const Moses::ChartHypothesis *hypo, long translationId);
void OutputBestHypo(search::Applied applied, long translationId);
void OutputBestHypo(const std::vector<const Moses::Factor*>& mbrBestHypo, long translationId);
void OutputNBestList(const Moses::ChartTrellisPathList &nBestList, const Moses::ChartHypothesis *bestHypo, const Moses::TranslationSystem* system, long translationId);
void OutputSparseFeatureScores(std::ostream& out, const Moses::ChartTrellisPath &path, const Moses::FeatureFunction *ff, std::string &lastName);
void OutputBestNone(long translationId);
void OutputNBestList(const Moses::ChartTrellisPathList &nBestList, const Moses::TranslationSystem* system, long translationId);
void OutputNBestList(const std::vector<search::Applied> &nbest, const Moses::TranslationSystem &system, long translationId);
void OutputDetailedTranslationReport(const Moses::ChartHypothesis *hypo, const Moses::Sentence &sentence, long translationId);
void Backtrack(const Moses::ChartHypothesis *hypo);
Moses::OutputCollector *ExposeSingleBest() { return m_singleBestOutputCollector; }
void ResetTranslationId();
Moses::OutputCollector *GetSearchGraphOutputCollector() {

14
moses-chart-cmd/Main.cpp
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@ -59,7 +59,7 @@ POSSIBILITY OF SUCH DAMAGE.
#include "moses/ChartHypothesis.h"
#include "moses/ChartTrellisPath.h"
#include "moses/ChartTrellisPathList.h"
#include "moses/Incremental/Manager.h"
#include "moses/Incremental.h"
#include "util/usage.hh"
@ -91,10 +91,14 @@ public:
if (staticData.GetSearchAlgorithm() == ChartIncremental) {
Incremental::Manager manager(*m_source, system);
manager.ProcessSentence();
if (m_ioWrapper.ExposeSingleBest()) {
m_ioWrapper.ExposeSingleBest()->Write(translationId, manager.String() + '\n');
const std::vector<search::Applied> &nbest = manager.ProcessSentence();
if (!nbest.empty()) {
m_ioWrapper.OutputBestHypo(nbest[0], translationId);
} else {
m_ioWrapper.OutputBestNone(translationId);
}
if (staticData.GetNBestSize() > 0)
m_ioWrapper.OutputNBestList(nbest, system, translationId);
return;
}
@ -125,7 +129,7 @@ public:
VERBOSE(2,"WRITING " << nBestSize << " TRANSLATION ALTERNATIVES TO " << staticData.GetNBestFilePath() << endl);
ChartTrellisPathList nBestList;
manager.CalcNBest(nBestSize, nBestList,staticData.GetDistinctNBest());
m_ioWrapper.OutputNBestList(nBestList, bestHypo, &system, translationId);
m_ioWrapper.OutputNBestList(nBestList, &system, translationId);
IFVERBOSE(2) {
PrintUserTime("N-Best Hypotheses Generation Time:");
}

4
moses/ChartCellLabel.h
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@ -23,7 +23,7 @@
#include "Word.h"
#include "WordsRange.h"
namespace search { class Vertex; class VertexGenerator; }
namespace search { class Vertex; }
namespace Moses
{
@ -41,7 +41,7 @@ class ChartCellLabel
union Stack {
const HypoList *cube; // cube pruning
const search::Vertex *incr; // incremental search after filling.
search::VertexGenerator *incr_generator; // incremental search during filling.
void *incr_generator; // incremental search during filling.
};

7
moses/CompactPT/PhraseDecoder.cpp
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@ -401,8 +401,8 @@ TargetPhraseVectorPtr PhraseDecoder::DecodeCollection(
if(m_phraseDictionary.m_useAlignmentInfo)
{
// reconstruct the alignment data based on the alignment of the subphrase
for(AlignmentInfo::const_iterator it = subTp.GetAlignNonTerm().begin();
it != subTp.GetAlignNonTerm().end(); it++)
for(AlignmentInfo::const_iterator it = subTp.GetAlignTerm().begin();
it != subTp.GetAlignTerm().end(); it++)
{
alignment.insert(AlignPointSizeT(srcStart + it->first,
targetPhrase->GetSize() + it->second));
@ -455,8 +455,9 @@ TargetPhraseVectorPtr PhraseDecoder::DecodeCollection(
if(state == Add)
{
if(m_phraseDictionary.m_useAlignmentInfo)
if(m_phraseDictionary.m_useAlignmentInfo) {
targetPhrase->SetAlignTerm(alignment);
}
if(m_coding == PREnc)
{

2
moses/CompactPT/PhraseDictionaryCompact.cpp
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@ -51,7 +51,7 @@ bool PhraseDictionaryCompact::Load(const std::vector<FactorType> &input
{
m_input = &input;
m_output = &output;
m_weight = &weight;
m_weight = new std::vector<float>(weight);
m_tableLimit = tableLimit;
m_languageModels = &languageModels;
m_weightWP = weightWP;

296
moses/Incremental.cpp Normal file
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@ -0,0 +1,296 @@
#include "moses/Incremental.h"
#include "moses/ChartCell.h"
#include "moses/ChartParserCallback.h"
#include "moses/FeatureVector.h"
#include "moses/StaticData.h"
#include "moses/TranslationSystem.h"
#include "moses/Util.h"
#include "lm/model.hh"
#include "search/applied.hh"
#include "search/config.hh"
#include "search/context.hh"
#include "search/edge_generator.hh"
#include "search/rule.hh"
#include "search/vertex_generator.hh"
#include <boost/lexical_cast.hpp>
namespace Moses {
namespace Incremental {
namespace {
// This is called by EdgeGenerator. Route hypotheses to separate vertices for
// each left hand side label, populating ChartCellLabelSet out.
template <class Best> class HypothesisCallback {
private:
typedef search::VertexGenerator<Best> Gen;
public:
HypothesisCallback(search::ContextBase &context, Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool)
: context_(context), best_(best), out_(out), vertex_pool_(vertex_pool) {}
void NewHypothesis(search::PartialEdge partial) {
// Get the LHS, look it up in the output ChartCellLabel, and upcast it.
// It's not part of the union because it would have been ugly to expose template types in ChartCellLabel.
ChartCellLabel::Stack &stack = out_.FindOrInsert(static_cast<const TargetPhrase *>(partial.GetNote().vp)->GetTargetLHS());
Gen *entry = static_cast<Gen*>(stack.incr_generator);
if (!entry) {
entry = generator_pool_.construct(context_, *vertex_pool_.construct(), best_);
stack.incr_generator = entry;
}
entry->NewHypothesis(partial);
}
void FinishedSearch() {
for (ChartCellLabelSet::iterator i(out_.mutable_begin()); i != out_.mutable_end(); ++i) {
ChartCellLabel::Stack &stack = i->second.MutableStack();
Gen *gen = static_cast<Gen*>(stack.incr_generator);
gen->FinishedSearch();
stack.incr = &gen->Generating();
}
}
private:
search::ContextBase &context_;
Best &best_;
ChartCellLabelSet &out_;
boost::object_pool<search::Vertex> &vertex_pool_;
boost::object_pool<Gen> generator_pool_;
};
// This is called by the moses parser to collect hypotheses. It converts to my
// edges (search::PartialEdge).
template <class Model> class Fill : public ChartParserCallback {
public:
Fill(search::Context<Model> &context, const std::vector<lm::WordIndex> &vocab_mapping, search::Score oov_weight)
: context_(context), vocab_mapping_(vocab_mapping), oov_weight_(oov_weight) {}
void Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &ignored);
void AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &waste_memory, const WordsRange &range);
bool Empty() const { return edges_.Empty(); }
template <class Best> void Search(Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool) {
HypothesisCallback<Best> callback(context_, best, out, vertex_pool);
edges_.Search(context_, callback);
}
// Root: everything into one vertex.
template <class Best> search::History RootSearch(Best &best) {
search::Vertex vertex;
search::RootVertexGenerator<Best> gen(vertex, best);
edges_.Search(context_, gen);
return vertex.BestChild();
}
private:
lm::WordIndex Convert(const Word &word) const;
search::Context<Model> &context_;
const std::vector<lm::WordIndex> &vocab_mapping_;
search::EdgeGenerator edges_;
const search::Score oov_weight_;
};
template <class Model> void Fill<Model>::Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &) {
std::vector<search::PartialVertex> vertices;
vertices.reserve(nts.size());
float below_score = 0.0;
for (StackVec::const_iterator i(nts.begin()); i != nts.end(); ++i) {
vertices.push_back((*i)->GetStack().incr->RootPartial());
if (vertices.back().Empty()) return;
below_score += vertices.back().Bound();
}
std::vector<lm::WordIndex> words;
for (TargetPhraseCollection::const_iterator p(targets.begin()); p != targets.end(); ++p) {
words.clear();
const TargetPhrase &phrase = **p;
const AlignmentInfo::NonTermIndexMap &align = phrase.GetAlignNonTerm().GetNonTermIndexMap();
search::PartialEdge edge(edges_.AllocateEdge(nts.size()));
search::PartialVertex *nt = edge.NT();
for (size_t i = 0; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
*(nt++) = vertices[align[i]];
words.push_back(search::kNonTerminal);
} else {
words.push_back(Convert(word));
}
}
edge.SetScore(phrase.GetFutureScore() + below_score);
// prob and oov were already accounted for.
search::ScoreRule(context_.LanguageModel(), words, edge.Between());
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edges_.AddEdge(edge);
}
}
template <class Model> void Fill<Model>::AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &, const WordsRange &) {
std::vector<lm::WordIndex> words;
CHECK(phrase.GetSize() <= 1);
if (phrase.GetSize())
words.push_back(Convert(phrase.GetWord(0)));
search::PartialEdge edge(edges_.AllocateEdge(0));
// Appears to be a bug that FutureScore does not already include language model.
search::ScoreRuleRet scored(search::ScoreRule(context_.LanguageModel(), words, edge.Between()));
edge.SetScore(phrase.GetFutureScore() + scored.prob * context_.LMWeight() + static_cast<search::Score>(scored.oov) * oov_weight_);
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edges_.AddEdge(edge);
}
// TODO: factors (but chart doesn't seem to support factors anyway).
template <class Model> lm::WordIndex Fill<Model>::Convert(const Word &word) const {
std::size_t factor = word.GetFactor(0)->GetId();
return (factor >= vocab_mapping_.size() ? 0 : vocab_mapping_[factor]);
}
struct ChartCellBaseFactory {
ChartCellBase *operator()(size_t startPos, size_t endPos) const {
return new ChartCellBase(startPos, endPos);
}
};
} // namespace
Manager::Manager(const InputType &source, const TranslationSystem &system) :
source_(source),
system_(system),
cells_(source, ChartCellBaseFactory()),
parser_(source, system, cells_),
n_best_(search::NBestConfig(StaticData::Instance().GetNBestSize())) {}
Manager::~Manager() {
system_.CleanUpAfterSentenceProcessing(source_);
}
template <class Model, class Best> search::History Manager::PopulateBest(const Model &model, const std::vector<lm::WordIndex> &words, Best &out) {
const LanguageModel &abstract = **system_.GetLanguageModels().begin();
const float oov_weight = abstract.OOVFeatureEnabled() ? abstract.GetOOVWeight() : 0.0;
const StaticData &data = StaticData::Instance();
search::Config config(abstract.GetWeight(), data.GetCubePruningPopLimit(), search::NBestConfig(data.GetNBestSize()));
search::Context<Model> context(config, model);
size_t size = source_.GetSize();
boost::object_pool<search::Vertex> vertex_pool(std::max<size_t>(size * size / 2, 32));
for (size_t width = 1; width < size; ++width) {
for (size_t startPos = 0; startPos <= size-width; ++startPos) {
WordsRange range(startPos, startPos + width - 1);
Fill<Model> filler(context, words, oov_weight);
parser_.Create(range, filler);
filler.Search(out, cells_.MutableBase(range).MutableTargetLabelSet(), vertex_pool);
}
}
WordsRange range(0, size - 1);
Fill<Model> filler(context, words, oov_weight);
parser_.Create(range, filler);
return filler.RootSearch(out);
}
template <class Model> void Manager::LMCallback(const Model &model, const std::vector<lm::WordIndex> &words) {
std::size_t nbest = StaticData::Instance().GetNBestSize();
if (nbest <= 1) {
search::History ret = PopulateBest(model, words, single_best_);
if (ret) {
backing_for_single_.resize(1);
backing_for_single_[0] = search::Applied(ret);
} else {
backing_for_single_.clear();
}
completed_nbest_ = &backing_for_single_;
} else {
search::History ret = PopulateBest(model, words, n_best_);
if (ret) {
completed_nbest_ = &n_best_.Extract(ret);
} else {
backing_for_single_.clear();
completed_nbest_ = &backing_for_single_;
}
}
}
template void Manager::LMCallback<lm::ngram::ProbingModel>(const lm::ngram::ProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::RestProbingModel>(const lm::ngram::RestProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::TrieModel>(const lm::ngram::TrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantTrieModel>(const lm::ngram::QuantTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::ArrayTrieModel>(const lm::ngram::ArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantArrayTrieModel>(const lm::ngram::QuantArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
const std::vector<search::Applied> &Manager::ProcessSentence() {
const LMList &lms = system_.GetLanguageModels();
UTIL_THROW_IF(lms.size() != 1, util::Exception, "Incremental search only supports one language model.");
(*lms.begin())->IncrementalCallback(*this);
return *completed_nbest_;
}
namespace {
struct NoOp {
void operator()(const TargetPhrase &) const {}
};
struct AccumScore {
AccumScore(ScoreComponentCollection &out) : out_(&out) {}
void operator()(const TargetPhrase &phrase) {
out_->PlusEquals(phrase.GetScoreBreakdown());
}
ScoreComponentCollection *out_;
};
template <class Action> void AppendToPhrase(const search::Applied final, Phrase &out, Action action) {
assert(final.Valid());
const TargetPhrase &phrase = *static_cast<const TargetPhrase*>(final.GetNote().vp);
action(phrase);
const search::Applied *child = final.Children();
for (std::size_t i = 0; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
AppendToPhrase(*child++, out, action);
} else {
out.AddWord(word);
}
}
}
} // namespace
void ToPhrase(const search::Applied final, Phrase &out) {
out.Clear();
AppendToPhrase(final, out, NoOp());
}
void PhraseAndFeatures(const TranslationSystem &system, const search::Applied final, Phrase &phrase, ScoreComponentCollection &features) {
phrase.Clear();
features.ZeroAll();
AppendToPhrase(final, phrase, AccumScore(features));
// If we made it this far, there is only one language model.
float full, ignored_ngram;
std::size_t ignored_oov;
const LanguageModel &model = **system.GetLanguageModels().begin();
model.CalcScore(phrase, full, ignored_ngram, ignored_oov);
// CalcScore transforms, but EvaluateChart doesn't.
features.Assign(&model, UntransformLMScore(full));
}
} // namespace Incremental
} // namespace Moses

60
moses/Incremental.h Normal file
View File

@ -0,0 +1,60 @@
#pragma once
#include "lm/word_index.hh"
#include "search/applied.hh"
#include "search/nbest.hh"
#include "moses/ChartCellCollection.h"
#include "moses/ChartParser.h"
#include <vector>
#include <string>
namespace Moses {
class ScoreComponentCollection;
class InputType;
class TranslationSystem;
namespace Incremental {
class Manager {
public:
Manager(const InputType &source, const TranslationSystem &system);
~Manager();
template <class Model> void LMCallback(const Model &model, const std::vector<lm::WordIndex> &words);
const std::vector<search::Applied> &ProcessSentence();
// Call to get the same value as ProcessSentence returned.
const std::vector<search::Applied> &Completed() const {
return *completed_nbest_;
}
private:
template <class Model, class Best> search::History PopulateBest(const Model &model, const std::vector<lm::WordIndex> &words, Best &out);
const InputType &source_;
const TranslationSystem &system_;
ChartCellCollectionBase cells_;
ChartParser parser_;
// Only one of single_best_ or n_best_ will be used, but it was easier to do this than a template.
search::SingleBest single_best_;
// ProcessSentence returns a reference to a vector. ProcessSentence
// doesn't have one, so this is populated and returned.
std::vector<search::Applied> backing_for_single_;
search::NBest n_best_;
const std::vector<search::Applied> *completed_nbest_;
};
// Just get the phrase.
void ToPhrase(const search::Applied final, Phrase &out);
// Get the phrase and the features.
void PhraseAndFeatures(const TranslationSystem &system, const search::Applied final, Phrase &phrase, ScoreComponentCollection &features);
} // namespace Incremental
} // namespace Moses

143
moses/Incremental/Fill.cpp
View File

@ -1,143 +0,0 @@
#include "Fill.h"
#include "moses/ChartCellLabel.h"
#include "moses/ChartCellLabelSet.h"
#include "moses/TargetPhraseCollection.h"
#include "moses/TargetPhrase.h"
#include "moses/Word.h"
#include "lm/model.hh"
#include "search/context.hh"
#include "search/note.hh"
#include "search/rule.hh"
#include "search/vertex.hh"
#include "search/vertex_generator.hh"
#include <math.h>
namespace Moses {
namespace Incremental {
template <class Model> Fill<Model>::Fill(search::Context<Model> &context, const std::vector<lm::WordIndex> &vocab_mapping)
: context_(context), vocab_mapping_(vocab_mapping) {}
template <class Model> void Fill<Model>::Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &) {
std::vector<search::PartialVertex> vertices;
vertices.reserve(nts.size());
float below_score = 0.0;
for (StackVec::const_iterator i(nts.begin()); i != nts.end(); ++i) {
vertices.push_back((*i)->GetStack().incr->RootPartial());
if (vertices.back().Empty()) return;
below_score += vertices.back().Bound();
}
std::vector<lm::WordIndex> words;
for (TargetPhraseCollection::const_iterator p(targets.begin()); p != targets.end(); ++p) {
words.clear();
const TargetPhrase &phrase = **p;
const AlignmentInfo::NonTermIndexMap &align = phrase.GetAlignNonTerm().GetNonTermIndexMap();
search::PartialEdge edge(edges_.AllocateEdge(nts.size()));
size_t i = 0;
bool bos = false;
search::PartialVertex *nt = edge.NT();
if (phrase.GetSize() && !phrase.GetWord(0).IsNonTerminal()) {
lm::WordIndex index = Convert(phrase.GetWord(0));
if (context_.LanguageModel().GetVocabulary().BeginSentence() == index) {
bos = true;
} else {
words.push_back(index);
}
i = 1;
}
for (; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
*(nt++) = vertices[align[i]];
words.push_back(search::kNonTerminal);
} else {
words.push_back(Convert(word));
}
}
edge.SetScore(phrase.GetFutureScore() + below_score);
search::ScoreRule(context_, words, bos, edge.Between());
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edges_.AddEdge(edge);
}
}
template <class Model> void Fill<Model>::AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &, const WordsRange &) {
std::vector<lm::WordIndex> words;
CHECK(phrase.GetSize() <= 1);
if (phrase.GetSize())
words.push_back(Convert(phrase.GetWord(0)));
search::PartialEdge edge(edges_.AllocateEdge(0));
// Appears to be a bug that FutureScore does not already include language model.
edge.SetScore(phrase.GetFutureScore() + search::ScoreRule(context_, words, false, edge.Between()));
search::Note note;
note.vp = &phrase;
edge.SetNote(note);
edges_.AddEdge(edge);
}
namespace {
// Route hypotheses to separate vertices for each left hand side label, populating ChartCellLabelSet out.
class HypothesisCallback {
public:
HypothesisCallback(search::ContextBase &context, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool)
: context_(context), out_(out), vertex_pool_(vertex_pool) {}
void NewHypothesis(search::PartialEdge partial) {
search::VertexGenerator *&entry = out_.FindOrInsert(static_cast<const TargetPhrase *>(partial.GetNote().vp)->GetTargetLHS()).incr_generator;
if (!entry) {
entry = generator_pool_.construct(context_, *vertex_pool_.construct());
}
entry->NewHypothesis(partial);
}
void FinishedSearch() {
for (ChartCellLabelSet::iterator i(out_.mutable_begin()); i != out_.mutable_end(); ++i) {
ChartCellLabel::Stack &stack = i->second.MutableStack();
stack.incr_generator->FinishedSearch();
stack.incr = &stack.incr_generator->Generating();
}
}
private:
search::ContextBase &context_;
ChartCellLabelSet &out_;
boost::object_pool<search::Vertex> &vertex_pool_;
boost::object_pool<search::VertexGenerator> generator_pool_;
};
} // namespace
template <class Model> void Fill<Model>::Search(ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool) {
HypothesisCallback callback(context_, out, vertex_pool);
edges_.Search(context_, callback);
}
// TODO: factors (but chart doesn't seem to support factors anyway).
template <class Model> lm::WordIndex Fill<Model>::Convert(const Word &word) const {
std::size_t factor = word.GetFactor(0)->GetId();
return (factor >= vocab_mapping_.size() ? 0 : vocab_mapping_[factor]);
}
template class Fill<lm::ngram::ProbingModel>;
template class Fill<lm::ngram::RestProbingModel>;
template class Fill<lm::ngram::TrieModel>;
template class Fill<lm::ngram::QuantTrieModel>;
template class Fill<lm::ngram::ArrayTrieModel>;
template class Fill<lm::ngram::QuantArrayTrieModel>;
} // namespace Incremental
} // namespace Moses

54
moses/Incremental/Fill.h
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@ -1,54 +0,0 @@
#pragma once
#include "moses/ChartParserCallback.h"
#include "moses/StackVec.h"
#include "lm/word_index.hh"
#include "search/edge_generator.hh"
#include <boost/pool/object_pool.hpp>
#include <list>
#include <vector>
namespace search {
template <class Model> class Context;
class Vertex;
} // namespace search
namespace Moses {
class Word;
class WordsRange;
class TargetPhraseCollection;
class WordsRange;
class ChartCellLabelSet;
class TargetPhrase;
namespace Incremental {
// Replacement for ChartTranslationOptionList
// TODO: implement count and score thresholding.
template <class Model> class Fill : public ChartParserCallback {
public:
Fill(search::Context<Model> &context, const std::vector<lm::WordIndex> &vocab_mapping);
void Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &ignored);
void AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &waste_memory, const WordsRange &range);
bool Empty() const { return edges_.Empty(); }
void Search(ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool);
private:
lm::WordIndex Convert(const Word &word) const ;
search::Context<Model> &context_;
const std::vector<lm::WordIndex> &vocab_mapping_;
search::EdgeGenerator edges_;
};
} // namespace Incremental
} // namespace Moses

122
moses/Incremental/Manager.cpp
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@ -1,122 +0,0 @@
#include "Manager.h"
#include "Fill.h"
#include "moses/ChartCell.h"
#include "moses/TranslationSystem.h"
#include "moses/StaticData.h"
#include "search/context.hh"
#include "search/config.hh"
#include "search/weights.hh"
#include <boost/lexical_cast.hpp>
namespace Moses {
namespace Incremental {
namespace {
struct ChartCellBaseFactory {
ChartCellBase *operator()(size_t startPos, size_t endPos) const {
return new ChartCellBase(startPos, endPos);
}
};
} // namespace
Manager::Manager(const InputType &source, const TranslationSystem &system) :
source_(source),
system_(system),
cells_(source, ChartCellBaseFactory()),
parser_(source, system, cells_) {
}
Manager::~Manager() {
system_.CleanUpAfterSentenceProcessing(source_);
}
namespace {
void ConstructString(const search::Final final, std::ostringstream &stream) {
assert(final.Valid());
const TargetPhrase &phrase = *static_cast<const TargetPhrase*>(final.GetNote().vp);
size_t child = 0;
for (std::size_t i = 0; i < phrase.GetSize(); ++i) {
const Word &word = phrase.GetWord(i);
if (word.IsNonTerminal()) {
assert(child < final.GetArity());
ConstructString(final.Children()[child++], stream);
} else {
stream << word[0]->GetString() << ' ';
}
}
}
void BestString(const ChartCellLabelSet &labels, std::string &out) {
search::Final best;
for (ChartCellLabelSet::const_iterator i = labels.begin(); i != labels.end(); ++i) {
const search::Final child(i->second.GetStack().incr->BestChild());
if (child.Valid() && (!best.Valid() || (child.GetScore() > best.GetScore()))) {
best = child;
}
}
if (!best.Valid()) {
out.clear();
return;
}
std::ostringstream stream;
ConstructString(best, stream);
out = stream.str();
CHECK(out.size() > 9);
// <s>
out.erase(0, 4);
// </s>
out.erase(out.size() - 5);
// Hack: include model score
out += " ||| ";
out += boost::lexical_cast<std::string>(best.GetScore());
}
} // namespace
template <class Model> void Manager::LMCallback(const Model &model, const std::vector<lm::WordIndex> &words) {
const LanguageModel &abstract = **system_.GetLanguageModels().begin();
search::Weights weights(
abstract.GetWeight(),
abstract.OOVFeatureEnabled() ? abstract.GetOOVWeight() : 0.0,
system_.GetWeightWordPenalty());
search::Config config(weights, StaticData::Instance().GetCubePruningPopLimit());
search::Context<Model> context(config, model);
size_t size = source_.GetSize();
boost::object_pool<search::Vertex> vertex_pool(std::max<size_t>(size * size / 2, 32));
for (size_t width = 1; width <= size; ++width) {
for (size_t startPos = 0; startPos <= size-width; ++startPos) {
size_t endPos = startPos + width - 1;
WordsRange range(startPos, endPos);
Fill<Model> filler(context, words);
parser_.Create(range, filler);
filler.Search(cells_.MutableBase(range).MutableTargetLabelSet(), vertex_pool);
}
}
BestString(cells_.GetBase(WordsRange(0, source_.GetSize() - 1)).GetTargetLabelSet(), output_);
}
template void Manager::LMCallback<lm::ngram::ProbingModel>(const lm::ngram::ProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::RestProbingModel>(const lm::ngram::RestProbingModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::TrieModel>(const lm::ngram::TrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantTrieModel>(const lm::ngram::QuantTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::ArrayTrieModel>(const lm::ngram::ArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
template void Manager::LMCallback<lm::ngram::QuantArrayTrieModel>(const lm::ngram::QuantArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
void Manager::ProcessSentence() {
const LMList &lms = system_.GetLanguageModels();
UTIL_THROW_IF(lms.size() != 1, util::Exception, "Incremental search only supports one language model.");
(*lms.begin())->IncrementalCallback(*this);
}
} // namespace Incremental
} // namespace Moses

35
moses/Incremental/Manager.h
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@ -1,35 +0,0 @@
#pragma once
#include "lm/word_index.hh"
#include "moses/ChartCellCollection.h"
#include "moses/ChartParser.h"
namespace Moses {
class InputType;
class TranslationSystem;
namespace Incremental {
class Manager {
public:
Manager(const InputType &source, const TranslationSystem &system);
~Manager();
template <class Model> void LMCallback(const Model &model, const std::vector<lm::WordIndex> &words);
void ProcessSentence();
const std::string &String() const { return output_; }
private:
const InputType &source_;
const TranslationSystem &system_;
ChartCellCollectionBase cells_;
ChartParser parser_;
std::string output_;
};
} // namespace Incremental
} // namespace Moses

1
moses/Jamfile
View File

@ -32,7 +32,6 @@ lib moses :
CYKPlusParser/*.cpp
RuleTable/*.cpp
fuzzy-match/*.cpp
Incremental/*.cpp
: #exceptions
ThreadPool.cpp
SyntacticLanguageModel.cpp

2
moses/LM/Ken.cpp
View File

@ -38,7 +38,7 @@ Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
#include "moses/InputFileStream.h"
#include "moses/StaticData.h"
#include "moses/ChartHypothesis.h"
#include "moses/Incremental/Manager.h"
#include "moses/Incremental.h"
#include <boost/shared_ptr.hpp>

28
scripts/training/filter-model-given-input.pl
View File

@ -119,7 +119,11 @@ while(<INI>) {
print INI_OUT "2 $source_factor $t $w $new_name.bin$table_flag\n";
}
elsif ($binarizer && $phrase_table_impl == 0) {
print INI_OUT "1 $source_factor $t $w $new_name$table_flag\n";
if ($binarizer =~ /processPhraseTableMin/) {
print INI_OUT "12 $source_factor $t $w $new_name$table_flag\n";
} else {
print INI_OUT "1 $source_factor $t $w $new_name$table_flag\n";
}
} else {
$new_name .= ".gz" if $opt_gzip;
print INI_OUT "$phrase_table_impl $source_factor $t $w $new_name$table_flag\n";
@ -147,7 +151,7 @@ while(<INI>) {
$file =~ s/^.*\/+([^\/]+)/$1/g;
my $new_name = "$dir/$file";
$new_name =~ s/\.gz//;
$new_name =~ s/\.gz//;
print INI_OUT "$factors $t $w $new_name\n";
push @TABLE_NEW_NAME,$new_name;
@ -275,11 +279,16 @@ for(my $i=0;$i<=$#TABLE;$i++) {
# ... hierarchical translation model
if ($opt_hierarchical) {
my $cmd = "$binarizer $new_file $new_file.bin";
print STDERR $cmd."\n";
print STDERR `$cmd`;
print STDERR $cmd."\n";
print STDERR `$cmd`;
}
# ... phrase translation model
else {
elsif ($binarizer =~ /processPhraseTableMin/) {
#compact phrase table
my $cmd = "LC_ALL=C sort -T $dir $new_file > $new_file.sorted; $binarizer -in $new_file.sorted -out $new_file -nscores $TABLE_WEIGHTS[$i]; rm $new_file.sorted";
print STDERR $cmd."\n";
print STDERR `$cmd`;
} else {
my $cmd = "cat $new_file | LC_ALL=C sort -T $dir | $binarizer -ttable 0 0 - -nscores $TABLE_WEIGHTS[$i] -out $new_file";
print STDERR $cmd."\n";
print STDERR `$cmd`;
@ -289,8 +298,13 @@ for(my $i=0;$i<=$#TABLE;$i++) {
else {
my $lexbin = $binarizer;
$lexbin =~ s/PhraseTable/LexicalTable/;
$lexbin =~ s/^\s*(\S+)\s.+/$1/; # no options
my $cmd = "$lexbin -in $new_file -out $new_file";
my $cmd;
if ($lexbin =~ /processLexicalTableMin/) {
$cmd = "LC_ALL=C sort -T $dir $new_file > $new_file.sorted; $lexbin -in $new_file.sorted -out $new_file; rm $new_file.sorted";
} else {
$lexbin =~ s/^\s*(\S+)\s.+/$1/; # no options
$cmd = "$lexbin -in $new_file -out $new_file";
}
print STDERR $cmd."\n";
print STDERR `$cmd`;
}

6
search/Jamfile
View File

@ -1,5 +1 @@
fakelib search : weights.cc vertex.cc vertex_generator.cc edge_generator.cc rule.cc ../lm//kenlm ../util//kenutil /top//boost_system : : : <include>.. ;
import testing ;
unit-test weights_test : weights_test.cc search /top//boost_unit_test_framework ;
fakelib search : edge_generator.cc nbest.cc rule.cc vertex.cc vertex_generator.cc ../lm//kenlm ../util//kenutil /top//boost_system : : : <include>.. ;

86
search/applied.hh Normal file
View File

@ -0,0 +1,86 @@
#ifndef SEARCH_APPLIED__
#define SEARCH_APPLIED__
#include "search/edge.hh"
#include "search/header.hh"
#include "util/pool.hh"
#include <math.h>
namespace search {
// A full hypothesis: a score, arity of the rule, a pointer to the decoder's rule (Note), and pointers to non-terminals that were substituted.
template <class Below> class GenericApplied : public Header {
public:
GenericApplied() {}
GenericApplied(void *location, PartialEdge partial)
: Header(location) {
memcpy(Base(), partial.Base(), kHeaderSize);
Below *child_out = Children();
const PartialVertex *part = partial.NT();
const PartialVertex *const part_end_loop = part + partial.GetArity();
for (; part != part_end_loop; ++part, ++child_out)
*child_out = Below(part->End());
}
GenericApplied(void *location, Score score, Arity arity, Note note) : Header(location, arity) {
SetScore(score);
SetNote(note);
}
explicit GenericApplied(History from) : Header(from) {}
// These are arrays of length GetArity().
Below *Children() {
return reinterpret_cast<Below*>(After());
}
const Below *Children() const {
return reinterpret_cast<const Below*>(After());
}
static std::size_t Size(Arity arity) {
return kHeaderSize + arity * sizeof(const Below);
}
};
// Applied rule that references itself.
class Applied : public GenericApplied<Applied> {
private:
typedef GenericApplied<Applied> P;
public:
Applied() {}
Applied(void *location, PartialEdge partial) : P(location, partial) {}
Applied(History from) : P(from) {}
};
// How to build single-best hypotheses.
class SingleBest {
public:
typedef PartialEdge Combine;
void Add(PartialEdge &existing, PartialEdge add) const {
if (!existing.Valid() || existing.GetScore() < add.GetScore())
existing = add;
}
NBestComplete Complete(PartialEdge partial) {
if (!partial.Valid())
return NBestComplete(NULL, lm::ngram::ChartState(), -INFINITY);
void *place_final = pool_.Allocate(Applied::Size(partial.GetArity()));
Applied(place_final, partial);
return NBestComplete(
place_final,
partial.CompletedState(),
partial.GetScore());
}
private:
util::Pool pool_;
};
} // namespace search
#endif // SEARCH_APPLIED__

25
search/config.hh
View File

@ -1,23 +1,36 @@
#ifndef SEARCH_CONFIG__
#define SEARCH_CONFIG__
#include "search/weights.hh"
#include "util/string_piece.hh"
#include "search/types.hh"
namespace search {
struct NBestConfig {
explicit NBestConfig(unsigned int in_size) {
keep = in_size;
size = in_size;
}
unsigned int keep, size;
};
class Config {
public:
Config(const Weights &weights, unsigned int pop_limit) :
weights_(weights), pop_limit_(pop_limit) {}
Config(Score lm_weight, unsigned int pop_limit, const NBestConfig &nbest) :
lm_weight_(lm_weight), pop_limit_(pop_limit), nbest_(nbest) {}
const Weights &GetWeights() const { return weights_; }
Score LMWeight() const { return lm_weight_; }
unsigned int PopLimit() const { return pop_limit_; }
const NBestConfig &GetNBest() const { return nbest_; }
private:
Weights weights_;
Score lm_weight_;
unsigned int pop_limit_;
NBestConfig nbest_;
};
} // namespace search

28
search/context.hh
View File

@ -1,30 +1,16 @@
#ifndef SEARCH_CONTEXT__
#define SEARCH_CONTEXT__
#include "lm/model.hh"
#include "search/config.hh"
#include "search/final.hh"
#include "search/types.hh"
#include "search/vertex.hh"
#include "util/exception.hh"
#include "util/pool.hh"
#include <boost/pool/object_pool.hpp>
#include <boost/ptr_container/ptr_vector.hpp>
#include <vector>
namespace search {
class Weights;
class ContextBase {
public:
explicit ContextBase(const Config &config) : pop_limit_(config.PopLimit()), weights_(config.GetWeights()) {}
util::Pool &FinalPool() {
return final_pool_;
}
explicit ContextBase(const Config &config) : config_(config) {}
VertexNode *NewVertexNode() {
VertexNode *ret = vertex_node_pool_.construct();
@ -36,18 +22,16 @@ class ContextBase {
vertex_node_pool_.destroy(node);
}
unsigned int PopLimit() const { return pop_limit_; }
unsigned int PopLimit() const { return config_.PopLimit(); }
const Weights &GetWeights() const { return weights_; }
Score LMWeight() const { return config_.LMWeight(); }
const Config &GetConfig() const { return config_; }
private:
util::Pool final_pool_;
boost::object_pool<VertexNode> vertex_node_pool_;
unsigned int pop_limit_;
const Weights &weights_;
Config config_;
};
template <class Model> class Context : public ContextBase {

3
search/edge_generator.cc
View File

@ -1,6 +1,7 @@
#include "search/edge_generator.hh"
#include "lm/left.hh"
#include "lm/model.hh"
#include "lm/partial.hh"
#include "search/context.hh"
#include "search/vertex.hh"
@ -38,7 +39,7 @@ template <class Model> void FastScore(const Context<Model> &context, Arity victi
*cover = *(cover + 1);
}
}
update.SetScore(update.GetScore() + adjustment * context.GetWeights().LM());
update.SetScore(update.GetScore() + adjustment * context.LMWeight());
}
} // namespace

1
search/edge_generator.hh
View File

@ -2,7 +2,6 @@
#define SEARCH_EDGE_GENERATOR__
#include "search/edge.hh"
#include "search/note.hh"
#include "search/types.hh"
#include <queue>

36
search/final.hh
View File

@ -1,36 +0,0 @@
#ifndef SEARCH_FINAL__
#define SEARCH_FINAL__
#include "search/header.hh"
#include "util/pool.hh"
namespace search {
// A full hypothesis with pointers to children.
class Final : public Header {
public:
Final() {}
Final(util::Pool &pool, Score score, Arity arity, Note note)
: Header(pool.Allocate(Size(arity)), arity) {
SetScore(score);
SetNote(note);
}
// These are arrays of length GetArity().
Final *Children() {
return reinterpret_cast<Final*>(After());
}
const Final *Children() const {
return reinterpret_cast<const Final*>(After());
}
private:
static std::size_t Size(Arity arity) {
return kHeaderSize + arity * sizeof(const Final);
}
};
} // namespace search
#endif // SEARCH_FINAL__

9
search/header.hh
View File

@ -3,7 +3,6 @@
// Header consisting of Score, Arity, and Note
#include "search/note.hh"
#include "search/types.hh"
#include <stdint.h>
@ -24,6 +23,9 @@ class Header {
bool operator<(const Header &other) const {
return GetScore() < other.GetScore();
}
bool operator>(const Header &other) const {
return GetScore() > other.GetScore();
}
Arity GetArity() const {
return *reinterpret_cast<const Arity*>(base_ + sizeof(Score));
@ -36,9 +38,14 @@ class Header {
*reinterpret_cast<Note*>(base_ + sizeof(Score) + sizeof(Arity)) = to;
}
uint8_t *Base() { return base_; }
const uint8_t *Base() const { return base_; }
protected:
Header() : base_(NULL) {}
explicit Header(void *base) : base_(static_cast<uint8_t*>(base)) {}
Header(void *base, Arity arity) : base_(static_cast<uint8_t*>(base)) {
*reinterpret_cast<Arity*>(base_ + sizeof(Score)) = arity;
}

106
search/nbest.cc Normal file
View File

@ -0,0 +1,106 @@
#include "search/nbest.hh"
#include "util/pool.hh"
#include <algorithm>
#include <functional>
#include <queue>
#include <assert.h>
#include <math.h>
namespace search {
NBestList::NBestList(std::vector<PartialEdge> &partials, util::Pool &entry_pool, std::size_t keep) {
assert(!partials.empty());
std::vector<PartialEdge>::iterator end;
if (partials.size() > keep) {
end = partials.begin() + keep;
std::nth_element(partials.begin(), end, partials.end(), std::greater<PartialEdge>());
} else {
end = partials.end();
}
for (std::vector<PartialEdge>::const_iterator i(partials.begin()); i != end; ++i) {
queue_.push(QueueEntry(entry_pool.Allocate(QueueEntry::Size(i->GetArity())), *i));
}
}
Score NBestList::TopAfterConstructor() const {
assert(revealed_.empty());
return queue_.top().GetScore();
}
const std::vector<Applied> &NBestList::Extract(util::Pool &pool, std::size_t n) {
while (revealed_.size() < n && !queue_.empty()) {
MoveTop(pool);
}
return revealed_;
}
Score NBestList::Visit(util::Pool &pool, std::size_t index) {
if (index + 1 < revealed_.size())
return revealed_[index + 1].GetScore() - revealed_[index].GetScore();
if (queue_.empty())
return -INFINITY;
if (index + 1 == revealed_.size())
return queue_.top().GetScore() - revealed_[index].GetScore();
assert(index == revealed_.size());
MoveTop(pool);
if (queue_.empty()) return -INFINITY;
return queue_.top().GetScore() - revealed_[index].GetScore();
}
Applied NBestList::Get(util::Pool &pool, std::size_t index) {
assert(index <= revealed_.size());
if (index == revealed_.size()) MoveTop(pool);
return revealed_[index];
}
void NBestList::MoveTop(util::Pool &pool) {
assert(!queue_.empty());
QueueEntry entry(queue_.top());
queue_.pop();
RevealedRef *const children_begin = entry.Children();
RevealedRef *const children_end = children_begin + entry.GetArity();
Score basis = entry.GetScore();
for (RevealedRef *child = children_begin; child != children_end; ++child) {
Score change = child->in_->Visit(pool, child->index_);
if (change != -INFINITY) {
assert(change < 0.001);
QueueEntry new_entry(pool.Allocate(QueueEntry::Size(entry.GetArity())), basis + change, entry.GetArity(), entry.GetNote());
std::copy(children_begin, child, new_entry.Children());
RevealedRef *update = new_entry.Children() + (child - children_begin);
update->in_ = child->in_;
update->index_ = child->index_ + 1;
std::copy(child + 1, children_end, update + 1);
queue_.push(new_entry);
}
// Gesmundo, A. and Henderson, J. Faster Cube Pruning, IWSLT 2010.
if (child->index_) break;
}
// Convert QueueEntry to Applied. This leaves some unused memory.
void *overwrite = entry.Children();
for (unsigned int i = 0; i < entry.GetArity(); ++i) {
RevealedRef from(*(static_cast<const RevealedRef*>(overwrite) + i));
*(static_cast<Applied*>(overwrite) + i) = from.in_->Get(pool, from.index_);
}
revealed_.push_back(Applied(entry.Base()));
}
NBestComplete NBest::Complete(std::vector<PartialEdge> &partials) {
assert(!partials.empty());
NBestList *list = list_pool_.construct(partials, entry_pool_, config_.keep);
return NBestComplete(
list,
partials.front().CompletedState(), // All partials have the same state
list->TopAfterConstructor());
}
const std::vector<Applied> &NBest::Extract(History history) {
return static_cast<NBestList*>(history)->Extract(entry_pool_, config_.size);
}
} // namespace search

81
search/nbest.hh Normal file
View File

@ -0,0 +1,81 @@
#ifndef SEARCH_NBEST__
#define SEARCH_NBEST__
#include "search/applied.hh"
#include "search/config.hh"
#include "search/edge.hh"
#include <boost/pool/object_pool.hpp>
#include <cstddef>
#include <queue>
#include <vector>
#include <assert.h>
namespace search {
class NBestList;
class NBestList {
private:
class RevealedRef {
public:
explicit RevealedRef(History history)
: in_(static_cast<NBestList*>(history)), index_(0) {}
private:
friend class NBestList;
NBestList *in_;
std::size_t index_;
};
typedef GenericApplied<RevealedRef> QueueEntry;
public:
NBestList(std::vector<PartialEdge> &existing, util::Pool &entry_pool, std::size_t keep);
Score TopAfterConstructor() const;
const std::vector<Applied> &Extract(util::Pool &pool, std::size_t n);
private:
Score Visit(util::Pool &pool, std::size_t index);
Applied Get(util::Pool &pool, std::size_t index);
void MoveTop(util::Pool &pool);
typedef std::vector<Applied> Revealed;
Revealed revealed_;
typedef std::priority_queue<QueueEntry> Queue;
Queue queue_;
};
class NBest {
public:
typedef std::vector<PartialEdge> Combine;
explicit NBest(const NBestConfig &config) : config_(config) {}
void Add(std::vector<PartialEdge> &existing, PartialEdge addition) const {
existing.push_back(addition);
}
NBestComplete Complete(std::vector<PartialEdge> &partials);
const std::vector<Applied> &Extract(History root);
private:
const NBestConfig config_;
boost::object_pool<NBestList> list_pool_;
util::Pool entry_pool_;
};
} // namespace search
#endif // SEARCH_NBEST__

12
search/note.hh
View File

@ -1,12 +0,0 @@
#ifndef SEARCH_NOTE__
#define SEARCH_NOTE__
namespace search {
union Note {
const void *vp;
};
} // namespace search
#endif // SEARCH_NOTE__

52
search/rule.cc
View File

@ -1,7 +1,7 @@
#include "search/rule.hh"
#include "lm/model.hh"
#include "search/context.hh"
#include "search/final.hh"
#include <ostream>
@ -9,35 +9,35 @@
namespace search {
template <class Model> float ScoreRule(const Context<Model> &context, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing) {
unsigned int oov_count = 0;
float prob = 0.0;
const Model &model = context.LanguageModel();
const lm::WordIndex oov = model.GetVocabulary().NotFound();
for (std::vector<lm::WordIndex>::const_iterator word = words.begin(); ; ++word) {
lm::ngram::RuleScore<Model> scorer(model, *(writing++));
// TODO: optimize
if (prepend_bos && (word == words.begin())) {
scorer.BeginSentence();
}
for (; ; ++word) {
if (word == words.end()) {
prob += scorer.Finish();
return static_cast<float>(oov_count) * context.GetWeights().OOV() + prob * context.GetWeights().LM();
}
if (*word == kNonTerminal) break;
if (*word == oov) ++oov_count;
template <class Model> ScoreRuleRet ScoreRule(const Model &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing) {
ScoreRuleRet ret;
ret.prob = 0.0;
ret.oov = 0;
const lm::WordIndex oov = model.GetVocabulary().NotFound(), bos = model.GetVocabulary().BeginSentence();
lm::ngram::RuleScore<Model> scorer(model, *(writing++));
std::vector<lm::WordIndex>::const_iterator word = words.begin();
if (word != words.end() && *word == bos) {
scorer.BeginSentence();
++word;
}
for (; word != words.end(); ++word) {
if (*word == kNonTerminal) {
ret.prob += scorer.Finish();
scorer.Reset(*(writing++));
} else {
if (*word == oov) ++ret.oov;
scorer.Terminal(*word);
}
prob += scorer.Finish();
}
ret.prob += scorer.Finish();
return ret;
}
template float ScoreRule(const Context<lm::ngram::RestProbingModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template float ScoreRule(const Context<lm::ngram::ProbingModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template float ScoreRule(const Context<lm::ngram::TrieModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template float ScoreRule(const Context<lm::ngram::QuantTrieModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template float ScoreRule(const Context<lm::ngram::ArrayTrieModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template float ScoreRule(const Context<lm::ngram::QuantArrayTrieModel> &model, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::RestProbingModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::ProbingModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::TrieModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::QuantTrieModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::ArrayTrieModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
template ScoreRuleRet ScoreRule(const lm::ngram::QuantArrayTrieModel &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *writing);
} // namespace search

11
search/rule.hh
View File

@ -9,11 +9,16 @@
namespace search {
template <class Model> class Context;
const lm::WordIndex kNonTerminal = lm::kMaxWordIndex;
template <class Model> float ScoreRule(const Context<Model> &context, const std::vector<lm::WordIndex> &words, bool prepend_bos, lm::ngram::ChartState *state_out);
struct ScoreRuleRet {
Score prob;
unsigned int oov;
};
// Pass <s> and </s> normally.
// Indicate non-terminals with kNonTerminal.
template <class Model> ScoreRuleRet ScoreRule(const Model &model, const std::vector<lm::WordIndex> &words, lm::ngram::ChartState *state_out);
} // namespace search

17
search/types.hh
View File

@ -3,12 +3,29 @@
#include <stdint.h>
namespace lm { namespace ngram { class ChartState; } }
namespace search {
typedef float Score;
typedef uint32_t Arity;
union Note {
const void *vp;
};
typedef void *History;
struct NBestComplete {
NBestComplete(History in_history, const lm::ngram::ChartState &in_state, Score in_score)
: history(in_history), state(&in_state), score(in_score) {}
History history;
const lm::ngram::ChartState *state;
Score score;
};
} // namespace search
#endif // SEARCH_TYPES__

27
search/vertex.cc
View File

@ -19,21 +19,34 @@ struct GreaterByBound : public std::binary_function<const VertexNode *, const Ve
} // namespace
void VertexNode::SortAndSet(ContextBase &context, VertexNode **parent_ptr) {
void VertexNode::RecursiveSortAndSet(ContextBase &context, VertexNode *&parent_ptr) {
if (Complete()) {
assert(end_.Valid());
assert(end_);
assert(extend_.empty());
bound_ = end_.GetScore();
return;
}
if (extend_.size() == 1 && parent_ptr) {
*parent_ptr = extend_[0];
extend_[0]->SortAndSet(context, parent_ptr);
if (extend_.size() == 1) {
parent_ptr = extend_[0];
extend_[0]->RecursiveSortAndSet(context, parent_ptr);
context.DeleteVertexNode(this);
return;
}
for (std::vector<VertexNode*>::iterator i = extend_.begin(); i != extend_.end(); ++i) {
(*i)->SortAndSet(context, &*i);
(*i)->RecursiveSortAndSet(context, *i);
}
std::sort(extend_.begin(), extend_.end(), GreaterByBound());
bound_ = extend_.front()->Bound();
}
void VertexNode::SortAndSet(ContextBase &context) {
// This is the root. The root might be empty.
if (extend_.empty()) {
bound_ = -INFINITY;
return;
}
// The root cannot be replaced. There's always one transition.
for (std::vector<VertexNode*>::iterator i = extend_.begin(); i != extend_.end(); ++i) {
(*i)->RecursiveSortAndSet(context, *i);
}
std::sort(extend_.begin(), extend_.end(), GreaterByBound());
bound_ = extend_.front()->Bound();

37
search/vertex.hh
View File

@ -2,7 +2,6 @@
#define SEARCH_VERTEX__
#include "lm/left.hh"
#include "search/final.hh"
#include "search/types.hh"
#include <boost/unordered_set.hpp>
@ -10,6 +9,7 @@
#include <queue>
#include <vector>
#include <math.h>
#include <stdint.h>
namespace search {
@ -18,7 +18,7 @@ class ContextBase;
class VertexNode {
public:
VertexNode() {}
VertexNode() : end_() {}
void InitRoot() {
extend_.clear();
@ -26,7 +26,7 @@ class VertexNode {
state_.left.length = 0;
state_.right.length = 0;
right_full_ = false;
end_ = Final();
end_ = History();
}
lm::ngram::ChartState &MutableState() { return state_; }
@ -36,20 +36,21 @@ class VertexNode {
extend_.push_back(next);
}
void SetEnd(Final end) {
assert(!end_.Valid());
void SetEnd(History end, Score score) {
assert(!end_);
end_ = end;
bound_ = score;
}
void SortAndSet(ContextBase &context, VertexNode **parent_pointer);
void SortAndSet(ContextBase &context);
// Should only happen to a root node when the entire vertex is empty.
bool Empty() const {
return !end_.Valid() && extend_.empty();
return !end_ && extend_.empty();
}
bool Complete() const {
return end_.Valid();
return end_;
}
const lm::ngram::ChartState &State() const { return state_; }
@ -64,7 +65,7 @@ class VertexNode {
}
// Will be invalid unless this is a leaf.
const Final End() const { return end_; }
const History End() const { return end_; }
const VertexNode &operator[](size_t index) const {
return *extend_[index];
@ -75,13 +76,15 @@ class VertexNode {
}
private:
void RecursiveSortAndSet(ContextBase &context, VertexNode *&parent);
std::vector<VertexNode*> extend_;
lm::ngram::ChartState state_;
bool right_full_;
Score bound_;
Final end_;
History end_;
};
class PartialVertex {
@ -97,7 +100,7 @@ class PartialVertex {
const lm::ngram::ChartState &State() const { return back_->State(); }
bool RightFull() const { return back_->RightFull(); }
Score Bound() const { return Complete() ? back_->End().GetScore() : (*back_)[index_].Bound(); }
Score Bound() const { return Complete() ? back_->Bound() : (*back_)[index_].Bound(); }
unsigned char Length() const { return back_->Length(); }
@ -121,7 +124,7 @@ class PartialVertex {
return ret;
}
const Final End() const {
const History End() const {
return back_->End();
}
@ -130,16 +133,18 @@ class PartialVertex {
unsigned int index_;
};
template <class Output> class VertexGenerator;
class Vertex {
public:
Vertex() {}
PartialVertex RootPartial() const { return PartialVertex(root_); }
const Final BestChild() const {
const History BestChild() const {
PartialVertex top(RootPartial());
if (top.Empty()) {
return Final();
return History();
} else {
PartialVertex continuation;
while (!top.Complete()) {
@ -150,8 +155,8 @@ class Vertex {
}
private:
friend class VertexGenerator;
template <class Output> friend class VertexGenerator;
template <class Output> friend class RootVertexGenerator;
VertexNode root_;
};

50
search/vertex_generator.cc
View File

@ -11,23 +11,11 @@
namespace search {
VertexGenerator::VertexGenerator(ContextBase &context, Vertex &gen) : context_(context), gen_(gen) {
gen.root_.InitRoot();
}
#if BOOST_VERSION > 104200
namespace {
const uint64_t kCompleteAdd = static_cast<uint64_t>(-1);
// Parallel structure to VertexNode.
struct Trie {
Trie() : under(NULL) {}
VertexNode *under;
boost::unordered_map<uint64_t, Trie> extend;
};
Trie &FindOrInsert(ContextBase &context, Trie &node, uint64_t added, const lm::ngram::ChartState &state, unsigned char left, bool left_full, unsigned char right, bool right_full) {
Trie &next = node.extend[added];
if (!next.under) {
@ -43,19 +31,10 @@ Trie &FindOrInsert(ContextBase &context, Trie &node, uint64_t added, const lm::n
return next;
}
void CompleteTransition(ContextBase &context, Trie &starter, PartialEdge partial) {
Final final(context.FinalPool(), partial.GetScore(), partial.GetArity(), partial.GetNote());
Final *child_out = final.Children();
const PartialVertex *part = partial.NT();
const PartialVertex *const part_end_loop = part + partial.GetArity();
for (; part != part_end_loop; ++part, ++child_out)
*child_out = part->End();
} // namespace
starter.under->SetEnd(final);
}
void AddHypothesis(ContextBase &context, Trie &root, PartialEdge partial) {
const lm::ngram::ChartState &state = partial.CompletedState();
void AddHypothesis(ContextBase &context, Trie &root, const NBestComplete &end) {
const lm::ngram::ChartState &state = *end.state;
unsigned char left = 0, right = 0;
Trie *node = &root;
@ -81,30 +60,9 @@ void AddHypothesis(ContextBase &context, Trie &root, PartialEdge partial) {
}
node = &FindOrInsert(context, *node, kCompleteAdd - state.left.full, state, state.left.length, true, state.right.length, true);
CompleteTransition(context, *node, partial);
}
} // namespace
#else // BOOST_VERSION
struct Trie {
VertexNode *under;
};
void AddHypothesis(ContextBase &context, Trie &root, PartialEdge partial) {
UTIL_THROW(util::Exception, "Upgrade Boost to >= 1.42.0 to use incremental search.");
node->under->SetEnd(end.history, end.score);
}
#endif // BOOST_VERSION
void VertexGenerator::FinishedSearch() {
Trie root;
root.under = &gen_.root_;
for (Existing::const_iterator i(existing_.begin()); i != existing_.end(); ++i) {
AddHypothesis(context_, root, i->second);
}
root.under->SortAndSet(context_, NULL);
}
} // namespace search

76
search/vertex_generator.hh
View File

@ -2,9 +2,11 @@
#define SEARCH_VERTEX_GENERATOR__
#include "search/edge.hh"
#include "search/types.hh"
#include "search/vertex.hh"
#include <boost/unordered_map.hpp>
#include <boost/version.hpp>
namespace lm {
namespace ngram {
@ -15,21 +17,44 @@ class ChartState;
namespace search {
class ContextBase;
class Final;
class VertexGenerator {
#if BOOST_VERSION > 104200
// Parallel structure to VertexNode.
struct Trie {
Trie() : under(NULL) {}
VertexNode *under;
boost::unordered_map<uint64_t, Trie> extend;
};
void AddHypothesis(ContextBase &context, Trie &root, const NBestComplete &end);
#endif // BOOST_VERSION
// Output makes the single-best or n-best list.
template <class Output> class VertexGenerator {
public:
VertexGenerator(ContextBase &context, Vertex &gen);
void NewHypothesis(PartialEdge partial) {
const lm::ngram::ChartState &state = partial.CompletedState();
std::pair<Existing::iterator, bool> ret(existing_.insert(std::make_pair(hash_value(state), partial)));
if (!ret.second && ret.first->second < partial) {
ret.first->second = partial;
}
VertexGenerator(ContextBase &context, Vertex &gen, Output &nbest) : context_(context), gen_(gen), nbest_(nbest) {
gen.root_.InitRoot();
}
void FinishedSearch();
void NewHypothesis(PartialEdge partial) {
nbest_.Add(existing_[hash_value(partial.CompletedState())], partial);
}
void FinishedSearch() {
#if BOOST_VERSION > 104200
Trie root;
root.under = &gen_.root_;
for (typename Existing::iterator i(existing_.begin()); i != existing_.end(); ++i) {
AddHypothesis(context_, root, nbest_.Complete(i->second));
}
existing_.clear();
root.under->SortAndSet(context_);
#else
UTIL_THROW(util::Exception, "Upgrade Boost to >= 1.42.0 to use incremental search.");
#endif
}
const Vertex &Generating() const { return gen_; }
@ -38,8 +63,35 @@ class VertexGenerator {
Vertex &gen_;
typedef boost::unordered_map<uint64_t, PartialEdge> Existing;
typedef boost::unordered_map<uint64_t, typename Output::Combine> Existing;
Existing existing_;
Output &nbest_;
};
// Special case for root vertex: everything should come together into the root
// node. In theory, this should happen naturally due to state collapsing with
// <s> and </s>. If that's the case, VertexGenerator is fine, though it will
// make one connection.
template <class Output> class RootVertexGenerator {
public:
RootVertexGenerator(Vertex &gen, Output &out) : gen_(gen), out_(out) {}
void NewHypothesis(PartialEdge partial) {
out_.Add(combine_, partial);
}
void FinishedSearch() {
gen_.root_.InitRoot();
NBestComplete completed(out_.Complete(combine_));
gen_.root_.SetEnd(completed.history, completed.score);
}
private:
Vertex &gen_;
typename Output::Combine combine_;
Output &out_;
};
} // namespace search

71
search/weights.cc
View File

@ -1,71 +0,0 @@
#include "search/weights.hh"
#include "util/tokenize_piece.hh"
#include <cstdlib>
namespace search {
namespace {
struct Insert {
void operator()(boost::unordered_map<std::string, search::Score> &map, StringPiece name, search::Score score) const {
std::string copy(name.data(), name.size());
map[copy] = score;
}
};
struct DotProduct {
search::Score total;
DotProduct() : total(0.0) {}
void operator()(const boost::unordered_map<std::string, search::Score> &map, StringPiece name, search::Score score) {
boost::unordered_map<std::string, search::Score>::const_iterator i(FindStringPiece(map, name));
if (i != map.end())
total += score * i->second;
}
};
template <class Map, class Op> void Parse(StringPiece text, Map &map, Op &op) {
for (util::TokenIter<util::SingleCharacter, true> spaces(text, ' '); spaces; ++spaces) {
util::TokenIter<util::SingleCharacter> equals(*spaces, '=');
UTIL_THROW_IF(!equals, WeightParseException, "Bad weight token " << *spaces);
StringPiece name(*equals);
UTIL_THROW_IF(!++equals, WeightParseException, "Bad weight token " << *spaces);
char *end;
// Assumes proper termination.
double value = std::strtod(equals->data(), &end);
UTIL_THROW_IF(end != equals->data() + equals->size(), WeightParseException, "Failed to parse weight" << *equals);
UTIL_THROW_IF(++equals, WeightParseException, "Too many equals in " << *spaces);
op(map, name, value);
}
}
} // namespace
Weights::Weights(StringPiece text) {
Insert op;
Parse<Map, Insert>(text, map_, op);
lm_ = Steal("LanguageModel");
oov_ = Steal("OOV");
word_penalty_ = Steal("WordPenalty");
}
Weights::Weights(Score lm, Score oov, Score word_penalty) : lm_(lm), oov_(oov), word_penalty_(word_penalty) {}
search::Score Weights::DotNoLM(StringPiece text) const {
DotProduct dot;
Parse<const Map, DotProduct>(text, map_, dot);
return dot.total;
}
float Weights::Steal(const std::string &str) {
Map::iterator i(map_.find(str));
if (i == map_.end()) {
return 0.0;
} else {
float ret = i->second;
map_.erase(i);
return ret;
}
}
} // namespace search

52
search/weights.hh
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@ -1,52 +0,0 @@
// For now, the individual features are not kept.
#ifndef SEARCH_WEIGHTS__
#define SEARCH_WEIGHTS__
#include "search/types.hh"
#include "util/exception.hh"
#include "util/string_piece.hh"
#include <boost/unordered_map.hpp>
#include <string>
namespace search {
class WeightParseException : public util::Exception {
public:
WeightParseException() {}
~WeightParseException() throw() {}
};
class Weights {
public:
// Parses weights, sets lm_weight_, removes it from map_.
explicit Weights(StringPiece text);
// Just the three scores we care about adding.
Weights(Score lm, Score oov, Score word_penalty);
Score DotNoLM(StringPiece text) const;
Score LM() const { return lm_; }
Score OOV() const { return oov_; }
Score WordPenalty() const { return word_penalty_; }
// Mostly for testing.
const boost::unordered_map<std::string, Score> &GetMap() const { return map_; }
private:
float Steal(const std::string &str);
typedef boost::unordered_map<std::string, Score> Map;
Map map_;
Score lm_, oov_, word_penalty_;
};
} // namespace search
#endif // SEARCH_WEIGHTS__

38
search/weights_test.cc
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#include "search/weights.hh"
#define BOOST_TEST_MODULE WeightTest
#include <boost/test/unit_test.hpp>
#include <boost/test/floating_point_comparison.hpp>
namespace search {
namespace {
#define CHECK_WEIGHT(value, string) \
i = parsed.find(string); \
BOOST_REQUIRE(i != parsed.end()); \
BOOST_CHECK_CLOSE((value), i->second, 0.001);
BOOST_AUTO_TEST_CASE(parse) {
// These are not real feature weights.
Weights w("rarity=0 phrase-SGT=0 phrase-TGS=9.45117 lhsGrhs=0 lexical-SGT=2.33833 lexical-TGS=-28.3317 abstract?=0 LanguageModel=3 lexical?=1 glue?=5");
const boost::unordered_map<std::string, search::Score> &parsed = w.GetMap();
boost::unordered_map<std::string, search::Score>::const_iterator i;
CHECK_WEIGHT(0.0, "rarity");
CHECK_WEIGHT(0.0, "phrase-SGT");
CHECK_WEIGHT(9.45117, "phrase-TGS");
CHECK_WEIGHT(2.33833, "lexical-SGT");
BOOST_CHECK(parsed.end() == parsed.find("lm"));
BOOST_CHECK_CLOSE(3.0, w.LM(), 0.001);
CHECK_WEIGHT(-28.3317, "lexical-TGS");
CHECK_WEIGHT(5.0, "glue?");
}
BOOST_AUTO_TEST_CASE(dot) {
Weights w("rarity=0 phrase-SGT=0 phrase-TGS=9.45117 lhsGrhs=0 lexical-SGT=2.33833 lexical-TGS=-28.3317 abstract?=0 LanguageModel=3 lexical?=1 glue?=5");
BOOST_CHECK_CLOSE(9.45117 * 3.0, w.DotNoLM("phrase-TGS=3.0"), 0.001);
BOOST_CHECK_CLOSE(9.45117 * 3.0, w.DotNoLM("phrase-TGS=3.0 LanguageModel=10"), 0.001);
BOOST_CHECK_CLOSE(9.45117 * 3.0 + 28.3317 * 17.4, w.DotNoLM("rarity=5 phrase-TGS=3.0 LanguageModel=10 lexical-TGS=-17.4"), 0.001);
}
} // namespace
} // namespace search