mirror of
https://github.com/moses-smt/mosesdecoder.git
synced 2024-12-27 22:14:57 +03:00
297 lines
11 KiB
C++
297 lines
11 KiB
C++
#include "moses/Incremental.h"
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#include "moses/ChartCell.h"
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#include "moses/ChartParserCallback.h"
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#include "moses/FeatureVector.h"
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#include "moses/StaticData.h"
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#include "moses/TranslationSystem.h"
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#include "moses/Util.h"
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#include "lm/model.hh"
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#include "search/applied.hh"
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#include "search/config.hh"
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#include "search/context.hh"
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#include "search/edge_generator.hh"
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#include "search/rule.hh"
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#include "search/vertex_generator.hh"
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#include <boost/lexical_cast.hpp>
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namespace Moses {
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namespace Incremental {
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namespace {
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// This is called by EdgeGenerator. Route hypotheses to separate vertices for
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// each left hand side label, populating ChartCellLabelSet out.
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template <class Best> class HypothesisCallback {
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private:
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typedef search::VertexGenerator<Best> Gen;
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public:
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HypothesisCallback(search::ContextBase &context, Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool)
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: context_(context), best_(best), out_(out), vertex_pool_(vertex_pool) {}
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void NewHypothesis(search::PartialEdge partial) {
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// Get the LHS, look it up in the output ChartCellLabel, and upcast it.
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// It's not part of the union because it would have been ugly to expose template types in ChartCellLabel.
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ChartCellLabel::Stack &stack = out_.FindOrInsert(static_cast<const TargetPhrase *>(partial.GetNote().vp)->GetTargetLHS());
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Gen *entry = static_cast<Gen*>(stack.incr_generator);
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if (!entry) {
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entry = generator_pool_.construct(context_, *vertex_pool_.construct(), best_);
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stack.incr_generator = entry;
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}
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entry->NewHypothesis(partial);
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}
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void FinishedSearch() {
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for (ChartCellLabelSet::iterator i(out_.mutable_begin()); i != out_.mutable_end(); ++i) {
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ChartCellLabel::Stack &stack = i->second.MutableStack();
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Gen *gen = static_cast<Gen*>(stack.incr_generator);
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gen->FinishedSearch();
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stack.incr = &gen->Generating();
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}
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}
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private:
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search::ContextBase &context_;
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Best &best_;
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ChartCellLabelSet &out_;
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boost::object_pool<search::Vertex> &vertex_pool_;
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boost::object_pool<Gen> generator_pool_;
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};
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// This is called by the moses parser to collect hypotheses. It converts to my
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// edges (search::PartialEdge).
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template <class Model> class Fill : public ChartParserCallback {
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public:
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Fill(search::Context<Model> &context, const std::vector<lm::WordIndex> &vocab_mapping, search::Score oov_weight)
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: context_(context), vocab_mapping_(vocab_mapping), oov_weight_(oov_weight) {}
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void Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &ignored);
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void AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &waste_memory, const WordsRange &range);
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bool Empty() const { return edges_.Empty(); }
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template <class Best> void Search(Best &best, ChartCellLabelSet &out, boost::object_pool<search::Vertex> &vertex_pool) {
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HypothesisCallback<Best> callback(context_, best, out, vertex_pool);
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edges_.Search(context_, callback);
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}
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// Root: everything into one vertex.
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template <class Best> search::History RootSearch(Best &best) {
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search::Vertex vertex;
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search::RootVertexGenerator<Best> gen(vertex, best);
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edges_.Search(context_, gen);
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return vertex.BestChild();
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}
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private:
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lm::WordIndex Convert(const Word &word) const;
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search::Context<Model> &context_;
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const std::vector<lm::WordIndex> &vocab_mapping_;
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search::EdgeGenerator edges_;
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const search::Score oov_weight_;
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};
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template <class Model> void Fill<Model>::Add(const TargetPhraseCollection &targets, const StackVec &nts, const WordsRange &) {
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std::vector<search::PartialVertex> vertices;
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vertices.reserve(nts.size());
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float below_score = 0.0;
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for (StackVec::const_iterator i(nts.begin()); i != nts.end(); ++i) {
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vertices.push_back((*i)->GetStack().incr->RootPartial());
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if (vertices.back().Empty()) return;
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below_score += vertices.back().Bound();
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}
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std::vector<lm::WordIndex> words;
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for (TargetPhraseCollection::const_iterator p(targets.begin()); p != targets.end(); ++p) {
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words.clear();
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const TargetPhrase &phrase = **p;
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const AlignmentInfo::NonTermIndexMap &align = phrase.GetAlignNonTerm().GetNonTermIndexMap();
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search::PartialEdge edge(edges_.AllocateEdge(nts.size()));
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search::PartialVertex *nt = edge.NT();
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for (size_t i = 0; i < phrase.GetSize(); ++i) {
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const Word &word = phrase.GetWord(i);
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if (word.IsNonTerminal()) {
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*(nt++) = vertices[align[i]];
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words.push_back(search::kNonTerminal);
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} else {
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words.push_back(Convert(word));
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}
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}
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edge.SetScore(phrase.GetFutureScore() + below_score);
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// prob and oov were already accounted for.
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search::ScoreRule(context_.LanguageModel(), words, edge.Between());
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search::Note note;
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note.vp = &phrase;
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edge.SetNote(note);
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edges_.AddEdge(edge);
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}
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}
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template <class Model> void Fill<Model>::AddPhraseOOV(TargetPhrase &phrase, std::list<TargetPhraseCollection*> &, const WordsRange &) {
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std::vector<lm::WordIndex> words;
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CHECK(phrase.GetSize() <= 1);
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if (phrase.GetSize())
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words.push_back(Convert(phrase.GetWord(0)));
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search::PartialEdge edge(edges_.AllocateEdge(0));
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// Appears to be a bug that FutureScore does not already include language model.
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search::ScoreRuleRet scored(search::ScoreRule(context_.LanguageModel(), words, edge.Between()));
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edge.SetScore(phrase.GetFutureScore() + scored.prob * context_.LMWeight() + static_cast<search::Score>(scored.oov) * oov_weight_);
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search::Note note;
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note.vp = &phrase;
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edge.SetNote(note);
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edges_.AddEdge(edge);
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}
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// TODO: factors (but chart doesn't seem to support factors anyway).
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template <class Model> lm::WordIndex Fill<Model>::Convert(const Word &word) const {
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std::size_t factor = word.GetFactor(0)->GetId();
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return (factor >= vocab_mapping_.size() ? 0 : vocab_mapping_[factor]);
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}
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struct ChartCellBaseFactory {
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ChartCellBase *operator()(size_t startPos, size_t endPos) const {
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return new ChartCellBase(startPos, endPos);
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}
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};
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} // namespace
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Manager::Manager(const InputType &source, const TranslationSystem &system) :
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source_(source),
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system_(system),
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cells_(source, ChartCellBaseFactory()),
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parser_(source, system, cells_),
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n_best_(search::NBestConfig(StaticData::Instance().GetNBestSize())) {}
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Manager::~Manager() {
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system_.CleanUpAfterSentenceProcessing(source_);
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}
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template <class Model, class Best> search::History Manager::PopulateBest(const Model &model, const std::vector<lm::WordIndex> &words, Best &out) {
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const LanguageModel &abstract = **system_.GetLanguageModels().begin();
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const float oov_weight = abstract.OOVFeatureEnabled() ? abstract.GetOOVWeight() : 0.0;
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const StaticData &data = StaticData::Instance();
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search::Config config(abstract.GetWeight(), data.GetCubePruningPopLimit(), search::NBestConfig(data.GetNBestSize()));
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search::Context<Model> context(config, model);
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size_t size = source_.GetSize();
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boost::object_pool<search::Vertex> vertex_pool(std::max<size_t>(size * size / 2, 32));
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for (size_t width = 1; width < size; ++width) {
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for (size_t startPos = 0; startPos <= size-width; ++startPos) {
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WordsRange range(startPos, startPos + width - 1);
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Fill<Model> filler(context, words, oov_weight);
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parser_.Create(range, filler);
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filler.Search(out, cells_.MutableBase(range).MutableTargetLabelSet(), vertex_pool);
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}
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}
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WordsRange range(0, size - 1);
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Fill<Model> filler(context, words, oov_weight);
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parser_.Create(range, filler);
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return filler.RootSearch(out);
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}
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template <class Model> void Manager::LMCallback(const Model &model, const std::vector<lm::WordIndex> &words) {
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std::size_t nbest = StaticData::Instance().GetNBestSize();
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if (nbest <= 1) {
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search::History ret = PopulateBest(model, words, single_best_);
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if (ret) {
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backing_for_single_.resize(1);
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backing_for_single_[0] = search::Applied(ret);
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} else {
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backing_for_single_.clear();
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}
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completed_nbest_ = &backing_for_single_;
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} else {
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search::History ret = PopulateBest(model, words, n_best_);
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if (ret) {
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completed_nbest_ = &n_best_.Extract(ret);
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} else {
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backing_for_single_.clear();
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completed_nbest_ = &backing_for_single_;
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}
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}
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}
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template void Manager::LMCallback<lm::ngram::ProbingModel>(const lm::ngram::ProbingModel &model, const std::vector<lm::WordIndex> &words);
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template void Manager::LMCallback<lm::ngram::RestProbingModel>(const lm::ngram::RestProbingModel &model, const std::vector<lm::WordIndex> &words);
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template void Manager::LMCallback<lm::ngram::TrieModel>(const lm::ngram::TrieModel &model, const std::vector<lm::WordIndex> &words);
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template void Manager::LMCallback<lm::ngram::QuantTrieModel>(const lm::ngram::QuantTrieModel &model, const std::vector<lm::WordIndex> &words);
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template void Manager::LMCallback<lm::ngram::ArrayTrieModel>(const lm::ngram::ArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
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template void Manager::LMCallback<lm::ngram::QuantArrayTrieModel>(const lm::ngram::QuantArrayTrieModel &model, const std::vector<lm::WordIndex> &words);
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const std::vector<search::Applied> &Manager::ProcessSentence() {
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const LMList &lms = system_.GetLanguageModels();
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UTIL_THROW_IF(lms.size() != 1, util::Exception, "Incremental search only supports one language model.");
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(*lms.begin())->IncrementalCallback(*this);
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return *completed_nbest_;
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}
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namespace {
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struct NoOp {
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void operator()(const TargetPhrase &) const {}
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};
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struct AccumScore {
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AccumScore(ScoreComponentCollection &out) : out_(&out) {}
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void operator()(const TargetPhrase &phrase) {
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out_->PlusEquals(phrase.GetScoreBreakdown());
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}
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ScoreComponentCollection *out_;
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};
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template <class Action> void AppendToPhrase(const search::Applied final, Phrase &out, Action action) {
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assert(final.Valid());
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const TargetPhrase &phrase = *static_cast<const TargetPhrase*>(final.GetNote().vp);
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action(phrase);
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const search::Applied *child = final.Children();
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for (std::size_t i = 0; i < phrase.GetSize(); ++i) {
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const Word &word = phrase.GetWord(i);
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if (word.IsNonTerminal()) {
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AppendToPhrase(*child++, out, action);
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} else {
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out.AddWord(word);
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}
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}
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}
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} // namespace
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void ToPhrase(const search::Applied final, Phrase &out) {
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out.Clear();
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AppendToPhrase(final, out, NoOp());
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}
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void PhraseAndFeatures(const TranslationSystem &system, const search::Applied final, Phrase &phrase, ScoreComponentCollection &features) {
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phrase.Clear();
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features.ZeroAll();
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AppendToPhrase(final, phrase, AccumScore(features));
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// If we made it this far, there is only one language model.
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float full, ignored_ngram;
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std::size_t ignored_oov;
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const LanguageModel &model = **system.GetLanguageModels().begin();
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model.CalcScore(phrase, full, ignored_ngram, ignored_oov);
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// CalcScore transforms, but EvaluateChart doesn't.
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features.Assign(&model, UntransformLMScore(full));
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}
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} // namespace Incremental
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} // namespace Moses
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