diff options
Diffstat (limited to 'training/mira')
| -rw-r--r-- | training/mira/Makefile.am | 6 | ||||
| -rw-r--r-- | training/mira/kbest_mira.cc | 322 |
2 files changed, 328 insertions, 0 deletions
diff --git a/training/mira/Makefile.am b/training/mira/Makefile.am new file mode 100644 index 00000000..fa4fb22d --- /dev/null +++ b/training/mira/Makefile.am @@ -0,0 +1,6 @@ +bin_PROGRAMS = kbest_mira + +kbest_mira_SOURCES = kbest_mira.cc +kbest_mira_LDADD = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a + +AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval diff --git a/training/mira/kbest_mira.cc b/training/mira/kbest_mira.cc new file mode 100644 index 00000000..d59b4224 --- /dev/null +++ b/training/mira/kbest_mira.cc @@ -0,0 +1,322 @@ +#include <sstream> +#include <iostream> +#include <vector> +#include <cassert> +#include <cmath> +#include <tr1/memory> + +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "stringlib.h" +#include "hg_sampler.h" +#include "sentence_metadata.h" +#include "ns.h" +#include "ns_docscorer.h" +#include "verbose.h" +#include "viterbi.h" +#include "hg.h" +#include "prob.h" +#include "kbest.h" +#include "ff_register.h" +#include "decoder.h" +#include "filelib.h" +#include "fdict.h" +#include "weights.h" +#include "sparse_vector.h" +#include "sampler.h" + +using namespace std; +namespace po = boost::program_options; + +bool invert_score; +std::tr1::shared_ptr<MT19937> rng; + +void RandomPermutation(int len, vector<int>* p_ids) { + vector<int>& ids = *p_ids; + ids.resize(len); + for (int i = 0; i < len; ++i) ids[i] = i; + for (int i = len; i > 0; --i) { + int j = rng->next() * i; + if (j == i) i--; + swap(ids[i-1], ids[j]); + } +} + +bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("input_weights,w",po::value<string>(),"Input feature weights file") + ("source,i",po::value<string>(),"Source file for development set") + ("passes,p", po::value<int>()->default_value(15), "Number of passes through the training data") + ("reference,r",po::value<vector<string> >(), "[REQD] Reference translation(s) (tokenized text file)") + ("mt_metric,m",po::value<string>()->default_value("ibm_bleu"), "Scoring metric (ibm_bleu, nist_bleu, koehn_bleu, ter, combi)") + ("max_step_size,C", po::value<double>()->default_value(0.01), "regularization strength (C)") + ("mt_metric_scale,s", po::value<double>()->default_value(1.0), "Amount to scale MT loss function by") + ("k_best_size,k", po::value<int>()->default_value(250), "Size of hypothesis list to search for oracles") + ("sample_forest,f", "Instead of a k-best list, sample k hypotheses from the decoder's forest") + ("sample_forest_unit_weight_vector,x", "Before sampling (must use -f option), rescale the weight vector used so it has unit length; this may improve the quality of the samples") + ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)") + ("decoder_config,c",po::value<string>(),"Decoder configuration file"); + po::options_description clo("Command line options"); + clo.add_options() + ("config", po::value<string>(), "Configuration file") + ("help,h", "Print this help message and exit"); + po::options_description dconfig_options, dcmdline_options; + dconfig_options.add(opts); + dcmdline_options.add(opts).add(clo); + + po::store(parse_command_line(argc, argv, dcmdline_options), *conf); + if (conf->count("config")) { + ifstream config((*conf)["config"].as<string>().c_str()); + po::store(po::parse_config_file(config, dconfig_options), *conf); + } + po::notify(*conf); + + if (conf->count("help") || !conf->count("input_weights") || !conf->count("source") || !conf->count("decoder_config") || !conf->count("reference")) { + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +static const double kMINUS_EPSILON = -1e-6; + +struct HypothesisInfo { + SparseVector<double> features; + double mt_metric; +}; + +struct GoodBadOracle { + std::tr1::shared_ptr<HypothesisInfo> good; + std::tr1::shared_ptr<HypothesisInfo> bad; +}; + +struct TrainingObserver : public DecoderObserver { + TrainingObserver(const int k, const DocumentScorer& d, const EvaluationMetric& m, bool sf, vector<GoodBadOracle>* o) : ds(d), metric(m), oracles(*o), kbest_size(k), sample_forest(sf) {} + const DocumentScorer& ds; + const EvaluationMetric& metric; + vector<GoodBadOracle>& oracles; + std::tr1::shared_ptr<HypothesisInfo> cur_best; + const int kbest_size; + const bool sample_forest; + + const HypothesisInfo& GetCurrentBestHypothesis() const { + return *cur_best; + } + + virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { + UpdateOracles(smeta.GetSentenceID(), *hg); + } + + std::tr1::shared_ptr<HypothesisInfo> MakeHypothesisInfo(const SparseVector<double>& feats, const double score) { + std::tr1::shared_ptr<HypothesisInfo> h(new HypothesisInfo); + h->features = feats; + h->mt_metric = score; + return h; + } + + void UpdateOracles(int sent_id, const Hypergraph& forest) { + std::tr1::shared_ptr<HypothesisInfo>& cur_good = oracles[sent_id].good; + std::tr1::shared_ptr<HypothesisInfo>& cur_bad = oracles[sent_id].bad; + cur_bad.reset(); // TODO get rid of?? + + if (sample_forest) { + vector<WordID> cur_prediction; + ViterbiESentence(forest, &cur_prediction); + SufficientStats sstats; + ds[sent_id]->Evaluate(cur_prediction, &sstats); + float sentscore = metric.ComputeScore(sstats); + cur_best = MakeHypothesisInfo(ViterbiFeatures(forest), sentscore); + + vector<HypergraphSampler::Hypothesis> samples; + HypergraphSampler::sample_hypotheses(forest, kbest_size, &*rng, &samples); + for (unsigned i = 0; i < samples.size(); ++i) { + ds[sent_id]->Evaluate(samples[i].words, &sstats); + float sentscore = metric.ComputeScore(sstats); + if (invert_score) sentscore *= -1.0; + if (!cur_good || sentscore > cur_good->mt_metric) + cur_good = MakeHypothesisInfo(samples[i].fmap, sentscore); + if (!cur_bad || sentscore < cur_bad->mt_metric) + cur_bad = MakeHypothesisInfo(samples[i].fmap, sentscore); + } + } else { + KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, kbest_size); + SufficientStats sstats; + for (int i = 0; i < kbest_size; ++i) { + const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = + kbest.LazyKthBest(forest.nodes_.size() - 1, i); + if (!d) break; + ds[sent_id]->Evaluate(d->yield, &sstats); + float sentscore = metric.ComputeScore(sstats); + if (invert_score) sentscore *= -1.0; + // cerr << TD::GetString(d->yield) << " ||| " << d->score << " ||| " << sentscore << endl; + if (i == 0) + cur_best = MakeHypothesisInfo(d->feature_values, sentscore); + if (!cur_good || sentscore > cur_good->mt_metric) + cur_good = MakeHypothesisInfo(d->feature_values, sentscore); + if (!cur_bad || sentscore < cur_bad->mt_metric) + cur_bad = MakeHypothesisInfo(d->feature_values, sentscore); + } + //cerr << "GOOD: " << cur_good->mt_metric << endl; + //cerr << " CUR: " << cur_best->mt_metric << endl; + //cerr << " BAD: " << cur_bad->mt_metric << endl; + } + } +}; + +void ReadTrainingCorpus(const string& fname, vector<string>* c) { + ReadFile rf(fname); + istream& in = *rf.stream(); + string line; + while(in) { + getline(in, line); + if (!in) break; + c->push_back(line); + } +} + +bool ApproxEqual(double a, double b) { + if (a == b) return true; + return (fabs(a-b)/fabs(b)) < 0.000001; +} + +int main(int argc, char** argv) { + register_feature_functions(); + SetSilent(true); // turn off verbose decoder output + + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + + if (conf.count("random_seed")) + rng.reset(new MT19937(conf["random_seed"].as<uint32_t>())); + else + rng.reset(new MT19937); + const bool sample_forest = conf.count("sample_forest") > 0; + const bool sample_forest_unit_weight_vector = conf.count("sample_forest_unit_weight_vector") > 0; + if (sample_forest_unit_weight_vector && !sample_forest) { + cerr << "Cannot --sample_forest_unit_weight_vector without --sample_forest" << endl; + return 1; + } + vector<string> corpus; + ReadTrainingCorpus(conf["source"].as<string>(), &corpus); + + string metric_name = UppercaseString(conf["mt_metric"].as<string>()); + if (metric_name == "COMBI") { + cerr << "WARNING: 'combi' metric is no longer supported, switching to 'COMB:TER=-0.5;IBM_BLEU=0.5'\n"; + metric_name = "COMB:TER=-0.5;IBM_BLEU=0.5"; + } else if (metric_name == "BLEU") { + cerr << "WARNING: 'BLEU' is ambiguous, assuming 'IBM_BLEU'\n"; + metric_name = "IBM_BLEU"; + } + EvaluationMetric* metric = EvaluationMetric::Instance(metric_name); + DocumentScorer ds(metric, conf["reference"].as<vector<string> >()); + cerr << "Loaded " << ds.size() << " references for scoring with " << metric_name << endl; + invert_score = metric->IsErrorMetric(); + + if (ds.size() != corpus.size()) { + cerr << "Mismatched number of references (" << ds.size() << ") and sources (" << corpus.size() << ")\n"; + return 1; + } + + ReadFile ini_rf(conf["decoder_config"].as<string>()); + Decoder decoder(ini_rf.stream()); + + // load initial weights + vector<weight_t>& dense_weights = decoder.CurrentWeightVector(); + SparseVector<weight_t> lambdas; + Weights::InitFromFile(conf["input_weights"].as<string>(), &dense_weights); + Weights::InitSparseVector(dense_weights, &lambdas); + + const double max_step_size = conf["max_step_size"].as<double>(); + const double mt_metric_scale = conf["mt_metric_scale"].as<double>(); + + assert(corpus.size() > 0); + vector<GoodBadOracle> oracles(corpus.size()); + + TrainingObserver observer(conf["k_best_size"].as<int>(), ds, *metric, sample_forest, &oracles); + int cur_sent = 0; + int lcount = 0; + int normalizer = 0; + double tot_loss = 0; + int dots = 0; + int cur_pass = 0; + SparseVector<double> tot; + tot += lambdas; // initial weights + normalizer++; // count for initial weights + int max_iteration = conf["passes"].as<int>() * corpus.size(); + string msg = "# MIRA tuned weights"; + string msga = "# MIRA tuned weights AVERAGED"; + vector<int> order; + RandomPermutation(corpus.size(), &order); + while (lcount <= max_iteration) { + lambdas.init_vector(&dense_weights); + if ((cur_sent * 40 / corpus.size()) > dots) { ++dots; cerr << '.'; } + if (corpus.size() == cur_sent) { + cerr << " [AVG METRIC LAST PASS=" << (tot_loss / corpus.size()) << "]\n"; + Weights::ShowLargestFeatures(dense_weights); + cur_sent = 0; + tot_loss = 0; + dots = 0; + ostringstream os; + os << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << ".gz"; + SparseVector<double> x = tot; + x /= normalizer; + ostringstream sa; + sa << "weights.mira-pass" << (cur_pass < 10 ? "0" : "") << cur_pass << "-avg.gz"; + x.init_vector(&dense_weights); + Weights::WriteToFile(os.str(), dense_weights, true, &msg); + ++cur_pass; + RandomPermutation(corpus.size(), &order); + } + if (cur_sent == 0) { + cerr << "PASS " << (lcount / corpus.size() + 1) << endl; + } + decoder.SetId(order[cur_sent]); + double sc = 1.0; + if (sample_forest_unit_weight_vector) { + sc = lambdas.l2norm(); + if (sc > 0) { + for (unsigned i = 0; i < dense_weights.size(); ++i) + dense_weights[i] /= sc; + } + } + decoder.Decode(corpus[order[cur_sent]], &observer); // update oracles + if (sc && sc != 1.0) { + for (unsigned i = 0; i < dense_weights.size(); ++i) + dense_weights[i] *= sc; + } + const HypothesisInfo& cur_hyp = observer.GetCurrentBestHypothesis(); + const HypothesisInfo& cur_good = *oracles[order[cur_sent]].good; + const HypothesisInfo& cur_bad = *oracles[order[cur_sent]].bad; + tot_loss += cur_hyp.mt_metric; + if (!ApproxEqual(cur_hyp.mt_metric, cur_good.mt_metric)) { + const double loss = cur_bad.features.dot(dense_weights) - cur_good.features.dot(dense_weights) + + mt_metric_scale * (cur_good.mt_metric - cur_bad.mt_metric); + //cerr << "LOSS: " << loss << endl; + if (loss > 0.0) { + SparseVector<double> diff = cur_good.features; + diff -= cur_bad.features; + double step_size = loss / diff.l2norm_sq(); + //cerr << loss << " " << step_size << " " << diff << endl; + if (step_size > max_step_size) step_size = max_step_size; + lambdas += (cur_good.features * step_size); + lambdas -= (cur_bad.features * step_size); + //cerr << "L: " << lambdas << endl; + } + } + tot += lambdas; + ++normalizer; + ++lcount; + ++cur_sent; + } + cerr << endl; + Weights::WriteToFile("weights.mira-final.gz", dense_weights, true, &msg); + tot /= normalizer; + tot.init_vector(dense_weights); + msg = "# MIRA tuned weights (averaged vector)"; + Weights::WriteToFile("weights.mira-final-avg.gz", dense_weights, true, &msg); + cerr << "Optimization complete.\nAVERAGED WEIGHTS: weights.mira-final-avg.gz\n"; + return 0; +} + |