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Diffstat (limited to 'training/crf/mpi_baum_welch.cc')
| -rw-r--r-- | training/crf/mpi_baum_welch.cc | 316 |
1 files changed, 316 insertions, 0 deletions
diff --git a/training/crf/mpi_baum_welch.cc b/training/crf/mpi_baum_welch.cc new file mode 100644 index 00000000..d69b1769 --- /dev/null +++ b/training/crf/mpi_baum_welch.cc @@ -0,0 +1,316 @@ +#include <sstream> +#include <iostream> +#include <vector> +#include <cassert> +#include <cmath> + +#include "config.h" +#ifdef HAVE_MPI +#include <boost/mpi/timer.hpp> +#include <boost/mpi.hpp> +namespace mpi = boost::mpi; +#endif + +#include <boost/unordered_map.hpp> +#include <boost/functional/hash.hpp> +#include <boost/shared_ptr.hpp> +#include <boost/program_options.hpp> +#include <boost/program_options/variables_map.hpp> + +#include "sentence_metadata.h" +#include "verbose.h" +#include "hg.h" +#include "prob.h" +#include "inside_outside.h" +#include "ff_register.h" +#include "decoder.h" +#include "filelib.h" +#include "stringlib.h" +#include "fdict.h" +#include "weights.h" +#include "sparse_vector.h" + +using namespace std; +namespace po = boost::program_options; + +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") + ("iterations,n",po::value<unsigned>()->default_value(50), "Number of training iterations") + ("training_data,t",po::value<string>(),"Training data") + ("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("training_data")) || !conf->count("decoder_config")) { + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c) { + ReadFile rf(fname); + istream& in = *rf.stream(); + string line; + int lc = 0; + while(in) { + getline(in, line); + if (!in) break; + if (lc % size == rank) c->push_back(line); + ++lc; + } +} + +static const double kMINUS_EPSILON = -1e-6; + +struct TrainingObserver : public DecoderObserver { + void Reset() { + acc_grad.clear(); + acc_obj = 0; + total_complete = 0; + trg_words = 0; + } + + void SetLocalGradientAndObjective(vector<double>* g, double* o) const { + *o = acc_obj; + for (SparseVector<double>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it) + (*g)[it->first] = it->second; + } + + virtual void NotifyDecodingStart(const SentenceMetadata& smeta) { + state = 1; + } + + // compute model expectations, denominator of objective + virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) { + assert(state == 1); + trg_words += smeta.GetSourceLength(); + state = 2; + SparseVector<prob_t> exps; + const prob_t z = InsideOutside<prob_t, + EdgeProb, + SparseVector<prob_t>, + EdgeFeaturesAndProbWeightFunction>(*hg, &exps); + exps /= z; + for (SparseVector<prob_t>::iterator it = exps.begin(); it != exps.end(); ++it) + acc_grad.add_value(it->first, it->second.as_float()); + + acc_obj += log(z); + } + + // compute "empirical" expectations, numerator of objective + virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) { + cerr << "Shouldn't get an alignment forest!\n"; + abort(); + } + + virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) { + ++total_complete; + } + + int total_complete; + SparseVector<double> acc_grad; + double acc_obj; + unsigned trg_words; + int state; +}; + +void ReadConfig(const string& ini, vector<string>* out) { + ReadFile rf(ini); + istream& in = *rf.stream(); + while(in) { + string line; + getline(in, line); + if (!in) continue; + out->push_back(line); + } +} + +void StoreConfig(const vector<string>& cfg, istringstream* o) { + ostringstream os; + for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; } + o->str(os.str()); +} + +#if 0 +template <typename T> +struct VectorPlus : public binary_function<vector<T>, vector<T>, vector<T> > { + vector<T> operator()(const vector<int>& a, const vector<int>& b) const { + assert(a.size() == b.size()); + vector<T> v(a.size()); + transform(a.begin(), a.end(), b.begin(), v.begin(), plus<T>()); + return v; + } +}; +#endif + +int main(int argc, char** argv) { +#ifdef HAVE_MPI + mpi::environment env(argc, argv); + mpi::communicator world; + const int size = world.size(); + const int rank = world.rank(); +#else + const int size = 1; + const int rank = 0; +#endif + SetSilent(true); // turn off verbose decoder output + register_feature_functions(); + + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + const unsigned iterations = conf["iterations"].as<unsigned>(); + + // load cdec.ini and set up decoder + vector<string> cdec_ini; + ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini); + istringstream ini; + StoreConfig(cdec_ini, &ini); + Decoder* decoder = new Decoder(&ini); + if (decoder->GetConf()["input"].as<string>() != "-") { + cerr << "cdec.ini must not set an input file\n"; + return 1; + } + + // load initial weights + if (rank == 0) { cerr << "Loading weights...\n"; } + vector<weight_t>& lambdas = decoder->CurrentWeightVector(); + Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas); + if (rank == 0) { cerr << "Done loading weights.\n"; } + + // freeze feature set (should be optional?) + const bool freeze_feature_set = true; + if (freeze_feature_set) FD::Freeze(); + + const int num_feats = FD::NumFeats(); + if (rank == 0) cerr << "Number of features: " << num_feats << endl; + lambdas.resize(num_feats); + + vector<double> gradient(num_feats, 0.0); + vector<double> rcv_grad; + rcv_grad.clear(); + bool converged = false; + + vector<string> corpus, test_corpus; + ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus); + assert(corpus.size() > 0); + if (conf.count("test_data")) + ReadTrainingCorpus(conf["test_data"].as<string>(), rank, size, &test_corpus); + + // build map from feature id to the accumulator that should normalize + boost::unordered_map<std::string, boost::unordered_map<int, double>, boost::hash<std::string> > ccs; + vector<boost::unordered_map<int, double>* > cpd_to_acc; + if (rank == 0) { + cpd_to_acc.resize(num_feats); + for (unsigned f = 1; f < num_feats; ++f) { + string normalizer; + //0 ||| 7 9 ||| Bi:BOS_7=1 Bi:7_9=1 Bi:9_EOS=1 Id:a:7=1 Uni:7=1 Id:b:9=1 Uni:9=1 ||| 0 + const string& fstr = FD::Convert(f); + if (fstr.find("Bi:") == 0) { + size_t pos = fstr.rfind('_'); + if (pos < fstr.size()) + normalizer = fstr.substr(0, pos); + } else if (fstr.find("Id:") == 0) { + size_t pos = fstr.rfind(':'); + if (pos < fstr.size()) { + normalizer = "Emit:"; + normalizer += fstr.substr(pos); + } + } + if (normalizer.size() > 0) { + boost::unordered_map<int, double>& acc = ccs[normalizer]; + cpd_to_acc[f] = &acc; + } + } + } + + TrainingObserver observer; + int iteration = 0; + while (!converged) { + ++iteration; + observer.Reset(); +#ifdef HAVE_MPI + mpi::timer timer; + world.barrier(); +#endif + if (rank == 0) { + cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n"; + cerr << " Testset size: " << test_corpus.size() << " sentences / proc)\n"; + for(boost::unordered_map<string, boost::unordered_map<int,double>, boost::hash<string> >::iterator it = ccs.begin(); it != ccs.end(); ++it) + it->second.clear(); + } + for (int i = 0; i < corpus.size(); ++i) + decoder->Decode(corpus[i], &observer); + cerr << " process " << rank << '/' << size << " done\n"; + fill(gradient.begin(), gradient.end(), 0); + double objective = 0; + observer.SetLocalGradientAndObjective(&gradient, &objective); + + unsigned total_words = 0; +#ifdef HAVE_MPI + double to = 0; + rcv_grad.resize(num_feats, 0.0); + mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus<double>(), 0); + swap(gradient, rcv_grad); + rcv_grad.clear(); + + reduce(world, observer.trg_words, total_words, std::plus<unsigned>(), 0); + mpi::reduce(world, objective, to, plus<double>(), 0); + objective = to; +#else + total_words = observer.trg_words; +#endif + if (rank == 0) { // run optimizer only on rank=0 node + cerr << "TRAINING CORPUS: ln p(x)=" << objective << "\t log_2 p(x) = " << (objective/log(2)) << "\t cross entropy = " << (objective/log(2) / total_words) << "\t ppl = " << pow(2, (-objective/log(2) / total_words)) << endl; + for (unsigned f = 1; f < num_feats; ++f) { + boost::unordered_map<int, double>* m = cpd_to_acc[f]; + if (m && gradient[f]) { + (*m)[f] += gradient[f]; + } + for(boost::unordered_map<string, boost::unordered_map<int,double>, boost::hash<string> >::iterator it = ccs.begin(); it != ccs.end(); ++it) { + const boost::unordered_map<int,double>& ccs = it->second; + double z = 0; + for (boost::unordered_map<int,double>::const_iterator ci = ccs.begin(); ci != ccs.end(); ++ci) + z += ci->second + 1e-09; + double lz = log(z); + for (boost::unordered_map<int,double>::const_iterator ci = ccs.begin(); ci != ccs.end(); ++ci) + lambdas[ci->first] = log(ci->second + 1e-09) - lz; + } + } + Weights::SanityCheck(lambdas); + Weights::ShowLargestFeatures(lambdas); + + converged = (iteration == iterations); + + string fname = "weights.cur.gz"; + if (converged) { fname = "weights.final.gz"; } + ostringstream vv; + vv << "Objective = " << objective << " (eval count=" << iteration << ")"; + const string svv = vv.str(); + Weights::WriteToFile(fname, lambdas, true, &svv); + } // rank == 0 + int cint = converged; +#ifdef HAVE_MPI + mpi::broadcast(world, &lambdas[0], lambdas.size(), 0); + mpi::broadcast(world, cint, 0); + if (rank == 0) { cerr << " ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; } +#endif + converged = cint; + } + return 0; +} + |