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-rw-r--r--training/crf/mpi_baum_welch.cc316
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diff --git a/training/crf/mpi_baum_welch.cc b/training/crf/mpi_baum_welch.cc
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+#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;
+}
+