major restructure of the training code

1 files changed, 0 insertions, 372 deletions

diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc
deleted file mode 100644
index 2eff07e4..00000000
--- a/training/mpi_batch_optimize.cc
+++ /dev/null
@@ -1,372 +0,0 @@
-#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/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "sentence_metadata.h"
-#include "cllh_observer.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 "optimize.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")
-        ("training_data,t",po::value<string>(),"Training data")
-        ("test_data,T",po::value<string>(),"(optional) test data")
-        ("decoder_config,c",po::value<string>(),"Decoder configuration file")
-        ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
-        ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
-	("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
-        ("gaussian_prior,p","Use a Gaussian prior on the weights")
-        ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior")
-        ("means,u", po::value<string>(), "(optional) file containing the means for Gaussian prior");
-  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<prob_t>::const_iterator it = acc_grad.begin(); it != acc_grad.end(); ++it)
-      (*g)[it->first] = it->second.as_float();
-  }
-
-  virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
-    cur_model_exp.clear();
-    cur_obj = 0;
-    state = 1;
-  }
-
-  // compute model expectations, denominator of objective
-  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
-    assert(state == 1);
-    state = 2;
-    const prob_t z = InsideOutside<prob_t,
-                                   EdgeProb,
-                                   SparseVector<prob_t>,
-                                   EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
-    cur_obj = log(z);
-    cur_model_exp /= z;
-  }
-
-  // compute "empirical" expectations, numerator of objective
-  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
-    assert(state == 2);
-    state = 3;
-    SparseVector<prob_t> ref_exp;
-    const prob_t ref_z = InsideOutside<prob_t,
-                                       EdgeProb,
-                                       SparseVector<prob_t>,
-                                       EdgeFeaturesAndProbWeightFunction>(*hg, &ref_exp);
-    ref_exp /= ref_z;
-
-    double log_ref_z;
-#if 0
-    if (crf_uniform_empirical) {
-      log_ref_z = ref_exp.dot(feature_weights);
-    } else {
-      log_ref_z = log(ref_z);
-    }
-#else
-    log_ref_z = log(ref_z);
-#endif
-
-    // rounding errors means that <0 is too strict
-    if ((cur_obj - log_ref_z) < kMINUS_EPSILON) {
-      cerr << "DIFF. ERR! log_model_z < log_ref_z: " << cur_obj << " " << log_ref_z << endl;
-      exit(1);
-    }
-    assert(!std::isnan(log_ref_z));
-    ref_exp -= cur_model_exp;
-    acc_grad -= ref_exp;
-    acc_obj += (cur_obj - log_ref_z);
-    trg_words += smeta.GetReference().size();
-  }
-
-  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
-    if (state == 3) {
-      ++total_complete;
-    } else {
-    }
-  }
-
-  int total_complete;
-  SparseVector<prob_t> cur_model_exp;
-  SparseVector<prob_t> acc_grad;
-  double acc_obj;
-  double cur_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());
-}
-
-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;
-  } 
-}; 
-
-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;
-
-  // 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);
-  if (rank == 0) cerr << "Loading grammar...\n";
-  Decoder* decoder = new Decoder(&ini);
-  if (decoder->GetConf()["input"].as<string>() != "-") {
-    cerr << "cdec.ini must not set an input file\n";
-    return 1;
-  }
-  if (rank == 0) cerr << "Done loading grammar!\n";
-
-  // 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);
-
-  const bool gaussian_prior = conf.count("gaussian_prior");
-  vector<weight_t> means(num_feats, 0);
-  if (conf.count("means")) {
-    if (!gaussian_prior) {
-      cerr << "Don't use --means without --gaussian_prior!\n";
-      exit(1);
-    }
-    Weights::InitFromFile(conf["means"].as<string>(), &means);
-  }
-  boost::shared_ptr<BatchOptimizer> o;
-  if (rank == 0) {
-    const string omethod = conf["optimization_method"].as<string>();
-    if (omethod == "rprop")
-      o.reset(new RPropOptimizer(num_feats));  // TODO add configuration
-    else
-      o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
-    cerr << "Optimizer: " << o->Name() << endl;
-  }
-  double objective = 0;
-  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);
-
-  TrainingObserver observer;
-  ConditionalLikelihoodObserver cllh_observer;
-  while (!converged) {
-    observer.Reset();
-    cllh_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 (int i = 0; i < corpus.size(); ++i)
-      decoder->Decode(corpus[i], &observer);
-    cerr << "  process " << rank << '/' << size << " done\n";
-    fill(gradient.begin(), gradient.end(), 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)
-      cerr << "TRAINING CORPUS: ln p(f|e)=" << objective << "\t log_2 p(f|e) = " << (objective/log(2)) << "\t cond. entropy = " << (objective/log(2) / total_words) << "\t ppl = " << pow(2, (objective/log(2) / total_words)) << endl;
-
-    for (int i = 0; i < test_corpus.size(); ++i)
-      decoder->Decode(test_corpus[i], &cllh_observer);
-
-    double test_objective = 0;
-    unsigned test_total_words = 0;
-#ifdef HAVE_MPI
-    reduce(world, cllh_observer.acc_obj, test_objective, std::plus<double>(), 0);
-    reduce(world, cllh_observer.trg_words, test_total_words, std::plus<unsigned>(), 0);
-#else
-    test_objective = cllh_observer.acc_obj;
-    test_total_words = cllh_observer.trg_words;
-#endif
-
-    if (rank == 0) {  // run optimizer only on rank=0 node
-      if (test_corpus.size())
-        cerr << "    TEST CORPUS: ln p(f|e)=" << test_objective << "\t log_2 p(f|e) = " << (test_objective/log(2)) << "\t cond. entropy = " << (test_objective/log(2) / test_total_words) << "\t ppl = " << pow(2, (test_objective/log(2) / test_total_words)) << endl;
-      if (gaussian_prior) {
-        const double sigsq = conf["sigma_squared"].as<double>();
-        double norm = 0;
-        for (int k = 1; k < lambdas.size(); ++k) {
-          const double& lambda_k = lambdas[k];
-          if (lambda_k) {
-            const double param = (lambda_k - means[k]);
-            norm += param * param;
-            gradient[k] += param / sigsq;
-          }
-        }
-        const double reg = norm / (2.0 * sigsq);
-        cerr << "REGULARIZATION TERM: " << reg << endl;
-        objective += reg;
-      }
-      cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
-      double gnorm = 0;
-      for (int i = 0; i < gradient.size(); ++i)
-        gnorm += gradient[i] * gradient[i];
-      cerr << "  GNORM=" << sqrt(gnorm) << endl;
-      vector<weight_t> old = lambdas;
-      int c = 0;
-      while (old == lambdas) {
-        ++c;
-        if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
-        o->Optimize(objective, gradient, &lambdas);
-        assert(c < 5);
-      }
-      old.clear();
-      Weights::SanityCheck(lambdas);
-      Weights::ShowLargestFeatures(lambdas);
-
-      converged = o->HasConverged();
-      if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
-
-      string fname = "weights.cur.gz";
-      if (converged) { fname = "weights.final.gz"; }
-      ostringstream vv;
-      vv << "Objective = " << objective << "  (eval count=" << o->EvaluationCount() << ")";
-      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;
-}
-