major restructure of the training code

1 files changed, 0 insertions, 386 deletions

diff --git a/training/mpi_flex_optimize.cc b/training/mpi_flex_optimize.cc
deleted file mode 100644
index b52decdc..00000000
--- a/training/mpi_flex_optimize.cc
+++ /dev/null
@@ -1,386 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <fstream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "stringlib.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 "optimize.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-#include "sampler.h"
-
-#ifdef HAVE_MPI
-#include <boost/mpi/timer.hpp>
-#include <boost/mpi.hpp>
-namespace mpi = boost::mpi;
-#endif
-
-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()
-        ("cdec_config,c",po::value<string>(),"Decoder configuration file")
-        ("weights,w",po::value<string>(),"Initial feature weights")
-        ("training_data,d",po::value<string>(),"Training data")
-        ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(6), "Number of training instances evaluated per processor in each minibatch")
-        ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch")
-        ("iterations,I", po::value<unsigned>()->default_value(50), "Number of passes through the training data before termination")
-        ("regularization_strength,C", po::value<double>()->default_value(0.2), "Regularization strength")
-        ("time_series_strength,T", po::value<double>()->default_value(0.0), "Time series regularization strength")
-        ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
-        ("lbfgs_memory_buffers,M", po::value<unsigned>()->default_value(10), "Number of memory buffers for LBFGS history");
-  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("training_data") || !conf->count("cdec_config")) {
-    cerr << "LBFGS minibatch online optimizer (MPI support "
-#if HAVE_MPI
-         << "enabled"
-#else
-         << "not enabled"
-#endif
-         << ")\n" << dcmdline_options << endl;
-    return false;
-  }
-  return true;
-}
-
-void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* order) {
-  ReadFile rf(fname);
-  istream& in = *rf.stream();
-  string line;
-  int id = 0;
-  while(in) {
-    getline(in, line);
-    if (!in) break;
-    if (id % size == rank) {
-      c->push_back(line);
-      order->push_back(id);
-    }
-    ++id;
-  }
-}
-
-static const double kMINUS_EPSILON = -1e-6;
-
-struct CopyHGsObserver : public DecoderObserver {
-  Hypergraph* hg_;
-  Hypergraph* gold_hg_;
-
-  // this can free up some memory
-  void RemoveRules(Hypergraph* h) {
-    for (unsigned i = 0; i < h->edges_.size(); ++i)
-      h->edges_[i].rule_.reset();
-  }
-
-  void SetCurrentHypergraphs(Hypergraph* h, Hypergraph* gold_h) {
-    hg_ = h;
-    gold_hg_ = gold_h;
-  }
-
-  virtual void NotifyDecodingStart(const SentenceMetadata&) {
-    state = 1;
-  }
-
-  // compute model expectations, denominator of objective
-  virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
-    *hg_ = *hg;
-    RemoveRules(hg_);
-    assert(state == 1);
-    state = 2;
-  }
-
-  // compute "empirical" expectations, numerator of objective
-  virtual void NotifyAlignmentForest(const SentenceMetadata&, Hypergraph* hg) {
-    assert(state == 2);
-    state = 3;
-    *gold_hg_ = *hg;
-    RemoveRules(gold_hg_);
-  }
-
-  virtual void NotifyDecodingComplete(const SentenceMetadata&) {
-    if (state == 3) {
-    } else {
-      hg_->clear();
-      gold_hg_->clear();
-    }
-  }
-
-  int state;
-};
-
-void ReadConfig(const string& ini, istringstream* out) {
-  ReadFile rf(ini);
-  istream& in = *rf.stream();
-  ostringstream os;
-  while(in) {
-    string line;
-    getline(in, line);
-    if (!in) continue;
-    os << line << endl;
-  }
-  out->str(os.str());
-}
-
-#ifdef HAVE_MPI
-namespace boost { namespace mpi {
-  template<>
-  struct is_commutative<std::plus<SparseVector<double> >, SparseVector<double> > 
-    : mpl::true_ { };
-} } // end namespace boost::mpi
-#endif
-
-void AddGrad(const SparseVector<prob_t> x, double s, SparseVector<double>* acc) {
-  for (SparseVector<prob_t>::const_iterator it = x.begin(); it != x.end(); ++it)
-    acc->add_value(it->first, it->second.as_float() * s);
-}
-
-double PNorm(const vector<double>& v, const double p) {
-  double acc = 0;
-  for (int i = 0; i < v.size(); ++i)
-    acc += pow(v[i], p);
-  return pow(acc, 1.0 / p);
-}
-
-void VV(ostream&os, const vector<double>& v) {
-  for (int i = 1; i < v.size(); ++i)
-    if (v[i]) os << FD::Convert(i) << "=" << v[i] << " ";
-}
-
-double ApplyRegularizationTerms(const double C,
-                                const double T,
-                                const vector<double>& weights,
-                                const vector<double>& prev_weights,
-                                double* g) {
-  double reg = 0;
-  for (size_t i = 0; i < weights.size(); ++i) {
-    const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
-    const double& w_i = weights[i];
-    reg += C * w_i * w_i;
-    g[i] += 2 * C * w_i;
-
-    reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
-    g[i] += 2 * T * (w_i - prev_w_i);
-  }
-  return reg;
-}
-
-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
-  if (size > 1) SetSilent(true);  // turn off verbose decoder output
-  register_feature_functions();
-  MT19937* rng = NULL;
-
-  po::variables_map conf;
-  if (!InitCommandLine(argc, argv, &conf))
-    return 1;
-
-  boost::shared_ptr<BatchOptimizer> o;
-  const unsigned lbfgs_memory_buffers = conf["lbfgs_memory_buffers"].as<unsigned>();
-  const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
-  const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();
-  const double regularization_strength = conf["regularization_strength"].as<double>();
-  const double time_series_strength = conf["time_series_strength"].as<double>();
-  const bool use_time_series_reg = time_series_strength > 0.0;
-  const unsigned max_iteration = conf["iterations"].as<unsigned>();
-
-  vector<string> corpus;
-  vector<int> ids;
-  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
-  assert(corpus.size() > 0);
-
-  if (size_per_proc > corpus.size()) {
-    cerr << "Minibatch size (per processor) must be smaller or equal to the local corpus size!\n";
-    return 1;
-  }
-
-  // initialize decoder (loads hash functions if necessary)
-  istringstream ins;
-  ReadConfig(conf["cdec_config"].as<string>(), &ins);
-  Decoder decoder(&ins);
-
-  // load initial weights
-  vector<weight_t> prev_weights;
-  if (conf.count("weights"))
-    Weights::InitFromFile(conf["weights"].as<string>(), &prev_weights);
-
-  if (conf.count("random_seed"))
-    rng = new MT19937(conf["random_seed"].as<uint32_t>());
-  else
-    rng = new MT19937;
-
-  size_t total_corpus_size = 0;
-#ifdef HAVE_MPI
-  reduce(world, corpus.size(), total_corpus_size, std::plus<size_t>(), 0);
-#else
-  total_corpus_size = corpus.size();
-#endif
-
-  if (rank == 0)
-    cerr << "Total corpus size: " << total_corpus_size << endl;
-
-  CopyHGsObserver observer;
-
-  int write_weights_every_ith = 100; // TODO configure
-  int titer = -1;
-
-  vector<weight_t>& cur_weights = decoder.CurrentWeightVector();
-  if (use_time_series_reg) {
-    cur_weights = prev_weights;
-  } else {
-    cur_weights.swap(prev_weights);
-    prev_weights.clear();
-  }
-
-  int iter = -1;
-  bool converged = false;
-  vector<double> gg;
-  while (!converged) {
-#ifdef HAVE_MPI
-    mpi::timer timer;
-#endif
-    ++iter; ++titer;
-    if (rank == 0) {
-      converged = (iter == max_iteration);
-        string fname = "weights.cur.gz";
-        if (iter % write_weights_every_ith == 0) {
-          ostringstream o; o << "weights.epoch_" << iter << ".gz";
-          fname = o.str();
-        }
-        if (converged) { fname = "weights.final.gz"; }
-        ostringstream vv;
-        vv << "total iter=" << titer << " (of current config iter=" << iter << ")  minibatch=" << size_per_proc << " sentences/proc x " << size << " procs.   num_feats=" << FD::NumFeats() << "   passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size()));
-        const string svv = vv.str();
-        Weights::WriteToFile(fname, cur_weights, true, &svv);
-      }
-
-      vector<Hypergraph> hgs(size_per_proc);
-      vector<Hypergraph> gold_hgs(size_per_proc);
-      for (int i = 0; i < size_per_proc; ++i) {
-        int ei = corpus.size() * rng->next();
-        int id = ids[ei];
-        observer.SetCurrentHypergraphs(&hgs[i], &gold_hgs[i]);
-        decoder.SetId(id);
-        decoder.Decode(corpus[ei], &observer);
-      }
-
-      SparseVector<double> local_grad, g;
-      double local_obj = 0;
-      o.reset();
-      for (unsigned mi = 0; mi < minibatch_iterations; ++mi) {
-        local_grad.clear();
-        g.clear();
-        local_obj = 0;
-
-        for (unsigned i = 0; i < size_per_proc; ++i) {
-          Hypergraph& hg = hgs[i];
-          Hypergraph& hg_gold = gold_hgs[i];
-          if (hg.edges_.size() < 2) continue;
-
-          hg.Reweight(cur_weights);
-          hg_gold.Reweight(cur_weights);
-          SparseVector<prob_t> model_exp, gold_exp;
-          const prob_t z = InsideOutside<prob_t,
-                                         EdgeProb,
-                                         SparseVector<prob_t>,
-                                         EdgeFeaturesAndProbWeightFunction>(hg, &model_exp);
-          local_obj += log(z);
-          model_exp /= z;
-          AddGrad(model_exp, 1.0, &local_grad);
-          model_exp.clear();
-
-          const prob_t goldz = InsideOutside<prob_t,
-                                         EdgeProb,
-                                         SparseVector<prob_t>,
-                                         EdgeFeaturesAndProbWeightFunction>(hg_gold, &gold_exp);
-          local_obj -= log(goldz);
-
-          if (log(z) - log(goldz) < kMINUS_EPSILON) {
-            cerr << "DIFF. ERR! log_model_z < log_gold_z: " << log(z) << " " << log(goldz) << endl;
-            return 1;
-          }
-
-          gold_exp /= goldz;
-          AddGrad(gold_exp, -1.0, &local_grad);
-        }
-
-        double obj = 0;
-#ifdef HAVE_MPI
-        reduce(world, local_obj, obj, std::plus<double>(), 0);
-        reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
-#else
-        obj = local_obj;
-        g.swap(local_grad);
-#endif
-        local_grad.clear();
-        if (rank == 0) {
-          // g /= (size_per_proc * size);
-          if (!o)
-            o.reset(new LBFGSOptimizer(FD::NumFeats(), lbfgs_memory_buffers));
-          gg.clear();
-          gg.resize(FD::NumFeats());
-          if (gg.size() != cur_weights.size()) { cur_weights.resize(gg.size()); }
-          for (SparseVector<double>::iterator it = g.begin(); it != g.end(); ++it)
-            if (it->first) { gg[it->first] = it->second; }
-          g.clear();
-          double r = ApplyRegularizationTerms(regularization_strength,
-                                time_series_strength, // * (iter == 0 ? 0.0 : 1.0),
-                                cur_weights,
-                                prev_weights,
-                                &gg[0]);
-          obj += r;
-          if (mi == 0 || mi == (minibatch_iterations - 1)) {
-            if (!mi) cerr << iter << ' '; else cerr << ' ';
-            cerr << "OBJ=" << obj << " (REG=" << r << ")" << " |g|=" << PNorm(gg, 2) << " |w|=" << PNorm(cur_weights, 2); 
-            if (mi > 0) cerr << endl << flush; else cerr << ' ';
-          } else { cerr << '.' << flush; }
-          // cerr << "w = "; VV(cerr, cur_weights); cerr << endl;
-          // cerr << "g = "; VV(cerr, gg); cerr << endl;
-          o->Optimize(obj, gg, &cur_weights);
-        }
-#ifdef HAVE_MPI
-        broadcast(world, cur_weights, 0);
-        broadcast(world, converged, 0);
-        world.barrier();
-#endif
-    }
-    prev_weights = cur_weights;
-  }
-  return 0;
-}