path: root/training/mpi_flex_optimize.cc

#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")
        ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (options: lbfgs, sgd, rprop)")
        ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch (1 = standard SGD)")
        ("iterations,I", po::value<unsigned>()->default_value(50), "Number of passes through the training data before termination")
        ("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")
        ("eta_0,e", po::value<double>()->default_value(0.1), "Initial learning rate for SGD")
        ("L1,1","Use L1 regularization")
        ("L2,2","Use L2 regularization")
        ("regularization_strength,C", po::value<double>()->default_value(1.0), "Regularization strength (C)");
  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 << "General-purpose 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);
}

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>();

  istringstream ins;
  ReadConfig(conf["cdec_config"].as<string>(), &ins);
  Decoder decoder(&ins);

  // load initial weights
  vector<weight_t> init_weights;
  if (conf.count("weights"))
    Weights::InitFromFile(conf["weights"].as<string>(), &init_weights);

  vector<string> corpus;
  vector<int> ids;
  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
  assert(corpus.size() > 0);

  const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
  if (size_per_proc > corpus.size()) {
    cerr << "Minibatch size must be smaller than corpus size!\n";
    return 1;
  }

  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 (conf.count("random_seed"))
    rng = new MT19937(conf["random_seed"].as<uint32_t>());
  else
    rng = new MT19937;

  const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();

  if (rank == 0) {
    cerr << "Total corpus size: " << total_corpus_size << endl;
    const unsigned batch_size = size_per_proc * size;
  }

  SparseVector<double> x;
  Weights::InitSparseVector(init_weights, &x);
  CopyHGsObserver observer;

  int write_weights_every_ith = 100; // TODO configure
  int titer = -1;

  vector<weight_t>& lambdas = decoder.CurrentWeightVector();
  lambdas.swap(init_weights);
  init_weights.clear();

  int iter = -1;
  bool converged = false;
  while (!converged) {
#ifdef HAVE_MPI
    mpi::timer timer;
#endif
    x.init_vector(&lambdas);
    ++iter; ++titer;
#if 0
    if (rank == 0) {
      converged = (iter == max_iteration);
      Weights::SanityCheck(lambdas);
      Weights::ShowLargestFeatures(lambdas);
        string fname = "weights.cur.gz";
        if (iter % write_weights_every_ith == 0) {
          ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz";
          fname = o.str();
        }
        if (converged && ((ai+1)==agenda.size())) { 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=" << x.size() << '/' << FD::NumFeats() << "   passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size())) << "   eta=" << lr->eta(titer);
        const string svv = vv.str();
        cerr << svv << endl;
        Weights::WriteToFile(fname, lambdas, true, &svv);
      }
#endif

      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(lambdas);
          hg_gold.Reweight(lambdas);
          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
        // TODO obj
        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));
          vector<double> gg(FD::NumFeats());
          if (gg.size() != lambdas.size()) { lambdas.resize(gg.size()); }
          for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it)
            if (it->first) { gg[it->first] = it->second; }
          cerr << "OBJ: " << obj << endl;
          o->Optimize(obj, gg, &lambdas);
        }
#ifdef HAVE_MPI
        broadcast(world, x, 0);
        broadcast(world, converged, 0);
        world.barrier();
        if (rank == 0) { cerr << "  ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
#endif
    }
  }
  return 0;
}