mpi support for training

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@654 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 322 insertions, 0 deletions

diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
new file mode 100644
index 00000000..95b462bb
--- /dev/null
+++ b/training/mpi_online_optimize.cc
@@ -0,0 +1,322 @@
+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <mpi.h>
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "verbose.h"
+#include "hg.h"
+#include "prob.h"
+#include "inside_outside.h"
+#include "ff_register.h"
+#include "decoder.h"
+#include "filelib.h"
+#include "online_optimizer.h"
+#include "fdict.h"
+#include "weights.h"
+#include "sparse_vector.h"
+#include "sampler.h"
+
+using namespace std;
+using boost::shared_ptr;
+namespace po = boost::program_options;
+
+void SanityCheck(const vector<double>& w) {
+  for (int i = 0; i < w.size(); ++i) {
+    assert(!isnan(w[i]));
+    assert(!isinf(w[i]));
+  }
+}
+
+struct FComp {
+  const vector<double>& w_;
+  FComp(const vector<double>& w) : w_(w) {}
+  bool operator()(int a, int b) const {
+    return fabs(w_[a]) > fabs(w_[b]);
+  }
+};
+
+void ShowLargestFeatures(const vector<double>& w) {
+  vector<int> fnums(w.size());
+  for (int i = 0; i < w.size(); ++i)
+    fnums[i] = i;
+  vector<int>::iterator mid = fnums.begin();
+  mid += (w.size() > 10 ? 10 : w.size());
+  partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
+  cerr << "TOP FEATURES:";
+  for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
+    cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
+  }
+  cerr << endl;
+}
+
+void 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 corpus")
+        ("decoder_config,c",po::value<string>(),"Decoder configuration file")
+        ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
+        ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(5), "Number of training instances evaluated per processor in each minibatch")
+        ("freeze_feature_set,Z", "The feature set specified in the initial weights file is frozen throughout the duration of training")
+        ("optimization_method,m", po::value<string>()->default_value("sgd"), "Optimization method (sgd)")
+        ("eta_0,e", po::value<double>()->default_value(0.2), "Initial learning rate for SGD (eta_0)")
+        ("L1,1","Use L1 regularization")
+        ("gaussian_prior,g","Use a Gaussian prior on the weights")
+        ("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical 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;
+    MPI::Finalize();
+    exit(1);
+  }
+}
+
+void ReadTrainingCorpus(const string& fname, vector<string>* c) {
+  ReadFile rf(fname);
+  istream& in = *rf.stream();
+  string line;
+  while(in) {
+    getline(in, line);
+    if (!in) break;
+    c->push_back(line);
+  }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct TrainingObserver : public DecoderObserver {
+  void Reset() {
+    acc_grad.clear();
+    acc_obj = 0;
+    total_complete = 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;
+  }
+
+  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(!isnan(log_ref_z));
+    ref_exp -= cur_model_exp;
+    acc_grad -= ref_exp;
+    acc_obj += (cur_obj - log_ref_z);
+  }
+
+  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;
+  int state;
+};
+
+template <typename T>
+inline void Shuffle(vector<T>* c, MT19937* rng) {
+  unsigned size = c->size();
+  for (unsigned i = size - 1; i > 0; --i) {
+    const unsigned j = static_cast<unsigned>(rng->next() * i);
+    swap((*c)[j], (*c)[i]);
+  }
+}
+
+int main(int argc, char** argv) {
+  MPI::Init(argc, argv);
+  const int size = MPI::COMM_WORLD.Get_size(); 
+  const int rank = MPI::COMM_WORLD.Get_rank();
+  SetSilent(true);  // turn off verbose decoder output
+  cerr << "MPI: I am " << rank << '/' << size << endl;
+  register_feature_functions();
+  MT19937* rng = NULL;
+  if (rank == 0) rng = new MT19937;
+
+  po::variables_map conf;
+  InitCommandLine(argc, argv, &conf);
+
+  // load initial weights
+  Weights weights;
+  weights.InitFromFile(conf["input_weights"].as<string>());
+
+  // freeze feature set
+  const bool freeze_feature_set = conf.count("freeze_feature_set");
+  if (freeze_feature_set) FD::Freeze();
+
+  // load cdec.ini and set up decoder
+  ReadFile ini_rf(conf["decoder_config"].as<string>());
+  Decoder decoder(ini_rf.stream());
+  if (decoder.GetConf()["input"].as<string>() != "-") {
+    cerr << "cdec.ini must not set an input file\n";
+    MPI::COMM_WORLD.Abort(1);
+  }
+
+  vector<string> corpus;
+  ReadTrainingCorpus(conf["training_data"].as<string>(), &corpus);
+  assert(corpus.size() > 0);
+
+  std::tr1::shared_ptr<OnlineOptimizer> o;
+  std::tr1::shared_ptr<LearningRateSchedule> lr;
+  if (rank == 0) {
+    // TODO config
+    lr.reset(new ExponentialDecayLearningRate(corpus.size(), conf["eta_0"].as<double>()));
+
+    const string omethod = conf["optimization_method"].as<string>();
+    if (omethod == "sgd") {
+      const double C = 1.0;
+      o.reset(new CumulativeL1OnlineOptimizer(lr, corpus.size(), C));
+    } else {
+      assert(!"fail");
+    }
+  }
+  double objective = 0;
+  vector<double> lambdas;
+  weights.InitVector(&lambdas);
+  bool converged = false;
+
+  TrainingObserver observer;
+  while (!converged) {
+    observer.Reset();
+    if (rank == 0) {
+      cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
+    }
+    decoder.SetWeights(lambdas);
+#if 0
+    for (int i = 0; i < corpus.size(); ++i)
+      decoder.Decode(corpus[i], &observer);
+
+    fill(gradient.begin(), gradient.end(), 0);
+    fill(rcv_grad.begin(), rcv_grad.end(), 0);
+    observer.SetLocalGradientAndObjective(&gradient, &objective);
+
+    double to = 0;
+    MPI::COMM_WORLD.Reduce(const_cast<double*>(&gradient.data()[0]), &rcv_grad[0], num_feats, MPI::DOUBLE, MPI::SUM, 0);
+    MPI::COMM_WORLD.Reduce(&objective, &to, 1, MPI::DOUBLE, MPI::SUM, 0);
+    swap(gradient, rcv_grad);
+    objective = to;
+
+    if (rank == 0) {  // run optimizer only on rank=0 node
+      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<double> 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();
+      SanityCheck(lambdas);
+      ShowLargestFeatures(lambdas);
+      weights.InitFromVector(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, true, &svv);
+    }  // rank == 0
+    int cint = converged;
+    MPI::COMM_WORLD.Bcast(const_cast<double*>(&lambdas.data()[0]), num_feats, MPI::DOUBLE, 0);
+    MPI::COMM_WORLD.Bcast(&cint, 1, MPI::INT, 0);
+    MPI::COMM_WORLD.Barrier();
+    converged = cint;
+#endif
+  }
+  MPI::Finalize(); 
+  return 0;
+}