get rid of bad Weights class so it no longer keeps a copy of a vector inside it

8 files changed, 72 insertions, 192 deletions

diff --git a/training/Makefile.am b/training/Makefile.am
index e075e417..6e2c06f5 100644
--- a/training/Makefile.am
+++ b/training/Makefile.am
@@ -12,9 +12,7 @@ bin_PROGRAMS = \
   cllh_filter_grammar \
   mpi_online_optimize \
   mpi_batch_optimize \
-  mpi_em_optimize \
   compute_cllh \
-  feature_expectations \
   augment_grammar
 
 noinst_PROGRAMS = \
@@ -29,12 +27,6 @@ mpi_online_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval
 mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc optimize.cc
 mpi_batch_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
-feature_expectations_SOURCES = feature_expectations.cc
-feature_expectations_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
-
-mpi_em_optimize_SOURCES = mpi_em_optimize.cc optimize.cc
-mpi_em_optimize_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
-
 compute_cllh_SOURCES = compute_cllh.cc
 compute_cllh_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz
 
diff --git a/training/augment_grammar.cc b/training/augment_grammar.cc
index df8d4ee8..e89a92d5 100644
--- a/training/augment_grammar.cc
+++ b/training/augment_grammar.cc
@@ -134,9 +134,7 @@ int main(int argc, char** argv) {
   } else { ngram = NULL; }
   extra_feature = conf.count("extra_lex_feature") > 0;
   if (conf.count("collapse_weights")) {
-    Weights w;
-    w.InitFromFile(conf["collapse_weights"].as<string>());
-    w.InitVector(&col_weights);
+    Weights::InitFromFile(conf["collapse_weights"].as<string>(), &col_weights);
   }
   clear_features = conf.count("clear_features_after_collapse") > 0;
   gather_rules = false;
diff --git a/training/collapse_weights.cc b/training/collapse_weights.cc
index 4fb742fb..dc480f6c 100644
--- a/training/collapse_weights.cc
+++ b/training/collapse_weights.cc
@@ -59,10 +59,8 @@ int main(int argc, char** argv) {
   InitCommandLine(argc, argv, &conf);
   const string wfile = conf["weights"].as<string>();
   const string gfile = conf["grammar"].as<string>();
-  Weights wm;
-  wm.InitFromFile(wfile);
-  vector<double> w;
-  wm.InitVector(&w);
+  vector<weight_t> w;
+  Weights::InitFromFile(wfile, &w);
   MarginalMap e_tots;
   MarginalMap f_tots;
   prob_t tot;
diff --git a/training/compute_cllh.cc b/training/compute_cllh.cc
index 332f6d0c..b496d196 100644
--- a/training/compute_cllh.cc
+++ b/training/compute_cllh.cc
@@ -148,15 +148,6 @@ int main(int argc, char** argv) {
   if (!InitCommandLine(argc, argv, &conf))
     return false;
 
-  // load initial weights
-  Weights weights;
-  if (conf.count("weights"))
-    weights.InitFromFile(conf["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());
@@ -165,17 +156,22 @@ int main(int argc, char** argv) {
     abort();
   }
 
+  // load weights
+  vector<weight_t>& weights = decoder.CurrentWeightVector();
+  if (conf.count("weights"))
+    Weights::InitFromFile(conf["weights"].as<string>(), &weights);
+
+  // freeze feature set
+  //const bool freeze_feature_set = conf.count("freeze_feature_set");
+  //if (freeze_feature_set) FD::Freeze();
+
   vector<string> corpus; vector<int> ids;
   ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
   assert(corpus.size() > 0);
   assert(corpus.size() == ids.size());
 
-  vector<double> wv;
-  weights.InitVector(&wv);
-  decoder.SetWeights(wv);
   TrainingObserver observer;
   double objective = 0;
-  bool converged = false;
 
   observer.Reset();
   if (rank == 0)
@@ -197,3 +193,4 @@ int main(int argc, char** argv) {
 
   return 0;
 }
+
diff --git a/training/grammar_convert.cc b/training/grammar_convert.cc
index 8d292f8a..bf8abb26 100644
--- a/training/grammar_convert.cc
+++ b/training/grammar_convert.cc
@@ -251,12 +251,10 @@ int main(int argc, char **argv) {
   const bool is_split_input = (conf["format"].as<string>() == "split");
   const bool is_json_input = is_split_input || (conf["format"].as<string>() == "json");
   const bool collapse_weights = conf.count("collapse_weights");
-  Weights wts;
   vector<double> w;
-  if (conf.count("weights")) {
-    wts.InitFromFile(conf["weights"].as<string>());
-    wts.InitVector(&w);
-  }
+  if (conf.count("weights"))
+    Weights::InitFromFile(conf["weights"].as<string>(), &w);
+
   if (collapse_weights && !w.size()) {
     cerr << "--collapse_weights requires a weights file to be specified!\n";
     exit(1);
diff --git a/training/mpi_batch_optimize.cc b/training/mpi_batch_optimize.cc
index 39a8af7d..cc5953f6 100644
--- a/training/mpi_batch_optimize.cc
+++ b/training/mpi_batch_optimize.cc
@@ -31,42 +31,12 @@ 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;
-}
-
 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")
         ("decoder_config,d",po::value<string>(),"Decoder configuration file")
-        ("sharded_input,s",po::value<string>(), "Corpus and grammar files are 'sharded' so each processor loads its own input and grammar file. Argument is the directory containing the shards.")
         ("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")
@@ -88,14 +58,10 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   }
   po::notify(*conf);
 
-  if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data") | conf->count("sharded_input")) || !conf->count("decoder_config")) {
+  if (conf->count("help") || !conf->count("input_weights") || !(conf->count("training_data")) || !conf->count("decoder_config")) {
     cerr << dcmdline_options << endl;
     return false;
   }
-  if (conf->count("training_data") && conf->count("sharded_input")) {
-    cerr << "Cannot specify both --training_data and --sharded_input\n";
-    return false;
-  }
   return true;
 }
 
@@ -236,42 +202,9 @@ int main(int argc, char** argv) {
   po::variables_map conf;
   if (!InitCommandLine(argc, argv, &conf)) return 1;
 
-  string shard_dir;
-  if (conf.count("sharded_input")) {
-    shard_dir = conf["sharded_input"].as<string>();
-    if (!DirectoryExists(shard_dir)) {
-      if (rank == 0) cerr << "Can't find shard directory: " << shard_dir << endl;
-      return 1;
-    }
-    if (rank == 0)
-      cerr << "Shard directory: " << shard_dir << endl;
-  }
-
-  // load initial weights
-  Weights weights;
-  if (rank == 0) { cerr << "Loading weights...\n"; }
-  weights.InitFromFile(conf["input_weights"].as<string>());
-  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();
-
   // load cdec.ini and set up decoder
   vector<string> cdec_ini;
   ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
-  if (shard_dir.size()) {
-    if (rank == 0) {
-      for (int i = 0; i < cdec_ini.size(); ++i) {
-        if (cdec_ini[i].find("grammar=") == 0) {
-          cerr << "!!! using sharded input and " << conf["decoder_config"].as<string>() << " contains a grammar specification:\n" << cdec_ini[i] << "\n  VERIFY THAT THIS IS CORRECT!\n";
-        }
-      }
-    }
-    ostringstream g;
-    g << "grammar=" << shard_dir << "/grammar." << rank << "_of_" << size << ".gz";
-    cdec_ini.push_back(g.str());
-  }
   istringstream ini;
   StoreConfig(cdec_ini, &ini);
   if (rank == 0) cerr << "Loading grammar...\n";
@@ -282,22 +215,28 @@ int main(int argc, char** argv) {
   }
   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<double> means(num_feats, 0);
+  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 wm; 
-    wm.InitFromFile(conf["means"].as<string>());
-    if (num_feats != FD::NumFeats()) {
-      cerr << "[ERROR] Means file had unexpected features!\n";
-      exit(1);
-    }
-    wm.InitVector(&means);
+    Weights::InitFromFile(conf["means"].as<string>(), &means);
   }
   shared_ptr<BatchOptimizer> o;
   if (rank == 0) {
@@ -309,26 +248,13 @@ int main(int argc, char** argv) {
     cerr << "Optimizer: " << o->Name() << endl;
   }
   double objective = 0;
-  vector<double> lambdas(num_feats, 0.0);
-  weights.InitVector(&lambdas);
-  if (lambdas.size() != num_feats) {
-    cerr << "Initial weights file did not have all features specified!\n  feats="
-         << num_feats << "\n  weights file=" << lambdas.size() << endl;
-    lambdas.resize(num_feats, 0.0);
-  }
   vector<double> gradient(num_feats, 0.0);
-  vector<double> rcv_grad(num_feats, 0.0);
+  vector<double> rcv_grad;
+  rcv_grad.clear();
   bool converged = false;
 
   vector<string> corpus;
-  if (shard_dir.size()) {
-    ostringstream os; os << shard_dir << "/corpus." << rank << "_of_" << size;
-    ReadTrainingCorpus(os.str(), 0, 1, &corpus);
-    cerr << os.str() << " has " << corpus.size() << " training examples. " << endl;
-    if (corpus.size() > 500) { corpus.resize(500); cerr << "  TRUNCATING\n"; }
-  } else {
-    ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
-  }
+  ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
   assert(corpus.size() > 0);
 
   TrainingObserver observer;
@@ -341,19 +267,20 @@ int main(int argc, char** argv) {
     if (rank == 0) {
       cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
     }
-    decoder->SetWeights(lambdas);
     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);
-    fill(rcv_grad.begin(), rcv_grad.end(), 0);
     observer.SetLocalGradientAndObjective(&gradient, &objective);
 
     double to = 0;
 #ifdef HAVE_MPI
+    rcv_grad.resize(num_feats, 0.0);
     mpi::reduce(world, &gradient[0], gradient.size(), &rcv_grad[0], plus<double>(), 0);
-    mpi::reduce(world, objective, to, plus<double>(), 0);
     swap(gradient, rcv_grad);
+    rcv_grad.clear();
+
+    mpi::reduce(world, objective, to, plus<double>(), 0);
     objective = to;
 #endif
 
@@ -378,7 +305,7 @@ int main(int argc, char** argv) {
       for (int i = 0; i < gradient.size(); ++i)
         gnorm += gradient[i] * gradient[i];
       cerr << "  GNORM=" << sqrt(gnorm) << endl;
-      vector<double> old = lambdas;
+      vector<weight_t> old = lambdas;
       int c = 0;
       while (old == lambdas) {
         ++c;
@@ -387,9 +314,8 @@ int main(int argc, char** argv) {
         assert(c < 5);
       }
       old.clear();
-      SanityCheck(lambdas);
-      ShowLargestFeatures(lambdas);
-      weights.InitFromVector(lambdas);
+      Weights::SanityCheck(lambdas);
+      Weights::ShowLargestFeatures(lambdas);
 
       converged = o->HasConverged();
       if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
@@ -399,7 +325,7 @@ int main(int argc, char** argv) {
       ostringstream vv;
       vv << "Objective = " << objective << "  (eval count=" << o->EvaluationCount() << ")";
       const string svv = vv.str();
-      weights.WriteToFile(fname, true, &svv);
+      Weights::WriteToFile(fname, lambdas, true, &svv);
     }  // rank == 0
     int cint = converged;
 #ifdef HAVE_MPI
@@ -411,3 +337,4 @@ int main(int argc, char** argv) {
   }
   return 0;
 }
+
diff --git a/training/mpi_online_optimize.cc b/training/mpi_online_optimize.cc
index 32033c19..2ef4a2e7 100644
--- a/training/mpi_online_optimize.cc
+++ b/training/mpi_online_optimize.cc
@@ -31,35 +31,6 @@ namespace mpi = boost::mpi;
 using namespace std;
 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;
-}
-
 bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
   po::options_description opts("Configuration options");
   opts.add_options()
@@ -250,10 +221,25 @@ int main(int argc, char** argv) {
   if (!InitCommandLine(argc, argv, &conf))
     return 1;
 
+  vector<pair<string, int> > agenda;
+  if (!LoadAgenda(conf["training_agenda"].as<string>(), &agenda))
+    return 1;
+  if (rank == 0)
+    cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n";
+
+  assert(agenda.size() > 0);
+
+  if (1) {  // hack to load the feature hash functions -- TODO this should not be in cdec.ini
+    const string& cur_config = agenda[0].first;
+    const unsigned max_iteration = agenda[0].second;
+    ReadFile ini_rf(cur_config);
+    Decoder decoder(ini_rf.stream());
+  }
+
   // load initial weights
-  Weights weights;
+  vector<weight_t> init_weights;
   if (conf.count("input_weights"))
-    weights.InitFromFile(conf["input_weights"].as<string>());
+    Weights::InitFromFile(conf["input_weights"].as<string>(), &init_weights);
 
   vector<int> frozen_fids;
   if (conf.count("frozen_features")) {
@@ -310,19 +296,12 @@ int main(int argc, char** argv) {
     rng.reset(new MT19937);
 
   SparseVector<double> x;
-  weights.InitSparseVector(&x);
+  Weights::InitSparseVector(init_weights, &x);
   TrainingObserver observer;
 
   int write_weights_every_ith = 100; // TODO configure
   int titer = -1;
 
-  vector<pair<string, int> > agenda;
-  if (!LoadAgenda(conf["training_agenda"].as<string>(), &agenda))
-    return 1;
-  if (rank == 0)
-    cerr << "Loaded agenda defining " << agenda.size() << " training epochs\n";
-
-  vector<double> lambdas;
   for (int ai = 0; ai < agenda.size(); ++ai) {
     const string& cur_config = agenda[ai].first;
     const unsigned max_iteration = agenda[ai].second;
@@ -331,6 +310,8 @@ int main(int argc, char** argv) {
     // load cdec.ini and set up decoder
     ReadFile ini_rf(cur_config);
     Decoder decoder(ini_rf.stream());
+    vector<weight_t>& lambdas = decoder.CurrentWeightVector();
+    if (ai == 0) { lambdas.swap(init_weights); init_weights.clear(); }
 
     if (rank == 0)
       o->ResetEpoch(); // resets the learning rate-- TODO is this good?
@@ -341,15 +322,13 @@ int main(int argc, char** argv) {
 #ifdef HAVE_MPI
       mpi::timer timer;
 #endif
-      weights.InitFromVector(x);
-      weights.InitVector(&lambdas);
+      x.init_vector(&lambdas);
       ++iter; ++titer;
       observer.Reset();
-      decoder.SetWeights(lambdas);
       if (rank == 0) {
         converged = (iter == max_iteration);
-        SanityCheck(lambdas);
-        ShowLargestFeatures(lambdas);
+        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";
@@ -360,7 +339,7 @@ int main(int argc, char** argv) {
         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, true, &svv);
+        Weights::WriteToFile(fname, lambdas, true, &svv);
       }
 
       for (int i = 0; i < size_per_proc; ++i) {
diff --git a/training/mr_optimize_reduce.cc b/training/mr_optimize_reduce.cc
index b931991d..15e28fa1 100644
--- a/training/mr_optimize_reduce.cc
+++ b/training/mr_optimize_reduce.cc
@@ -88,25 +88,19 @@ int main(int argc, char** argv) {
 
   const bool use_b64 = conf["input_format"].as<string>() == "b64";
 
-  Weights weights;
-  weights.InitFromFile(conf["input_weights"].as<string>());
+  vector<weight_t> lambdas;
+  Weights::InitFromFile(conf["input_weights"].as<string>(), &lambdas);
   const string s_obj = "**OBJ**";
   int num_feats = FD::NumFeats();
   cerr << "Number of features: " << num_feats << endl;
   const bool gaussian_prior = conf.count("gaussian_prior");
-  vector<double> means(num_feats, 0);
+  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 wm; 
-    wm.InitFromFile(conf["means"].as<string>());
-    if (num_feats != FD::NumFeats()) {
-      cerr << "[ERROR] Means file had unexpected features!\n";
-      exit(1);
-    }
-    wm.InitVector(&means);
+    Weights::InitFromFile(conf["means"].as<string>(), &means);
   }
   shared_ptr<BatchOptimizer> o;
   const string omethod = conf["optimization_method"].as<string>();
@@ -124,8 +118,6 @@ int main(int argc, char** argv) {
       cerr << "No state file found, assuming ITERATION 1\n";
   }
 
-  vector<double> lambdas(num_feats, 0);
-  weights.InitVector(&lambdas);
   double objective = 0;
   vector<double> gradient(num_feats, 0);
   // 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
@@ -223,8 +215,7 @@ int main(int argc, char** argv) {
   old.clear();
   SanityCheck(lambdas);
   ShowLargestFeatures(lambdas);
-  weights.InitFromVector(lambdas);
-  weights.WriteToFile(conf["output_weights"].as<string>(), false);
+  Weights::WriteToFile(conf["output_weights"].as<string>(), lambdas, false);
 
   const bool conv = o->HasConverged();
   if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }