big refactor

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@649 ec762483-ff6d-05da-a07a-a48fb63a330f

5 files changed, 731 insertions, 899 deletions

diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index 56e103aa..97ec6798 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -1,888 +1,28 @@
 #include <iostream>
-#include <fstream>
-#include <tr1/unordered_map>
-#include <tr1/unordered_set>
 
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "decoder.h"
-#include "oracle_bleu.h"
-#include "timing_stats.h"
-#include "translator.h"
-#include "phrasebased_translator.h"
-#include "aligner.h"
-#include "stringlib.h"
-#include "forest_writer.h"
-#include "hg_io.h"
 #include "filelib.h"
-#include "sampler.h"
-#include "sparse_vector.h"
-#include "tagger.h"
-#include "lextrans.h"
-#include "lexalign.h"
-#include "csplit.h"
-#include "weights.h"
-#include "tdict.h"
-#include "ff.h"
-#include "ff_fsa_dynamic.h"
-#include "ff_factory.h"
-#include "hg_intersect.h"
-#include "apply_models.h"
-#include "viterbi.h"
-#include "kbest.h"
-#include "inside_outside.h"
-#include "exp_semiring.h"
-#include "sentence_metadata.h"
-#include "scorer.h"
-#include "apply_fsa_models.h"
-#include "program_options.h"
-#include "cfg_options.h"
-
-CFGOptions cfg_options;
+#include "decoder.h"
 
 using namespace std;
-using namespace std::tr1;
-using boost::shared_ptr;
-namespace po = boost::program_options;
-
-vector<string> cfg_files;
-bool show_config=false;
-bool show_weights=false;
-bool verbose_feature_functions=true;
-
-// some globals ...
-boost::shared_ptr<RandomNumberGenerator<boost::mt19937> > rng;
-static const double kMINUS_EPSILON = -1e-6;  // don't be too strict
-
-namespace Hack { void MaxTrans(const Hypergraph& in, int beam_size); }
-namespace NgramCache { void Clear(); }
-
-void ShowBanner() {
-  cerr << "cdec v1.0 (c) 2009-2010 by Chris Dyer\n";
-}
-
-void ParseTranslatorInputLattice(const string& line, string* input, Lattice* ref) {
-  string sref;
-  ParseTranslatorInput(line, input, &sref);
-  if (sref.size() > 0) {
-    assert(ref);
-    LatticeTools::ConvertTextOrPLF(sref, ref);
-  }
-}
-
-void ConvertSV(const SparseVector<prob_t>& src, SparseVector<double>* trg) {
-  for (SparseVector<prob_t>::const_iterator it = src.begin(); it != src.end(); ++it)
-    trg->set_value(it->first, it->second);
-}
-
-inline string str(char const* name,po::variables_map const& conf) {
-  return conf[name].as<string>();
-}
-
-shared_ptr<FeatureFunction> make_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
-  string ff, param;
-  SplitCommandAndParam(ffp, &ff, &param);
-  cerr << pre << "feature: " << ff;
-  if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
-  else cerr << " (no config parameters)\n";
-  shared_ptr<FeatureFunction> pf = ff_registry.Create(ff, param);
-  if (!pf) exit(1);
-  int nbyte=pf->NumBytesContext();
-  if (verbose_feature_functions)
-    cerr<<"State is "<<nbyte<<" bytes for "<<pre<<"feature "<<ffp<<endl;
-  return pf;
-}
-
-shared_ptr<FsaFeatureFunction> make_fsa_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
-  string ff, param;
-  SplitCommandAndParam(ffp, &ff, &param);
-  cerr << "FSA Feature: " << ff;
-  if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
-  else cerr << " (no config parameters)\n";
-  shared_ptr<FsaFeatureFunction> pf = fsa_ff_registry.Create(ff, param);
-  if (!pf) exit(1);
-  if (verbose_feature_functions)
-    cerr<<"State is "<<pf->state_bytes()<<" bytes for "<<pre<<"feature "<<ffp<<endl;
-  return pf;
-}
-
-// print just the --long_opt names suitable for bash compgen
-void print_options(std::ostream &out,po::options_description const& opts) {
-  typedef std::vector< shared_ptr<po::option_description> > Ds;
-  Ds const& ds=opts.options();
-  out << '"';
-  for (unsigned i=0;i<ds.size();++i) {
-    if (i) out<<' ';
-    out<<"--"<<ds[i]->long_name();
-  }
-  out << '"';
-}
-
-void InitCommandLine(int argc, char** argv, OracleBleu &ob, po::variables_map* confp) {
-  po::variables_map &conf=*confp;
-  po::options_description opts("Configuration options");
-  opts.add_options()
-        ("formalism,f",po::value<string>(),"Decoding formalism; values include SCFG, FST, PB, LexTrans (lexical translation model, also disc training), CSplit (compound splitting), Tagger (sequence labeling), LexAlign (alignment only, or EM training)")
-        ("input,i",po::value<string>()->default_value("-"),"Source file")
-        ("grammar,g",po::value<vector<string> >()->composing(),"Either SCFG grammar file(s) or phrase tables file(s)")
-        ("weights,w",po::value<string>(),"Feature weights file")
-    ("prelm_weights",po::value<string>(),"Feature weights file for prelm_beam_prune.  Requires --weights.")
-    ("prelm_copy_weights","use --weights as value for --prelm_weights.")
-    ("prelm_feature_function",po::value<vector<string> >()->composing(),"Additional feature functions for prelm pass only (in addition to the 0-state subset of feature_function")
-    ("keep_prelm_cube_order","DEPRECATED (always enabled).  when forest rescoring with final models, use the edge ordering from the prelm pruning features*weights.  only meaningful if --prelm_weights given.  UNTESTED but assume that cube pruning gives a sensible result, and that 'good' (as tuned for bleu w/ prelm features) edges come first.")
-    ("warn_0_weight","Warn about any feature id that has a 0 weight (this is perfectly safe if you intend 0 weight, though)")
-        ("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
-        ("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
-        ("fsa_feature_function,A",po::value<vector<string> >()->composing(), "Additional FSA feature function(s) (-L for list)")
-    ("apply_fsa_by",po::value<string>()->default_value("BU_CUBE"), "Method for applying fsa_feature_functions - BU_FULL BU_CUBE EARLEY") //+ApplyFsaBy::all_names()
-        ("list_feature_functions,L","List available feature functions")
-        ("add_pass_through_rules,P","Add rules to translate OOV words as themselves")
-	("k_best,k",po::value<int>(),"Extract the k best derivations")
-	("unique_k_best,r", "Unique k-best translation list")
-        ("aligner,a", "Run as a word/phrase aligner (src & ref required)")
-        ("intersection_strategy,I",po::value<string>()->default_value("cube_pruning"), "Intersection strategy for incorporating finite-state features; values include Cube_pruning, Full")
-        ("cubepruning_pop_limit,K",po::value<int>()->default_value(200), "Max number of pops from the candidate heap at each node")
-        ("goal",po::value<string>()->default_value("S"),"Goal symbol (SCFG & FST)")
-        ("scfg_extra_glue_grammar", po::value<string>(), "Extra glue grammar file (Glue grammars apply when i=0 but have no other span restrictions)")
-        ("scfg_no_hiero_glue_grammar,n", "No Hiero glue grammar (nb. by default the SCFG decoder adds Hiero glue rules)")
-        ("scfg_default_nt,d",po::value<string>()->default_value("X"),"Default non-terminal symbol in SCFG")
-        ("scfg_max_span_limit,S",po::value<int>()->default_value(10),"Maximum non-terminal span limit (except \"glue\" grammar)")
-    ("show_config", po::bool_switch(&show_config), "show contents of loaded -c config files.")
-    ("show_weights", po::bool_switch(&show_weights), "show effective feature weights")
-        ("show_joshua_visualization,J", "Produce output compatible with the Joshua visualization tools")
-        ("show_tree_structure", "Show the Viterbi derivation structure")
-        ("show_expected_length", "Show the expected translation length under the model")
-        ("show_partition,z", "Compute and show the partition (inside score)")
-        ("show_cfg_search_space", "Show the search space as a CFG")
-    ("show_features","Show the feature vector for the viterbi translation")
-    ("prelm_density_prune", po::value<double>(), "Applied to -LM forest just before final LM rescoring: keep no more than this many times the number of edges used in the best derivation tree (>=1.0)")
-    ("density_prune", po::value<double>(), "Keep no more than this many times the number of edges used in the best derivation tree (>=1.0)")
-        ("prelm_beam_prune", po::value<double>(), "Prune paths from -LM forest before LM rescoring, keeping paths within exp(alpha>=0)")
-        ("coarse_to_fine_beam_prune", po::value<double>(), "Prune paths from coarse parse forest before fine parse, keeping paths within exp(alpha>=0)")
-        ("ctf_beam_widen", po::value<double>()->default_value(2.0), "Expand coarse pass beam by this factor if no fine parse is found")
-        ("ctf_num_widenings", po::value<int>()->default_value(2), "Widen coarse beam this many times before backing off to full parse")
-        ("ctf_no_exhaustive", "Do not fall back to exhaustive parse if coarse-to-fine parsing fails")
-        ("beam_prune", po::value<double>(), "Prune paths from +LM forest, keep paths within exp(alpha>=0)")
-    ("scale_prune_srclen", "scale beams by the input length (in # of tokens; may not be what you want for lattices")
-    ("promise_power",po::value<double>()->default_value(0), "Give more beam budget to more promising previous-pass nodes when pruning - but allocate the same average beams.  0 means off, 1 means beam proportional to inside*outside prob, n means nth power (affects just --cubepruning_pop_limit).  note: for the same pop_limit, this gives more search error unless very close to 0 (recommend disabled; even 0.01 is slightly worse than 0) which is a bad sign and suggests this isn't doing a good job; further it's slightly slower to LM cube rescore with 0.01 compared to 0, as well as giving (very insignificantly) lower BLEU.  TODO: test under more conditions, or try idea with different formula, or prob. cube beams.")
-        ("lexalign_use_null", "Support source-side null words in lexical translation")
-        ("tagger_tagset,t", po::value<string>(), "(Tagger) file containing tag set")
-        ("csplit_output_plf", "(Compound splitter) Output lattice in PLF format")
-        ("csplit_preserve_full_word", "(Compound splitter) Always include the unsegmented form in the output lattice")
-        ("extract_rules", po::value<string>(), "Extract the rules used in translation (de-duped) to this file")
-        ("graphviz","Show (constrained) translation forest in GraphViz format")
-        ("max_translation_beam,x", po::value<int>(), "Beam approximation to get max translation from the chart")
-        ("max_translation_sample,X", po::value<int>(), "Sample the max translation from the chart")
-        ("pb_max_distortion,D", po::value<int>()->default_value(4), "Phrase-based decoder: maximum distortion")
-        ("cll_gradient,G","Compute conditional log-likelihood gradient and write to STDOUT (src & ref required)")
-        ("crf_uniform_empirical", "If there are multple references use (i.e., lattice) a uniform distribution rather than posterior weighting a la EM")
-    ("get_oracle_forest,o", "Calculate rescored hypregraph using approximate BLEU scoring of rules")
-    ("feature_expectations","Write feature expectations for all features in chart (**OBJ** will be the partition)")
-        ("vector_format",po::value<string>()->default_value("b64"), "Sparse vector serialization format for feature expectations or gradients, includes (text or b64)")
-        ("combine_size,C",po::value<int>()->default_value(1), "When option -G is used, process this many sentence pairs before writing the gradient (1=emit after every sentence pair)")
-        ("forest_output,O",po::value<string>(),"Directory to write forests to")
-        ("minimal_forests,m","Write minimal forests (excludes Rule information). Such forests can be used for ML/MAP training, but not rescoring, etc.");
-  ob.AddOptions(&opts);
-  po::options_description cfgo(cfg_options.description());
-  cfg_options.AddOptions(&cfgo);
-  po::options_description clo("Command line options");
-  clo.add_options()
-    ("config,c", po::value<vector<string> >(&cfg_files), "Configuration file(s) - latest has priority")
-        ("help,h", "Print this help message and exit")
-    ("usage,u", po::value<string>(), "Describe a feature function type")
-    ("compgen", "Print just option names suitable for bash command line completion builtin 'compgen'")
-    ;
-
-  po::options_description dconfig_options, dcmdline_options;
-  dconfig_options.add(opts).add(cfgo);
-  //add(opts).add(cfgo)
-  dcmdline_options.add(dconfig_options).add(clo);
-  argv_minus_to_underscore(argc,argv);
-  po::store(parse_command_line(argc, argv, dcmdline_options), conf);
-  if (conf.count("compgen")) {
-    print_options(cout,dcmdline_options);
-    cout << endl;
-    exit(0);
-  }
-  ShowBanner();
-  if (conf.count("show_config")) // special handling needed because we only want to notify() once.
-    show_config=true;
-  if (conf.count("config")) {
-    typedef vector<string> Cs;
-    Cs cs=conf["config"].as<Cs>();
-    for (int i=0;i<cs.size();++i) {
-      string cfg=cs[i];
-      cerr << "Configuration file: " << cfg << endl;
-      ReadFile conff(cfg);
-      po::store(po::parse_config_file(*conff, dconfig_options), conf);
-    }
-  }
-  po::notify(conf);
-  if (show_config && !cfg_files.empty()) {
-    cerr<< "\nConfig files:\n\n";
-    for (int i=0;i<cfg_files.size();++i) {
-      string cfg=cfg_files[i];
-      cerr << "Configuration file: " << cfg << endl;
-      CopyFile(cfg,cerr);
-      cerr << "(end config "<<cfg<<"\n\n";
-    }
-    cerr <<"Command line:";
-    for (int i=0;i<argc;++i)
-      cerr<<" "<<argv[i];
-    cerr << "\n\n";
-  }
-
-
-  if (conf.count("list_feature_functions")) {
-    cerr << "Available feature functions (specify with -F; describe with -u FeatureName):\n";
-    ff_registry.DisplayList();
-    cerr << "Available FSA feature functions (specify with --fsa_feature_function):\n";
-    fsa_ff_registry.DisplayList();
-    cerr << endl;
-    exit(1);
-  }
-
-  if (conf.count("usage")) {
-    ff_usage(str("usage",conf));
-    exit(0);
-  }
-  if (conf.count("help")) {
-    cout << dcmdline_options << endl;
-    exit(0);
-  }
-  if (conf.count("help") || conf.count("formalism") == 0) {
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-
-  const string formalism = LowercaseString(str("formalism",conf));
-  if (formalism != "scfg" && formalism != "fst" && formalism != "lextrans" && formalism != "pb" && formalism != "csplit" && formalism != "tagger" && formalism != "lexalign") {
-    cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lextrans', 'lexalign', or 'tagger'\n";
-    cerr << dcmdline_options << endl;
-    exit(1);
-  }
-
-}
-
-// TODO move out of cdec into some sampling decoder file
-void SampleRecurse(const Hypergraph& hg, const vector<SampleSet<prob_t> >& ss, int n, vector<WordID>* out) {
-  const SampleSet<prob_t>& s = ss[n];
-  int i = rng->SelectSample(s);
-  const Hypergraph::Edge& edge = hg.edges_[hg.nodes_[n].in_edges_[i]];
-  vector<vector<WordID> > ants(edge.tail_nodes_.size());
-  for (int j = 0; j < ants.size(); ++j)
-    SampleRecurse(hg, ss, edge.tail_nodes_[j], &ants[j]);
-
-  vector<const vector<WordID>*> pants(ants.size());
-  for (int j = 0; j < ants.size(); ++j) pants[j] = &ants[j];
-  edge.rule_->ESubstitute(pants, out);
-}
-
-struct SampleSort {
-  bool operator()(const pair<int,string>& a, const pair<int,string>& b) const {
-    return a.first > b.first;
-  }
-};
-
-// TODO move out of cdec into some sampling decoder file
-void MaxTranslationSample(Hypergraph* hg, const int samples, const int k) {
-  unordered_map<string, int, boost::hash<string> > m;
-  hg->PushWeightsToGoal();
-  const int num_nodes = hg->nodes_.size();
-  vector<SampleSet<prob_t> > ss(num_nodes);
-  for (int i = 0; i < num_nodes; ++i) {
-    SampleSet<prob_t>& s = ss[i];
-    const vector<int>& in_edges = hg->nodes_[i].in_edges_;
-    for (int j = 0; j < in_edges.size(); ++j) {
-      s.add(hg->edges_[in_edges[j]].edge_prob_);
-    }
-  }
-  for (int i = 0; i < samples; ++i) {
-    vector<WordID> yield;
-    SampleRecurse(*hg, ss, hg->nodes_.size() - 1, &yield);
-    const string trans = TD::GetString(yield);
-    ++m[trans];
-  }
-  vector<pair<int, string> > dist;
-  for (unordered_map<string, int, boost::hash<string> >::iterator i = m.begin();
-         i != m.end(); ++i) {
-    dist.push_back(make_pair(i->second, i->first));
-  }
-  sort(dist.begin(), dist.end(), SampleSort());
-  if (k) {
-    for (int i = 0; i < k; ++i)
-      cout << dist[i].first << " ||| " << dist[i].second << endl;
-  } else {
-    cout << dist[0].second << endl;
-  }
-}
-
-
-
-struct ELengthWeightFunction {
-  double operator()(const Hypergraph::Edge& e) const {
-    return e.rule_->ELength() - e.rule_->Arity();
-  }
-};
-
-
-struct TRPHash {
-  size_t operator()(const TRulePtr& o) const { return reinterpret_cast<size_t>(o.get()); }
-};
-static void ExtractRulesDedupe(const Hypergraph& hg, ostream* os) {
-  static unordered_set<TRulePtr, TRPHash> written;
-  for (int i = 0; i < hg.edges_.size(); ++i) {
-    const TRulePtr& rule = hg.edges_[i].rule_;
-    if (written.insert(rule).second) {
-      (*os) << rule->AsString() << endl;
-    }
-  }
-}
 
 void register_feature_functions();
 
-bool beam_param(po::variables_map const& conf,string const& name,double *val,bool scale_srclen=false,double srclen=1)
-{
-  if (conf.count(name)) {
-    *val=conf[name].as<double>()*(scale_srclen?srclen:1);
-    return true;
-  }
-  return false;
-}
-
-bool prelm_weights_string(po::variables_map const& conf,string &s)
-{
-  if (conf.count("prelm_weights")) {
-    s=str("prelm_weights",conf);
-    return true;
-  }
-  if (conf.count("prelm_copy_weights")) {
-    s=str("weights",conf);
-    return true;
-  }
-  return false;
-}
-
-
-void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,WeightVector *weights=0,bool show_deriv=false) {
-  cerr << viterbi_stats(forest,name,true,show_tree,show_deriv);
-    if (show_features) {
-      cerr << name<<"     features: ";
-/*      Hypergraph::Edge const* best=forest.ViterbiGoalEdge();
-      if (!best)
-        cerr << name<<" has no goal edge.";
-      else
-        cerr<<best->feature_values_;
-*/
-      cerr << ViterbiFeatures(forest,weights);
-      cerr << endl;
-    }
-}
-
-void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,DenseWeightVector const& feature_weights,bool sd=false) {
-    WeightVector fw(feature_weights);
-    forest_stats(forest,name,show_tree,show_features,&fw,sd);
-}
-
-
-void maybe_prune(Hypergraph &forest,po::variables_map const& conf,string nbeam,string ndensity,string forestname,double srclen) {
-    double beam_prune=0,density_prune=0;
-    bool use_beam_prune=beam_param(conf,nbeam,&beam_prune,conf.count("scale_prune_srclen"),srclen);
-    bool use_density_prune=beam_param(conf,ndensity,&density_prune);
-    if (use_beam_prune || use_density_prune) {
-      double presize=forest.edges_.size();
-      vector<bool> preserve_mask,*pm=0;
-      if (conf.count("csplit_preserve_full_word")) {
-        preserve_mask.resize(forest.edges_.size());
-        preserve_mask[CompoundSplit::GetFullWordEdgeIndex(forest)] = true;
-        pm=&preserve_mask;
-      }
-      forest.PruneInsideOutside(beam_prune,density_prune,pm,false,1,conf["promise_power"].as<double>());
-      if (!forestname.empty()) forestname=" "+forestname;
-      forest_stats(forest,"  Pruned "+forestname+" forest",false,false,0,false);
-      cerr << "  Pruned "<<forestname<<" forest portion of edges kept: "<<forest.edges_.size()/presize<<endl;
-    }
-}
-
-void show_models(po::variables_map const& conf,ModelSet &ms,char const* header) {
-  cerr<<header<<": ";
-  ms.show_features(cerr,cerr,conf.count("warn_0_weight"));
-}
-
-template <class V>
-bool store_conf(po::variables_map const& conf,std::string const& name,V *v) {
-  if (conf.count(name)) {
-    *v=conf[name].as<V>();
-    return true;
-  }
-  return false;
-}
-
-
 int main(int argc, char** argv) {
   register_feature_functions();
-  po::variables_map conf;
-  OracleBleu oracle;
-
-  InitCommandLine(argc, argv, oracle, &conf);
-  const bool write_gradient = conf.count("cll_gradient");
-  const bool feature_expectations = conf.count("feature_expectations");
-  if (write_gradient && feature_expectations) {
-    cerr << "You can only specify --gradient or --feature_expectations, not both!\n";
-    exit(1);
-  }
-  const bool output_training_vector = (write_gradient || feature_expectations);
-
-  boost::shared_ptr<Translator> translator;
-  const string formalism = LowercaseString(str("formalism",conf));
-  const bool csplit_preserve_full_word = conf.count("csplit_preserve_full_word");
-  if (csplit_preserve_full_word &&
-      (formalism != "csplit" || !(conf.count("beam_prune")||conf.count("density_prune")||conf.count("prelm_beam_prune")||conf.count("prelm_density_prune")))) {
-    cerr << "--csplit_preserve_full_word should only be "
-         << "used with csplit AND --*_prune!\n";
-    exit(1);
-  }
-  const bool csplit_output_plf = conf.count("csplit_output_plf");
-  if (csplit_output_plf && formalism != "csplit") {
-    cerr << "--csplit_output_plf should only be used with csplit!\n";
-    exit(1);
-  }
+  Decoder decoder(argc, argv);
 
-  const string input = str("input",conf);
+  const string input = decoder.GetConf()["input"].as<string>();
   cerr << "Reading input from " << ((input == "-") ? "STDIN" : input.c_str()) << endl;
   ReadFile in_read(input);
   istream *in = in_read.stream();
   assert(*in);
 
-  // load feature weights (and possibly freeze feature set)
-  vector<double> feature_weights,prelm_feature_weights;
-  Weights w,prelm_w;
-  bool has_prelm_models = false;
-  if (conf.count("weights")) {
-    w.InitFromFile(str("weights",conf));
-    feature_weights.resize(FD::NumFeats());
-    w.InitVector(&feature_weights);
-    string plmw;
-    if (prelm_weights_string(conf,plmw)) {
-      has_prelm_models = true;
-      prelm_w.InitFromFile(plmw);
-      prelm_feature_weights.resize(FD::NumFeats());
-      prelm_w.InitVector(&prelm_feature_weights);
-      if (show_weights)
-        cerr << "prelm_weights: " << WeightVector(prelm_feature_weights)<<endl;
-    }
-    if (show_weights)
-      cerr << "+LM weights: " << WeightVector(feature_weights)<<endl;
-  }
-  bool warn0=conf.count("warn_0_weight");
-  bool freeze=!conf.count("no_freeze_feature_set");
-  bool early_freeze=freeze && !warn0;
-  bool late_freeze=freeze && warn0;
-  if (early_freeze) {
-    cerr << "Freezing feature set (use --no_freeze_feature_set or --warn_0_weight to prevent)." << endl;
-    FD::Freeze(); // this means we can't see the feature names of not-weighted features
-  }
-
-  // set up translation back end
-  if (formalism == "scfg")
-    translator.reset(new SCFGTranslator(conf));
-  else if (formalism == "fst")
-    translator.reset(new FSTTranslator(conf));
-  else if (formalism == "pb")
-    translator.reset(new PhraseBasedTranslator(conf));
-  else if (formalism == "csplit")
-    translator.reset(new CompoundSplit(conf));
-  else if (formalism == "lextrans")
-    translator.reset(new LexicalTrans(conf));
-  else if (formalism == "lexalign")
-    translator.reset(new LexicalAlign(conf));
-  else if (formalism == "tagger")
-    translator.reset(new Tagger(conf));
-  else
-    assert(!"error");
-
-  // set up additional scoring features
-  vector<shared_ptr<FeatureFunction> > pffs,prelm_only_ffs;
-  vector<const FeatureFunction*> late_ffs,prelm_ffs;
-  if (conf.count("feature_function") > 0) {
-    vector<string> add_ffs;
-//    const vector<string>& add_ffs = conf["feature_function"].as<vector<string> >();
-    store_conf(conf,"feature_function",&add_ffs);
-    for (int i = 0; i < add_ffs.size(); ++i) {
-      pffs.push_back(make_ff(add_ffs[i],verbose_feature_functions));
-      FeatureFunction const* p=pffs.back().get();
-      late_ffs.push_back(p);
-      if (has_prelm_models) {
-        if (p->NumBytesContext()==0)
-          prelm_ffs.push_back(p);
-        else
-          cerr << "Excluding stateful feature from prelm pruning: "<<add_ffs[i]<<endl;
-      }
-    }
-  }
-  if (conf.count("prelm_feature_function") > 0) {
-    vector<string> add_ffs;
-    store_conf(conf,"prelm_feature_function",&add_ffs);
-//    const vector<string>& add_ffs = conf["prelm_feature_function"].as<vector<string> >();
-    for (int i = 0; i < add_ffs.size(); ++i) {
-      prelm_only_ffs.push_back(make_ff(add_ffs[i],verbose_feature_functions,"prelm-only "));
-      prelm_ffs.push_back(prelm_only_ffs.back().get());
-    }
-  }
-
-  vector<shared_ptr<FsaFeatureFunction> > fsa_ffs;
-  vector<string> fsa_names;
-  store_conf(conf,"fsa_feature_function",&fsa_names);
-  for (int i=0;i<fsa_names.size();++i)
-    fsa_ffs.push_back(make_fsa_ff(fsa_names[i],verbose_feature_functions,"FSA "));
-  if (fsa_ffs.size()>1) {
-    //FIXME: support N fsa ffs.
-    cerr<<"Only the first fsa FF will be used (FIXME).\n";
-    fsa_ffs.resize(1);
-  }
-  if (!fsa_ffs.empty()) {
-    cerr<<"FSA: ";
-    show_all_features(fsa_ffs,feature_weights,cerr,cerr,true,true);
-  }
-
-  if (late_freeze) {
-    cerr << "Late freezing feature set (use --no_freeze_feature_set to prevent)." << endl;
-    FD::Freeze(); // this means we can't see the feature names of not-weighted features
-  }
-
-  if (has_prelm_models)
-        cerr << "prelm rescoring with "<<prelm_ffs.size()<<" 0-state feature functions.  +LM pass will use "<<late_ffs.size()<<" features (not counting rule features)."<<endl;
-
-  ModelSet late_models(feature_weights, late_ffs);
-  show_models(conf,late_models,"late ");
-  ModelSet prelm_models(prelm_feature_weights, prelm_ffs);
-  if (has_prelm_models)
-    show_models(conf,prelm_models,"prelm ");
-
-  int palg = 1;
-  if (LowercaseString(str("intersection_strategy",conf)) == "full") {
-    palg = 0;
-    cerr << "Using full intersection (no pruning).\n";
-  }
-  int pop_limit=conf["cubepruning_pop_limit"].as<int>();
-  const IntersectionConfiguration inter_conf(palg, pop_limit);
-
-  const int sample_max_trans = conf.count("max_translation_sample") ?
-    conf["max_translation_sample"].as<int>() : 0;
-  if (sample_max_trans)
-    rng.reset(new RandomNumberGenerator<boost::mt19937>);
-  const bool aligner_mode = conf.count("aligner");
-  const bool minimal_forests = conf.count("minimal_forests");
-  const bool graphviz = conf.count("graphviz");
-  const bool joshua_viz = conf.count("show_joshua_visualization");
-  const bool encode_b64 = str("vector_format",conf) == "b64";
-  const bool kbest = conf.count("k_best");
-  const bool unique_kbest = conf.count("unique_k_best");
-  const bool crf_uniform_empirical = conf.count("crf_uniform_empirical");
-  const bool get_oracle_forest = conf.count("get_oracle_forest");
-
-  cfg_options.Validate();
-  if (get_oracle_forest)
-    oracle.UseConf(conf);
-
-  shared_ptr<WriteFile> extract_file;
-  if (conf.count("extract_rules"))
-    extract_file.reset(new WriteFile(str("extract_rules",conf)));
-
-  int combine_size = conf["combine_size"].as<int>();
-  if (combine_size < 1) combine_size = 1;
-
-  SparseVector<prob_t> acc_vec;  // accumulate gradient
-  double acc_obj = 0; // accumulate objective
-  int g_count = 0;    // number of gradient pieces computed
-  int sent_id = -1;         // line counter
-
+  string buf;
   while(*in) {
-    NgramCache::Clear();   // clear ngram cache for remote LM (if used)
-    Timer::Summarize();
-    ++sent_id;
-    string buf;
     getline(*in, buf);
     if (buf.empty()) continue;
-    map<string, string> sgml;
-    ProcessAndStripSGML(&buf, &sgml);
-    if (sgml.find("id") != sgml.end())
-      sent_id = atoi(sgml["id"].c_str());
-
-    cerr << "\nINPUT: ";
-    if (buf.size() < 100)
-      cerr << buf << endl;
-    else {
-     size_t x = buf.rfind(" ", 100);
-     if (x == string::npos) x = 100;
-     cerr << buf.substr(0, x) << " ..." << endl;
-    }
-    cerr << "  id = " << sent_id << endl;
-    string to_translate;
-    Lattice ref;
-    ParseTranslatorInputLattice(buf, &to_translate, &ref);
-    const unsigned srclen=NTokens(to_translate,' ');
-//FIXME: should get the avg. or max source length of the input lattice (like Lattice::dist_(start,end)); but this is only used to scale beam parameters (optionally) anyway so fidelity isn't important.
-    const bool has_ref = ref.size() > 0;
-    SentenceMetadata smeta(sent_id, ref);
-    const bool hadoop_counters = (write_gradient);
-    Hypergraph forest;          // -LM forest
-    translator->ProcessMarkupHints(sgml);
-    Timer t("Translation");
-    const bool translation_successful =
-      translator->Translate(to_translate, &smeta, feature_weights, &forest);
-    //TODO: modify translator to incorporate all 0-state model scores immediately?
-    translator->SentenceComplete();
-    if (!translation_successful) {
-      cerr << "  NO PARSE FOUND.\n";
-      if (hadoop_counters)
-        cerr << "reporter:counter:UserCounters,FParseFailed,1" << endl;
-      cout << endl << flush;
-      continue;
-    }
-    const bool show_tree_structure=conf.count("show_tree_structure");
-    const bool show_features=conf.count("show_features");
-    forest_stats(forest,"  -LM forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
-    if (conf.count("show_expected_length")) {
-      const PRPair<double, double> res =
-        Inside<PRPair<double, double>,
-               PRWeightFunction<double, EdgeProb, double, ELengthWeightFunction> >(forest);
-      cerr << "  Expected length  (words): " << res.r / res.p << "\t" << res << endl;
-    }
-    if (conf.count("show_partition")) {
-      const prob_t z = Inside<prob_t, EdgeProb>(forest);
-      cerr << "  -LM partition     log(Z): " << log(z) << endl;
-    }
-    if (extract_file)
-      ExtractRulesDedupe(forest, extract_file->stream());
-
-    if (has_prelm_models) {
-      Timer t("prelm rescoring");
-      forest.Reweight(prelm_feature_weights);
-      Hypergraph prelm_forest;
-      ApplyModelSet(forest,
-                    smeta,
-                    prelm_models,
-                    inter_conf, // this is now reduced to exhaustive if all are stateless
-                    &prelm_forest);
-      forest.swap(prelm_forest);
-      forest.Reweight(prelm_feature_weights); //FIXME: why the reweighting? here and below.  maybe in case we already had a featval for that id and ApplyModelSet only adds prob, doesn't recompute it?
-      forest_stats(forest," prelm forest",show_tree_structure,show_features,prelm_feature_weights,oracle.show_derivation);
-    }
-
-    maybe_prune(forest,conf,"prelm_beam_prune","prelm_density_prune","-LM",srclen);
-
-    cfg_options.maybe_output_source(forest);
-
-    bool has_late_models = !late_models.empty();
-    if (has_late_models) {
-      Timer t("Forest rescoring:");
-      forest.Reweight(feature_weights);
-      Hypergraph lm_forest;
-      ApplyModelSet(forest,
-                    smeta,
-                    late_models,
-                    inter_conf,
-                    &lm_forest);
-      forest.swap(lm_forest);
-      forest.Reweight(feature_weights);
-      forest_stats(forest,"  +LM forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
-    }
-
-    maybe_prune(forest,conf,"beam_prune","density_prune","+LM",srclen);
-
-    HgCFG hgcfg(forest);
-    cfg_options.prepare(hgcfg);
-    if (!fsa_ffs.empty()) {
-      Timer t("Target FSA rescoring:");
-      if (!has_late_models)
-        forest.Reweight(feature_weights);
-      Hypergraph fsa_forest;
-      assert(fsa_ffs.size()==1);
-      ApplyFsaBy cfg(str("apply_fsa_by",conf),pop_limit);
-      cerr << "FSA rescoring with "<<cfg<<" "<<fsa_ffs[0]->describe()<<endl;
-      ApplyFsaModels(hgcfg,smeta,*fsa_ffs[0],feature_weights,cfg,&fsa_forest);
-      forest.swap(fsa_forest);
-      forest.Reweight(feature_weights);
-      forest_stats(forest,"  +FSA forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
-    }
-
-    /*Oracle Rescoring*/
-    if(get_oracle_forest) {
-      Oracle o=oracle.ComputeOracle(smeta,&forest,FeatureVector(feature_weights),10,conf["forest_output"].as<std::string>());
-      cerr << "  +Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
-      cerr << "  +Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
-      o.hope.Print(cerr,"  +Oracle BLEU");
-      o.fear.Print(cerr,"  -Oracle BLEU");
-      //Add 1-best translation (trans) to psuedo-doc vectors
-      oracle.IncludeLastScore(&cerr);
-    }
-
-
-    if (conf.count("forest_output") && !has_ref) {
-      ForestWriter writer(str("forest_output",conf), sent_id);
-      if (FileExists(writer.fname_)) {
-        cerr << "  Unioning...\n";
-        Hypergraph new_hg;
-        {
-          ReadFile rf(writer.fname_);
-          bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
-          assert(succeeded);
-        }
-        new_hg.Union(forest);
-        bool succeeded = writer.Write(new_hg, minimal_forests);
-        assert(succeeded);
-      } else {
-        bool succeeded = writer.Write(forest, minimal_forests);
-        assert(succeeded);
-      }
-    }
-
-    if (sample_max_trans) {
-      MaxTranslationSample(&forest, sample_max_trans, conf.count("k_best") ? conf["k_best"].as<int>() : 0);
-    } else {
-      if (kbest) {
-        //TODO: does this work properly?
-        oracle.DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest,"-");
-      } else if (csplit_output_plf) {
-        cout << HypergraphIO::AsPLF(forest, false) << endl;
-      } else {
-        if (!graphviz && !has_ref && !joshua_viz) {
-          vector<WordID> trans;
-          ViterbiESentence(forest, &trans);
-          cout << TD::GetString(trans) << endl << flush;
-        }
-        if (joshua_viz) {
-          cout << sent_id << " ||| " << JoshuaVisualizationString(forest) << " ||| 1.0 ||| " << -1.0 << endl << flush;
-        }
-      }
-    }
-
-    const int max_trans_beam_size = conf.count("max_translation_beam") ?
-      conf["max_translation_beam"].as<int>() : 0;
-    if (max_trans_beam_size) {
-      Hack::MaxTrans(forest, max_trans_beam_size);
-      continue;
-    }
-
-    if (graphviz && !has_ref) forest.PrintGraphviz();
-
-    // the following are only used if write_gradient is true!
-    SparseVector<prob_t> full_exp, ref_exp, gradient;
-    double log_z = 0, log_ref_z = 0;
-    if (write_gradient) {
-      const prob_t z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &full_exp);
-      log_z = log(z);
-      full_exp /= z;
-    }
-    if (conf.count("show_cfg_search_space"))
-      HypergraphIO::WriteAsCFG(forest);
-    if (has_ref) {
-      if (HG::Intersect(ref, &forest)) {
-        cerr << "  Constr. forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
-        cerr << "  Constr. forest       (paths): " << forest.NumberOfPaths() << endl;
-        if (crf_uniform_empirical) {
-          cerr << "  USING UNIFORM WEIGHTS\n";
-          for (int i = 0; i < forest.edges_.size(); ++i)
-            forest.edges_[i].edge_prob_=prob_t::One();
-        } else {
-          forest.Reweight(feature_weights);
-          cerr << "  Constr. VitTree: " << ViterbiFTree(forest) << endl;
-        }
-	if (hadoop_counters)
-          cerr << "reporter:counter:UserCounters,SentencePairsParsed,1" << endl;
-        if (conf.count("show_partition")) {
-           const prob_t z = Inside<prob_t, EdgeProb>(forest);
-           cerr << "  Contst. partition  log(Z): " << log(z) << endl;
-        }
-        //DumpKBest(sent_id, forest, 1000);
-        if (conf.count("forest_output")) {
-          ForestWriter writer(str("forest_output",conf), sent_id);
-          if (FileExists(writer.fname_)) {
-            cerr << "  Unioning...\n";
-            Hypergraph new_hg;
-            {
-              ReadFile rf(writer.fname_);
-              bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
-              assert(succeeded);
-            }
-            new_hg.Union(forest);
-            bool succeeded = writer.Write(new_hg, minimal_forests);
-            assert(succeeded);
-          } else {
-            bool succeeded = writer.Write(forest, minimal_forests);
-            assert(succeeded);
-          }
-        }
-        if (aligner_mode && !output_training_vector)
-          AlignerTools::WriteAlignment(smeta.GetSourceLattice(), smeta.GetReference(), forest, &cout);
-        if (write_gradient) {
-          const prob_t ref_z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
-          ref_exp /= ref_z;
-          if (crf_uniform_empirical) {
-            log_ref_z = ref_exp.dot(feature_weights);
-          } else {
-            log_ref_z = log(ref_z);
-          }
-          //cerr << "      MODEL LOG Z: " << log_z << endl;
-          //cerr << "  EMPIRICAL LOG Z: " << log_ref_z << endl;
-          if ((log_z - log_ref_z) < kMINUS_EPSILON) {
-            cerr << "DIFF. ERR! log_z < log_ref_z: " << log_z << " " << log_ref_z << endl;
-            exit(1);
-          }
-          assert(!isnan(log_ref_z));
-          ref_exp -= full_exp;
-          acc_vec += ref_exp;
-          acc_obj += (log_z - log_ref_z);
-        }
-        if (feature_expectations) {
-          const prob_t z =
-            InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
-          ref_exp /= z;
-          acc_obj += log(z);
-          acc_vec += ref_exp;
-        }
-
-        if (output_training_vector) {
-          acc_vec.erase(0);
-          ++g_count;
-          if (g_count % combine_size == 0) {
-            if (encode_b64) {
-              cout << "0\t";
-              SparseVector<double> dav; ConvertSV(acc_vec, &dav);
-              B64::Encode(acc_obj, dav, &cout);
-              cout << endl << flush;
-            } else {
-              cout << "0\t**OBJ**=" << acc_obj << ';' <<  acc_vec << endl << flush;
-            }
-            acc_vec.clear();
-            acc_obj = 0;
-          }
-        }
-        if (conf.count("graphviz")) forest.PrintGraphviz();
-      } else {
-        cerr << "  REFERENCE UNREACHABLE.\n";
-        if (write_gradient) {
-	  if (hadoop_counters)
-            cerr << "reporter:counter:UserCounters,EFParseFailed,1" << endl;
-          cout << endl << flush;
-	}
-      }
-    }
-  }
-  if (output_training_vector && !acc_vec.empty()) {
-    if (encode_b64) {
-      cout << "0\t";
-      SparseVector<double> dav; ConvertSV(acc_vec, &dav);
-      B64::Encode(acc_obj, dav, &cout);
-      cout << endl << flush;
-    } else {
-      cout << "0\t**OBJ**=" << acc_obj << ';' << acc_vec << endl << flush;
-    }
+    decoder.Decode(buf);
   }
-  exit(0); // maybe this will save some destruction overhead.  or g++ without cxx_atexit needed?
   return 0;
 }
 
diff --git a/decoder/cdec_ff.cc b/decoder/cdec_ff.cc
index 98d4711f..84ba19fa 100644
--- a/decoder/cdec_ff.cc
+++ b/decoder/cdec_ff.cc
@@ -13,6 +13,12 @@
 #include "ff_register.h"
 
 void register_feature_functions() {
+  static bool registered = false;
+  if (registered) {
+    assert(!"register_feature_functions() called twice!");
+  }
+  registered = true;
+
   //TODO: these are worthless example target FSA ffs.  remove later
   RegisterFsaImpl<SameFirstLetter>(true);
   RegisterFsaImpl<LongerThanPrev>(true);
diff --git a/decoder/decoder.cc b/decoder/decoder.cc
index 196ffa46..f90e6bc0 100644
--- a/decoder/decoder.cc
+++ b/decoder/decoder.cc
@@ -1,25 +1,94 @@
 #include "decoder.h"
 
+#include <tr1/unordered_map>
 #include <boost/program_options.hpp>
 #include <boost/program_options/variables_map.hpp>
 
-#include "ff_factory.h"
-#include "cfg_options.h"
+#include "sampler.h"
 #include "stringlib.h"
+#include "weights.h"
+#include "filelib.h"
+#include "fdict.h"
+#include "timing_stats.h"
+
+#include "translator.h"
+#include "phrasebased_translator.h"
+#include "tagger.h"
+#include "lextrans.h"
+#include "lexalign.h"
+#include "csplit.h"
+
+#include "lattice.h"
 #include "hg.h"
+#include "sentence_metadata.h"
+#include "hg_intersect.h"
+
+#include "apply_fsa_models.h"
+#include "oracle_bleu.h"
+#include "apply_models.h"
+#include "ff.h"
+#include "ff_factory.h"
+#include "cfg_options.h"
+#include "viterbi.h"
+#include "kbest.h"
+#include "inside_outside.h"
+#include "exp_semiring.h"
+#include "sentence_metadata.h"
+#include "hg_cfg.h"
+
+#include "forest_writer.h" // TODO this section should probably be handled by an Observer
+#include "hg_io.h"
+#include "aligner.h"
+static const double kMINUS_EPSILON = -1e-6;  // don't be too strict
 
 using namespace std;
+using namespace std::tr1;
 using boost::shared_ptr;
 namespace po = boost::program_options;
 
+static bool verbose_feature_functions=true;
+
+namespace Hack { void MaxTrans(const Hypergraph& in, int beam_size); }
+namespace NgramCache { void Clear(); }
+
+void DecoderObserver::NotifySourceParseFailure(const SentenceMetadata&) {}
+void DecoderObserver::NotifyTranslationForest(const SentenceMetadata&, Hypergraph*) {}
+void DecoderObserver::NotifyAlignmentFailure(const SentenceMetadata&) {}
+void DecoderObserver::NotifyAlignmentForest(const SentenceMetadata&, Hypergraph*) {}
+void DecoderObserver::NotifyDecodingComplete(const SentenceMetadata&) {}
+
+struct ELengthWeightFunction {
+  double operator()(const Hypergraph::Edge& e) const {
+    return e.rule_->ELength() - e.rule_->Arity();
+  }
+};
 inline void ShowBanner() {
   cerr << "cdec v1.0 (c) 2009-2010 by Chris Dyer\n";
 }
 
+inline void show_models(po::variables_map const& conf,ModelSet &ms,char const* header) {
+  cerr<<header<<": ";
+  ms.show_features(cerr,cerr,conf.count("warn_0_weight"));
+}
+
 inline string str(char const* name,po::variables_map const& conf) {
   return conf[name].as<string>();
 }
 
+inline bool prelm_weights_string(po::variables_map const& conf,string &s) {
+  if (conf.count("prelm_weights")) {
+    s=str("prelm_weights",conf);
+    return true;
+  }
+  if (conf.count("prelm_copy_weights")) {
+    s=str("weights",conf);
+    return true;
+  }
+  return false;
+}
+
+
+
 // print just the --long_opt names suitable for bash compgen
 inline void print_options(std::ostream &out,po::options_description const& opts) {
   typedef std::vector< shared_ptr<po::option_description> > Ds;
@@ -32,22 +101,214 @@ inline void print_options(std::ostream &out,po::options_description const& opts)
   out << '"';
 }
 
+template <class V>
+inline bool store_conf(po::variables_map const& conf,std::string const& name,V *v) {
+  if (conf.count(name)) {
+    *v=conf[name].as<V>();
+    return true;
+  }
+  return false;
+}
+
+inline shared_ptr<FeatureFunction> make_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
+  string ff, param;
+  SplitCommandAndParam(ffp, &ff, &param);
+  cerr << pre << "feature: " << ff;
+  if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
+  else cerr << " (no config parameters)\n";
+  shared_ptr<FeatureFunction> pf = ff_registry.Create(ff, param);
+  if (!pf) exit(1);
+  int nbyte=pf->NumBytesContext();
+  if (verbose_feature_functions)
+    cerr<<"State is "<<nbyte<<" bytes for "<<pre<<"feature "<<ffp<<endl;
+  return pf;
+}
+
+inline shared_ptr<FsaFeatureFunction> make_fsa_ff(string const& ffp,bool verbose_feature_functions,char const* pre="") {
+  string ff, param;
+  SplitCommandAndParam(ffp, &ff, &param);
+  cerr << "FSA Feature: " << ff;
+  if (param.size() > 0) cerr << " (with config parameters '" << param << "')\n";
+  else cerr << " (no config parameters)\n";
+  shared_ptr<FsaFeatureFunction> pf = fsa_ff_registry.Create(ff, param);
+  if (!pf) exit(1);
+  if (verbose_feature_functions)
+    cerr<<"State is "<<pf->state_bytes()<<" bytes for "<<pre<<"feature "<<ffp<<endl;
+  return pf;
+}
+
 struct DecoderImpl {
-  DecoderImpl(int argc, char** argv, istream* cfg);
-  bool Decode(const string& input) {
-    return false;
+  DecoderImpl(po::variables_map& conf, int argc, char** argv, istream* cfg);
+  ~DecoderImpl();
+  bool Decode(const string& input, DecoderObserver*);
+  void SetWeights(const vector<double>& weights) {
   }
-  bool DecodeProduceHypergraph(const string& input, Hypergraph* hg) {
+
+  void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,WeightVector *weights=0,bool show_deriv=false) {
+    cerr << viterbi_stats(forest,name,true,show_tree,show_deriv);
+    if (show_features) {
+      cerr << name<<"     features: ";
+/*      Hypergraph::Edge const* best=forest.ViterbiGoalEdge();
+      if (!best)
+        cerr << name<<" has no goal edge.";
+      else
+        cerr<<best->feature_values_;
+*/
+    cerr << ViterbiFeatures(forest,weights);
+    cerr << endl;
+    }
+  }
+
+  void forest_stats(Hypergraph &forest,string name,bool show_tree,bool show_features,DenseWeightVector const& feature_weights, bool sd=false) {
+      WeightVector fw(feature_weights);
+      forest_stats(forest,name,show_tree,show_features,&fw,sd);
+  }
+
+  bool beam_param(po::variables_map const& conf,string const& name,double *val,bool scale_srclen=false,double srclen=1) {
+    if (conf.count(name)) {
+      *val=conf[name].as<double>()*(scale_srclen?srclen:1);
+      return true;
+    }
     return false;
   }
-  void SetWeights(const vector<double>& weights) {
+
+  void maybe_prune(Hypergraph &forest,po::variables_map const& conf,string nbeam,string ndensity,string forestname,double srclen) {
+    double beam_prune=0,density_prune=0;
+    bool use_beam_prune=beam_param(conf,nbeam,&beam_prune,conf.count("scale_prune_srclen"),srclen);
+    bool use_density_prune=beam_param(conf,ndensity,&density_prune);
+    if (use_beam_prune || use_density_prune) {
+      double presize=forest.edges_.size();
+      vector<bool> preserve_mask,*pm=0;
+      if (conf.count("csplit_preserve_full_word")) {
+        preserve_mask.resize(forest.edges_.size());
+        preserve_mask[CompoundSplit::GetFullWordEdgeIndex(forest)] = true;
+        pm=&preserve_mask;
+      }
+      forest.PruneInsideOutside(beam_prune,density_prune,pm,false,1,conf["promise_power"].as<double>());
+      if (!forestname.empty()) forestname=" "+forestname;
+      forest_stats(forest,"  Pruned "+forestname+" forest",false,false,0,false);
+      cerr << "  Pruned "<<forestname<<" forest portion of edges kept: "<<forest.edges_.size()/presize<<endl;
+    }
+  }
+
+  void SampleRecurse(const Hypergraph& hg, const vector<SampleSet<prob_t> >& ss, int n, vector<WordID>* out) {
+    const SampleSet<prob_t>& s = ss[n];
+    int i = rng->SelectSample(s);
+    const Hypergraph::Edge& edge = hg.edges_[hg.nodes_[n].in_edges_[i]];
+    vector<vector<WordID> > ants(edge.tail_nodes_.size());
+    for (int j = 0; j < ants.size(); ++j)
+      SampleRecurse(hg, ss, edge.tail_nodes_[j], &ants[j]);
+
+    vector<const vector<WordID>*> pants(ants.size());
+    for (int j = 0; j < ants.size(); ++j) pants[j] = &ants[j];
+    edge.rule_->ESubstitute(pants, out);
+  }
+
+  struct SampleSort {
+    bool operator()(const pair<int,string>& a, const pair<int,string>& b) const {
+      return a.first > b.first;
+    }
+  };
+
+  // TODO this should be handled by an Observer
+  void MaxTranslationSample(Hypergraph* hg, const int samples, const int k) {
+    unordered_map<string, int, boost::hash<string> > m;
+    hg->PushWeightsToGoal();
+    const int num_nodes = hg->nodes_.size();
+    vector<SampleSet<prob_t> > ss(num_nodes);
+    for (int i = 0; i < num_nodes; ++i) {
+      SampleSet<prob_t>& s = ss[i];
+      const vector<int>& in_edges = hg->nodes_[i].in_edges_;
+      for (int j = 0; j < in_edges.size(); ++j) {
+        s.add(hg->edges_[in_edges[j]].edge_prob_);
+      }
+    }
+    for (int i = 0; i < samples; ++i) {
+      vector<WordID> yield;
+      SampleRecurse(*hg, ss, hg->nodes_.size() - 1, &yield);
+      const string trans = TD::GetString(yield);
+      ++m[trans];
+    }
+    vector<pair<int, string> > dist;
+    for (unordered_map<string, int, boost::hash<string> >::iterator i = m.begin();
+           i != m.end(); ++i) {
+      dist.push_back(make_pair(i->second, i->first));
+    }
+    sort(dist.begin(), dist.end(), SampleSort());
+    if (k) {
+      for (int i = 0; i < k; ++i)
+        cout << dist[i].first << " ||| " << dist[i].second << endl;
+    } else {
+      cout << dist[0].second << endl;
+    }
   }
 
-  po::variables_map conf;
+  void ParseTranslatorInputLattice(const string& line, string* input, Lattice* ref) {
+    string sref;
+    ParseTranslatorInput(line, input, &sref);
+    if (sref.size() > 0) {
+      assert(ref);
+      LatticeTools::ConvertTextOrPLF(sref, ref);
+    }
+  } 
+
+  po::variables_map& conf;
+  OracleBleu oracle;
   CFGOptions cfg_options;
+  string formalism;
+  shared_ptr<Translator> translator;
+  vector<double> feature_weights,prelm_feature_weights;
+  Weights w,prelm_w;
+  vector<shared_ptr<FeatureFunction> > pffs,prelm_only_ffs;
+  vector<const FeatureFunction*> late_ffs,prelm_ffs;
+  vector<shared_ptr<FsaFeatureFunction> > fsa_ffs;
+  vector<string> fsa_names;
+  ModelSet* late_models, *prelm_models;
+  IntersectionConfiguration* inter_conf;
+  shared_ptr<RandomNumberGenerator<boost::mt19937> > rng;
+  int sample_max_trans;
+  bool aligner_mode;
+  bool minimal_forests;
+  bool graphviz; 
+  bool joshua_viz;
+  bool encode_b64;
+  bool kbest;
+  bool unique_kbest;
+  bool crf_uniform_empirical;
+  bool get_oracle_forest;
+  shared_ptr<WriteFile> extract_file;
+  int combine_size;
+  int sent_id;
+  SparseVector<prob_t> acc_vec;  // accumulate gradient
+  double acc_obj; // accumulate objective
+  int g_count;    // number of gradient pieces computed
+  bool has_prelm_models;
+  int pop_limit;
+  bool csplit_output_plf;
+  bool write_gradient; // TODO Observer
+  bool feature_expectations; // TODO Observer
+  bool output_training_vector; // TODO Observer
+
+  static void ConvertSV(const SparseVector<prob_t>& src, SparseVector<double>* trg) {
+    for (SparseVector<prob_t>::const_iterator it = src.begin(); it != src.end(); ++it)
+      trg->set_value(it->first, it->second);
+  }
 };
 
-DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
+DecoderImpl::~DecoderImpl() {
+  if (output_training_vector && !acc_vec.empty()) {
+    if (encode_b64) {
+      cout << "0\t";
+      SparseVector<double> dav; ConvertSV(acc_vec, &dav);
+      B64::Encode(acc_obj, dav, &cout);
+      cout << endl << flush;
+    } else {
+      cout << "0\t**OBJ**=" << acc_obj << ';' << acc_vec << endl << flush;
+    }
+  }
+}
+
+DecoderImpl::DecoderImpl(po::variables_map& conf, int argc, char** argv, istream* cfg) : conf(conf) {
   if (cfg) { if (argc || argv) { cerr << "DecoderImpl() can only take a file or command line options, not both\n"; exit(1); } }
   bool show_config;
   bool show_weights;
@@ -64,7 +325,7 @@ DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
     ("prelm_feature_function",po::value<vector<string> >()->composing(),"Additional feature functions for prelm pass only (in addition to the 0-state subset of feature_function")
     ("keep_prelm_cube_order","DEPRECATED (always enabled).  when forest rescoring with final models, use the edge ordering from the prelm pruning features*weights.  only meaningful if --prelm_weights given.  UNTESTED but assume that cube pruning gives a sensible result, and that 'good' (as tuned for bleu w/ prelm features) edges come first.")
     ("warn_0_weight","Warn about any feature id that has a 0 weight (this is perfectly safe if you intend 0 weight, though)")
-        ("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
+        ("freeze_feature_set,Z", "Freeze feature set after reading feature weights file")
         ("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
         ("fsa_feature_function,A",po::value<vector<string> >()->composing(), "Additional FSA feature function(s) (-L for list)")
     ("apply_fsa_by",po::value<string>()->default_value("BU_CUBE"), "Method for applying fsa_feature_functions - BU_FULL BU_CUBE EARLEY") //+ApplyFsaBy::all_names()
@@ -142,7 +403,7 @@ DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
   }
   if (conf.count("show_config")) // special handling needed because we only want to notify() once.
     show_config=true;
-  if (conf.count("config") || cfg) {
+  if (conf.count("config") && !cfg) {
     typedef vector<string> Cs;
     Cs cs=conf["config"].as<Cs>();
     for (int i=0;i<cs.size();++i) {
@@ -151,8 +412,8 @@ DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
       ReadFile conff(cfg);
       po::store(po::parse_config_file(*conff, dconfig_options), conf);
     }
-    if (cfg) po::store(po::parse_config_file(*cfg, dconfig_options), conf);
   }
+  if (cfg) po::store(po::parse_config_file(*cfg, dconfig_options), conf);
   po::notify(conf);
   if (show_config && !cfg_files.empty()) {
     cerr<< "\nConfig files:\n\n";
@@ -171,9 +432,9 @@ DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
 
   if (conf.count("list_feature_functions")) {
     cerr << "Available feature functions (specify with -F; describe with -u FeatureName):\n";
-    // ff_registry.DisplayList(); //TODO
+    ff_registry.DisplayList(); //TODO
     cerr << "Available FSA feature functions (specify with --fsa_feature_function):\n";
-   // fsa_ff_registry.DisplayList(); // TODO
+    fsa_ff_registry.DisplayList(); // TODO
     cerr << endl;
     exit(1);
   }
@@ -191,38 +452,449 @@ DecoderImpl::DecoderImpl(int argc, char** argv, istream* cfg) {
     exit(1);
   }
 
-  const string formalism = LowercaseString(str("formalism",conf));
+  formalism = LowercaseString(str("formalism",conf));
   if (formalism != "scfg" && formalism != "fst" && formalism != "lextrans" && formalism != "pb" && formalism != "csplit" && formalism != "tagger" && formalism != "lexalign") {
     cerr << "Error: --formalism takes only 'scfg', 'fst', 'pb', 'csplit', 'lextrans', 'lexalign', or 'tagger'\n";
     cerr << dcmdline_options << endl;
     exit(1);
   }
 
-}
 
-Decoder::Decoder(istream* cfg) {
-  pimpl_.reset(new DecoderImpl(0,0,cfg));
-}
+  write_gradient = conf.count("cll_gradient");
+  feature_expectations = conf.count("feature_expectations");
+  if (write_gradient && feature_expectations) {
+    cerr << "You can only specify --gradient or --feature_expectations, not both!\n";
+    exit(1);
+  }
+  output_training_vector = (write_gradient || feature_expectations);
 
-Decoder::Decoder(int argc, char** argv) {
-  pimpl_.reset(new DecoderImpl(argc, argv, 0));
-}
+  const string formalism = LowercaseString(str("formalism",conf));
+  const bool csplit_preserve_full_word = conf.count("csplit_preserve_full_word");
+  if (csplit_preserve_full_word &&
+      (formalism != "csplit" || !(conf.count("beam_prune")||conf.count("density_prune")||conf.count("prelm_beam_prune")||conf.count("prelm_density_prune")))) {
+    cerr << "--csplit_preserve_full_word should only be "
+         << "used with csplit AND --*_prune!\n";
+    exit(1);
+  }
+  csplit_output_plf = conf.count("csplit_output_plf");
+  if (csplit_output_plf && formalism != "csplit") {
+    cerr << "--csplit_output_plf should only be used with csplit!\n";
+    exit(1);
+  }
 
-Decoder::~Decoder() {}
+  // load feature weights (and possibly freeze feature set)
+  has_prelm_models = false;
+  if (conf.count("weights")) {
+    w.InitFromFile(str("weights",conf));
+    feature_weights.resize(FD::NumFeats());
+    w.InitVector(&feature_weights);
+    string plmw;
+    if (prelm_weights_string(conf,plmw)) {
+      has_prelm_models = true;
+      prelm_w.InitFromFile(plmw);
+      prelm_feature_weights.resize(FD::NumFeats());
+      prelm_w.InitVector(&prelm_feature_weights);
+      if (show_weights)
+        cerr << "prelm_weights: " << WeightVector(prelm_feature_weights)<<endl;
+    }
+    if (show_weights)
+      cerr << "+LM weights: " << WeightVector(feature_weights)<<endl;
+  }
+  bool warn0=conf.count("warn_0_weight");
+  bool freeze=conf.count("freeze_feature_set");
+  bool early_freeze=freeze && !warn0;
+  bool late_freeze=freeze && warn0;
+  if (early_freeze) {
+    cerr << "Freezing feature set" << endl;
+    FD::Freeze(); // this means we can't see the feature names of not-weighted features
+  }
 
-bool Decoder::Decode(const string& input) {
-  return pimpl_->Decode(input);
-}
+  // set up translation back end
+  if (formalism == "scfg")
+    translator.reset(new SCFGTranslator(conf));
+  else if (formalism == "fst")
+    translator.reset(new FSTTranslator(conf));
+  else if (formalism == "pb")
+    translator.reset(new PhraseBasedTranslator(conf));
+  else if (formalism == "csplit")
+    translator.reset(new CompoundSplit(conf));
+  else if (formalism == "lextrans")
+    translator.reset(new LexicalTrans(conf));
+  else if (formalism == "lexalign")
+    translator.reset(new LexicalAlign(conf));
+  else if (formalism == "tagger")
+    translator.reset(new Tagger(conf));
+  else
+    assert(!"error");
+
+  // set up additional scoring features
+  if (conf.count("feature_function") > 0) {
+    vector<string> add_ffs;
+//    const vector<string>& add_ffs = conf["feature_function"].as<vector<string> >();
+    store_conf(conf,"feature_function",&add_ffs);
+    for (int i = 0; i < add_ffs.size(); ++i) {
+      pffs.push_back(make_ff(add_ffs[i],verbose_feature_functions));
+      FeatureFunction const* p=pffs.back().get();
+      late_ffs.push_back(p);
+      if (has_prelm_models) {
+        if (p->NumBytesContext()==0)
+          prelm_ffs.push_back(p);
+        else
+          cerr << "Excluding stateful feature from prelm pruning: "<<add_ffs[i]<<endl;
+      }
+    }
+  }
+  if (conf.count("prelm_feature_function") > 0) {
+    vector<string> add_ffs;
+    store_conf(conf,"prelm_feature_function",&add_ffs);
+//    const vector<string>& add_ffs = conf["prelm_feature_function"].as<vector<string> >();
+    for (int i = 0; i < add_ffs.size(); ++i) {
+      prelm_only_ffs.push_back(make_ff(add_ffs[i],verbose_feature_functions,"prelm-only "));
+      prelm_ffs.push_back(prelm_only_ffs.back().get());
+    }
+  }
 
-bool Decoder::DecodeProduceHypergraph(const string& input, Hypergraph* hg) {
-  return pimpl_->DecodeProduceHypergraph(input, hg);
+  store_conf(conf,"fsa_feature_function",&fsa_names);
+  for (int i=0;i<fsa_names.size();++i)
+    fsa_ffs.push_back(make_fsa_ff(fsa_names[i],verbose_feature_functions,"FSA "));
+  if (fsa_ffs.size()>1) {
+    //FIXME: support N fsa ffs.
+    cerr<<"Only the first fsa FF will be used (FIXME).\n";
+    fsa_ffs.resize(1);
+  }
+  if (!fsa_ffs.empty()) {
+    cerr<<"FSA: ";
+    show_all_features(fsa_ffs,feature_weights,cerr,cerr,true,true);
+  }
+
+  if (late_freeze) {
+    cerr << "Late freezing feature set (use --no_freeze_feature_set to prevent)." << endl;
+    FD::Freeze(); // this means we can't see the feature names of not-weighted features
+  }
+
+  if (has_prelm_models)
+        cerr << "prelm rescoring with "<<prelm_ffs.size()<<" 0-state feature functions.  +LM pass will use "<<late_ffs.size()<<" features (not counting rule features)."<<endl;
+
+  late_models = new ModelSet(feature_weights, late_ffs);
+  show_models(conf,*late_models,"late ");
+  prelm_models = new ModelSet(prelm_feature_weights, prelm_ffs);
+  if (has_prelm_models)
+    show_models(conf,*prelm_models,"prelm ");
+
+  int palg = 1;
+  if (LowercaseString(str("intersection_strategy",conf)) == "full") {
+    palg = 0;
+    cerr << "Using full intersection (no pruning).\n";
+  }
+  pop_limit=conf["cubepruning_pop_limit"].as<int>();
+  inter_conf = new IntersectionConfiguration(palg, pop_limit);
+
+  sample_max_trans = conf.count("max_translation_sample") ?
+    conf["max_translation_sample"].as<int>() : 0;
+  if (sample_max_trans)
+    rng.reset(new RandomNumberGenerator<boost::mt19937>);
+  aligner_mode = conf.count("aligner");
+  minimal_forests = conf.count("minimal_forests");
+  graphviz = conf.count("graphviz");
+  joshua_viz = conf.count("show_joshua_visualization");
+  encode_b64 = str("vector_format",conf) == "b64";
+  kbest = conf.count("k_best");
+  unique_kbest = conf.count("unique_k_best");
+  crf_uniform_empirical = conf.count("crf_uniform_empirical");
+  get_oracle_forest = conf.count("get_oracle_forest");
+
+  cfg_options.Validate();
+
+  if (conf.count("extract_rules"))
+    extract_file.reset(new WriteFile(str("extract_rules",conf)));
+
+  combine_size = conf["combine_size"].as<int>();
+  if (combine_size < 1) combine_size = 1;
+  sent_id = -1;
+  acc_obj = 0; // accumulate objective
+  g_count = 0;    // number of gradient pieces computed
 }
 
-void Decoder::SetWeights(const vector<double>& weights) {
-  pimpl_->SetWeights(weights);
+Decoder::Decoder(istream* cfg) { pimpl_.reset(new DecoderImpl(conf,0,0,cfg)); }
+Decoder::Decoder(int argc, char** argv) { pimpl_.reset(new DecoderImpl(conf,argc, argv, 0)); }
+Decoder::~Decoder() {}
+bool Decoder::Decode(const string& input, DecoderObserver* o) {
+  bool del = false;
+  if (!o) { o = new DecoderObserver; del = true; }
+  const bool res = pimpl_->Decode(input, o);
+  if (del) delete o;
+  return res;
 }
+void Decoder::SetWeights(const vector<double>& weights) { pimpl_->SetWeights(weights); }
+
+
+bool DecoderImpl::Decode(const string& input, DecoderObserver* o) {
+  string buf = input;
+  NgramCache::Clear();   // clear ngram cache for remote LM (if used)
+  Timer::Summarize();
+  ++sent_id;
+  map<string, string> sgml;
+  ProcessAndStripSGML(&buf, &sgml);
+  if (sgml.find("id") != sgml.end())
+    sent_id = atoi(sgml["id"].c_str());
+
+  cerr << "\nINPUT: ";
+  if (buf.size() < 100)
+    cerr << buf << endl;
+  else {
+    size_t x = buf.rfind(" ", 100);
+    if (x == string::npos) x = 100;
+    cerr << buf.substr(0, x) << " ..." << endl;
+  }
+  cerr << "  id = " << sent_id << endl;
+  string to_translate;
+  Lattice ref;
+  ParseTranslatorInputLattice(buf, &to_translate, &ref);
+  const unsigned srclen=NTokens(to_translate,' ');
+//FIXME: should get the avg. or max source length of the input lattice (like Lattice::dist_(start,end)); but this is only used to scale beam parameters (optionally) anyway so fidelity isn't important.
+  const bool has_ref = ref.size() > 0;
+  SentenceMetadata smeta(sent_id, ref);
+  Hypergraph forest;          // -LM forest
+  translator->ProcessMarkupHints(sgml);
+  Timer t("Translation");
+  const bool translation_successful =
+    translator->Translate(to_translate, &smeta, feature_weights, &forest);
+  //TODO: modify translator to incorporate all 0-state model scores immediately?
+  translator->SentenceComplete();
+
+  if (!translation_successful) {
+    cerr << "  NO PARSE FOUND.\n";
+    o->NotifySourceParseFailure(smeta);
+    o->NotifyDecodingComplete(smeta);
+    return false;
+  }
+
+  const bool show_tree_structure=conf.count("show_tree_structure");
+  const bool show_features=conf.count("show_features");
+  forest_stats(forest,"  -LM forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
+  if (conf.count("show_expected_length")) {
+    const PRPair<double, double> res =
+      Inside<PRPair<double, double>,
+             PRWeightFunction<double, EdgeProb, double, ELengthWeightFunction> >(forest);
+    cerr << "  Expected length  (words): " << res.r / res.p << "\t" << res << endl;
+  }
+  if (conf.count("show_partition")) {
+    const prob_t z = Inside<prob_t, EdgeProb>(forest);
+    cerr << "  -LM partition     log(Z): " << log(z) << endl;
+  }
+
+  if (has_prelm_models) {
+    Timer t("prelm rescoring");
+    forest.Reweight(prelm_feature_weights);
+    Hypergraph prelm_forest;
+    ApplyModelSet(forest,
+                  smeta,
+                  *prelm_models,
+                  *inter_conf, // this is now reduced to exhaustive if all are stateless
+                  &prelm_forest);
+    forest.swap(prelm_forest);
+    forest.Reweight(prelm_feature_weights); //FIXME: why the reweighting? here and below.  maybe in case we already had a featval for that id and ApplyModelSet only adds prob, doesn't recompute it?
+    forest_stats(forest," prelm forest",show_tree_structure,show_features,prelm_feature_weights,oracle.show_derivation);
+  }
+
+  maybe_prune(forest,conf,"prelm_beam_prune","prelm_density_prune","-LM",srclen);
+
+  cfg_options.maybe_output_source(forest);
+
+  bool has_late_models = !late_models->empty();
+  if (has_late_models) {
+    Timer t("Forest rescoring:");
+    forest.Reweight(feature_weights);
+    Hypergraph lm_forest;
+    ApplyModelSet(forest,
+                  smeta,
+                  *late_models,
+                  *inter_conf,
+                  &lm_forest);
+    forest.swap(lm_forest);
+    forest.Reweight(feature_weights);
+    forest_stats(forest,"  +LM forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
+  }
+
+  maybe_prune(forest,conf,"beam_prune","density_prune","+LM",srclen);
+
+  HgCFG hgcfg(forest);
+  cfg_options.prepare(hgcfg);
+
+  if (!fsa_ffs.empty()) {
+    Timer t("Target FSA rescoring:");
+    if (!has_late_models)
+      forest.Reweight(feature_weights);
+    Hypergraph fsa_forest;
+    assert(fsa_ffs.size()==1);
+    ApplyFsaBy cfg(str("apply_fsa_by",conf),pop_limit);
+    cerr << "FSA rescoring with "<<cfg<<" "<<fsa_ffs[0]->describe()<<endl;
+    ApplyFsaModels(hgcfg,smeta,*fsa_ffs[0],feature_weights,cfg,&fsa_forest);
+    forest.swap(fsa_forest);
+    forest.Reweight(feature_weights);
+    forest_stats(forest,"  +FSA forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
+  }
+
+  // Oracle Rescoring
+  if(get_oracle_forest) {
+    Oracle oc=oracle.ComputeOracle(smeta,&forest,FeatureVector(feature_weights),10,conf["forest_output"].as<std::string>());
+    cerr << "  +Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+    cerr << "  +Oracle BLEU (paths): " << forest.NumberOfPaths() << endl;
+    oc.hope.Print(cerr,"  +Oracle BLEU");
+    oc.fear.Print(cerr,"  -Oracle BLEU");
+    //Add 1-best translation (trans) to psuedo-doc vectors
+    oracle.IncludeLastScore(&cerr);
+  }
+
+  // TODO I think this should probably be handled by an Observer
+  if (conf.count("forest_output") && !has_ref) {
+    ForestWriter writer(str("forest_output",conf), sent_id);
+    if (FileExists(writer.fname_)) {
+      cerr << "  Unioning...\n";
+      Hypergraph new_hg;
+      {
+        ReadFile rf(writer.fname_);
+        bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
+        assert(succeeded);
+      }
+      new_hg.Union(forest);
+      bool succeeded = writer.Write(new_hg, minimal_forests);
+      assert(succeeded);
+    } else {
+      bool succeeded = writer.Write(forest, minimal_forests);
+      assert(succeeded);
+    }
+  }
+
+  // TODO I think this should probably be handled by an Observer
+  if (sample_max_trans) {
+    MaxTranslationSample(&forest, sample_max_trans, conf.count("k_best") ? conf["k_best"].as<int>() : 0);
+  } else {
+    if (kbest) {
+      //TODO: does this work properly?
+      oracle.DumpKBest(sent_id, forest, conf["k_best"].as<int>(), unique_kbest,"-");
+    } else if (csplit_output_plf) {
+      cout << HypergraphIO::AsPLF(forest, false) << endl;
+    } else {
+      if (!graphviz && !has_ref && !joshua_viz) {
+        vector<WordID> trans;
+        ViterbiESentence(forest, &trans);
+        cout << TD::GetString(trans) << endl << flush;
+      }
+      if (joshua_viz) {
+        cout << sent_id << " ||| " << JoshuaVisualizationString(forest) << " ||| 1.0 ||| " << -1.0 << endl << flush;
+      }
+    }
+  }
+
+  // TODO this should be handled by an Observer
+  const int max_trans_beam_size = conf.count("max_translation_beam") ?
+    conf["max_translation_beam"].as<int>() : 0;
+  if (max_trans_beam_size) {
+    Hack::MaxTrans(forest, max_trans_beam_size);
+    return true;
+  }
+
+  // TODO this should be handled by an Observer
+  if (graphviz && !has_ref) forest.PrintGraphviz();
 
-void InitCommandLine(int argc, char** argv, po::variables_map* confp) {
-  po::variables_map &conf=*confp;
+  // the following are only used if write_gradient is true!
+  SparseVector<prob_t> full_exp, ref_exp, gradient;
+  double log_z = 0, log_ref_z = 0;
+  if (write_gradient) {
+    const prob_t z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &full_exp);
+    log_z = log(z);
+    full_exp /= z;
+  }
+  if (conf.count("show_cfg_search_space"))
+    HypergraphIO::WriteAsCFG(forest);
+  if (has_ref) {
+    if (HG::Intersect(ref, &forest)) {
+      cerr << "  Constr. forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
+      cerr << "  Constr. forest       (paths): " << forest.NumberOfPaths() << endl;
+      if (crf_uniform_empirical) {
+        cerr << "  USING UNIFORM WEIGHTS\n";
+        for (int i = 0; i < forest.edges_.size(); ++i)
+          forest.edges_[i].edge_prob_=prob_t::One();
+      } else {
+        forest.Reweight(feature_weights);
+        cerr << "  Constr. VitTree: " << ViterbiFTree(forest) << endl;
+      }
+      if (conf.count("show_partition")) {
+         const prob_t z = Inside<prob_t, EdgeProb>(forest);
+         cerr << "  Contst. partition  log(Z): " << log(z) << endl;
+      }
+      if (conf.count("forest_output")) {
+        ForestWriter writer(str("forest_output",conf), sent_id);
+        if (FileExists(writer.fname_)) {
+          cerr << "  Unioning...\n";
+          Hypergraph new_hg;
+          {
+            ReadFile rf(writer.fname_);
+            bool succeeded = HypergraphIO::ReadFromJSON(rf.stream(), &new_hg);
+            assert(succeeded);
+          }
+          new_hg.Union(forest);
+          bool succeeded = writer.Write(new_hg, minimal_forests);
+          assert(succeeded);
+        } else {
+          bool succeeded = writer.Write(forest, minimal_forests);
+          assert(succeeded);
+        }
+      }
+      if (aligner_mode && !output_training_vector)
+        AlignerTools::WriteAlignment(smeta.GetSourceLattice(), smeta.GetReference(), forest, &cout);
+      if (write_gradient) {
+        const prob_t ref_z = InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
+        ref_exp /= ref_z;
+        if (crf_uniform_empirical) {
+          log_ref_z = ref_exp.dot(feature_weights);
+        } else {
+          log_ref_z = log(ref_z);
+        }
+        //cerr << "      MODEL LOG Z: " << log_z << endl;
+        //cerr << "  EMPIRICAL LOG Z: " << log_ref_z << endl;
+        if ((log_z - log_ref_z) < kMINUS_EPSILON) {
+          cerr << "DIFF. ERR! log_z < log_ref_z: " << log_z << " " << log_ref_z << endl;
+          exit(1);
+        }
+        assert(!isnan(log_ref_z));
+        ref_exp -= full_exp;
+        acc_vec += ref_exp;
+        acc_obj += (log_z - log_ref_z);
+      }
+      if (feature_expectations) {
+        const prob_t z =
+          InsideOutside<prob_t, EdgeProb, SparseVector<prob_t>, EdgeFeaturesAndProbWeightFunction>(forest, &ref_exp);
+        ref_exp /= z;
+        acc_obj += log(z);
+        acc_vec += ref_exp;
+      }
 
+      if (output_training_vector) {
+        acc_vec.erase(0);
+        ++g_count;
+        if (g_count % combine_size == 0) {
+          if (encode_b64) {
+            cout << "0\t";
+            SparseVector<double> dav; ConvertSV(acc_vec, &dav);
+            B64::Encode(acc_obj, dav, &cout);
+            cout << endl << flush;
+          } else {
+            cout << "0\t**OBJ**=" << acc_obj << ';' <<  acc_vec << endl << flush;
+          }
+          acc_vec.clear();
+          acc_obj = 0;
+        }
+      }
+      if (conf.count("graphviz")) forest.PrintGraphviz();
+    } else {
+      cerr << "  REFERENCE UNREACHABLE.\n";
+      if (write_gradient) {
+        cout << endl << flush;
+      }
+    }
+  }
+  return true;
 }
+
diff --git a/decoder/decoder.h b/decoder/decoder.h
index ae1b9bb0..5dd6e1aa 100644
--- a/decoder/decoder.h
+++ b/decoder/decoder.h
@@ -5,17 +5,29 @@
 #include <string>
 #include <vector>
 #include <boost/shared_ptr.hpp>
+#include <boost/program_options/variables_map.hpp>
 
+class SentenceMetadata;
 struct Hypergraph;
 struct DecoderImpl;
+
+struct DecoderObserver {
+  virtual void NotifySourceParseFailure(const SentenceMetadata& smeta);
+  virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg);
+  virtual void NotifyAlignmentFailure(const SentenceMetadata& semta);
+  virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg);
+  virtual void NotifyDecodingComplete(const SentenceMetadata& smeta);
+};
+
 struct Decoder {
   Decoder(int argc, char** argv);
   Decoder(std::istream* config_file);
-  bool Decode(const std::string& input);
-  bool DecodeProduceHypergraph(const std::string& input, Hypergraph* hg);
+  bool Decode(const std::string& input, DecoderObserver* observer = NULL);
   void SetWeights(const std::vector<double>& weights);
   ~Decoder();
+  const boost::program_options::variables_map& GetConf() const { return conf; }
  private:
+  boost::program_options::variables_map conf;
   boost::shared_ptr<DecoderImpl> pimpl_;
 };
 
diff --git a/decoder/ff_factory.h b/decoder/ff_factory.h
index 5eb68c8b..92334396 100644
--- a/decoder/ff_factory.h
+++ b/decoder/ff_factory.h
@@ -20,6 +20,8 @@
 
 #include <boost/shared_ptr.hpp>
 
+#include "ff_fsa_dynamic.h"
+
 class FeatureFunction;
 
 class FsaFeatureFunction;