#include "decoder.h"
#include <tr1/unordered_map>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "sampler.h"
#include "stringlib.h"
#include "weights.h"
#include "filelib.h"
#include "fdict.h"
#include "timing_stats.h"
#include "verbose.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(); }
DecoderObserver::~DecoderObserver() {}
void DecoderObserver::NotifyDecodingStart(const SentenceMetadata& smeta) {}
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;
Ds const& ds=opts.options();
out << '"';
for (unsigned i=0;i<ds.size();++i) {
if (i) out<<' ';
out<<"--"<<ds[i]->long_name();
}
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, ¶m);
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, ¶m);
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(po::variables_map& conf, int argc, char** argv, istream* cfg);
~DecoderImpl();
bool Decode(const string& input, DecoderObserver*);
void SetWeights(const vector<double>& weights) {
feature_weights = weights;
}
void SetId(int next_sent_id) { sent_id = next_sent_id - 1; }
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 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;
}
}
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() {
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;
vector<string> cfg_files;
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)")
("per_sentence_grammar_file", po::value<string>(), "Optional (and possibly not implemented) per sentence grammar file enables all per sentence grammars to be stored in a single large file and accessed by offset")
("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)")
("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()
("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.")
("lextrans_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);
if (argc) {
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") && !cfg) {
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);
}
}
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";
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(); //TODO
cerr << "Available FSA feature functions (specify with --fsa_feature_function):\n";
fsa_ff_registry.DisplayList(); // TODO
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);
}
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);
}
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);
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);
}
// 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
}
// 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());
}
}
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);
if (!SILENT) show_models(conf,*late_models,"late ");
prelm_models = new ModelSet(prelm_feature_weights, prelm_ffs);
if (has_prelm_models) {
if (!SILENT) 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
}
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() {}
void Decoder::SetId(int next_sent_id) { pimpl_->SetId(next_sent_id); }
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());
if (!SILENT) {
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);
smeta.sgml_.swap(sgml);
o->NotifyDecodingStart(smeta);
Hypergraph forest; // -LM forest
translator->ProcessMarkupHints(smeta.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) {
if (!SILENT) 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");
if (!SILENT) 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;
prelm_models->PrepareForInput(smeta);
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:");
late_models->PrepareForInput(smeta);
forest.Reweight(feature_weights);
Hypergraph lm_forest;
ApplyModelSet(forest,
smeta,
*late_models,
*inter_conf,
&lm_forest);
forest.swap(lm_forest);
forest.Reweight(feature_weights);
if (!SILENT) 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);
if (!SILENT) 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);
if (!SILENT) 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>());
if (!SILENT) cerr << " +Oracle BLEU forest (nodes/edges): " << forest.nodes_.size() << '/' << forest.edges_.size() << endl;
if (!SILENT) 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
if (!SILENT) oracle.IncludeLastScore(&cerr);
}
o->NotifyTranslationForest(smeta, &forest);
// 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_)) {
if (!SILENT) 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();
// 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)) {
if (!SILENT) forest_stats(forest," Constr. forest",show_tree_structure,show_features,feature_weights,oracle.show_derivation);
if (crf_uniform_empirical) {
if (!SILENT) 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);
if (!SILENT) 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;
}
o->NotifyAlignmentForest(smeta, &forest);
if (conf.count("forest_output")) {
ForestWriter writer(str("forest_output",conf), sent_id);
if (FileExists(writer.fname_)) {
if (!SILENT) 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 {
o->NotifyAlignmentFailure(smeta);
if (!SILENT) cerr << " REFERENCE UNREACHABLE.\n";
if (write_gradient) {
cout << endl << flush;
}
}
}
o->NotifyDecodingComplete(smeta);
return true;
}