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#include <sstream>
#include <iostream>
#include <fstream>
#include <vector>
#include <cassert>
#include <cmath>
#include <mpi.h>
#include <boost/mpi.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "verbose.h"
#include "hg.h"
#include "prob.h"
#include "inside_outside.h"
#include "ff_register.h"
#include "decoder.h"
#include "filelib.h"
#include "weights.h"
using namespace std;
namespace po = boost::program_options;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("weights,w",po::value<string>(),"Input feature weights file")
("training_data,t",po::value<string>(),"Training data corpus")
("decoder_config,c",po::value<string>(),"Decoder configuration file");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
po::store(po::parse_config_file(config, dconfig_options), *conf);
}
po::notify(*conf);
if (conf->count("help") || !conf->count("training_data") || !conf->count("decoder_config")) {
cerr << dcmdline_options << endl;
MPI::Finalize();
exit(1);
}
}
void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* ids) {
ReadFile rf(fname);
istream& in = *rf.stream();
string line;
int lc = 0;
while(in) {
getline(in, line);
if (!in) break;
if (lc % size == rank) {
c->push_back(line);
ids->push_back(lc);
}
++lc;
}
}
static const double kMINUS_EPSILON = -1e-6;
struct TrainingObserver : public DecoderObserver {
void Reset() {
acc_obj = 0;
}
virtual void NotifyDecodingStart(const SentenceMetadata&) {
cur_obj = 0;
state = 1;
}
// compute model expectations, denominator of objective
virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
assert(state == 1);
state = 2;
SparseVector<prob_t> cur_model_exp;
const prob_t z = InsideOutside<prob_t,
EdgeProb,
SparseVector<prob_t>,
EdgeFeaturesAndProbWeightFunction>(*hg, &cur_model_exp);
cur_obj = log(z);
}
// compute "empirical" expectations, numerator of objective
virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
assert(state == 2);
state = 3;
SparseVector<prob_t> ref_exp;
const prob_t ref_z = InsideOutside<prob_t,
EdgeProb,
SparseVector<prob_t>,
EdgeFeaturesAndProbWeightFunction>(*hg, &ref_exp);
double log_ref_z;
#if 0
if (crf_uniform_empirical) {
log_ref_z = ref_exp.dot(feature_weights);
} else {
log_ref_z = log(ref_z);
}
#else
log_ref_z = log(ref_z);
#endif
// rounding errors means that <0 is too strict
if ((cur_obj - log_ref_z) < kMINUS_EPSILON) {
cerr << "DIFF. ERR! log_model_z < log_ref_z: " << cur_obj << " " << log_ref_z << endl;
exit(1);
}
assert(!isnan(log_ref_z));
acc_obj += (cur_obj - log_ref_z);
}
double acc_obj;
double cur_obj;
int state;
};
namespace mpi = boost::mpi;
int main(int argc, char** argv) {
mpi::environment env(argc, argv);
mpi::communicator world;
const int size = world.size();
const int rank = world.rank();
if (size > 1) SetSilent(true); // turn off verbose decoder output
register_feature_functions();
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
// load initial weights
Weights weights;
if (conf.count("weights"))
weights.InitFromFile(conf["weights"].as<string>());
// freeze feature set
//const bool freeze_feature_set = conf.count("freeze_feature_set");
//if (freeze_feature_set) FD::Freeze();
// load cdec.ini and set up decoder
ReadFile ini_rf(conf["decoder_config"].as<string>());
Decoder decoder(ini_rf.stream());
if (decoder.GetConf()["input"].as<string>() != "-") {
cerr << "cdec.ini must not set an input file\n";
abort();
}
vector<string> corpus; vector<int> ids;
ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
assert(corpus.size() > 0);
assert(corpus.size() == ids.size());
vector<double> wv;
weights.InitVector(&wv);
decoder.SetWeights(wv);
TrainingObserver observer;
double objective = 0;
bool converged = false;
observer.Reset();
if (rank == 0)
cerr << "Each processor is decoding " << corpus.size() << " training examples...\n";
for (int i = 0; i < corpus.size(); ++i) {
decoder.SetId(ids[i]);
decoder.Decode(corpus[i], &observer);
}
reduce(world, observer.acc_obj, objective, std::plus<double>(), 0);
if (rank == 0)
cout << "OBJECTIVE: " << objective << endl;
return 0;
}
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