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-rw-r--r--training/mpi_em_optimize.cc389
1 files changed, 0 insertions, 389 deletions
diff --git a/training/mpi_em_optimize.cc b/training/mpi_em_optimize.cc
deleted file mode 100644
index 48683b15..00000000
--- a/training/mpi_em_optimize.cc
+++ /dev/null
@@ -1,389 +0,0 @@
-#include <sstream>
-#include <iostream>
-#include <vector>
-#include <cassert>
-#include <cmath>
-
-#ifdef HAVE_MPI
-#include <mpi.h>
-#endif
-
-#include <boost/shared_ptr.hpp>
-#include <boost/program_options.hpp>
-#include <boost/program_options/variables_map.hpp>
-
-#include "verbose.h"
-#include "hg.h"
-#include "prob.h"
-#include "inside_outside.h"
-#include "ff_register.h"
-#include "decoder.h"
-#include "filelib.h"
-#include "optimize.h"
-#include "fdict.h"
-#include "weights.h"
-#include "sparse_vector.h"
-
-using namespace std;
-using boost::shared_ptr;
-namespace po = boost::program_options;
-
-void SanityCheck(const vector<double>& w) {
- for (int i = 0; i < w.size(); ++i) {
- assert(!isnan(w[i]));
- assert(!isinf(w[i]));
- }
-}
-
-struct FComp {
- const vector<double>& w_;
- FComp(const vector<double>& w) : w_(w) {}
- bool operator()(int a, int b) const {
- return fabs(w_[a]) > fabs(w_[b]);
- }
-};
-
-void ShowLargestFeatures(const vector<double>& w) {
- vector<int> fnums(w.size());
- for (int i = 0; i < w.size(); ++i)
- fnums[i] = i;
- vector<int>::iterator mid = fnums.begin();
- mid += (w.size() > 10 ? 10 : w.size());
- partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
- cerr << "TOP FEATURES:";
- for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
- cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
- }
- cerr << endl;
-}
-
-void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
- po::options_description opts("Configuration options");
- opts.add_options()
- ("input_weights,w",po::value<string>(),"Input feature weights file")
- ("training_data,t",po::value<string>(),"Training data")
- ("decoder_config,c",po::value<string>(),"Decoder configuration file")
- ("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights 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;
-#ifdef HAVE_MPI
- MPI::Finalize();
-#endif
- exit(1);
- }
-}
-
-void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c) {
- 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);
- ++lc;
- }
-}
-
-static const double kMINUS_EPSILON = -1e-6;
-
-struct TrainingObserver : public DecoderObserver {
- void Reset() {
- total_complete = 0;
- cur_obj = 0;
- tot_obj = 0;
- tot.clear();
- }
-
- void SetLocalGradientAndObjective(SparseVector<double>* g, double* o) const {
- *o = tot_obj;
- *g = tot;
- }
-
- virtual void NotifyDecodingStart(const SentenceMetadata& smeta) {
- cur_obj = 0;
- state = 1;
- }
-
- void ExtractExpectedCounts(Hypergraph* hg) {
- vector<prob_t> posts;
- cur.clear();
- const prob_t z = hg->ComputeEdgePosteriors(1.0, &posts);
- cur_obj = log(z);
- for (int i = 0; i < posts.size(); ++i) {
- const SparseVector<double>& efeats = hg->edges_[i].feature_values_;
- const double post = static_cast<double>(posts[i] / z);
- for (SparseVector<double>::const_iterator j = efeats.begin(); j != efeats.end(); ++j)
- cur.add_value(j->first, post);
- }
- }
-
- // compute model expectations, denominator of objective
- virtual void NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) {
- assert(state == 1);
- state = 2;
- ExtractExpectedCounts(hg);
- }
-
- // replace translation forest, since we're doing EM training (we don't know which)
- virtual void NotifyAlignmentForest(const SentenceMetadata& smeta, Hypergraph* hg) {
- assert(state == 2);
- state = 3;
- ExtractExpectedCounts(hg);
- }
-
- virtual void NotifyDecodingComplete(const SentenceMetadata& smeta) {
- ++total_complete;
- tot_obj += cur_obj;
- tot += cur;
- }
-
- int total_complete;
- double cur_obj;
- double tot_obj;
- SparseVector<double> cur, tot;
- int state;
-};
-
-void ReadConfig(const string& ini, vector<string>* out) {
- ReadFile rf(ini);
- istream& in = *rf.stream();
- while(in) {
- string line;
- getline(in, line);
- if (!in) continue;
- out->push_back(line);
- }
-}
-
-void StoreConfig(const vector<string>& cfg, istringstream* o) {
- ostringstream os;
- for (int i = 0; i < cfg.size(); ++i) { os << cfg[i] << endl; }
- o->str(os.str());
-}
-
-struct OptimizableMultinomialFamily {
- struct CPD {
- CPD() : z() {}
- double z;
- map<WordID, double> c2counts;
- };
- map<WordID, CPD> counts;
- double Value(WordID conditioning, WordID generated) const {
- map<WordID, CPD>::const_iterator it = counts.find(conditioning);
- assert(it != counts.end());
- map<WordID,double>::const_iterator r = it->second.c2counts.find(generated);
- if (r == it->second.c2counts.end()) return 0;
- return r->second;
- }
- void Increment(WordID conditioning, WordID generated, double count) {
- CPD& cc = counts[conditioning];
- cc.z += count;
- cc.c2counts[generated] += count;
- }
- void Optimize() {
- for (map<WordID, CPD>::iterator i = counts.begin(); i != counts.end(); ++i) {
- CPD& cpd = i->second;
- for (map<WordID, double>::iterator j = cpd.c2counts.begin(); j != cpd.c2counts.end(); ++j) {
- j->second /= cpd.z;
- // cerr << "P(" << TD::Convert(j->first) << " | " << TD::Convert(i->first) << " ) = " << j->second << endl;
- }
- }
- }
- void Clear() {
- counts.clear();
- }
-};
-
-struct CountManager {
- CountManager(size_t num_types) : oms_(num_types) {}
- virtual ~CountManager();
- virtual void AddCounts(const SparseVector<double>& c) = 0;
- void Optimize(SparseVector<double>* weights) {
- for (int i = 0; i < oms_.size(); ++i) {
- oms_[i].Optimize();
- }
- GetOptimalValues(weights);
- for (int i = 0; i < oms_.size(); ++i) {
- oms_[i].Clear();
- }
- }
- virtual void GetOptimalValues(SparseVector<double>* wv) const = 0;
- vector<OptimizableMultinomialFamily> oms_;
-};
-CountManager::~CountManager() {}
-
-struct TaggerCountManager : public CountManager {
- // 0 = transitions, 2 = emissions
- TaggerCountManager() : CountManager(2) {}
- void AddCounts(const SparseVector<double>& c);
- void GetOptimalValues(SparseVector<double>* wv) const {
- for (set<int>::const_iterator it = fids_.begin(); it != fids_.end(); ++it) {
- int ftype;
- WordID cond, gen;
- bool is_optimized = TaggerCountManager::GetFeature(*it, &ftype, &cond, &gen);
- assert(is_optimized);
- wv->set_value(*it, log(oms_[ftype].Value(cond, gen)));
- }
- }
- // Id:0:a=1 Bi:a_b=1 Bi:b_c=1 Bi:c_d=1 Uni:a=1 Uni:b=1 Uni:c=1 Uni:d=1 Id:1:b=1 Bi:BOS_a=1 Id:2:c=1
- static bool GetFeature(const int fid, int* feature_type, WordID* cond, WordID* gen) {
- const string& feat = FD::Convert(fid);
- if (feat.size() > 5 && feat[0] == 'I' && feat[1] == 'd' && feat[2] == ':') {
- // emission
- const size_t p = feat.rfind(':');
- assert(p != string::npos);
- *cond = TD::Convert(feat.substr(p+1));
- *gen = TD::Convert(feat.substr(3, p - 3));
- *feature_type = 1;
- return true;
- } else if (feat[0] == 'B' && feat.size() > 5 && feat[2] == ':' && feat[1] == 'i') {
- // transition
- const size_t p = feat.rfind('_');
- assert(p != string::npos);
- *gen = TD::Convert(feat.substr(p+1));
- *cond = TD::Convert(feat.substr(3, p - 3));
- *feature_type = 0;
- return true;
- } else if (feat[0] == 'U' && feat.size() > 4 && feat[1] == 'n' && feat[2] == 'i' && feat[3] == ':') {
- // ignore
- return false;
- } else {
- cerr << "Don't know how to deal with feature of type: " << feat << endl;
- abort();
- }
- }
- set<int> fids_;
-};
-
-void TaggerCountManager::AddCounts(const SparseVector<double>& c) {
- for (SparseVector<double>::const_iterator it = c.begin(); it != c.end(); ++it) {
- const double& val = it->second;
- int ftype;
- WordID cond, gen;
- if (GetFeature(it->first, &ftype, &cond, &gen)) {
- oms_[ftype].Increment(cond, gen, val);
- fids_.insert(it->first);
- }
- }
-}
-
-int main(int argc, char** argv) {
-#ifdef HAVE_MPI
- MPI::Init(argc, argv);
- const int size = MPI::COMM_WORLD.Get_size();
- const int rank = MPI::COMM_WORLD.Get_rank();
-#else
- const int size = 1;
- const int rank = 0;
-#endif
- SetSilent(true); // turn off verbose decoder output
- register_feature_functions();
-
- po::variables_map conf;
- InitCommandLine(argc, argv, &conf);
-
- TaggerCountManager tcm;
-
- // load cdec.ini and set up decoder
- vector<string> cdec_ini;
- ReadConfig(conf["decoder_config"].as<string>(), &cdec_ini);
- istringstream ini;
- StoreConfig(cdec_ini, &ini);
- if (rank == 0) cerr << "Loading grammar...\n";
- Decoder* decoder = new Decoder(&ini);
- if (decoder->GetConf()["input"].as<string>() != "-") {
- cerr << "cdec.ini must not set an input file\n";
-#ifdef HAVE_MPI
- MPI::COMM_WORLD.Abort(1);
-#endif
- }
- if (rank == 0) cerr << "Done loading grammar!\n";
- Weights w;
- if (conf.count("input_weights"))
- w.InitFromFile(conf["input_weights"].as<string>());
-
- double objective = 0;
- bool converged = false;
-
- vector<double> lambdas;
- w.InitVector(&lambdas);
- vector<string> corpus;
- ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus);
- assert(corpus.size() > 0);
-
- int iteration = 0;
- TrainingObserver observer;
- while (!converged) {
- ++iteration;
- observer.Reset();
- if (rank == 0) {
- cerr << "Starting decoding... (~" << corpus.size() << " sentences / proc)\n";
- }
- decoder->SetWeights(lambdas);
- for (int i = 0; i < corpus.size(); ++i)
- decoder->Decode(corpus[i], &observer);
-
- SparseVector<double> x;
- observer.SetLocalGradientAndObjective(&x, &objective);
- cerr << "COUNTS = " << x << endl;
- cerr << " OBJ = " << objective << endl;
- tcm.AddCounts(x);
-
-#if 0
-#ifdef HAVE_MPI
- MPI::COMM_WORLD.Reduce(const_cast<double*>(&gradient.data()[0]), &rcv_grad[0], num_feats, MPI::DOUBLE, MPI::SUM, 0);
- MPI::COMM_WORLD.Reduce(&objective, &to, 1, MPI::DOUBLE, MPI::SUM, 0);
- swap(gradient, rcv_grad);
- objective = to;
-#endif
-#endif
-
- if (rank == 0) {
- SparseVector<double> wsv;
- tcm.Optimize(&wsv);
-
- w.InitFromVector(wsv);
- w.InitVector(&lambdas);
-
- ShowLargestFeatures(lambdas);
-
- converged = iteration > 100;
- if (converged) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
-
- string fname = "weights.cur.gz";
- if (converged) { fname = "weights.final.gz"; }
- ostringstream vv;
- vv << "Objective = " << objective << " (ITERATION=" << iteration << ")";
- const string svv = vv.str();
- w.WriteToFile(fname, true, &svv);
- } // rank == 0
- int cint = converged;
-#ifdef HAVE_MPI
- MPI::COMM_WORLD.Bcast(const_cast<double*>(&lambdas.data()[0]), num_feats, MPI::DOUBLE, 0);
- MPI::COMM_WORLD.Bcast(&cint, 1, MPI::INT, 0);
- MPI::COMM_WORLD.Barrier();
-#endif
- converged = cint;
- }
-#ifdef HAVE_MPI
- MPI::Finalize();
-#endif
- return 0;
-}
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