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authorAvneesh Saluja <asaluja@gmail.com>2013-03-28 18:28:16 -0700
committerAvneesh Saluja <asaluja@gmail.com>2013-03-28 18:28:16 -0700
commit3d8d656fa7911524e0e6885647173474524e0784 (patch)
tree81b1ee2fcb67980376d03f0aa48e42e53abff222 /training/crf/mpi_flex_optimize.cc
parentbe7f57fdd484e063775d7abf083b9fa4c403b610 (diff)
parent96fedabebafe7a38a6d5928be8fff767e411d705 (diff)
fixed conflicts
Diffstat (limited to 'training/crf/mpi_flex_optimize.cc')
-rw-r--r--training/crf/mpi_flex_optimize.cc386
1 files changed, 386 insertions, 0 deletions
diff --git a/training/crf/mpi_flex_optimize.cc b/training/crf/mpi_flex_optimize.cc
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+++ b/training/crf/mpi_flex_optimize.cc
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+#include <sstream>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <cassert>
+#include <cmath>
+
+#include <boost/shared_ptr.hpp>
+#include <boost/program_options.hpp>
+#include <boost/program_options/variables_map.hpp>
+
+#include "stringlib.h"
+#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"
+#include "sampler.h"
+
+#ifdef HAVE_MPI
+#include <boost/mpi/timer.hpp>
+#include <boost/mpi.hpp>
+namespace mpi = boost::mpi;
+#endif
+
+using namespace std;
+namespace po = boost::program_options;
+
+bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
+ po::options_description opts("Configuration options");
+ opts.add_options()
+ ("cdec_config,c",po::value<string>(),"Decoder configuration file")
+ ("weights,w",po::value<string>(),"Initial feature weights")
+ ("training_data,d",po::value<string>(),"Training data")
+ ("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(6), "Number of training instances evaluated per processor in each minibatch")
+ ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch")
+ ("iterations,I", po::value<unsigned>()->default_value(50), "Number of passes through the training data before termination")
+ ("regularization_strength,C", po::value<double>()->default_value(0.2), "Regularization strength")
+ ("time_series_strength,T", po::value<double>()->default_value(0.0), "Time series regularization strength")
+ ("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
+ ("lbfgs_memory_buffers,M", po::value<unsigned>()->default_value(10), "Number of memory buffers for LBFGS history");
+ 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("cdec_config")) {
+ cerr << "LBFGS minibatch online optimizer (MPI support "
+#if HAVE_MPI
+ << "enabled"
+#else
+ << "not enabled"
+#endif
+ << ")\n" << dcmdline_options << endl;
+ return false;
+ }
+ return true;
+}
+
+void ReadTrainingCorpus(const string& fname, int rank, int size, vector<string>* c, vector<int>* order) {
+ ReadFile rf(fname);
+ istream& in = *rf.stream();
+ string line;
+ int id = 0;
+ while(in) {
+ getline(in, line);
+ if (!in) break;
+ if (id % size == rank) {
+ c->push_back(line);
+ order->push_back(id);
+ }
+ ++id;
+ }
+}
+
+static const double kMINUS_EPSILON = -1e-6;
+
+struct CopyHGsObserver : public DecoderObserver {
+ Hypergraph* hg_;
+ Hypergraph* gold_hg_;
+
+ // this can free up some memory
+ void RemoveRules(Hypergraph* h) {
+ for (unsigned i = 0; i < h->edges_.size(); ++i)
+ h->edges_[i].rule_.reset();
+ }
+
+ void SetCurrentHypergraphs(Hypergraph* h, Hypergraph* gold_h) {
+ hg_ = h;
+ gold_hg_ = gold_h;
+ }
+
+ virtual void NotifyDecodingStart(const SentenceMetadata&) {
+ state = 1;
+ }
+
+ // compute model expectations, denominator of objective
+ virtual void NotifyTranslationForest(const SentenceMetadata&, Hypergraph* hg) {
+ *hg_ = *hg;
+ RemoveRules(hg_);
+ assert(state == 1);
+ state = 2;
+ }
+
+ // compute "empirical" expectations, numerator of objective
+ virtual void NotifyAlignmentForest(const SentenceMetadata&, Hypergraph* hg) {
+ assert(state == 2);
+ state = 3;
+ *gold_hg_ = *hg;
+ RemoveRules(gold_hg_);
+ }
+
+ virtual void NotifyDecodingComplete(const SentenceMetadata&) {
+ if (state == 3) {
+ } else {
+ hg_->clear();
+ gold_hg_->clear();
+ }
+ }
+
+ int state;
+};
+
+void ReadConfig(const string& ini, istringstream* out) {
+ ReadFile rf(ini);
+ istream& in = *rf.stream();
+ ostringstream os;
+ while(in) {
+ string line;
+ getline(in, line);
+ if (!in) continue;
+ os << line << endl;
+ }
+ out->str(os.str());
+}
+
+#ifdef HAVE_MPI
+namespace boost { namespace mpi {
+ template<>
+ struct is_commutative<std::plus<SparseVector<double> >, SparseVector<double> >
+ : mpl::true_ { };
+} } // end namespace boost::mpi
+#endif
+
+void AddGrad(const SparseVector<prob_t> x, double s, SparseVector<double>* acc) {
+ for (SparseVector<prob_t>::const_iterator it = x.begin(); it != x.end(); ++it)
+ acc->add_value(it->first, it->second.as_float() * s);
+}
+
+double PNorm(const vector<double>& v, const double p) {
+ double acc = 0;
+ for (int i = 0; i < v.size(); ++i)
+ acc += pow(v[i], p);
+ return pow(acc, 1.0 / p);
+}
+
+void VV(ostream&os, const vector<double>& v) {
+ for (int i = 1; i < v.size(); ++i)
+ if (v[i]) os << FD::Convert(i) << "=" << v[i] << " ";
+}
+
+double ApplyRegularizationTerms(const double C,
+ const double T,
+ const vector<double>& weights,
+ const vector<double>& prev_weights,
+ double* g) {
+ double reg = 0;
+ for (size_t i = 0; i < weights.size(); ++i) {
+ const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
+ const double& w_i = weights[i];
+ reg += C * w_i * w_i;
+ g[i] += 2 * C * w_i;
+
+ reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
+ g[i] += 2 * T * (w_i - prev_w_i);
+ }
+ return reg;
+}
+
+int main(int argc, char** argv) {
+#ifdef HAVE_MPI
+ mpi::environment env(argc, argv);
+ mpi::communicator world;
+ const int size = world.size();
+ const int rank = world.rank();
+#else
+ const int size = 1;
+ const int rank = 0;
+#endif
+ if (size > 1) SetSilent(true); // turn off verbose decoder output
+ register_feature_functions();
+ MT19937* rng = NULL;
+
+ po::variables_map conf;
+ if (!InitCommandLine(argc, argv, &conf))
+ return 1;
+
+ boost::shared_ptr<BatchOptimizer> o;
+ const unsigned lbfgs_memory_buffers = conf["lbfgs_memory_buffers"].as<unsigned>();
+ const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
+ const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();
+ const double regularization_strength = conf["regularization_strength"].as<double>();
+ const double time_series_strength = conf["time_series_strength"].as<double>();
+ const bool use_time_series_reg = time_series_strength > 0.0;
+ const unsigned max_iteration = conf["iterations"].as<unsigned>();
+
+ vector<string> corpus;
+ vector<int> ids;
+ ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
+ assert(corpus.size() > 0);
+
+ if (size_per_proc > corpus.size()) {
+ cerr << "Minibatch size (per processor) must be smaller or equal to the local corpus size!\n";
+ return 1;
+ }
+
+ // initialize decoder (loads hash functions if necessary)
+ istringstream ins;
+ ReadConfig(conf["cdec_config"].as<string>(), &ins);
+ Decoder decoder(&ins);
+
+ // load initial weights
+ vector<weight_t> prev_weights;
+ if (conf.count("weights"))
+ Weights::InitFromFile(conf["weights"].as<string>(), &prev_weights);
+
+ if (conf.count("random_seed"))
+ rng = new MT19937(conf["random_seed"].as<uint32_t>());
+ else
+ rng = new MT19937;
+
+ size_t total_corpus_size = 0;
+#ifdef HAVE_MPI
+ reduce(world, corpus.size(), total_corpus_size, std::plus<size_t>(), 0);
+#else
+ total_corpus_size = corpus.size();
+#endif
+
+ if (rank == 0)
+ cerr << "Total corpus size: " << total_corpus_size << endl;
+
+ CopyHGsObserver observer;
+
+ int write_weights_every_ith = 100; // TODO configure
+ int titer = -1;
+
+ vector<weight_t>& cur_weights = decoder.CurrentWeightVector();
+ if (use_time_series_reg) {
+ cur_weights = prev_weights;
+ } else {
+ cur_weights.swap(prev_weights);
+ prev_weights.clear();
+ }
+
+ int iter = -1;
+ bool converged = false;
+ vector<double> gg;
+ while (!converged) {
+#ifdef HAVE_MPI
+ mpi::timer timer;
+#endif
+ ++iter; ++titer;
+ if (rank == 0) {
+ converged = (iter == max_iteration);
+ string fname = "weights.cur.gz";
+ if (iter % write_weights_every_ith == 0) {
+ ostringstream o; o << "weights.epoch_" << iter << ".gz";
+ fname = o.str();
+ }
+ if (converged) { fname = "weights.final.gz"; }
+ ostringstream vv;
+ vv << "total iter=" << titer << " (of current config iter=" << iter << ") minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << FD::NumFeats() << " passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size()));
+ const string svv = vv.str();
+ Weights::WriteToFile(fname, cur_weights, true, &svv);
+ }
+
+ vector<Hypergraph> hgs(size_per_proc);
+ vector<Hypergraph> gold_hgs(size_per_proc);
+ for (int i = 0; i < size_per_proc; ++i) {
+ int ei = corpus.size() * rng->next();
+ int id = ids[ei];
+ observer.SetCurrentHypergraphs(&hgs[i], &gold_hgs[i]);
+ decoder.SetId(id);
+ decoder.Decode(corpus[ei], &observer);
+ }
+
+ SparseVector<double> local_grad, g;
+ double local_obj = 0;
+ o.reset();
+ for (unsigned mi = 0; mi < minibatch_iterations; ++mi) {
+ local_grad.clear();
+ g.clear();
+ local_obj = 0;
+
+ for (unsigned i = 0; i < size_per_proc; ++i) {
+ Hypergraph& hg = hgs[i];
+ Hypergraph& hg_gold = gold_hgs[i];
+ if (hg.edges_.size() < 2) continue;
+
+ hg.Reweight(cur_weights);
+ hg_gold.Reweight(cur_weights);
+ SparseVector<prob_t> model_exp, gold_exp;
+ const prob_t z = InsideOutside<prob_t,
+ EdgeProb,
+ SparseVector<prob_t>,
+ EdgeFeaturesAndProbWeightFunction>(hg, &model_exp);
+ local_obj += log(z);
+ model_exp /= z;
+ AddGrad(model_exp, 1.0, &local_grad);
+ model_exp.clear();
+
+ const prob_t goldz = InsideOutside<prob_t,
+ EdgeProb,
+ SparseVector<prob_t>,
+ EdgeFeaturesAndProbWeightFunction>(hg_gold, &gold_exp);
+ local_obj -= log(goldz);
+
+ if (log(z) - log(goldz) < kMINUS_EPSILON) {
+ cerr << "DIFF. ERR! log_model_z < log_gold_z: " << log(z) << " " << log(goldz) << endl;
+ return 1;
+ }
+
+ gold_exp /= goldz;
+ AddGrad(gold_exp, -1.0, &local_grad);
+ }
+
+ double obj = 0;
+#ifdef HAVE_MPI
+ reduce(world, local_obj, obj, std::plus<double>(), 0);
+ reduce(world, local_grad, g, std::plus<SparseVector<double> >(), 0);
+#else
+ obj = local_obj;
+ g.swap(local_grad);
+#endif
+ local_grad.clear();
+ if (rank == 0) {
+ // g /= (size_per_proc * size);
+ if (!o)
+ o.reset(new LBFGSOptimizer(FD::NumFeats(), lbfgs_memory_buffers));
+ gg.clear();
+ gg.resize(FD::NumFeats());
+ if (gg.size() != cur_weights.size()) { cur_weights.resize(gg.size()); }
+ for (SparseVector<double>::iterator it = g.begin(); it != g.end(); ++it)
+ if (it->first) { gg[it->first] = it->second; }
+ g.clear();
+ double r = ApplyRegularizationTerms(regularization_strength,
+ time_series_strength, // * (iter == 0 ? 0.0 : 1.0),
+ cur_weights,
+ prev_weights,
+ &gg[0]);
+ obj += r;
+ if (mi == 0 || mi == (minibatch_iterations - 1)) {
+ if (!mi) cerr << iter << ' '; else cerr << ' ';
+ cerr << "OBJ=" << obj << " (REG=" << r << ")" << " |g|=" << PNorm(gg, 2) << " |w|=" << PNorm(cur_weights, 2);
+ if (mi > 0) cerr << endl << flush; else cerr << ' ';
+ } else { cerr << '.' << flush; }
+ // cerr << "w = "; VV(cerr, cur_weights); cerr << endl;
+ // cerr << "g = "; VV(cerr, gg); cerr << endl;
+ o->Optimize(obj, gg, &cur_weights);
+ }
+#ifdef HAVE_MPI
+ broadcast(world, cur_weights, 0);
+ broadcast(world, converged, 0);
+ world.barrier();
+#endif
+ }
+ prev_weights = cur_weights;
+ }
+ return 0;
+}