From 78518f1f417616633b300a361cd5e0c1bcb1ff24 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Thu, 20 Sep 2012 21:51:11 -0400 Subject: rename model1 to fast_aligner, since it does way more than model1 but is mostly just fast --- training/Makefile.am | 6 +- training/fast_align.cc | 271 +++++++++++++++++++++++++++++++++++++++++++++++++ training/model1.cc | 264 ----------------------------------------------- 3 files changed, 274 insertions(+), 267 deletions(-) create mode 100644 training/fast_align.cc delete mode 100644 training/model1.cc (limited to 'training') diff --git a/training/Makefile.am b/training/Makefile.am index 4cef0d5b..5254333a 100644 --- a/training/Makefile.am +++ b/training/Makefile.am @@ -1,5 +1,5 @@ bin_PROGRAMS = \ - model1 \ + fast_align \ lbl_model \ test_ngram \ mr_em_map_adapter \ @@ -55,8 +55,8 @@ augment_grammar_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/lib test_ngram_SOURCES = test_ngram.cc test_ngram_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz -model1_SOURCES = model1.cc ttables.cc -model1_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz +fast_align_SOURCES = fast_align.cc ttables.cc +fast_align_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz lbl_model_SOURCES = lbl_model.cc lbl_model_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz diff --git a/training/fast_align.cc b/training/fast_align.cc new file mode 100644 index 00000000..0d7b0202 --- /dev/null +++ b/training/fast_align.cc @@ -0,0 +1,271 @@ +#include +#include + +#include +#include + +#include "m.h" +#include "corpus_tools.h" +#include "stringlib.h" +#include "filelib.h" +#include "ttables.h" +#include "tdict.h" + +namespace po = boost::program_options; +using namespace std; + +bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { + po::options_description opts("Configuration options"); + opts.add_options() + ("input,i",po::value(),"Parallel corpus input file") + ("reverse,r","Reverse estimation (swap source and target during training)") + ("iterations,I",po::value()->default_value(5),"Number of iterations of EM training") + //("bidir,b", "Run bidirectional alignment") + ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal") + ("prob_align_null", po::value()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?") + ("diagonal_tension,T", po::value()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)") + ("variational_bayes,v","Infer VB estimate of parameters under a symmetric Dirichlet prior") + ("alpha,a", po::value()->default_value(0.01), "Hyperparameter for optional Dirichlet prior") + ("no_null_word,N","Do not generate from a null token") + ("output_parameters,p", "Write model parameters instead of alignments") + ("beam_threshold,t",po::value()->default_value(-4),"When writing parameters, log_10 of beam threshold for writing parameter (-10000 to include everything, 0 max parameter only)") + ("testset,x", po::value(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model") + ("no_add_viterbi,V","When writing model parameters, do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)"); + po::options_description clo("Command line options"); + clo.add_options() + ("config", po::value(), "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().c_str()); + po::store(po::parse_config_file(config, dconfig_options), *conf); + } + po::notify(*conf); + + if (conf->count("help") || conf->count("input") == 0) { + cerr << "Usage " << argv[0] << " [OPTIONS] -i corpus.fr-en\n"; + cerr << dcmdline_options << endl; + return false; + } + return true; +} + +double PosteriorInference(const vector& src, const vector& trg) { + double llh = 0; + static vector unnormed_a_i; + if (src.size() > unnormed_a_i.size()) + unnormed_a_i.resize(src.size()); + return llh; +} + +int main(int argc, char** argv) { + po::variables_map conf; + if (!InitCommandLine(argc, argv, &conf)) return 1; + const string fname = conf["input"].as(); + const bool reverse = conf.count("reverse") > 0; + const int ITERATIONS = conf["iterations"].as(); + const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as()); + const bool use_null = (conf.count("no_null_word") == 0); + const WordID kNULL = TD::Convert(""); + const bool add_viterbi = (conf.count("no_add_viterbi") == 0); + const bool variational_bayes = (conf.count("variational_bayes") > 0); + const bool write_alignments = (conf.count("output_parameters") == 0); + const double diagonal_tension = conf["diagonal_tension"].as(); + const double prob_align_null = conf["prob_align_null"].as(); + string testset; + if (conf.count("testset")) testset = conf["testset"].as(); + const double prob_align_not_null = 1.0 - prob_align_null; + const double alpha = conf["alpha"].as(); + const bool favor_diagonal = conf.count("favor_diagonal"); + if (variational_bayes && alpha <= 0.0) { + cerr << "--alpha must be > 0\n"; + return 1; + } + + TTable s2t, t2s; + TTable::Word2Word2Double s2t_viterbi; + double tot_len_ratio = 0; + double mean_srclen_multiplier = 0; + vector unnormed_a_i; + for (int iter = 0; iter < ITERATIONS; ++iter) { + const bool final_iteration = (iter == (ITERATIONS - 1)); + cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl; + ReadFile rf(fname); + istream& in = *rf.stream(); + double likelihood = 0; + double denom = 0.0; + int lc = 0; + bool flag = false; + string line; + string ssrc, strg; + vector src, trg; + while(true) { + getline(in, line); + if (!in) break; + ++lc; + if (lc % 1000 == 0) { cerr << '.'; flag = true; } + if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; } + src.clear(); trg.clear(); + CorpusTools::ReadLine(line, &src, &trg); + if (reverse) swap(src, trg); + if (src.size() == 0 || trg.size() == 0) { + cerr << "Error: " << lc << "\n" << line << endl; + return 1; + } + if (src.size() > unnormed_a_i.size()) + unnormed_a_i.resize(src.size()); + if (iter == 0) + tot_len_ratio += static_cast(trg.size()) / static_cast(src.size()); + denom += trg.size(); + vector probs(src.size() + 1); + bool first_al = true; // used for write_alignments + for (int j = 0; j < trg.size(); ++j) { + const WordID& f_j = trg[j]; + double sum = 0; + const double j_over_ts = double(j) / trg.size(); + double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) + if (use_null) { + if (favor_diagonal) prob_a_i = prob_align_null; + probs[0] = s2t.prob(kNULL, f_j) * prob_a_i; + sum += probs[0]; + } + double az = 0; + if (favor_diagonal) { + for (int ta = 0; ta < src.size(); ++ta) { + unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + az += unnormed_a_i[ta]; + } + az /= prob_align_not_null; + } + for (int i = 1; i <= src.size(); ++i) { + if (favor_diagonal) + prob_a_i = unnormed_a_i[i-1] / az; + probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i; + sum += probs[i]; + } + if (final_iteration) { + if (add_viterbi || write_alignments) { + WordID max_i = 0; + double max_p = -1; + int max_index = -1; + if (use_null) { + max_i = kNULL; + max_index = 0; + max_p = probs[0]; + } + for (int i = 1; i <= src.size(); ++i) { + if (probs[i] > max_p) { + max_index = i; + max_p = probs[i]; + max_i = src[i-1]; + } + } + if (write_alignments) { + if (max_index > 0) { + if (first_al) first_al = false; else cout << ' '; + if (reverse) + cout << j << '-' << (max_index - 1); + else + cout << (max_index - 1) << '-' << j; + } + } + s2t_viterbi[max_i][f_j] = 1.0; + } + } else { + if (use_null) + s2t.Increment(kNULL, f_j, probs[0] / sum); + for (int i = 1; i <= src.size(); ++i) + s2t.Increment(src[i-1], f_j, probs[i] / sum); + } + likelihood += log(sum); + } + if (write_alignments && final_iteration) cout << endl; + } + + // log(e) = 1.0 + double base2_likelihood = likelihood / log(2); + + if (flag) { cerr << endl; } + if (iter == 0) { + mean_srclen_multiplier = tot_len_ratio / lc; + cerr << "expected target length = source length * " << mean_srclen_multiplier << endl; + } + cerr << " log_e likelihood: " << likelihood << endl; + cerr << " log_2 likelihood: " << base2_likelihood << endl; + cerr << " cross entropy: " << (-base2_likelihood / denom) << endl; + cerr << " perplexity: " << pow(2.0, -base2_likelihood / denom) << endl; + if (!final_iteration) { + if (variational_bayes) + s2t.NormalizeVB(alpha); + else + s2t.Normalize(); + } + } + if (testset.size()) { + ReadFile rf(testset); + istream& in = *rf.stream(); + int lc = 0; + double tlp = 0; + string ssrc, strg, line; + while (getline(in, line)) { + ++lc; + vector src, trg; + CorpusTools::ReadLine(line, &src, &trg); + double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier); + if (src.size() > unnormed_a_i.size()) + unnormed_a_i.resize(src.size()); + + // compute likelihood + for (int j = 0; j < trg.size(); ++j) { + const WordID& f_j = trg[j]; + double sum = 0; + const double j_over_ts = double(j) / trg.size(); + double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) + if (use_null) { + if (favor_diagonal) prob_a_i = prob_align_null; + sum += s2t.prob(kNULL, f_j) * prob_a_i; + } + double az = 0; + if (favor_diagonal) { + for (int ta = 0; ta < src.size(); ++ta) { + unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); + az += unnormed_a_i[ta]; + } + az /= prob_align_not_null; + } + for (int i = 1; i <= src.size(); ++i) { + if (favor_diagonal) + prob_a_i = unnormed_a_i[i-1] / az; + sum += s2t.prob(src[i-1], f_j) * prob_a_i; + } + log_prob += log(sum); + } + tlp += log_prob; + cerr << ssrc << " ||| " << strg << " ||| " << log_prob << endl; + } + cerr << "TOTAL LOG PROB " << tlp << endl; + } + + if (write_alignments) return 0; + + for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) { + const TTable::Word2Double& cpd = ei->second; + const TTable::Word2Double& vit = s2t_viterbi[ei->first]; + const string& esym = TD::Convert(ei->first); + double max_p = -1; + for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) + if (fi->second > max_p) max_p = fi->second; + const double threshold = max_p * BEAM_THRESHOLD; + for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) { + if (fi->second > threshold || (vit.find(fi->first) != vit.end())) { + cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl; + } + } + } + return 0; +} + diff --git a/training/model1.cc b/training/model1.cc deleted file mode 100644 index 19692b9a..00000000 --- a/training/model1.cc +++ /dev/null @@ -1,264 +0,0 @@ -#include -#include - -#include -#include - -#include "m.h" -#include "corpus_tools.h" -#include "stringlib.h" -#include "filelib.h" -#include "ttables.h" -#include "tdict.h" - -namespace po = boost::program_options; -using namespace std; - -bool InitCommandLine(int argc, char** argv, po::variables_map* conf) { - po::options_description opts("Configuration options"); - opts.add_options() - ("iterations,i",po::value()->default_value(5),"Number of iterations of EM training") - ("beam_threshold,t",po::value()->default_value(-4),"log_10 of beam threshold (-10000 to include everything, 0 max)") - ("bidir,b", "Run bidirectional alignment") - ("no_null_word,N","Do not generate from the null token") - ("write_alignments,A", "Write alignments instead of parameters") - ("favor_diagonal,d", "Use a static alignment distribution that assigns higher probabilities to alignments near the diagonal") - ("diagonal_tension,T", po::value()->default_value(4.0), "How sharp or flat around the diagonal is the alignment distribution (<1 = flat >1 = sharp)") - ("prob_align_null", po::value()->default_value(0.08), "When --favor_diagonal is set, what's the probability of a null alignment?") - ("variational_bayes,v","Add a symmetric Dirichlet prior and infer VB estimate of weights") - ("testset,x", po::value(), "After training completes, compute the log likelihood of this set of sentence pairs under the learned model") - ("alpha,a", po::value()->default_value(0.01), "Hyperparameter for optional Dirichlet prior") - ("no_add_viterbi,V","Do not add Viterbi alignment points (may generate a grammar where some training sentence pairs are unreachable)"); - po::options_description clo("Command line options"); - clo.add_options() - ("config", po::value(), "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().c_str()); - po::store(po::parse_config_file(config, dconfig_options), *conf); - } - po::notify(*conf); - - if (argc < 2 || conf->count("help")) { - cerr << "Usage " << argv[0] << " [OPTIONS] corpus.fr-en\n"; - cerr << dcmdline_options << endl; - return false; - } - return true; -} - -// src and trg are source and target strings, respectively (not really lattices) -double PosteriorInference(const vector& src, const vector& trg) { - double llh = 0; - static vector unnormed_a_i; - if (src.size() > unnormed_a_i.size()) - unnormed_a_i.resize(src.size()); - return llh; -} - -int main(int argc, char** argv) { - po::variables_map conf; - if (!InitCommandLine(argc, argv, &conf)) return 1; - const string fname = argv[argc - 1]; - const int ITERATIONS = conf["iterations"].as(); - const double BEAM_THRESHOLD = pow(10.0, conf["beam_threshold"].as()); - const bool use_null = (conf.count("no_null_word") == 0); - const WordID kNULL = TD::Convert(""); - const bool add_viterbi = (conf.count("no_add_viterbi") == 0); - const bool variational_bayes = (conf.count("variational_bayes") > 0); - const bool write_alignments = (conf.count("write_alignments") > 0); - const double diagonal_tension = conf["diagonal_tension"].as(); - const double prob_align_null = conf["prob_align_null"].as(); - string testset; - if (conf.count("testset")) testset = conf["testset"].as(); - const double prob_align_not_null = 1.0 - prob_align_null; - const double alpha = conf["alpha"].as(); - const bool favor_diagonal = conf.count("favor_diagonal"); - if (variational_bayes && alpha <= 0.0) { - cerr << "--alpha must be > 0\n"; - return 1; - } - - TTable s2t, t2s; - TTable::Word2Word2Double s2t_viterbi; - double tot_len_ratio = 0; - double mean_srclen_multiplier = 0; - vector unnormed_a_i; - for (int iter = 0; iter < ITERATIONS; ++iter) { - const bool final_iteration = (iter == (ITERATIONS - 1)); - cerr << "ITERATION " << (iter + 1) << (final_iteration ? " (FINAL)" : "") << endl; - ReadFile rf(fname); - istream& in = *rf.stream(); - double likelihood = 0; - double denom = 0.0; - int lc = 0; - bool flag = false; - string line; - string ssrc, strg; - while(true) { - getline(in, line); - if (!in) break; - ++lc; - if (lc % 1000 == 0) { cerr << '.'; flag = true; } - if (lc %50000 == 0) { cerr << " [" << lc << "]\n" << flush; flag = false; } - vector src, trg; - CorpusTools::ReadLine(line, &src, &trg); - if (src.size() == 0 || trg.size() == 0) { - cerr << "Error: " << lc << "\n" << line << endl; - return 1; - } - if (src.size() > unnormed_a_i.size()) - unnormed_a_i.resize(src.size()); - if (iter == 0) - tot_len_ratio += static_cast(trg.size()) / static_cast(src.size()); - denom += trg.size(); - vector probs(src.size() + 1); - bool first_al = true; // used for write_alignments - for (int j = 0; j < trg.size(); ++j) { - const WordID& f_j = trg[j]; - double sum = 0; - const double j_over_ts = double(j) / trg.size(); - double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) - if (use_null) { - if (favor_diagonal) prob_a_i = prob_align_null; - probs[0] = s2t.prob(kNULL, f_j) * prob_a_i; - sum += probs[0]; - } - double az = 0; - if (favor_diagonal) { - for (int ta = 0; ta < src.size(); ++ta) { - unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); - az += unnormed_a_i[ta]; - } - az /= prob_align_not_null; - } - for (int i = 1; i <= src.size(); ++i) { - if (favor_diagonal) - prob_a_i = unnormed_a_i[i-1] / az; - probs[i] = s2t.prob(src[i-1], f_j) * prob_a_i; - sum += probs[i]; - } - if (final_iteration) { - if (add_viterbi || write_alignments) { - WordID max_i = 0; - double max_p = -1; - int max_index = -1; - if (use_null) { - max_i = kNULL; - max_index = 0; - max_p = probs[0]; - } - for (int i = 1; i <= src.size(); ++i) { - if (probs[i] > max_p) { - max_index = i; - max_p = probs[i]; - max_i = src[i-1]; - } - } - if (write_alignments) { - if (max_index > 0) { - if (first_al) first_al = false; else cout << ' '; - cout << (max_index - 1) << "-" << j; - } - } - s2t_viterbi[max_i][f_j] = 1.0; - } - } else { - if (use_null) - s2t.Increment(kNULL, f_j, probs[0] / sum); - for (int i = 1; i <= src.size(); ++i) - s2t.Increment(src[i-1], f_j, probs[i] / sum); - } - likelihood += log(sum); - } - if (write_alignments && final_iteration) cout << endl; - } - - // log(e) = 1.0 - double base2_likelihood = likelihood / log(2); - - if (flag) { cerr << endl; } - if (iter == 0) { - mean_srclen_multiplier = tot_len_ratio / lc; - cerr << "expected target length = source length * " << mean_srclen_multiplier << endl; - } - cerr << " log_e likelihood: " << likelihood << endl; - cerr << " log_2 likelihood: " << base2_likelihood << endl; - cerr << " cross entropy: " << (-base2_likelihood / denom) << endl; - cerr << " perplexity: " << pow(2.0, -base2_likelihood / denom) << endl; - if (!final_iteration) { - if (variational_bayes) - s2t.NormalizeVB(alpha); - else - s2t.Normalize(); - } - } - if (testset.size()) { - ReadFile rf(testset); - istream& in = *rf.stream(); - int lc = 0; - double tlp = 0; - string ssrc, strg, line; - while (getline(in, line)) { - ++lc; - vector src, trg; - CorpusTools::ReadLine(line, &src, &trg); - double log_prob = Md::log_poisson(trg.size(), 0.05 + src.size() * mean_srclen_multiplier); - if (src.size() > unnormed_a_i.size()) - unnormed_a_i.resize(src.size()); - - // compute likelihood - for (int j = 0; j < trg.size(); ++j) { - const WordID& f_j = trg[j]; - double sum = 0; - const double j_over_ts = double(j) / trg.size(); - double prob_a_i = 1.0 / (src.size() + use_null); // uniform (model 1) - if (use_null) { - if (favor_diagonal) prob_a_i = prob_align_null; - sum += s2t.prob(kNULL, f_j) * prob_a_i; - } - double az = 0; - if (favor_diagonal) { - for (int ta = 0; ta < src.size(); ++ta) { - unnormed_a_i[ta] = exp(-fabs(double(ta) / src.size() - j_over_ts) * diagonal_tension); - az += unnormed_a_i[ta]; - } - az /= prob_align_not_null; - } - for (int i = 1; i <= src.size(); ++i) { - if (favor_diagonal) - prob_a_i = unnormed_a_i[i-1] / az; - sum += s2t.prob(src[i-1], f_j) * prob_a_i; - } - log_prob += log(sum); - } - tlp += log_prob; - cerr << ssrc << " ||| " << strg << " ||| " << log_prob << endl; - } - cerr << "TOTAL LOG PROB " << tlp << endl; - } - - if (write_alignments) return 0; - - for (TTable::Word2Word2Double::iterator ei = s2t.ttable.begin(); ei != s2t.ttable.end(); ++ei) { - const TTable::Word2Double& cpd = ei->second; - const TTable::Word2Double& vit = s2t_viterbi[ei->first]; - const string& esym = TD::Convert(ei->first); - double max_p = -1; - for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) - if (fi->second > max_p) max_p = fi->second; - const double threshold = max_p * BEAM_THRESHOLD; - for (TTable::Word2Double::const_iterator fi = cpd.begin(); fi != cpd.end(); ++fi) { - if (fi->second > threshold || (vit.find(fi->first) != vit.end())) { - cout << esym << ' ' << TD::Convert(fi->first) << ' ' << log(fi->second) << endl; - } - } - } - return 0; -} - -- cgit v1.2.3