diff options
Diffstat (limited to 'dtrain')
-rw-r--r-- | dtrain/Makefile.am | 7 | ||||
-rw-r--r-- | dtrain/README.md | 48 | ||||
-rw-r--r-- | dtrain/dtrain.cc | 623 | ||||
-rw-r--r-- | dtrain/dtrain.h | 95 | ||||
-rwxr-xr-x | dtrain/hstreaming/avg.rb | 32 | ||||
-rw-r--r-- | dtrain/hstreaming/cdec.ini | 22 | ||||
-rw-r--r-- | dtrain/hstreaming/dtrain.ini | 15 | ||||
-rwxr-xr-x | dtrain/hstreaming/dtrain.sh | 9 | ||||
-rwxr-xr-x | dtrain/hstreaming/hadoop-streaming-job.sh | 30 | ||||
-rwxr-xr-x | dtrain/hstreaming/lplp.rb | 131 | ||||
-rw-r--r-- | dtrain/hstreaming/red-test | 9 | ||||
-rw-r--r-- | dtrain/kbestget.h | 145 | ||||
-rw-r--r-- | dtrain/ksampler.h | 52 | ||||
-rw-r--r-- | dtrain/pairsampling.h | 112 | ||||
-rw-r--r-- | dtrain/score.cc | 145 | ||||
-rw-r--r-- | dtrain/score.h | 154 | ||||
-rw-r--r-- | dtrain/test/example/README | 6 | ||||
-rw-r--r-- | dtrain/test/example/cdec.ini | 24 | ||||
-rw-r--r-- | dtrain/test/example/dtrain.ini | 21 | ||||
-rw-r--r-- | dtrain/test/example/nc-wmt11.1k.gz | bin | 0 -> 21185883 bytes | |||
-rw-r--r-- | dtrain/test/example/nc-wmt11.en.srilm.gz | bin | 0 -> 16017291 bytes | |||
-rw-r--r-- | dtrain/test/toy/cdec.ini | 2 | ||||
-rw-r--r-- | dtrain/test/toy/dtrain.ini | 12 | ||||
-rw-r--r-- | dtrain/test/toy/input | 2 |
24 files changed, 1696 insertions, 0 deletions
diff --git a/dtrain/Makefile.am b/dtrain/Makefile.am new file mode 100644 index 00000000..f39d161e --- /dev/null +++ b/dtrain/Makefile.am @@ -0,0 +1,7 @@ +bin_PROGRAMS = dtrain + +dtrain_SOURCES = dtrain.cc score.cc +dtrain_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteval.a $(top_srcdir)/utils/libutils.a ../klm/lm/libklm.a ../klm/util/libklm_util.a -lz + +AM_CPPFLAGS = -O3 -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval + diff --git a/dtrain/README.md b/dtrain/README.md new file mode 100644 index 00000000..92d6ba0d --- /dev/null +++ b/dtrain/README.md @@ -0,0 +1,48 @@ +This is a simple (and parallelizable) tuning method for cdec +which is able to train the weights of very many (sparse) features. +It was used here: + "Joint Feature Selection in Distributed Stochastic + Learning for Large-Scale Discriminative Training in + SMT" +(Simianer, Riezler, Dyer; ACL 2012) + + +Building +-------- +Builds when building cdec, see ../BUILDING . +To build only parts needed for dtrain do +``` + autoreconf -ifv + ./configure [--disable-test] + cd dtrain/; make +``` + +Running +------- +To run this on a dev set locally: +``` + #define DTRAIN_LOCAL +``` +otherwise remove that line or undef, then recompile. You need a single +grammar file or input annotated with per-sentence grammars (psg) as you +would use with cdec. Additionally you need to give dtrain a file with +references (--refs) when running locally. + +The input for use with hadoop streaming looks like this: +``` + <sid>\t<source>\t<ref>\t<grammar rules separated by \t> +``` +To convert a psg to this format you need to replace all "\n" +by "\t". Make sure there are no tabs in your data. + +For an example of local usage (with the 'distributed' format) +the see test/example/ . This expects dtrain to be built without +DTRAIN_LOCAL. + +Legal +----- +Copyright (c) 2012 by Patrick Simianer <p@simianer.de> + +See the file ../LICENSE.txt for the licensing terms that this software is +released under. + diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc new file mode 100644 index 00000000..e817e7ab --- /dev/null +++ b/dtrain/dtrain.cc @@ -0,0 +1,623 @@ +#include "dtrain.h" + + +bool +dtrain_init(int argc, char** argv, po::variables_map* cfg) +{ + po::options_description ini("Configuration File Options"); + ini.add_options() + ("input", po::value<string>()->default_value("-"), "input file") + ("output", po::value<string>()->default_value("-"), "output weights file, '-' for STDOUT") + ("input_weights", po::value<string>(), "input weights file (e.g. from previous iteration)") + ("decoder_config", po::value<string>(), "configuration file for cdec") + ("print_weights", po::value<string>(), "weights to print on each iteration") + ("stop_after", po::value<unsigned>()->default_value(0), "stop after X input sentences") + ("tmp", po::value<string>()->default_value("/tmp"), "temp dir to use") + ("keep", po::value<bool>()->zero_tokens(), "keep weights files for each iteration") + ("hstreaming", po::value<string>(), "run in hadoop streaming mode, arg is a task id") + ("epochs", po::value<unsigned>()->default_value(10), "# of iterations T (per shard)") + ("k", po::value<unsigned>()->default_value(100), "how many translations to sample") + ("sample_from", po::value<string>()->default_value("kbest"), "where to sample translations from: 'kbest', 'forest'") + ("filter", po::value<string>()->default_value("uniq"), "filter kbest list: 'not', 'uniq'") + ("pair_sampling", po::value<string>()->default_value("XYX"), "how to sample pairs: 'all', 'XYX' or 'PRO'") + ("hi_lo", po::value<float>()->default_value(0.1), "hi and lo (X) for XYX (default 0.1), <= 0.5") + ("pair_threshold", po::value<score_t>()->default_value(0), "bleu [0,1] threshold to filter pairs") + ("N", po::value<unsigned>()->default_value(4), "N for Ngrams (BLEU)") + ("scorer", po::value<string>()->default_value("stupid_bleu"), "scoring: bleu, stupid_, smooth_, approx_") + ("learning_rate", po::value<weight_t>()->default_value(0.0001), "learning rate") + ("gamma", po::value<weight_t>()->default_value(0), "gamma for SVM (0 for perceptron)") + ("select_weights", po::value<string>()->default_value("last"), "output best, last, avg weights ('VOID' to throw away)") + ("rescale", po::value<bool>()->zero_tokens(), "rescale weight vector after each input") + ("l1_reg", po::value<string>()->default_value("none"), "apply l1 regularization as in 'Tsuroka et al' (2010)") + ("l1_reg_strength", po::value<weight_t>(), "l1 regularization strength") + ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent of features after each epoch") + ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU") +#ifdef DTRAIN_LOCAL + ("refs,r", po::value<string>(), "references in local mode") +#endif + ("noup", po::value<bool>()->zero_tokens(), "do not update weights"); + po::options_description cl("Command Line Options"); + cl.add_options() + ("config,c", po::value<string>(), "dtrain config file") + ("quiet,q", po::value<bool>()->zero_tokens(), "be quiet") + ("verbose,v", po::value<bool>()->zero_tokens(), "be verbose"); + cl.add(ini); + po::store(parse_command_line(argc, argv, cl), *cfg); + if (cfg->count("config")) { + ifstream ini_f((*cfg)["config"].as<string>().c_str()); + po::store(po::parse_config_file(ini_f, ini), *cfg); + } + po::notify(*cfg); + if (!cfg->count("decoder_config")) { + cerr << cl << endl; + return false; + } + if (cfg->count("hstreaming") && (*cfg)["output"].as<string>() != "-") { + cerr << "When using 'hstreaming' the 'output' param should be '-'." << endl; + return false; + } +#ifdef DTRAIN_LOCAL + if ((*cfg)["input"].as<string>() == "-") { + cerr << "Can't use stdin as input with this binary. Recompile without DTRAIN_LOCAL" << endl; + return false; + } +#endif + if ((*cfg)["sample_from"].as<string>() != "kbest" + && (*cfg)["sample_from"].as<string>() != "forest") { + cerr << "Wrong 'sample_from' param: '" << (*cfg)["sample_from"].as<string>() << "', use 'kbest' or 'forest'." << endl; + return false; + } + if ((*cfg)["sample_from"].as<string>() == "kbest" && (*cfg)["filter"].as<string>() != "uniq" && + (*cfg)["filter"].as<string>() != "not") { + cerr << "Wrong 'filter' param: '" << (*cfg)["filter"].as<string>() << "', use 'uniq' or 'not'." << endl; + return false; + } + if ((*cfg)["pair_sampling"].as<string>() != "all" && (*cfg)["pair_sampling"].as<string>() != "XYX" && + (*cfg)["pair_sampling"].as<string>() != "PRO") { + cerr << "Wrong 'pair_sampling' param: '" << (*cfg)["pair_sampling"].as<string>() << "'." << endl; + return false; + } + if(cfg->count("hi_lo") && (*cfg)["pair_sampling"].as<string>() != "XYX") { + cerr << "Warning: hi_lo only works with pair_sampling XYX." << endl; + } + if((*cfg)["hi_lo"].as<float>() > 0.5 || (*cfg)["hi_lo"].as<float>() < 0.01) { + cerr << "hi_lo must lie in [0.01, 0.5]" << endl; + return false; + } + if ((*cfg)["pair_threshold"].as<score_t>() < 0) { + cerr << "The threshold must be >= 0!" << endl; + return false; + } + if ((*cfg)["select_weights"].as<string>() != "last" && (*cfg)["select_weights"].as<string>() != "best" && + (*cfg)["select_weights"].as<string>() != "avg" && (*cfg)["select_weights"].as<string>() != "VOID") { + cerr << "Wrong 'select_weights' param: '" << (*cfg)["select_weights"].as<string>() << "', use 'last' or 'best'." << endl; + return false; + } + return true; +} + +int +main(int argc, char** argv) +{ + // handle most parameters + po::variables_map cfg; + if (!dtrain_init(argc, argv, &cfg)) exit(1); // something is wrong + bool quiet = false; + if (cfg.count("quiet")) quiet = true; + bool verbose = false; + if (cfg.count("verbose")) verbose = true; + bool noup = false; + if (cfg.count("noup")) noup = true; + bool hstreaming = false; + string task_id; + if (cfg.count("hstreaming")) { + hstreaming = true; + quiet = true; + task_id = cfg["hstreaming"].as<string>(); + cerr.precision(17); + } + bool rescale = false; + if (cfg.count("rescale")) rescale = true; + HSReporter rep(task_id); + bool keep = false; + if (cfg.count("keep")) keep = true; + + const unsigned k = cfg["k"].as<unsigned>(); + const unsigned N = cfg["N"].as<unsigned>(); + const unsigned T = cfg["epochs"].as<unsigned>(); + const unsigned stop_after = cfg["stop_after"].as<unsigned>(); + const string filter_type = cfg["filter"].as<string>(); + const string sample_from = cfg["sample_from"].as<string>(); + const string pair_sampling = cfg["pair_sampling"].as<string>(); + const score_t pair_threshold = cfg["pair_threshold"].as<score_t>(); + const string select_weights = cfg["select_weights"].as<string>(); + const float hi_lo = cfg["hi_lo"].as<float>(); + const score_t approx_bleu_d = cfg["approx_bleu_d"].as<score_t>(); + bool average = false; + if (select_weights == "avg") + average = true; + vector<string> print_weights; + if (cfg.count("print_weights")) + boost::split(print_weights, cfg["print_weights"].as<string>(), boost::is_any_of(" ")); + + // setup decoder + register_feature_functions(); + SetSilent(true); + ReadFile ini_rf(cfg["decoder_config"].as<string>()); + if (!quiet) + cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; + Decoder decoder(ini_rf.stream()); + + // scoring metric/scorer + string scorer_str = cfg["scorer"].as<string>(); + LocalScorer* scorer; + if (scorer_str == "bleu") { + scorer = dynamic_cast<BleuScorer*>(new BleuScorer); + } else if (scorer_str == "stupid_bleu") { + scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer); + } else if (scorer_str == "smooth_bleu") { + scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer); + } else if (scorer_str == "approx_bleu") { + scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d)); + } else { + cerr << "Don't know scoring metric: '" << scorer_str << "', exiting." << endl; + exit(1); + } + vector<score_t> bleu_weights; + scorer->Init(N, bleu_weights); + + // setup decoder observer + MT19937 rng; // random number generator, only for forest sampling + HypSampler* observer; + if (sample_from == "kbest") + observer = dynamic_cast<KBestGetter*>(new KBestGetter(k, filter_type)); + else + observer = dynamic_cast<KSampler*>(new KSampler(k, &rng)); + observer->SetScorer(scorer); + + // init weights + vector<weight_t>& dense_weights = decoder.CurrentWeightVector(); + SparseVector<weight_t> lambdas, cumulative_penalties, w_average; + if (cfg.count("input_weights")) Weights::InitFromFile(cfg["input_weights"].as<string>(), &dense_weights); + Weights::InitSparseVector(dense_weights, &lambdas); + + // meta params for perceptron, SVM + weight_t eta = cfg["learning_rate"].as<weight_t>(); + weight_t gamma = cfg["gamma"].as<weight_t>(); + + // l1 regularization + bool l1naive = false; + bool l1clip = false; + bool l1cumul = false; + weight_t l1_reg = 0; + if (cfg["l1_reg"].as<string>() != "none") { + string s = cfg["l1_reg"].as<string>(); + if (s == "naive") l1naive = true; + else if (s == "clip") l1clip = true; + else if (s == "cumul") l1cumul = true; + l1_reg = cfg["l1_reg_strength"].as<weight_t>(); + } + + // output + string output_fn = cfg["output"].as<string>(); + // input + string input_fn = cfg["input"].as<string>(); + ReadFile input(input_fn); + // buffer input for t > 0 + vector<string> src_str_buf; // source strings (decoder takes only strings) + vector<vector<WordID> > ref_ids_buf; // references as WordID vecs + // where temp files go + string tmp_path = cfg["tmp"].as<string>(); +#ifdef DTRAIN_LOCAL + string refs_fn = cfg["refs"].as<string>(); + ReadFile refs(refs_fn); +#else + string grammar_buf_fn = gettmpf(tmp_path, "dtrain-grammars"); + ogzstream grammar_buf_out; + grammar_buf_out.open(grammar_buf_fn.c_str()); +#endif + + unsigned in_sz = UINT_MAX; // input index, input size + vector<pair<score_t, score_t> > all_scores; + score_t max_score = 0.; + unsigned best_it = 0; + float overall_time = 0.; + + // output cfg + if (!quiet) { + cerr << _p5; + cerr << endl << "dtrain" << endl << "Parameters:" << endl; + cerr << setw(25) << "k " << k << endl; + cerr << setw(25) << "N " << N << endl; + cerr << setw(25) << "T " << T << endl; + cerr << setw(25) << "scorer '" << scorer_str << "'" << endl; + if (scorer_str == "approx_bleu") + cerr << setw(25) << "approx. B discount " << approx_bleu_d << endl; + cerr << setw(25) << "sample from " << "'" << sample_from << "'" << endl; + if (sample_from == "kbest") + cerr << setw(25) << "filter " << "'" << filter_type << "'" << endl; + cerr << setw(25) << "learning rate " << eta << endl; + cerr << setw(25) << "gamma " << gamma << endl; + cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl; + if (pair_sampling == "XYX") + cerr << setw(25) << "hi lo " << hi_lo << endl; + cerr << setw(25) << "pair threshold " << pair_threshold << endl; + cerr << setw(25) << "select weights " << "'" << select_weights << "'" << endl; + if (cfg.count("l1_reg")) + cerr << setw(25) << "l1 reg " << l1_reg << " '" << cfg["l1_reg"].as<string>() << "'" << endl; + if (rescale) + cerr << setw(25) << "rescale " << rescale << endl; + cerr << setw(25) << "cdec cfg " << "'" << cfg["decoder_config"].as<string>() << "'" << endl; + cerr << setw(25) << "input " << "'" << input_fn << "'" << endl; +#ifdef DTRAIN_LOCAL + cerr << setw(25) << "refs " << "'" << refs_fn << "'" << endl; +#endif + cerr << setw(25) << "output " << "'" << output_fn << "'" << endl; + if (cfg.count("input_weights")) + cerr << setw(25) << "weights in " << "'" << cfg["input_weights"].as<string>() << "'" << endl; + if (cfg.count("stop-after")) + cerr << setw(25) << "stop_after " << stop_after << endl; + if (!verbose) cerr << "(a dot represents " << DTRAIN_DOTS << " inputs)" << endl; + } + + + for (unsigned t = 0; t < T; t++) // T epochs + { + + if (hstreaming) cerr << "reporter:status:Iteration #" << t+1 << " of " << T << endl; + + time_t start, end; + time(&start); +#ifndef DTRAIN_LOCAL + igzstream grammar_buf_in; + if (t > 0) grammar_buf_in.open(grammar_buf_fn.c_str()); +#endif + score_t score_sum = 0.; + score_t model_sum(0); + unsigned ii = 0, rank_errors = 0, margin_violations = 0, npairs = 0; + if (!quiet) cerr << "Iteration #" << t+1 << " of " << T << "." << endl; + + while(true) + { + + string in; + bool next = false, stop = false; // next iteration or premature stop + if (t == 0) { + if(!getline(*input, in)) next = true; + } else { + if (ii == in_sz) next = true; // stop if we reach the end of our input + } + // stop after X sentences (but still go on for those) + if (stop_after > 0 && stop_after == ii && !next) stop = true; + + // produce some pretty output + if (!quiet && !verbose) { + if (ii == 0) cerr << " "; + if ((ii+1) % (DTRAIN_DOTS) == 0) { + cerr << "."; + cerr.flush(); + } + if ((ii+1) % (20*DTRAIN_DOTS) == 0) { + cerr << " " << ii+1 << endl; + if (!next && !stop) cerr << " "; + } + if (stop) { + if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl; + cerr << "Stopping after " << stop_after << " input sentences." << endl; + } else { + if (next) { + if (ii % (20*DTRAIN_DOTS) != 0) cerr << " " << ii << endl; + } + } + } + + // next iteration + if (next || stop) break; + + // weights + lambdas.init_vector(&dense_weights); + + // getting input + vector<WordID> ref_ids; // reference as vector<WordID> +#ifndef DTRAIN_LOCAL + vector<string> in_split; // input: sid\tsrc\tref\tpsg + if (t == 0) { + // handling input + split_in(in, in_split); + if (hstreaming && ii == 0) cerr << "reporter:counter:" << task_id << ",First ID," << in_split[0] << endl; + // getting reference + vector<string> ref_tok; + boost::split(ref_tok, in_split[2], boost::is_any_of(" ")); + register_and_convert(ref_tok, ref_ids); + ref_ids_buf.push_back(ref_ids); + // process and set grammar + bool broken_grammar = true; // ignore broken grammars + for (string::iterator it = in.begin(); it != in.end(); it++) { + if (!isspace(*it)) { + broken_grammar = false; + break; + } + } + if (broken_grammar) { + cerr << "Broken grammar for " << ii+1 << "! Ignoring this input." << endl; + continue; + } + boost::replace_all(in, "\t", "\n"); + in += "\n"; + grammar_buf_out << in << DTRAIN_GRAMMAR_DELIM << " " << in_split[0] << endl; + decoder.SetSentenceGrammarFromString(in); + src_str_buf.push_back(in_split[1]); + // decode + observer->SetRef(ref_ids); + decoder.Decode(in_split[1], observer); + } else { + // get buffered grammar + string grammar_str; + while (true) { + string rule; + getline(grammar_buf_in, rule); + if (boost::starts_with(rule, DTRAIN_GRAMMAR_DELIM)) break; + grammar_str += rule + "\n"; + } + decoder.SetSentenceGrammarFromString(grammar_str); + // decode + observer->SetRef(ref_ids_buf[ii]); + decoder.Decode(src_str_buf[ii], observer); + } +#else + if (t == 0) { + string r_; + getline(*refs, r_); + vector<string> ref_tok; + boost::split(ref_tok, r_, boost::is_any_of(" ")); + register_and_convert(ref_tok, ref_ids); + ref_ids_buf.push_back(ref_ids); + src_str_buf.push_back(in); + } else { + ref_ids = ref_ids_buf[ii]; + } + observer->SetRef(ref_ids); + if (t == 0) + decoder.Decode(in, observer); + else + decoder.Decode(src_str_buf[ii], observer); +#endif + + // get (scored) samples + vector<ScoredHyp>* samples = observer->GetSamples(); + + if (verbose) { + cerr << "--- ref for " << ii << ": "; + if (t > 0) printWordIDVec(ref_ids_buf[ii]); + else printWordIDVec(ref_ids); + cerr << endl; + for (unsigned u = 0; u < samples->size(); u++) { + cerr << _p5 << _np << "[" << u << ". '"; + printWordIDVec((*samples)[u].w); + cerr << "'" << endl; + cerr << "SCORE=" << (*samples)[u].score << ",model="<< (*samples)[u].model << endl; + cerr << "F{" << (*samples)[u].f << "} ]" << endl << endl; + } + } + + score_sum += (*samples)[0].score; // stats for 1best + model_sum += (*samples)[0].model; + + // weight updates + if (!noup) { + vector<pair<ScoredHyp,ScoredHyp> > pairs; + if (pair_sampling == "all") + all_pairs(samples, pairs, pair_threshold); + if (pair_sampling == "XYX") + partXYX(samples, pairs, pair_threshold, hi_lo); + if (pair_sampling == "PRO") + PROsampling(samples, pairs, pair_threshold); + npairs += pairs.size(); + + for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); + it != pairs.end(); it++) { + bool rank_error = it->first.model <= it->second.model; + if (rank_error) rank_errors++; + score_t margin = fabs(it->first.model - it->second.model); + if (!rank_error && margin < 1) margin_violations++; + if (rank_error || (gamma && margin<1)) { + SparseVector<weight_t> diff_vec = it->first.f - it->second.f; + lambdas.plus_eq_v_times_s(diff_vec, eta); + if (gamma) + lambdas.plus_eq_v_times_s(lambdas, -2*gamma*eta*(1./npairs)); + } + } + + // l1 regularization + if (l1naive) { + for (unsigned d = 0; d < lambdas.size(); d++) { + weight_t v = lambdas.get(d); + lambdas.set_value(d, v - sign(v) * l1_reg); + } + } else if (l1clip) { + for (unsigned d = 0; d < lambdas.size(); d++) { + if (lambdas.nonzero(d)) { + weight_t v = lambdas.get(d); + if (v > 0) { + lambdas.set_value(d, max(0., v - l1_reg)); + } else { + lambdas.set_value(d, min(0., v + l1_reg)); + } + } + } + } else if (l1cumul) { + weight_t acc_penalty = (ii+1) * l1_reg; // ii is the index of the current input + for (unsigned d = 0; d < lambdas.size(); d++) { + if (lambdas.nonzero(d)) { + weight_t v = lambdas.get(d); + weight_t penalty = 0; + if (v > 0) { + penalty = max(0., v-(acc_penalty + cumulative_penalties.get(d))); + } else { + penalty = min(0., v+(acc_penalty - cumulative_penalties.get(d))); + } + lambdas.set_value(d, penalty); + cumulative_penalties.set_value(d, cumulative_penalties.get(d)+penalty); + } + } + } + + } + + if (rescale) lambdas /= lambdas.l2norm(); + + ++ii; + + if (hstreaming) { + rep.update_counter("Seen #"+boost::lexical_cast<string>(t+1), 1u); + rep.update_counter("Seen", 1u); + } + + } // input loop + + if (average) w_average += lambdas; + + if (scorer_str == "approx_bleu") scorer->Reset(); + + if (t == 0) { + in_sz = ii; // remember size of input (# lines) + if (hstreaming) { + rep.update_counter("|Input|", ii); + rep.update_gcounter("|Input|", ii); + rep.update_gcounter("Shards", 1u); + } + } + +#ifndef DTRAIN_LOCAL + if (t == 0) { + grammar_buf_out.close(); + } else { + grammar_buf_in.close(); + } +#endif + + // print some stats + score_t score_avg = score_sum/(score_t)in_sz; + score_t model_avg = model_sum/(score_t)in_sz; + score_t score_diff, model_diff; + if (t > 0) { + score_diff = score_avg - all_scores[t-1].first; + model_diff = model_avg - all_scores[t-1].second; + } else { + score_diff = score_avg; + model_diff = model_avg; + } + + unsigned nonz = 0; + if (!quiet || hstreaming) nonz = (unsigned)lambdas.size_nonzero(); + + if (!quiet) { + cerr << _p9 << _p << "WEIGHTS" << endl; + for (vector<string>::iterator it = print_weights.begin(); it != print_weights.end(); it++) { + cerr << setw(18) << *it << " = " << lambdas.get(FD::Convert(*it)) << endl; + } + cerr << " ---" << endl; + cerr << _np << " 1best avg score: " << score_avg; + cerr << _p << " (" << score_diff << ")" << endl; + cerr << _np << " 1best avg model score: " << model_avg; + cerr << _p << " (" << model_diff << ")" << endl; + cerr << " avg # pairs: "; + cerr << _np << npairs/(float)in_sz << endl; + cerr << " avg # rank err: "; + cerr << rank_errors/(float)in_sz << endl; + cerr << " avg # margin viol: "; + cerr << margin_violations/(float)in_sz << endl; + cerr << " non0 feature count: " << nonz << endl; + } + + if (hstreaming) { + rep.update_counter("Score 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(score_avg*DTRAIN_SCALE)); + rep.update_counter("Model 1best avg #"+boost::lexical_cast<string>(t+1), (unsigned)(model_avg*DTRAIN_SCALE)); + rep.update_counter("Pairs avg #"+boost::lexical_cast<string>(t+1), (unsigned)((npairs/(weight_t)in_sz)*DTRAIN_SCALE)); + rep.update_counter("Rank errors avg #"+boost::lexical_cast<string>(t+1), (unsigned)((rank_errors/(weight_t)in_sz)*DTRAIN_SCALE)); + rep.update_counter("Margin violations avg #"+boost::lexical_cast<string>(t+1), (unsigned)((margin_violations/(weight_t)in_sz)*DTRAIN_SCALE)); + rep.update_counter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz); + rep.update_gcounter("Non zero feature count #"+boost::lexical_cast<string>(t+1), nonz); + } + + pair<score_t,score_t> remember; + remember.first = score_avg; + remember.second = model_avg; + all_scores.push_back(remember); + if (score_avg > max_score) { + max_score = score_avg; + best_it = t; + } + time (&end); + float time_diff = difftime(end, start); + overall_time += time_diff; + if (!quiet) { + cerr << _p2 << _np << "(time " << time_diff/60. << " min, "; + cerr << time_diff/in_sz << " s/S)" << endl; + } + if (t+1 != T && !quiet) cerr << endl; + + if (noup) break; + + // write weights to file + if (select_weights == "best" || keep) { + lambdas.init_vector(&dense_weights); + string w_fn = "weights." + boost::lexical_cast<string>(t) + ".gz"; + Weights::WriteToFile(w_fn, dense_weights, true); + } + + } // outer loop + + if (average) w_average /= (weight_t)T; + +#ifndef DTRAIN_LOCAL + unlink(grammar_buf_fn.c_str()); +#endif + + if (!noup) { + if (!quiet) cerr << endl << "Writing weights file to '" << output_fn << "' ..." << endl; + if (select_weights == "last" || average) { // last, average + WriteFile of(output_fn); // works with '-' + ostream& o = *of.stream(); + o.precision(17); + o << _np; + if (average) { + for (SparseVector<weight_t>::const_iterator it = w_average.begin(); it != w_average.end(); ++it) { + if (it->second == 0) continue; + o << FD::Convert(it->first) << '\t' << it->second << endl; + } + } else { + for (SparseVector<weight_t>::const_iterator it = lambdas.begin(); it != lambdas.end(); ++it) { + if (it->second == 0) continue; + o << FD::Convert(it->first) << '\t' << it->second << endl; + } + } + } else if (select_weights == "VOID") { // do nothing with the weights + } else { // best + if (output_fn != "-") { + CopyFile("weights."+boost::lexical_cast<string>(best_it)+".gz", output_fn); + } else { + ReadFile bestw("weights."+boost::lexical_cast<string>(best_it)+".gz"); + string o; + cout.precision(17); + cout << _np; + while(getline(*bestw, o)) cout << o << endl; + } + if (!keep) { + for (unsigned i = 0; i < T; i++) { + string s = "weights." + boost::lexical_cast<string>(i) + ".gz"; + unlink(s.c_str()); + } + } + } + if (output_fn == "-" && hstreaming) cout << "__SHARD_COUNT__\t1" << endl; + if (!quiet) cerr << "done" << endl; + } + + if (!quiet) { + cerr << _p5 << _np << endl << "---" << endl << "Best iteration: "; + cerr << best_it+1 << " [SCORE '" << scorer_str << "'=" << max_score << "]." << endl; + cerr << _p2 << "This took " << overall_time/60. << " min." << endl; + } +} + diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h new file mode 100644 index 00000000..15d32e36 --- /dev/null +++ b/dtrain/dtrain.h @@ -0,0 +1,95 @@ +#ifndef _DTRAIN_H_ +#define _DTRAIN_H_ + +#include <iomanip> +#include <climits> +#include <string.h> + +#include <boost/algorithm/string.hpp> +#include <boost/program_options.hpp> + +#include "ksampler.h" +#include "pairsampling.h" + +#include "filelib.h" + +#define DTRAIN_LOCAL + +#define DTRAIN_DOTS 10 // after how many inputs to display a '.' +#define DTRAIN_GRAMMAR_DELIM "########EOS########" +#define DTRAIN_SCALE 100000 + +using namespace std; +using namespace dtrain; +namespace po = boost::program_options; + +inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids) +{ + vector<string>::const_iterator it; + for (it = strs.begin(); it < strs.end(); it++) + ids.push_back(TD::Convert(*it)); +} + +inline string gettmpf(const string path, const string infix) +{ + char fn[1024]; + strcpy(fn, path.c_str()); + strcat(fn, "/"); + strcat(fn, infix.c_str()); + strcat(fn, "-XXXXXX"); + if (!mkstemp(fn)) { + cerr << "Cannot make temp file in" << path << " , exiting." << endl; + exit(1); + } + return string(fn); +} + +inline void split_in(string& s, vector<string>& parts) +{ + unsigned f = 0; + for(unsigned i = 0; i < 3; i++) { + unsigned e = f; + f = s.find("\t", f+1); + if (e != 0) parts.push_back(s.substr(e+1, f-e-1)); + else parts.push_back(s.substr(0, f)); + } + s.erase(0, f+1); +} + +struct HSReporter +{ + string task_id_; + + HSReporter(string task_id) : task_id_(task_id) {} + + inline void update_counter(string name, unsigned amount) { + cerr << "reporter:counter:" << task_id_ << "," << name << "," << amount << endl; + } + inline void update_gcounter(string name, unsigned amount) { + cerr << "reporter:counter:Global," << name << "," << amount << endl; + } +}; + +inline ostream& _np(ostream& out) { return out << resetiosflags(ios::showpos); } +inline ostream& _p(ostream& out) { return out << setiosflags(ios::showpos); } +inline ostream& _p2(ostream& out) { return out << setprecision(2); } +inline ostream& _p5(ostream& out) { return out << setprecision(5); } +inline ostream& _p9(ostream& out) { return out << setprecision(9); } + +inline void printWordIDVec(vector<WordID>& v) +{ + for (unsigned i = 0; i < v.size(); i++) { + cerr << TD::Convert(v[i]); + if (i < v.size()-1) cerr << " "; + } +} + +template<typename T> +inline T sign(T z) +{ + if (z == 0) return 0; + return z < 0 ? -1 : +1; +} + +#endif + diff --git a/dtrain/hstreaming/avg.rb b/dtrain/hstreaming/avg.rb new file mode 100755 index 00000000..2599c732 --- /dev/null +++ b/dtrain/hstreaming/avg.rb @@ -0,0 +1,32 @@ +#!/usr/bin/env ruby +# first arg may be an int of custom shard count + +shard_count_key = "__SHARD_COUNT__" + +STDIN.set_encoding 'utf-8' +STDOUT.set_encoding 'utf-8' + +w = {} +c = {} +w.default = 0 +c.default = 0 +while line = STDIN.gets + key, val = line.split /\s/ + w[key] += val.to_f + c[key] += 1 +end + +if ARGV.size == 0 + shard_count = w["__SHARD_COUNT__"] +else + shard_count = ARGV[0].to_f +end +w.each_key { |k| + if k == shard_count_key + next + else + puts "#{k}\t#{w[k]/shard_count}" + #puts "# #{c[k]}" + end +} + diff --git a/dtrain/hstreaming/cdec.ini b/dtrain/hstreaming/cdec.ini new file mode 100644 index 00000000..d4f5cecd --- /dev/null +++ b/dtrain/hstreaming/cdec.ini @@ -0,0 +1,22 @@ +formalism=scfg +add_pass_through_rules=true +scfg_max_span_limit=15 +intersection_strategy=cube_pruning +cubepruning_pop_limit=30 +feature_function=WordPenalty +feature_function=KLanguageModel nc-wmt11.en.srilm.gz +#feature_function=ArityPenalty +#feature_function=CMR2008ReorderingFeatures +#feature_function=Dwarf +#feature_function=InputIndicator +#feature_function=LexNullJump +#feature_function=NewJump +#feature_function=NgramFeatures +#feature_function=NonLatinCount +#feature_function=OutputIndicator +#feature_function=RuleIdentityFeatures +#feature_function=RuleNgramFeatures +#feature_function=RuleShape +#feature_function=SourceSpanSizeFeatures +#feature_function=SourceWordPenalty +#feature_function=SpanFeatures diff --git a/dtrain/hstreaming/dtrain.ini b/dtrain/hstreaming/dtrain.ini new file mode 100644 index 00000000..a2c219a1 --- /dev/null +++ b/dtrain/hstreaming/dtrain.ini @@ -0,0 +1,15 @@ +input=- +output=- +decoder_config=cdec.ini +tmp=/var/hadoop/mapred/local/ +epochs=1 +k=100 +N=4 +learning_rate=0.0001 +gamma=0 +scorer=stupid_bleu +sample_from=kbest +filter=uniq +pair_sampling=XYX +pair_threshold=0 +select_weights=last diff --git a/dtrain/hstreaming/dtrain.sh b/dtrain/hstreaming/dtrain.sh new file mode 100755 index 00000000..877ff94c --- /dev/null +++ b/dtrain/hstreaming/dtrain.sh @@ -0,0 +1,9 @@ +#!/bin/bash +# script to run dtrain with a task id + +pushd . &>/dev/null +cd .. +ID=$(basename $(pwd)) # attempt_... +popd &>/dev/null +./dtrain -c dtrain.ini --hstreaming $ID + diff --git a/dtrain/hstreaming/hadoop-streaming-job.sh b/dtrain/hstreaming/hadoop-streaming-job.sh new file mode 100755 index 00000000..92419956 --- /dev/null +++ b/dtrain/hstreaming/hadoop-streaming-job.sh @@ -0,0 +1,30 @@ +#!/bin/sh + +EXP=a_simple_test + +# change these vars to fit your hadoop installation +HADOOP_HOME=/usr/lib/hadoop-0.20 +JAR=contrib/streaming/hadoop-streaming-0.20.2-cdh3u1.jar +HSTREAMING="$HADOOP_HOME/bin/hadoop jar $HADOOP_HOME/$JAR" + + IN=input_on_hdfs +OUT=output_weights_on_hdfs + +# you can -reducer to NONE if you want to +# do feature selection/averaging locally (e.g. to +# keep weights of all epochs) +$HSTREAMING \ + -mapper "dtrain.sh" \ + -reducer "ruby lplp.rb l2 select_k 100000" \ + -input $IN \ + -output $OUT \ + -file dtrain.sh \ + -file lplp.rb \ + -file ../dtrain \ + -file dtrain.ini \ + -file cdec.ini \ + -file ../test/example/nc-wmt11.en.srilm.gz \ + -jobconf mapred.reduce.tasks=30 \ + -jobconf mapred.max.map.failures.percent=0 \ + -jobconf mapred.job.name="dtrain $EXP" + diff --git a/dtrain/hstreaming/lplp.rb b/dtrain/hstreaming/lplp.rb new file mode 100755 index 00000000..f0cd58c5 --- /dev/null +++ b/dtrain/hstreaming/lplp.rb @@ -0,0 +1,131 @@ +# lplp.rb + +# norms +def l0(feature_column, n) + if feature_column.size >= n then return 1 else return 0 end +end + +def l1(feature_column, n=-1) + return feature_column.map { |i| i.abs }.reduce { |sum,i| sum+i } +end + +def l2(feature_column, n=-1) + return Math.sqrt feature_column.map { |i| i.abs2 }.reduce { |sum,i| sum+i } +end + +def linfty(feature_column, n=-1) + return feature_column.map { |i| i.abs }.max +end + +# stats +def median(feature_column, n) + return feature_column.concat(0.step(n-feature_column.size-1).map{|i|0}).sort[feature_column.size/2] +end + +def mean(feature_column, n) + return feature_column.reduce { |sum, i| sum+i } / n +end + +# selection +def select_k(weights, norm_fun, n, k=10000) + weights.sort{|a,b| norm_fun.call(b[1], n) <=> norm_fun.call(a[1], n)}.each { |p| + puts "#{p[0]}\t#{mean(p[1], n)}" + k -= 1 + if k == 0 then break end + } +end + +def cut(weights, norm_fun, n, epsilon=0.0001) + weights.each { |k,v| + if norm_fun.call(v, n).abs >= epsilon + puts "#{k}\t#{mean(v, n)}" + end + } +end + +# test +def _test() + puts + w = {} + w["a"] = [1, 2, 3] + w["b"] = [1, 2] + w["c"] = [66] + w["d"] = [10, 20, 30] + n = 3 + puts w.to_s + puts + puts "select_k" + puts "l0 expect ad" + select_k(w, method(:l0), n, 2) + puts "l1 expect cd" + select_k(w, method(:l1), n, 2) + puts "l2 expect c" + select_k(w, method(:l2), n, 1) + puts + puts "cut" + puts "l1 expect cd" + cut(w, method(:l1), n, 7) + puts + puts "median" + a = [1,2,3,4,5] + puts a.to_s + puts median(a, 5) + puts + puts "#{median(a, 7)} <- that's because we add missing 0s:" + puts a.concat(0.step(7-a.size-1).map{|i|0}).to_s + puts + puts "mean expect bc" + w.clear + w["a"] = [2] + w["b"] = [2.1] + w["c"] = [2.2] + cut(w, method(:mean), 1, 2.05) + exit +end +#_test() + +# actually do something +def usage() + puts "lplp.rb <l0,l1,l2,linfty,mean,median> <cut|select_k> <k|threshold> [n] < <input>" + puts " l0...: norms for selection" + puts "select_k: only output top k (according to the norm of their column vector) features" + puts " cut: output features with weight >= threshold" + puts " n: if we do not have a shard count use this number for averaging" + exit +end + +if ARGV.size < 3 then usage end +norm_fun = method(ARGV[0].to_sym) +type = ARGV[1] +x = ARGV[2].to_f + +shard_count_key = "__SHARD_COUNT__" + +STDIN.set_encoding 'utf-8' +STDOUT.set_encoding 'utf-8' + +w = {} +shard_count = 0 +while line = STDIN.gets + key, val = line.split /\s+/ + if key == shard_count_key + shard_count += 1 + next + end + if w.has_key? key + w[key].push val.to_f + else + w[key] = [val.to_f] + end +end + +if ARGV.size == 4 then shard_count = ARGV[3].to_f end + +if type == 'cut' + cut(w, norm_fun, shard_count, x) +elsif type == 'select_k' + select_k(w, norm_fun, shard_count, x) +else + puts "oh oh" +end + diff --git a/dtrain/hstreaming/red-test b/dtrain/hstreaming/red-test new file mode 100644 index 00000000..2623d697 --- /dev/null +++ b/dtrain/hstreaming/red-test @@ -0,0 +1,9 @@ +a 1 +b 2 +c 3.5 +a 1 +b 2 +c 3.5 +d 1 +e 2 +__SHARD_COUNT__ 2 diff --git a/dtrain/kbestget.h b/dtrain/kbestget.h new file mode 100644 index 00000000..77d4a139 --- /dev/null +++ b/dtrain/kbestget.h @@ -0,0 +1,145 @@ +#ifndef _DTRAIN_KBESTGET_H_ +#define _DTRAIN_KBESTGET_H_ + +#include "kbest.h" // cdec +#include "sentence_metadata.h" + +#include "verbose.h" +#include "viterbi.h" +#include "ff_register.h" +#include "decoder.h" +#include "weights.h" +#include "logval.h" + +using namespace std; + +namespace dtrain +{ + + +typedef double score_t; + +struct ScoredHyp +{ + vector<WordID> w; + SparseVector<double> f; + score_t model; + score_t score; + unsigned rank; +}; + +struct LocalScorer +{ + unsigned N_; + vector<score_t> w_; + + virtual score_t + Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len)=0; + + void Reset() {} // only for approx bleu + + inline void + Init(unsigned N, vector<score_t> weights) + { + assert(N > 0); + N_ = N; + if (weights.empty()) for (unsigned i = 0; i < N_; i++) w_.push_back(1./N_); + else w_ = weights; + } + + inline score_t + brevity_penalty(const unsigned hyp_len, const unsigned ref_len) + { + if (hyp_len > ref_len) return 1; + return exp(1 - (score_t)ref_len/hyp_len); + } +}; + +struct HypSampler : public DecoderObserver +{ + LocalScorer* scorer_; + vector<WordID>* ref_; + virtual vector<ScoredHyp>* GetSamples()=0; + inline void SetScorer(LocalScorer* scorer) { scorer_ = scorer; } + inline void SetRef(vector<WordID>& ref) { ref_ = &ref; } +}; +//////////////////////////////////////////////////////////////////////////////// + + + + +struct KBestGetter : public HypSampler +{ + const unsigned k_; + const string filter_type_; + vector<ScoredHyp> s_; + unsigned src_len_; + + KBestGetter(const unsigned k, const string filter_type) : + k_(k), filter_type_(filter_type) {} + + virtual void + NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) + { + src_len_ = smeta.GetSourceLength(); + KBestScored(*hg); + } + + vector<ScoredHyp>* GetSamples() { return &s_; } + + void + KBestScored(const Hypergraph& forest) + { + if (filter_type_ == "uniq") { + KBestUnique(forest); + } else if (filter_type_ == "not") { + KBestNoFilter(forest); + } + } + + void + KBestUnique(const Hypergraph& forest) + { + s_.clear(); + KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, + KBest::FilterUnique, prob_t, EdgeProb> kbest(forest, k_); + for (unsigned i = 0; i < k_; ++i) { + const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal, KBest::FilterUnique, + prob_t, EdgeProb>::Derivation* d = + kbest.LazyKthBest(forest.nodes_.size() - 1, i); + if (!d) break; + ScoredHyp h; + h.w = d->yield; + h.f = d->feature_values; + h.model = log(d->score); + h.rank = i; + h.score = scorer_->Score(h.w, *ref_, i, src_len_); + s_.push_back(h); + } + } + + void + KBestNoFilter(const Hypergraph& forest) + { + s_.clear(); + KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(forest, k_); + for (unsigned i = 0; i < k_; ++i) { + const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = + kbest.LazyKthBest(forest.nodes_.size() - 1, i); + if (!d) break; + ScoredHyp h; + h.w = d->yield; + h.f = d->feature_values; + h.model = log(d->score); + h.rank = i; + h.score = scorer_->Score(h.w, *ref_, i, src_len_); + s_.push_back(h); + } + } +}; + + +} // namespace + +#endif + diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h new file mode 100644 index 00000000..0783f98b --- /dev/null +++ b/dtrain/ksampler.h @@ -0,0 +1,52 @@ +#ifndef _DTRAIN_KSAMPLER_H_ +#define _DTRAIN_KSAMPLER_H_ + +#include "hg_sampler.h" // cdec +#include "kbestget.h" +#include "score.h" + +namespace dtrain +{ + + +struct KSampler : public HypSampler +{ + const unsigned k_; + vector<ScoredHyp> s_; + MT19937* prng_; + score_t (*scorer)(NgramCounts&, const unsigned, const unsigned, unsigned, vector<score_t>); + unsigned src_len_; + + explicit KSampler(const unsigned k, MT19937* prng) : + k_(k), prng_(prng) {} + + virtual void + NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg) + { + src_len_ = smeta.GetSourceLength(); + ScoredSamples(*hg); + } + + vector<ScoredHyp>* GetSamples() { return &s_; } + + void ScoredSamples(const Hypergraph& forest) { + s_.clear(); + std::vector<HypergraphSampler::Hypothesis> samples; + HypergraphSampler::sample_hypotheses(forest, k_, prng_, &samples); + for (unsigned i = 0; i < k_; ++i) { + ScoredHyp h; + h.w = samples[i].words; + h.f = samples[i].fmap; + h.model = log(samples[i].model_score); + h.rank = i; + h.score = scorer_->Score(h.w, *ref_, i, src_len_); + s_.push_back(h); + } + } +}; + + +} // namespace + +#endif + diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h new file mode 100644 index 00000000..bb01cf4f --- /dev/null +++ b/dtrain/pairsampling.h @@ -0,0 +1,112 @@ +#ifndef _DTRAIN_PAIRSAMPLING_H_ +#define _DTRAIN_PAIRSAMPLING_H_ + +namespace dtrain +{ + + +bool +accept_pair(score_t a, score_t b, score_t threshold) +{ + if (fabs(a - b) < threshold) return false; + return true; +} + +inline void +all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused = 1) +{ + unsigned sz = s->size(); + for (unsigned i = 0; i < sz-1; i++) { + for (unsigned j = i+1; j < sz; j++) { + if (threshold > 0) { + if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) + training.push_back(make_pair((*s)[i], (*s)[j])); + } else { + training.push_back(make_pair((*s)[i], (*s)[j])); + } + } + } +} + +/* + * multipartite ranking + * sort (descending) by bleu + * compare top X to middle Y and low X + * cmp middle Y to low X + */ +bool +_XYX_cmp_hyp_by_score(ScoredHyp a, ScoredHyp b) +{ + return a.score > b.score; +} +inline void +partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo) +{ + sort(s->begin(), s->end(), _XYX_cmp_hyp_by_score); + unsigned sz = s->size(); + unsigned sep = round(sz*hi_lo); + for (unsigned i = 0; i < sep; i++) { + for (unsigned j = sep; j < sz; j++) { + if (threshold > 0) { + if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) + training.push_back(make_pair((*s)[i], (*s)[j])); + } else { + if((*s)[i].score != (*s)[j].score) + training.push_back(make_pair((*s)[i], (*s)[j])); + } + } + } + for (unsigned i = sep; i < sz-sep; i++) { + for (unsigned j = sz-sep; j < sz; j++) { + if (threshold > 0) { + if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) + training.push_back(make_pair((*s)[i], (*s)[j])); + } else { + if((*s)[i].score != (*s)[j].score) + training.push_back(make_pair((*s)[i], (*s)[j])); + } + } + } +} + +/* + * pair sampling as in + * 'Tuning as Ranking' (Hopkins & May, 2011) + * count = 5000 + * threshold = 5% BLEU (0.05 for param 3) + * cut = top 50 + */ +bool +_PRO_cmp_pair_by_diff(pair<ScoredHyp,ScoredHyp> a, pair<ScoredHyp,ScoredHyp> b) +{ + return (fabs(a.first.score - a.second.score)) > (fabs(b.first.score - b.second.score)); +} +inline void +PROsampling(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float _unused = 1) +{ + unsigned max_count = 5000, count = 0; + bool b = false; + for (unsigned i = 0; i < s->size()-1; i++) { + for (unsigned j = i+1; j < s->size(); j++) { + if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) { + training.push_back(make_pair((*s)[i], (*s)[j])); + if (++count == max_count) { + b = true; + break; + } + } + } + if (b) break; + } + if (training.size() > 50) { + sort(training.begin(), training.end(), _PRO_cmp_pair_by_diff); + training.erase(training.begin()+50, training.end()); + } + return; +} + + +} // namespace + +#endif + diff --git a/dtrain/score.cc b/dtrain/score.cc new file mode 100644 index 00000000..b09d32ba --- /dev/null +++ b/dtrain/score.cc @@ -0,0 +1,145 @@ +#include "score.h" + +namespace dtrain +{ + + +/* + * bleu + * + * as in "BLEU: a Method for Automatic Evaluation + * of Machine Translation" + * (Papineni et al. '02) + * + * NOTE: 0 if for one n \in {1..N} count is 0 + */ +score_t +BleuScorer::Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len) +{ + if (hyp_len == 0 || ref_len == 0) return 0.; + unsigned M = N_; + vector<score_t> v = w_; + if (ref_len < N_) { + M = ref_len; + for (unsigned i = 0; i < M; i++) v[i] = 1./((score_t)M); + } + score_t sum = 0; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) return 0.; + sum += v[i] * log((score_t)counts.clipped_[i]/counts.sum_[i]); + } + return brevity_penalty(hyp_len, ref_len) * exp(sum); +} + +score_t +BleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + return Bleu(counts, hyp_len, ref_len); +} + +/* + * 'stupid' bleu + * + * as in "ORANGE: a Method for Evaluating + * Automatic Evaluation Metrics + * for Machine Translation" + * (Lin & Och '04) + * + * NOTE: 0 iff no 1gram match + */ +score_t +StupidBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + unsigned M = N_; + vector<score_t> v = w_; + if (ref_len < N_) { + M = ref_len; + for (unsigned i = 0; i < M; i++) v[i] = 1./((score_t)M); + } + score_t sum = 0, add = 0; + for (unsigned i = 0; i < M; i++) { + if (i == 0 && (counts.sum_[i] == 0 || counts.clipped_[i] == 0)) return 0.; + if (i == 1) add = 1; + sum += v[i] * log(((score_t)counts.clipped_[i] + add)/((counts.sum_[i] + add))); + } + return brevity_penalty(hyp_len, ref_len) * exp(sum); +} + +/* + * smooth bleu + * + * as in "An End-to-End Discriminative Approach + * to Machine Translation" + * (Liang et al. '06) + * + * NOTE: max is 0.9375 + */ +score_t +SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned /*rank*/, const unsigned /*src_len*/) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (hyp_len == 0 || ref_len == 0) return 0.; + NgramCounts counts = make_ngram_counts(hyp, ref, N_); + unsigned M = N_; + if (ref_len < N_) M = ref_len; + score_t sum = 0.; + vector<score_t> i_bleu; + for (unsigned i = 0; i < M; i++) i_bleu.push_back(0.); + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) { + break; + } else { + score_t i_ng = log((score_t)counts.clipped_[i]/counts.sum_[i]); + for (unsigned j = i; j < M; j++) { + i_bleu[j] += (1/((score_t)j+1)) * i_ng; + } + } + sum += exp(i_bleu[i])/(pow(2, N_-i)); + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +/* + * approx. bleu + * + * as in "Online Large-Margin Training of Syntactic + * and Structural Translation Features" + * (Chiang et al. '08) + * + * NOTE: needs some more code in dtrain.cc + */ +score_t +ApproxBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, + const unsigned rank, const unsigned src_len) +{ + unsigned hyp_len = hyp.size(), ref_len = ref.size(); + if (ref_len == 0) return 0.; + score_t score = 0.; + NgramCounts counts(N_); + if (hyp_len > 0) { + counts = make_ngram_counts(hyp, ref, N_); + NgramCounts tmp = glob_onebest_counts_ + counts; + score = Bleu(tmp, hyp_len, ref_len); + } + if (rank == 0) { // 'context of 1best translations' + glob_onebest_counts_ += counts; + glob_onebest_counts_ *= discount_; + glob_hyp_len_ = discount_ * (glob_hyp_len_ + hyp_len); + glob_ref_len_ = discount_ * (glob_ref_len_ + ref_len); + glob_src_len_ = discount_ * (glob_src_len_ + src_len); + } + return (score_t)glob_src_len_ * score; +} + + +} // namespace + diff --git a/dtrain/score.h b/dtrain/score.h new file mode 100644 index 00000000..eb8ad912 --- /dev/null +++ b/dtrain/score.h @@ -0,0 +1,154 @@ +#ifndef _DTRAIN_SCORE_H_ +#define _DTRAIN_SCORE_H_ + +#include "kbestget.h" + +using namespace std; + +namespace dtrain +{ + + +struct NgramCounts +{ + unsigned N_; + map<unsigned, score_t> clipped_; + map<unsigned, score_t> sum_; + + NgramCounts(const unsigned N) : N_(N) { Zero(); } + + inline void + operator+=(const NgramCounts& rhs) + { + assert(N_ == rhs.N_); + for (unsigned i = 0; i < N_; i++) { + this->clipped_[i] += rhs.clipped_.find(i)->second; + this->sum_[i] += rhs.sum_.find(i)->second; + } + } + + inline const NgramCounts + operator+(const NgramCounts &other) const + { + NgramCounts result = *this; + result += other; + return result; + } + + inline void + operator*=(const score_t rhs) + { + for (unsigned i = 0; i < N_; i++) { + this->clipped_[i] *= rhs; + this->sum_[i] *= rhs; + } + } + + inline void + Add(const unsigned count, const unsigned ref_count, const unsigned i) + { + assert(i < N_); + if (count > ref_count) { + clipped_[i] += ref_count; + } else { + clipped_[i] += count; + } + sum_[i] += count; + } + + inline void + Zero() + { + unsigned i; + for (i = 0; i < N_; i++) { + clipped_[i] = 0.; + sum_[i] = 0.; + } + } + + inline void + Print() + { + for (unsigned i = 0; i < N_; i++) { + cout << i+1 << "grams (clipped):\t" << clipped_[i] << endl; + cout << i+1 << "grams:\t\t\t" << sum_[i] << endl; + } + } +}; + +typedef map<vector<WordID>, unsigned> Ngrams; + +inline Ngrams +make_ngrams(const vector<WordID>& s, const unsigned N) +{ + Ngrams ngrams; + vector<WordID> ng; + for (size_t i = 0; i < s.size(); i++) { + ng.clear(); + for (unsigned j = i; j < min(i+N, s.size()); j++) { + ng.push_back(s[j]); + ngrams[ng]++; + } + } + return ngrams; +} + +inline NgramCounts +make_ngram_counts(const vector<WordID>& hyp, const vector<WordID>& ref, const unsigned N) +{ + Ngrams hyp_ngrams = make_ngrams(hyp, N); + Ngrams ref_ngrams = make_ngrams(ref, N); + NgramCounts counts(N); + Ngrams::iterator it; + Ngrams::iterator ti; + for (it = hyp_ngrams.begin(); it != hyp_ngrams.end(); it++) { + ti = ref_ngrams.find(it->first); + if (ti != ref_ngrams.end()) { + counts.Add(it->second, ti->second, it->first.size() - 1); + } else { + counts.Add(it->second, 0, it->first.size() - 1); + } + } + return counts; +} + +struct BleuScorer : public LocalScorer +{ + score_t Bleu(NgramCounts& counts, const unsigned hyp_len, const unsigned ref_len); + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + +struct StupidBleuScorer : public LocalScorer +{ + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + +struct SmoothBleuScorer : public LocalScorer +{ + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); +}; + +struct ApproxBleuScorer : public BleuScorer +{ + NgramCounts glob_onebest_counts_; + unsigned glob_hyp_len_, glob_ref_len_, glob_src_len_; + score_t discount_; + + ApproxBleuScorer(unsigned N, score_t d) : glob_onebest_counts_(NgramCounts(N)), discount_(d) + { + glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0; + } + + inline void Reset() { + glob_onebest_counts_.Zero(); + glob_hyp_len_ = glob_ref_len_ = glob_src_len_ = 0.; + } + + score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned rank, const unsigned src_len); +}; + + +} // namespace + +#endif + diff --git a/dtrain/test/example/README b/dtrain/test/example/README new file mode 100644 index 00000000..e5a5de59 --- /dev/null +++ b/dtrain/test/example/README @@ -0,0 +1,6 @@ +Small example of input format for distributed training. +Call dtrain from cdec/dtrain/ with ./dtrain -c test/example/dtrain.ini . + +For this to work, disable '#define DTRAIN_LOCAL' from dtrain.h +and recompile. + diff --git a/dtrain/test/example/cdec.ini b/dtrain/test/example/cdec.ini new file mode 100644 index 00000000..6642107f --- /dev/null +++ b/dtrain/test/example/cdec.ini @@ -0,0 +1,24 @@ +formalism=scfg +add_pass_through_rules=true +scfg_max_span_limit=15 +intersection_strategy=cube_pruning +cubepruning_pop_limit=30 +feature_function=WordPenalty +feature_function=KLanguageModel test/example/nc-wmt11.en.srilm.gz +# all currently working feature functions for translation: +# (with those features active that were used in the ACL paper) +#feature_function=ArityPenalty +#feature_function=CMR2008ReorderingFeatures +#feature_function=Dwarf +#feature_function=InputIndicator +#feature_function=LexNullJump +#feature_function=NewJump +#feature_function=NgramFeatures +#feature_function=NonLatinCount +#feature_function=OutputIndicator +feature_function=RuleIdentityFeatures +feature_function=RuleNgramFeatures +feature_function=RuleShape +#feature_function=SourceSpanSizeFeatures +#feature_function=SourceWordPenalty +#feature_function=SpanFeatures diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini new file mode 100644 index 00000000..2ad44688 --- /dev/null +++ b/dtrain/test/example/dtrain.ini @@ -0,0 +1,21 @@ +input=test/example/nc-wmt11.1k.gz # use '-' for STDIN +output=- # a weights file (add .gz for gzip compression) or STDOUT '-' +decoder_config=test/example/cdec.ini # config for cdec +# weights for these features will be printed on each iteration +print_weights=Glue WordPenalty LanguageModel LanguageModel_OOV PhraseModel_0 PhraseModel_1 PhraseModel_2 PhraseModel_3 PhraseModel_4 PhraseModel_5 PhraseModel_6 PassThrough +tmp=/tmp +stop_after=20 # stop epoch after 20 inputs + +# interesting stuff +epochs=3 # run over input 3 times +k=100 # use 100best lists +N=4 # optimize (approx) BLEU4 +scorer=stupid_bleu # use 'stupid' BLEU+1 +learning_rate=0.0001 # learning rate +gamma=0 # use SVM reg +sample_from=kbest # use kbest lists (as opposed to forest) +filter=uniq # only unique entries in kbest (surface form) +pair_sampling=XYX +hi_lo=0.1 # 10 vs 80 vs 10 and 80 vs 10 here +pair_threshold=0 # minimum distance in BLEU (this will still only use pairs with diff > 0) +select_weights=VOID # don't output weights diff --git a/dtrain/test/example/nc-wmt11.1k.gz b/dtrain/test/example/nc-wmt11.1k.gz Binary files differnew file mode 100644 index 00000000..45496cd8 --- /dev/null +++ b/dtrain/test/example/nc-wmt11.1k.gz diff --git a/dtrain/test/example/nc-wmt11.en.srilm.gz b/dtrain/test/example/nc-wmt11.en.srilm.gz Binary files differnew file mode 100644 index 00000000..7ce81057 --- /dev/null +++ b/dtrain/test/example/nc-wmt11.en.srilm.gz diff --git a/dtrain/test/toy/cdec.ini b/dtrain/test/toy/cdec.ini new file mode 100644 index 00000000..98b02d44 --- /dev/null +++ b/dtrain/test/toy/cdec.ini @@ -0,0 +1,2 @@ +formalism=scfg +add_pass_through_rules=true diff --git a/dtrain/test/toy/dtrain.ini b/dtrain/test/toy/dtrain.ini new file mode 100644 index 00000000..a091732f --- /dev/null +++ b/dtrain/test/toy/dtrain.ini @@ -0,0 +1,12 @@ +decoder_config=test/toy/cdec.ini +input=test/toy/input +output=- +print_weights=logp shell_rule house_rule small_rule little_rule PassThrough +k=4 +N=4 +epochs=2 +scorer=bleu +sample_from=kbest +filter=uniq +pair_sampling=all +learning_rate=1 diff --git a/dtrain/test/toy/input b/dtrain/test/toy/input new file mode 100644 index 00000000..4d10a9ea --- /dev/null +++ b/dtrain/test/toy/input @@ -0,0 +1,2 @@ +0 ich sah ein kleines haus i saw a little house [S] ||| [NP,1] [VP,2] ||| [1] [2] ||| logp=0 [NP] ||| ich ||| i ||| logp=0 [NP] ||| ein [NN,1] ||| a [1] ||| logp=0 [NN] ||| [JJ,1] haus ||| [1] house ||| logp=0 house_rule=1 [NN] ||| [JJ,1] haus ||| [1] shell ||| logp=0 shell_rule=1 [JJ] ||| kleines ||| small ||| logp=0 small_rule=1 [JJ] ||| kleines ||| little ||| logp=0 little_rule=1 [JJ] ||| grosses ||| big ||| logp=0 [JJ] ||| grosses ||| large ||| logp=0 [VP] ||| [V,1] [NP,2] ||| [1] [2] ||| logp=0 [V] ||| sah ||| saw ||| logp=0 [V] ||| fand ||| found ||| logp=0 +1 ich fand ein kleines haus i found a little house [S] ||| [NP,1] [VP,2] ||| [1] [2] ||| logp=0 [NP] ||| ich ||| i ||| logp=0 [NP] ||| ein [NN,1] ||| a [1] ||| logp=0 [NN] ||| [JJ,1] haus ||| [1] house ||| logp=0 house_rule=1 [NN] ||| [JJ,1] haus ||| [1] shell ||| logp=0 shell_rule=1 [JJ] ||| kleines ||| small ||| logp=0 small_rule=1 [JJ] ||| kleines ||| little ||| logp=0 little_rule=1 [JJ] ||| grosses ||| big ||| logp=0 [JJ] ||| grosses ||| large ||| logp=0 [VP] ||| [V,1] [NP,2] ||| [1] [2] ||| logp=0 [V] ||| sah ||| saw ||| logp=0 [V] ||| fand ||| found ||| logp=0 |