diff options
| -rw-r--r-- | dtrain/Makefile.am | 2 | ||||
| -rw-r--r-- | dtrain/README.md | 9 | ||||
| -rw-r--r-- | dtrain/dtrain.cc | 22 | ||||
| -rw-r--r-- | dtrain/dtrain.h | 14 | ||||
| -rw-r--r-- | dtrain/ksampler.h | 7 | ||||
| -rw-r--r-- | dtrain/pairsampling.h | 28 | ||||
| -rw-r--r-- | dtrain/score.cc | 22 | ||||
| -rw-r--r-- | dtrain/score.h | 5 | ||||
| -rw-r--r-- | dtrain/test/example/README | 4 | ||||
| -rw-r--r-- | dtrain/test/example/dtrain.ini | 3 | ||||
| -rw-r--r-- | dtrain/test/example/expected-output | 125 |
11 files changed, 222 insertions, 19 deletions
diff --git a/dtrain/Makefile.am b/dtrain/Makefile.am index f39d161e..64fef489 100644 --- a/dtrain/Makefile.am +++ b/dtrain/Makefile.am @@ -3,5 +3,5 @@ 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 +AM_CPPFLAGS = -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 index 9580df6d..350c7423 100644 --- a/dtrain/README.md +++ b/dtrain/README.md @@ -41,6 +41,8 @@ DTRAIN_LOCAL. Next ---- ++ approx. Bleu? ++ turn off inclusion + (dtrain|decoder) more meta-parameters testing + feature selection directly in dtrain + feature template: target side rule ngrams @@ -48,6 +50,13 @@ Next + make svm doable; no subgradient? + reranking while sgd? + try PRO, mira emulations ++ sampling (MBR) ++ forest (on train)? ++ best BLEU transl (Sokolov)? ++ entire reg. path ++ resharding [nfold cross val.] ++ bigger LM, feats (target side Ng., word alignments etc.) ++ merge kbest lists Legal ----- diff --git a/dtrain/dtrain.cc b/dtrain/dtrain.cc index 8b1fc953..717d47a2 100644 --- a/dtrain/dtrain.cc +++ b/dtrain/dtrain.cc @@ -33,6 +33,7 @@ dtrain_init(int argc, char** argv, po::variables_map* cfg) ("fselect", po::value<weight_t>()->default_value(-1), "TODO select top x percent (or by threshold) of features after each epoch") ("approx_bleu_d", po::value<score_t>()->default_value(0.9), "discount for approx. BLEU") ("scale_bleu_diff", po::value<bool>()->zero_tokens(), "learning rate <- bleu diff of a misranked pair") + ("loss_margin", po::value<weight_t>()->default_value(0.), "update if no error in pref pair but model scores this near") #ifdef DTRAIN_LOCAL ("refs,r", po::value<string>(), "references in local mode") #endif @@ -134,6 +135,8 @@ main(int argc, char** argv) 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>(); + weight_t loss_margin = cfg["loss_margin"].as<weight_t>(); + if (loss_margin > 9998.) loss_margin = std::numeric_limits<float>::max(); bool scale_bleu_diff = false; if (cfg.count("scale_bleu_diff")) scale_bleu_diff = true; bool average = false; @@ -160,6 +163,8 @@ main(int argc, char** argv) scorer = dynamic_cast<StupidBleuScorer*>(new StupidBleuScorer); } else if (scorer_str == "smooth_bleu") { scorer = dynamic_cast<SmoothBleuScorer*>(new SmoothBleuScorer); + } else if (scorer_str == "smooth_single_bleu") { + scorer = dynamic_cast<SmoothSingleBleuScorer*>(new SmoothSingleBleuScorer); } else if (scorer_str == "approx_bleu") { scorer = dynamic_cast<ApproxBleuScorer*>(new ApproxBleuScorer(N, approx_bleu_d)); } else { @@ -220,7 +225,7 @@ main(int argc, char** argv) grammar_buf_out.open(grammar_buf_fn.c_str()); #endif - unsigned in_sz = UINT_MAX; // input index, input size + unsigned in_sz = std::numeric_limits<unsigned>::max(); // input index, input size vector<pair<score_t, score_t> > all_scores; score_t max_score = 0.; unsigned best_it = 0; @@ -242,6 +247,7 @@ main(int argc, char** argv) if (!scale_bleu_diff) cerr << setw(25) << "learning rate " << eta << endl; else cerr << setw(25) << "learning rate " << "bleu diff" << endl; cerr << setw(25) << "gamma " << gamma << endl; + cerr << setw(25) << "loss margin " << loss_margin << endl; cerr << setw(25) << "pairs " << "'" << pair_sampling << "'" << endl; if (pair_sampling == "XYX") cerr << setw(25) << "hi lo " << hi_lo << endl; @@ -424,12 +430,18 @@ main(int argc, char** argv) for (vector<pair<ScoredHyp,ScoredHyp> >::iterator it = pairs.begin(); it != pairs.end(); it++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + bool rank_error = true; // pair filtering already did this for us + rank_errors++; + score_t margin = std::numeric_limits<float>::max(); +#else 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++; + score_t margin = fabs(fabs(it->first.model) - fabs(it->second.model)); + if (!rank_error && margin < loss_margin) margin_violations++; +#endif if (scale_bleu_diff) eta = it->first.score - it->second.score; - if (rank_error || (gamma && margin<1)) { + if (rank_error || margin < loss_margin) { SparseVector<weight_t> diff_vec = it->first.f - it->second.f; lambdas.plus_eq_v_times_s(diff_vec, eta); if (gamma) @@ -534,8 +546,10 @@ main(int argc, char** argv) cerr << _np << npairs/(float)in_sz << endl; cerr << " avg # rank err: "; cerr << rank_errors/(float)in_sz << endl; +#ifndef DTRAIN_FASTER_PERCEPTRON cerr << " avg # margin viol: "; cerr << margin_violations/(float)in_sz << endl; +#endif cerr << " non0 feature count: " << nonz << endl; cerr << " avg list sz: " << list_sz/(float)in_sz << endl; cerr << " avg f count: " << f_count/(float)list_sz << endl; diff --git a/dtrain/dtrain.h b/dtrain/dtrain.h index 94d149ce..d8dc14b6 100644 --- a/dtrain/dtrain.h +++ b/dtrain/dtrain.h @@ -1,6 +1,14 @@ #ifndef _DTRAIN_H_ #define _DTRAIN_H_ +#undef DTRAIN_FASTER_PERCEPTRON // only look at misranked pairs + // DO NOT USE WITH SVM! +#undef DTRAIN_LOCAL +#define DTRAIN_DOTS 10 // after how many inputs to display a '.' +#define DTRAIN_GRAMMAR_DELIM "########EOS########" +#define DTRAIN_SCALE 100000 + + #include <iomanip> #include <climits> #include <string.h> @@ -13,11 +21,7 @@ #include "filelib.h" -#undef 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; @@ -32,7 +36,7 @@ inline void register_and_convert(const vector<string>& strs, vector<WordID>& ids inline string gettmpf(const string path, const string infix) { - char fn[1024]; + char fn[path.size() + infix.size() + 8]; strcpy(fn, path.c_str()); strcat(fn, "/"); strcat(fn, infix.c_str()); diff --git a/dtrain/ksampler.h b/dtrain/ksampler.h index f52fb649..bc2f56cd 100644 --- a/dtrain/ksampler.h +++ b/dtrain/ksampler.h @@ -8,6 +8,11 @@ namespace dtrain { +bool +cmp_hyp_by_model_d(ScoredHyp a, ScoredHyp b) +{ + return a.model > b.model; +} struct KSampler : public HypSampler { @@ -44,6 +49,8 @@ struct KSampler : public HypSampler sz_++; f_count_ += h.f.size(); } + sort(s_.begin(), s_.end(), cmp_hyp_by_model_d); + for (unsigned i = 0; i < s_.size(); i++) s_[i].rank = i; } }; diff --git a/dtrain/pairsampling.h b/dtrain/pairsampling.h index bac132c6..32006a41 100644 --- a/dtrain/pairsampling.h +++ b/dtrain/pairsampling.h @@ -46,11 +46,18 @@ all_pairs(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, sc inline void partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, score_t threshold, float hi_lo) { - sort(s->begin(), s->end(), cmp_hyp_by_score_d); unsigned sz = s->size(); + if (sz < 2) return; + sort(s->begin(), s->end(), cmp_hyp_by_score_d); unsigned sep = round(sz*hi_lo); - for (unsigned i = 0; i < sep; i++) { - for (unsigned j = sep; j < sz; j++) { + unsigned sep_hi = sep; + if (sz > 4) while (sep_hi < sz && (*s)[sep_hi-1].score == (*s)[sep_hi].score) ++sep_hi; + else sep_hi = 1; + for (unsigned i = 0; i < sep_hi; i++) { + for (unsigned j = sep_hi; j < sz; j++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + if ((*s)[i].model <= (*s)[j].model) { +#endif if (threshold > 0) { if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) training.push_back(make_pair((*s)[i], (*s)[j])); @@ -58,10 +65,18 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor if ((*s)[i].score != (*s)[j].score) training.push_back(make_pair((*s)[i], (*s)[j])); } +#ifdef DTRAIN_FASTER_PERCEPTRON + } +#endif } } - for (unsigned i = sep; i < sz-sep; i++) { - for (unsigned j = sz-sep; j < sz; j++) { + unsigned sep_lo = sz-sep; + while (sep_lo > 0 && (*s)[sep_lo-1].score == (*s)[sep_lo].score) --sep_lo; + for (unsigned i = sep_hi; i < sz-sep_lo; i++) { + for (unsigned j = sz-sep_lo; j < sz; j++) { +#ifdef DTRAIN_FASTER_PERCEPTRON + if ((*s)[i].model <= (*s)[j].model) { +#endif if (threshold > 0) { if (accept_pair((*s)[i].score, (*s)[j].score, threshold)) training.push_back(make_pair((*s)[i], (*s)[j])); @@ -69,6 +84,9 @@ partXYX(vector<ScoredHyp>* s, vector<pair<ScoredHyp,ScoredHyp> >& training, scor if ((*s)[i].score != (*s)[j].score) training.push_back(make_pair((*s)[i], (*s)[j])); } +#ifdef DTRAIN_FASTER_PERCEPTRON + } +#endif } } } diff --git a/dtrain/score.cc b/dtrain/score.cc index 7b1f6be4..b331dc4f 100644 --- a/dtrain/score.cc +++ b/dtrain/score.cc @@ -103,7 +103,27 @@ SmoothBleuScorer::Score(vector<WordID>& hyp, vector<WordID>& ref, i_bleu[j] += (1/((score_t)j+1)) * i_ng; } } - sum += exp(i_bleu[i])/(pow(2.0, static_cast<double>(N_-i))); + sum += exp(i_bleu[i])/(pow(2.0, N_-i)); + } + return brevity_penalty(hyp_len, ref_len) * sum; +} + +// variant of smooth_bleu; i-Bleu scores only single 'i' +score_t +SmoothSingleBleuScorer::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.; + unsigned j = 1; + for (unsigned i = 0; i < M; i++) { + if (counts.sum_[i] == 0 || counts.clipped_[i] == 0) break; + sum += ((score_t)counts.clipped_[i]/counts.sum_[i])/pow(2.0, N_-j+1); + j++; } return brevity_penalty(hyp_len, ref_len) * sum; } diff --git a/dtrain/score.h b/dtrain/score.h index eb8ad912..d4fba22c 100644 --- a/dtrain/score.h +++ b/dtrain/score.h @@ -128,6 +128,11 @@ struct SmoothBleuScorer : public LocalScorer score_t Score(vector<WordID>& hyp, vector<WordID>& ref, const unsigned /*rank*/, const unsigned /*src_len*/); }; +struct SmoothSingleBleuScorer : 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_; diff --git a/dtrain/test/example/README b/dtrain/test/example/README index b3ea5f06..6937b11b 100644 --- a/dtrain/test/example/README +++ b/dtrain/test/example/README @@ -1,8 +1,8 @@ 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 +For this to work, undef 'DTRAIN_LOCAL' in dtrain.h and recompile. -Data is here: http://simianer.de/dtrain +Data is here: http://simianer.de/#dtrain diff --git a/dtrain/test/example/dtrain.ini b/dtrain/test/example/dtrain.ini index f87ee9cf..c8ac7c3f 100644 --- a/dtrain/test/example/dtrain.ini +++ b/dtrain/test/example/dtrain.ini @@ -5,7 +5,7 @@ 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=10 # stop epoch after 20 inputs +stop_after=10 # stop epoch after 10 inputs # interesting stuff epochs=3 # run over input 3 times @@ -19,3 +19,4 @@ 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) +loss_margin=0 diff --git a/dtrain/test/example/expected-output b/dtrain/test/example/expected-output new file mode 100644 index 00000000..25d2c069 --- /dev/null +++ b/dtrain/test/example/expected-output @@ -0,0 +1,125 @@ + cdec cfg 'test/example/cdec.ini' +feature: WordPenalty (no config parameters) +State is 0 bytes for feature WordPenalty +feature: KLanguageModel (with config parameters 'test/example/nc-wmt11.en.srilm.gz') +Loading the LM will be faster if you build a binary file. +Reading test/example/nc-wmt11.en.srilm.gz +----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100 +**************************************************************************************************** +Loaded 5-gram KLM from test/example/nc-wmt11.en.srilm.gz (MapSize=49581) +State is 98 bytes for feature KLanguageModel test/example/nc-wmt11.en.srilm.gz +feature: RuleIdentityFeatures (no config parameters) +State is 0 bytes for feature RuleIdentityFeatures +feature: RuleNgramFeatures (no config parameters) +State is 0 bytes for feature RuleNgramFeatures +feature: RuleShape (no config parameters) + Example feature: Shape_S00000_T00000 +State is 0 bytes for feature RuleShape +Seeding random number sequence to 1072059181 + +dtrain +Parameters: + k 100 + N 4 + T 3 + scorer 'stupid_bleu' + sample from 'kbest' + filter 'uniq' + learning rate 0.0001 + gamma 0 + loss margin 0 + pairs 'XYX' + hi lo 0.1 + pair threshold 0 + select weights 'VOID' + l1 reg 0 'none' + cdec cfg 'test/example/cdec.ini' + input 'test/example/nc-wmt11.1k.gz' + output '-' + stop_after 10 +(a dot represents 10 inputs) +Iteration #1 of 3. + . 10 +Stopping after 10 input sentences. +WEIGHTS + Glue = -0.0293 + WordPenalty = +0.049075 + LanguageModel = +0.24345 + LanguageModel_OOV = -0.2029 + PhraseModel_0 = +0.0084102 + PhraseModel_1 = +0.021729 + PhraseModel_2 = +0.014922 + PhraseModel_3 = +0.104 + PhraseModel_4 = -0.14308 + PhraseModel_5 = +0.0247 + PhraseModel_6 = -0.012 + PassThrough = -0.2161 + --- + 1best avg score: 0.16872 (+0.16872) + 1best avg model score: -1.8276 (-1.8276) + avg # pairs: 1121.1 + avg # rank err: 555.6 + avg # margin viol: 0 + non0 feature count: 277 + avg list sz: 77.2 + avg f count: 90.96 +(time 0.1 min, 0.6 s/S) + +Iteration #2 of 3. + . 10 +WEIGHTS + Glue = -0.3526 + WordPenalty = +0.067576 + LanguageModel = +1.155 + LanguageModel_OOV = -0.2728 + PhraseModel_0 = -0.025529 + PhraseModel_1 = +0.095869 + PhraseModel_2 = +0.094567 + PhraseModel_3 = +0.12482 + PhraseModel_4 = -0.36533 + PhraseModel_5 = +0.1068 + PhraseModel_6 = -0.1517 + PassThrough = -0.286 + --- + 1best avg score: 0.18394 (+0.015221) + 1best avg model score: 3.205 (+5.0326) + avg # pairs: 1168.3 + avg # rank err: 594.8 + avg # margin viol: 0 + non0 feature count: 543 + avg list sz: 77.5 + avg f count: 85.916 +(time 0.083 min, 0.5 s/S) + +Iteration #3 of 3. + . 10 +WEIGHTS + Glue = -0.392 + WordPenalty = +0.071963 + LanguageModel = +0.81266 + LanguageModel_OOV = -0.4177 + PhraseModel_0 = -0.2649 + PhraseModel_1 = -0.17931 + PhraseModel_2 = +0.038261 + PhraseModel_3 = +0.20261 + PhraseModel_4 = -0.42621 + PhraseModel_5 = +0.3198 + PhraseModel_6 = -0.1437 + PassThrough = -0.4309 + --- + 1best avg score: 0.2962 (+0.11225) + 1best avg model score: -36.274 (-39.479) + avg # pairs: 1109.6 + avg # rank err: 515.9 + avg # margin viol: 0 + non0 feature count: 741 + avg list sz: 77 + avg f count: 88.982 +(time 0.083 min, 0.5 s/S) + +Writing weights file to '-' ... +done + +--- +Best iteration: 3 [SCORE 'stupid_bleu'=0.2962]. +This took 0.26667 min. |