diff options
Diffstat (limited to 'training')
| -rw-r--r-- | training/Makefile.am | 30 | ||||
| -rw-r--r-- | training/candidate_set.cc | 169 | ||||
| -rw-r--r-- | training/candidate_set.h | 53 | ||||
| -rw-r--r-- | training/kbest_repository.cc | 37 | ||||
| -rw-r--r-- | training/kbest_repository.h | 19 |
5 files changed, 240 insertions, 68 deletions
diff --git a/training/Makefile.am b/training/Makefile.am index 991ac210..8124b107 100644 --- a/training/Makefile.am +++ b/training/Makefile.am @@ -23,11 +23,17 @@ noinst_PROGRAMS = \ TESTS = lbfgs_test optimize_test -mpi_online_optimize_SOURCES = mpi_online_optimize.cc online_optimizer.cc -mpi_online_optimize_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 +noinst_LIBRARIES = libtraining.a +libtraining_a_SOURCES = \ + candidate_set.cc \ + optimize.cc \ + online_optimizer.cc -mpi_flex_optimize_SOURCES = mpi_flex_optimize.cc online_optimizer.cc optimize.cc -mpi_flex_optimize_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 +mpi_online_optimize_SOURCES = mpi_online_optimize.cc +mpi_online_optimize_LDADD = libtraining.a $(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 + +mpi_flex_optimize_SOURCES = mpi_flex_optimize.cc +mpi_flex_optimize_LDADD = libtraining.a $(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 mpi_extract_reachable_SOURCES = mpi_extract_reachable.cc mpi_extract_reachable_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 @@ -35,8 +41,8 @@ mpi_extract_reachable_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mtev mpi_extract_features_SOURCES = mpi_extract_features.cc mpi_extract_features_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 -mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc optimize.cc -mpi_batch_optimize_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 +mpi_batch_optimize_SOURCES = mpi_batch_optimize.cc +mpi_batch_optimize_LDADD = libtraining.a $(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 mpi_compute_cllh_SOURCES = mpi_compute_cllh.cc mpi_compute_cllh_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 @@ -50,14 +56,14 @@ test_ngram_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/mteval/libmteva model1_SOURCES = model1.cc ttables.cc model1_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz -lbl_model_SOURCES = lbl_model.cc optimize.cc -lbl_model_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 grammar_convert_SOURCES = grammar_convert.cc grammar_convert_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz -optimize_test_SOURCES = optimize_test.cc optimize.cc online_optimizer.cc -optimize_test_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz +optimize_test_SOURCES = optimize_test.cc +optimize_test_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz collapse_weights_SOURCES = collapse_weights.cc collapse_weights_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz @@ -65,8 +71,8 @@ collapse_weights_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/lib lbfgs_test_SOURCES = lbfgs_test.cc lbfgs_test_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz -mr_optimize_reduce_SOURCES = mr_optimize_reduce.cc optimize.cc -mr_optimize_reduce_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz +mr_optimize_reduce_SOURCES = mr_optimize_reduce.cc +mr_optimize_reduce_LDADD = libtraining.a $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz mr_em_map_adapter_SOURCES = mr_em_map_adapter.cc mr_em_map_adapter_LDADD = $(top_srcdir)/decoder/libcdec.a $(top_srcdir)/utils/libutils.a -lz diff --git a/training/candidate_set.cc b/training/candidate_set.cc new file mode 100644 index 00000000..5ab4558a --- /dev/null +++ b/training/candidate_set.cc @@ -0,0 +1,169 @@ +#include "candidate_set.h" + +#include <tr1/unordered_set> + +#include <boost/functional/hash.hpp> + +#include "ns.h" +#include "filelib.h" +#include "wordid.h" +#include "tdict.h" +#include "hg.h" +#include "kbest.h" +#include "viterbi.h" + +using namespace std; + +namespace training { + +struct ApproxVectorHasher { + static const size_t MASK = 0xFFFFFFFFull; + union UType { + double f; // leave as double + size_t i; + }; + static inline double round(const double x) { + UType t; + t.f = x; + size_t r = t.i & MASK; + if ((r << 1) > MASK) + t.i += MASK - r + 1; + else + t.i &= (1ull - MASK); + return t.f; + } + size_t operator()(const SparseVector<double>& x) const { + size_t h = 0x573915839; + for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) { + UType t; + t.f = it->second; + if (t.f) { + size_t z = (t.i >> 32); + boost::hash_combine(h, it->first); + boost::hash_combine(h, z); + } + } + return h; + } +}; + +struct ApproxVectorEquals { + bool operator()(const SparseVector<double>& a, const SparseVector<double>& b) const { + SparseVector<double>::const_iterator bit = b.begin(); + for (SparseVector<double>::const_iterator ait = a.begin(); ait != a.end(); ++ait) { + if (bit == b.end() || + ait->first != bit->first || + ApproxVectorHasher::round(ait->second) != ApproxVectorHasher::round(bit->second)) + return false; + ++bit; + } + if (bit != b.end()) return false; + return true; + } +}; + +double Candidate::g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const { + if (g_ == -100.0f) { + SufficientStats ss; + scorer.Evaluate(ewords, &ss); + g_ = metric->ComputeScore(ss); + } + return g_; +} + +struct CandidateCompare { + bool operator()(const Candidate& a, const Candidate& b) const { + ApproxVectorEquals eq; + return (a.ewords == b.ewords && eq(a.fmap,b.fmap)); + } +}; + +struct CandidateHasher { + size_t operator()(const Candidate& x) const { + boost::hash<vector<WordID> > hhasher; + ApproxVectorHasher vhasher; + size_t ha = hhasher(x.ewords); + boost::hash_combine(ha, vhasher(x.fmap)); + return ha; + } +}; + +void CandidateSet::WriteToFile(const string& file) const { + WriteFile wf(file); + ostream& out = *wf.stream(); + out.precision(10); + for (unsigned i = 0; i < cs.size(); ++i) { + out << TD::GetString(cs[i].ewords) << endl; + out << cs[i].fmap << endl; + } +} + +static void ParseSparseVector(string& line, size_t cur, SparseVector<double>* out) { + SparseVector<double>& x = *out; + size_t last_start = cur; + size_t last_comma = string::npos; + while(cur <= line.size()) { + if (line[cur] == ' ' || cur == line.size()) { + if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) { + cerr << "[ERROR] " << line << endl << " position = " << cur << endl; + exit(1); + } + const int fid = FD::Convert(line.substr(last_start, last_comma - last_start)); + if (cur < line.size()) line[cur] = 0; + const double val = strtod(&line[last_comma + 1], NULL); + x.set_value(fid, val); + + last_comma = string::npos; + last_start = cur+1; + } else { + if (line[cur] == '=') + last_comma = cur; + } + ++cur; + } +} + +void CandidateSet::ReadFromFile(const string& file) { + cerr << "Reading candidates from " << file << endl; + ReadFile rf(file); + istream& in = *rf.stream(); + string cand; + string feats; + while(getline(in, cand)) { + getline(in, feats); + assert(in); + cs.push_back(Candidate()); + TD::ConvertSentence(cand, &cs.back().ewords); + ParseSparseVector(feats, 0, &cs.back().fmap); + } + cerr << " read " << cs.size() << " candidates\n"; +} + +void CandidateSet::Dedup() { + cerr << "Dedup in=" << cs.size(); + tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare> u; + while(cs.size() > 0) { + u.insert(cs.back()); + cs.pop_back(); + } + tr1::unordered_set<Candidate, CandidateHasher, CandidateCompare>::iterator it = u.begin(); + while (it != u.end()) { + cs.push_back(*it); + it = u.erase(it); + } + cerr << " out=" << cs.size() << endl; +} + +void CandidateSet::AddKBestCandidates(const Hypergraph& hg, size_t kbest_size) { + KBest::KBestDerivations<vector<WordID>, ESentenceTraversal> kbest(hg, kbest_size); + + for (unsigned i = 0; i < kbest_size; ++i) { + const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal>::Derivation* d = + kbest.LazyKthBest(hg.nodes_.size() - 1, i); + if (!d) break; + cs.push_back(Candidate(d->yield, d->feature_values)); + } + Dedup(); +} + +} diff --git a/training/candidate_set.h b/training/candidate_set.h new file mode 100644 index 00000000..e2b0b1ba --- /dev/null +++ b/training/candidate_set.h @@ -0,0 +1,53 @@ +#ifndef _CANDIDATE_SET_H_ +#define _CANDIDATE_SET_H_ + +#include <vector> +#include <algorithm> + +#include "wordid.h" +#include "sparse_vector.h" + +class Hypergraph; +struct SegmentEvaluator; +struct EvaluationMetric; + +namespace training { + +struct Candidate { + Candidate() : g_(-100.0f) {} + Candidate(const std::vector<WordID>& e, const SparseVector<double>& fm) : ewords(e), fmap(fm), g_(-100.0f) {} + std::vector<WordID> ewords; + SparseVector<double> fmap; + double g(const SegmentEvaluator& scorer, const EvaluationMetric* metric) const; + void swap(Candidate& other) { + std::swap(g_, other.g_); + ewords.swap(other.ewords); + fmap.swap(other.fmap); + } + private: + mutable float g_; + //SufficientStats score_stats; +}; + +// represents some kind of collection of translation candidates, e.g. +// aggregated k-best lists, sample lists, etc. +class CandidateSet { + public: + CandidateSet() {} + inline size_t size() const { return cs.size(); } + const Candidate& operator[](size_t i) const { return cs[i]; } + + void ReadFromFile(const std::string& file); + void WriteToFile(const std::string& file) const; + void AddKBestCandidates(const Hypergraph& hg, size_t kbest_size); + // TODO add code to do unique k-best + // TODO add code to draw k samples + + private: + void Dedup(); + std::vector<Candidate> cs; +}; + +} + +#endif diff --git a/training/kbest_repository.cc b/training/kbest_repository.cc deleted file mode 100644 index 145b40a2..00000000 --- a/training/kbest_repository.cc +++ /dev/null @@ -1,37 +0,0 @@ -#include "kbest_repository.h" - -#include <boost/functional/hash.hpp> - -using namespace std; - -struct ApproxVectorHasher { - static const size_t MASK = 0xFFFFFFFFull; - union UType { - double f; // leave as double - size_t i; - }; - static inline double round(const double x) { - UType t; - t.f = x; - size_t r = t.i & MASK; - if ((r << 1) > MASK) - t.i += MASK - r + 1; - else - t.i &= (1ull - MASK); - return t.f; - } - size_t operator()(const SparseVector<double>& x) const { - size_t h = 0x573915839; - for (SparseVector<double>::const_iterator it = x.begin(); it != x.end(); ++it) { - UType t; - t.f = it->second; - if (t.f) { - size_t z = (t.i >> 32); - boost::hash_combine(h, it->first); - boost::hash_combine(h, z); - } - } - return h; - } -}; - diff --git a/training/kbest_repository.h b/training/kbest_repository.h deleted file mode 100644 index 0345394a..00000000 --- a/training/kbest_repository.h +++ /dev/null @@ -1,19 +0,0 @@ -#ifndef _KBEST_REPOSITORY_H_ -#define _KBEST_REPOSITORY_H_ - -#include <vector> -#include "wordid.h" -#include "ns.h" -#include "sparse_vector.h" - -class KBestRepository { - struct HypInfo { - std::vector<WordID> words; - SparseVector<double> x; - SufficientStats score_stats; - }; - - std::vector<HypInfo> candidates; -}; - -#endif |