mosesdecoder/lm/model.cc
2011-11-17 12:49:55 +00:00

283 lines
12 KiB
C++

#include "lm/model.hh"
#include "lm/blank.hh"
#include "lm/lm_exception.hh"
#include "lm/search_hashed.hh"
#include "lm/search_trie.hh"
#include "lm/read_arpa.hh"
#include "util/murmur_hash.hh"
#include <algorithm>
#include <functional>
#include <numeric>
#include <cmath>
namespace lm {
namespace ngram {
namespace detail {
template <class Search, class VocabularyT> const ModelType GenericModel<Search, VocabularyT>::kModelType = Search::kModelType;
template <class Search, class VocabularyT> size_t GenericModel<Search, VocabularyT>::Size(const std::vector<uint64_t> &counts, const Config &config) {
return VocabularyT::Size(counts[0], config) + Search::Size(counts, config);
}
template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::SetupMemory(void *base, const std::vector<uint64_t> &counts, const Config &config) {
uint8_t *start = static_cast<uint8_t*>(base);
size_t allocated = VocabularyT::Size(counts[0], config);
vocab_.SetupMemory(start, allocated, counts[0], config);
start += allocated;
start = search_.SetupMemory(start, counts, config);
if (static_cast<std::size_t>(start - static_cast<uint8_t*>(base)) != Size(counts, config)) UTIL_THROW(FormatLoadException, "The data structures took " << (start - static_cast<uint8_t*>(base)) << " but Size says they should take " << Size(counts, config));
}
template <class Search, class VocabularyT> GenericModel<Search, VocabularyT>::GenericModel(const char *file, const Config &config) {
LoadLM(file, config, *this);
// g++ prints warnings unless these are fully initialized.
State begin_sentence = State();
begin_sentence.length = 1;
begin_sentence.words[0] = vocab_.BeginSentence();
begin_sentence.backoff[0] = search_.unigram.Lookup(begin_sentence.words[0]).backoff;
State null_context = State();
null_context.length = 0;
P::Init(begin_sentence, null_context, vocab_, search_.MiddleEnd() - search_.MiddleBegin() + 2);
}
template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::InitializeFromBinary(void *start, const Parameters &params, const Config &config, int fd) {
SetupMemory(start, params.counts, config);
vocab_.LoadedBinary(fd, config.enumerate_vocab);
search_.LoadedBinary();
}
template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::InitializeFromARPA(const char *file, const Config &config) {
// Backing file is the ARPA. Steal it so we can make the backing file the mmap output if any.
util::FilePiece f(backing_.file.release(), file, config.messages);
try {
std::vector<uint64_t> counts;
// File counts do not include pruned trigrams that extend to quadgrams etc. These will be fixed by search_.
ReadARPACounts(f, counts);
if (counts.size() > kMaxOrder) UTIL_THROW(FormatLoadException, "This model has order " << counts.size() << ". Edit lm/max_order.hh, set kMaxOrder to at least this value, and recompile.");
if (counts.size() < 2) UTIL_THROW(FormatLoadException, "This ngram implementation assumes at least a bigram model.");
if (config.probing_multiplier <= 1.0) UTIL_THROW(ConfigException, "probing multiplier must be > 1.0");
std::size_t vocab_size = VocabularyT::Size(counts[0], config);
// Setup the binary file for writing the vocab lookup table. The search_ is responsible for growing the binary file to its needs.
vocab_.SetupMemory(SetupJustVocab(config, counts.size(), vocab_size, backing_), vocab_size, counts[0], config);
if (config.write_mmap) {
WriteWordsWrapper wrap(config.enumerate_vocab);
vocab_.ConfigureEnumerate(&wrap, counts[0]);
search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_);
wrap.Write(backing_.file.get());
} else {
vocab_.ConfigureEnumerate(config.enumerate_vocab, counts[0]);
search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_);
}
if (!vocab_.SawUnk()) {
assert(config.unknown_missing != THROW_UP);
// Default probabilities for unknown.
search_.unigram.Unknown().backoff = 0.0;
search_.unigram.Unknown().prob = config.unknown_missing_logprob;
}
FinishFile(config, kModelType, kVersion, counts, backing_);
} catch (util::Exception &e) {
e << " Byte: " << f.Offset();
throw;
}
}
template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::UpdateConfigFromBinary(int fd, const std::vector<uint64_t> &counts, Config &config) {
util::AdvanceOrThrow(fd, VocabularyT::Size(counts[0], config));
Search::UpdateConfigFromBinary(fd, counts, config);
}
template <class Search, class VocabularyT> FullScoreReturn GenericModel<Search, VocabularyT>::FullScore(const State &in_state, const WordIndex new_word, State &out_state) const {
FullScoreReturn ret = ScoreExceptBackoff(in_state.words, in_state.words + in_state.length, new_word, out_state);
for (const float *i = in_state.backoff + ret.ngram_length - 1; i < in_state.backoff + in_state.length; ++i) {
ret.prob += *i;
}
return ret;
}
template <class Search, class VocabularyT> FullScoreReturn GenericModel<Search, VocabularyT>::FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const {
context_rend = std::min(context_rend, context_rbegin + P::Order() - 1);
FullScoreReturn ret = ScoreExceptBackoff(context_rbegin, context_rend, new_word, out_state);
// Add the backoff weights for n-grams of order start to (context_rend - context_rbegin).
unsigned char start = ret.ngram_length;
if (context_rend - context_rbegin < static_cast<std::ptrdiff_t>(start)) return ret;
if (start <= 1) {
ret.prob += search_.unigram.Lookup(*context_rbegin).backoff;
start = 2;
}
typename Search::Node node;
if (!search_.FastMakeNode(context_rbegin, context_rbegin + start - 1, node)) {
return ret;
}
float backoff;
// i is the order of the backoff we're looking for.
const Middle *mid_iter = search_.MiddleBegin() + start - 2;
for (const WordIndex *i = context_rbegin + start - 1; i < context_rend; ++i, ++mid_iter) {
if (!search_.LookupMiddleNoProb(*mid_iter, *i, backoff, node)) break;
ret.prob += backoff;
}
return ret;
}
template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::GetState(const WordIndex *context_rbegin, const WordIndex *context_rend, State &out_state) const {
// Generate a state from context.
context_rend = std::min(context_rend, context_rbegin + P::Order() - 1);
if (context_rend == context_rbegin) {
out_state.length = 0;
return;
}
FullScoreReturn ignored;
typename Search::Node node;
search_.LookupUnigram(*context_rbegin, out_state.backoff[0], node, ignored);
out_state.length = HasExtension(out_state.backoff[0]) ? 1 : 0;
float *backoff_out = out_state.backoff + 1;
const typename Search::Middle *mid = search_.MiddleBegin();
for (const WordIndex *i = context_rbegin + 1; i < context_rend; ++i, ++backoff_out, ++mid) {
if (!search_.LookupMiddleNoProb(*mid, *i, *backoff_out, node)) {
std::copy(context_rbegin, context_rbegin + out_state.length, out_state.words);
return;
}
if (HasExtension(*backoff_out)) out_state.length = i - context_rbegin + 1;
}
std::copy(context_rbegin, context_rbegin + out_state.length, out_state.words);
}
template <class Search, class VocabularyT> FullScoreReturn GenericModel<Search, VocabularyT>::ExtendLeft(
const WordIndex *add_rbegin, const WordIndex *add_rend,
const float *backoff_in,
uint64_t extend_pointer,
unsigned char extend_length,
float *backoff_out,
unsigned char &next_use) const {
FullScoreReturn ret;
float subtract_me;
typename Search::Node node(search_.Unpack(extend_pointer, extend_length, subtract_me));
ret.prob = subtract_me;
ret.ngram_length = extend_length;
next_use = 0;
// If this function is called, then it does depend on left words.
ret.independent_left = false;
ret.extend_left = extend_pointer;
const typename Search::Middle *mid_iter = search_.MiddleBegin() + extend_length - 1;
const WordIndex *i = add_rbegin;
for (; ; ++i, ++backoff_out, ++mid_iter) {
if (i == add_rend) {
// Ran out of words.
for (const float *b = backoff_in + ret.ngram_length - extend_length; b < backoff_in + (add_rend - add_rbegin); ++b) ret.prob += *b;
ret.prob -= subtract_me;
return ret;
}
if (mid_iter == search_.MiddleEnd()) break;
if (ret.independent_left || !search_.LookupMiddle(*mid_iter, *i, *backoff_out, node, ret)) {
// Didn't match a word.
ret.independent_left = true;
for (const float *b = backoff_in + ret.ngram_length - extend_length; b < backoff_in + (add_rend - add_rbegin); ++b) ret.prob += *b;
ret.prob -= subtract_me;
return ret;
}
ret.ngram_length = mid_iter - search_.MiddleBegin() + 2;
if (HasExtension(*backoff_out)) next_use = i - add_rbegin + 1;
}
if (ret.independent_left || !search_.LookupLongest(*i, ret.prob, node)) {
// The last backoff weight, for Order() - 1.
ret.prob += backoff_in[i - add_rbegin];
} else {
ret.ngram_length = P::Order();
}
ret.independent_left = true;
ret.prob -= subtract_me;
return ret;
}
namespace {
// Do a paraonoid copy of history, assuming new_word has already been copied
// (hence the -1). out_state.length could be zero so I avoided using
// std::copy.
void CopyRemainingHistory(const WordIndex *from, State &out_state) {
WordIndex *out = out_state.words + 1;
const WordIndex *in_end = from + static_cast<ptrdiff_t>(out_state.length) - 1;
for (const WordIndex *in = from; in < in_end; ++in, ++out) *out = *in;
}
} // namespace
/* Ugly optimized function. Produce a score excluding backoff.
* The search goes in increasing order of ngram length.
* Context goes backward, so context_begin is the word immediately preceeding
* new_word.
*/
template <class Search, class VocabularyT> FullScoreReturn GenericModel<Search, VocabularyT>::ScoreExceptBackoff(
const WordIndex *context_rbegin,
const WordIndex *context_rend,
const WordIndex new_word,
State &out_state) const {
FullScoreReturn ret;
// ret.ngram_length contains the last known non-blank ngram length.
ret.ngram_length = 1;
float *backoff_out(out_state.backoff);
typename Search::Node node;
search_.LookupUnigram(new_word, *backoff_out, node, ret);
// This is the length of the context that should be used for continuation to the right.
out_state.length = HasExtension(*backoff_out) ? 1 : 0;
// We'll write the word anyway since it will probably be used and does no harm being there.
out_state.words[0] = new_word;
if (context_rbegin == context_rend) return ret;
++backoff_out;
// Ok start by looking up the bigram.
const WordIndex *hist_iter = context_rbegin;
const typename Search::Middle *mid_iter = search_.MiddleBegin();
for (; ; ++mid_iter, ++hist_iter, ++backoff_out) {
if (hist_iter == context_rend) {
// Ran out of history. Typically no backoff, but this could be a blank.
CopyRemainingHistory(context_rbegin, out_state);
// ret.prob was already set.
return ret;
}
if (mid_iter == search_.MiddleEnd()) break;
if (ret.independent_left || !search_.LookupMiddle(*mid_iter, *hist_iter, *backoff_out, node, ret)) {
// Didn't find an ngram using hist_iter.
CopyRemainingHistory(context_rbegin, out_state);
// ret.prob was already set.
ret.independent_left = true;
return ret;
}
ret.ngram_length = hist_iter - context_rbegin + 2;
if (HasExtension(*backoff_out)) {
out_state.length = ret.ngram_length;
}
}
// It passed every lookup in search_.middle. All that's left is to check search_.longest.
if (!ret.independent_left && search_.LookupLongest(*hist_iter, ret.prob, node)) {
// It's an P::Order()-gram.
// There is no blank in longest_.
ret.ngram_length = P::Order();
}
// This handles (N-1)-grams and N-grams.
CopyRemainingHistory(context_rbegin, out_state);
ret.independent_left = true;
return ret;
}
template class GenericModel<ProbingHashedSearch, ProbingVocabulary>; // HASH_PROBING
template class GenericModel<trie::TrieSearch<DontQuantize, trie::DontBhiksha>, SortedVocabulary>; // TRIE_SORTED
template class GenericModel<trie::TrieSearch<DontQuantize, trie::ArrayBhiksha>, SortedVocabulary>;
template class GenericModel<trie::TrieSearch<SeparatelyQuantize, trie::DontBhiksha>, SortedVocabulary>; // TRIE_SORTED_QUANT
template class GenericModel<trie::TrieSearch<SeparatelyQuantize, trie::ArrayBhiksha>, SortedVocabulary>;
} // namespace detail
} // namespace ngram
} // namespace lm