From 14e02978fcbbe9ec6ed3faa232fcb3f30664f40e Mon Sep 17 00:00:00 2001
From: Kenneth Heafield <github@kheafield.com>
Date: Mon, 27 Jan 2014 16:51:35 -0800
Subject: KenLM 5cc905bc2d214efa7de2db56a9a672b749a95591

Avoid unspecified behavior of mmap when a file is resized reported by Christian Hardmeier
Fixes for Mavericks and a workaround for Boost's broken semaphore
Clean clang compile (of kenlm)

Merged some of 744376b3fbebc41c4a270bf549826d5eb9219ae0 but also undid some of it because it was just masking a fundaemntal problem with pread rather than working around windows limitations
---
 lm/model.cc | 84 ++++++++++++++++++++++++++++++++++++++-----------------------
 1 file changed, 52 insertions(+), 32 deletions(-)

(limited to 'lm/model.cc')

diff --git a/lm/model.cc b/lm/model.cc
index a26654a6f..a5a16bf8e 100644
--- a/lm/model.cc
+++ b/lm/model.cc
@@ -34,23 +34,17 @@ template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT
   if (static_cast<std::size_t>(start - static_cast<uint8_t*>(base)) != goal_size) UTIL_THROW(FormatLoadException, "The data structures took " << (start - static_cast<uint8_t*>(base)) << " but Size says they should take " << goal_size);
 }
 
-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();
-  typename Search::Node ignored_node;
-  bool ignored_independent_left;
-  uint64_t ignored_extend_left;
-  begin_sentence.backoff[0] = search_.LookupUnigram(begin_sentence.words[0], ignored_node, ignored_independent_left, ignored_extend_left).Backoff();
-  State null_context = State();
-  null_context.length = 0;
-  P::Init(begin_sentence, null_context, vocab_, search_.Order());
+namespace {
+void ComplainAboutARPA(const Config &config, ModelType model_type) {
+  if (config.write_mmap || !config.messages) return;
+  if (config.arpa_complain == Config::ALL) {
+    *config.messages << "Loading the LM will be faster if you build a binary file." << std::endl;
+  } else if (config.arpa_complain == Config::EXPENSIVE &&
+             (model_type == TRIE || model_type == QUANT_TRIE || model_type == ARRAY_TRIE || model_type == QUANT_ARRAY_TRIE)) {
+    *config.messages << "Building " << kModelNames[model_type] << " from ARPA is expensive.  Save time by building a binary format." << std::endl;
+  }
 }
 
-namespace {
 void CheckCounts(const std::vector<uint64_t> &counts) {
   UTIL_THROW_IF(counts.size() > KENLM_MAX_ORDER, FormatLoadException, "This model has order " << counts.size() << " but KenLM was compiled to support up to " << KENLM_MAX_ORDER << ".  " << KENLM_ORDER_MESSAGE);
   if (sizeof(uint64_t) > sizeof(std::size_t)) {
@@ -59,18 +53,45 @@ void CheckCounts(const std::vector<uint64_t> &counts) {
     }
   }
 }
+
 } // namespace
 
-template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::InitializeFromBinary(void *start, const Parameters &params, const Config &config, int fd) {
-  CheckCounts(params.counts);
-  SetupMemory(start, params.counts, config);
-  vocab_.LoadedBinary(params.fixed.has_vocabulary, fd, config.enumerate_vocab);
-  search_.LoadedBinary();
+template <class Search, class VocabularyT> GenericModel<Search, VocabularyT>::GenericModel(const char *file, const Config &init_config) : backing_(init_config) {
+  util::scoped_fd fd(util::OpenReadOrThrow(file));
+  if (IsBinaryFormat(fd.get())) {
+    Parameters parameters;
+    int fd_shallow = fd.release();
+    backing_.InitializeBinary(fd_shallow, kModelType, kVersion, parameters);
+    CheckCounts(parameters.counts);
+
+    Config new_config(init_config);
+    new_config.probing_multiplier = parameters.fixed.probing_multiplier;
+    Search::UpdateConfigFromBinary(backing_, parameters.counts, VocabularyT::Size(parameters.counts[0], new_config), new_config);
+    UTIL_THROW_IF(new_config.enumerate_vocab && !parameters.fixed.has_vocabulary, FormatLoadException, "The decoder requested all the vocabulary strings, but this binary file does not have them.  You may need to rebuild the binary file with an updated version of build_binary.");
+
+    SetupMemory(backing_.LoadBinary(Size(parameters.counts, new_config)), parameters.counts, new_config);
+    vocab_.LoadedBinary(parameters.fixed.has_vocabulary, fd_shallow, new_config.enumerate_vocab, backing_.VocabStringReadingOffset());
+  } else {
+    ComplainAboutARPA(init_config, kModelType);
+    InitializeFromARPA(fd.release(), file, init_config);
+  }
+
+  // g++ prints warnings unless these are fully initialized.
+  State begin_sentence = State();
+  begin_sentence.length = 1;
+  begin_sentence.words[0] = vocab_.BeginSentence();
+  typename Search::Node ignored_node;
+  bool ignored_independent_left;
+  uint64_t ignored_extend_left;
+  begin_sentence.backoff[0] = search_.LookupUnigram(begin_sentence.words[0], ignored_node, ignored_independent_left, ignored_extend_left).Backoff();
+  State null_context = State();
+  null_context.length = 0;
+  P::Init(begin_sentence, null_context, vocab_, search_.Order());
 }
 
-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.ProgressMessages());
+template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT>::InitializeFromARPA(int fd, const char *file, const Config &config) {
+  // Backing file is the ARPA.
+  util::FilePiece f(fd, file, config.ProgressMessages());
   try {
     std::vector<uint64_t> counts;
     // File counts do not include pruned trigrams that extend to quadgrams etc.   These will be fixed by search_.
@@ -81,13 +102,17 @@ template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT
 
     std::size_t vocab_size = util::CheckOverflow(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);
+    vocab_.SetupMemory(backing_.SetupJustVocab(vocab_size, counts.size()), vocab_size, counts[0], config);
 
-    if (config.write_mmap) {
+    if (config.write_mmap && config.include_vocab) {
       WriteWordsWrapper wrap(config.enumerate_vocab);
       vocab_.ConfigureEnumerate(&wrap, counts[0]);
       search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_);
-      wrap.Write(backing_.file.get(), backing_.vocab.size() + vocab_.UnkCountChangePadding() + Search::Size(counts, config));
+      void *vocab_rebase, *search_rebase;
+      backing_.WriteVocabWords(wrap.Buffer(), vocab_rebase, search_rebase);
+      // Due to writing at the end of file, mmap may have relocated data.  So remap.
+      vocab_.Relocate(vocab_rebase);
+      search_.SetupMemory(reinterpret_cast<uint8_t*>(search_rebase), counts, config);
     } else {
       vocab_.ConfigureEnumerate(config.enumerate_vocab, counts[0]);
       search_.InitializeFromARPA(file, f, counts, config, vocab_, backing_);
@@ -99,18 +124,13 @@ template <class Search, class VocabularyT> void GenericModel<Search, VocabularyT
       search_.UnknownUnigram().backoff = 0.0;
       search_.UnknownUnigram().prob = config.unknown_missing_logprob;
     }
-    FinishFile(config, kModelType, kVersion, counts, vocab_.UnkCountChangePadding(), backing_);
+    backing_.FinishFile(config, kModelType, kVersion, counts);
   } 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) {
-- 
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