KenLM dffafbf with lmplz source (but not built)

1 files changed, 320 insertions, 0 deletions

diff --git a/klm/lm/builder/pipeline.cc b/klm/lm/builder/pipeline.cc
new file mode 100644
index 00000000..14a1f721
--- /dev/null
+++ b/klm/lm/builder/pipeline.cc
@@ -0,0 +1,320 @@
+#include "lm/builder/pipeline.hh"
+
+#include "lm/builder/adjust_counts.hh"
+#include "lm/builder/corpus_count.hh"
+#include "lm/builder/initial_probabilities.hh"
+#include "lm/builder/interpolate.hh"
+#include "lm/builder/print.hh"
+#include "lm/builder/sort.hh"
+
+#include "lm/sizes.hh"
+
+#include "util/exception.hh"
+#include "util/file.hh"
+#include "util/stream/io.hh"
+
+#include <algorithm>
+#include <iostream>
+#include <vector>
+
+namespace lm { namespace builder {
+
+namespace {
+void PrintStatistics(const std::vector<uint64_t> &counts, const std::vector<Discount> &discounts) {
+  std::cerr << "Statistics:\n";
+  for (size_t i = 0; i < counts.size(); ++i) {
+    std::cerr << (i + 1) << ' ' << counts[i];
+    for (size_t d = 1; d <= 3; ++d)
+      std::cerr << " D" << d << (d == 3 ? "+=" : "=") << discounts[i].amount[d];
+    std::cerr << '\n';
+  }
+}
+
+class Master {
+  public:
+    explicit Master(const PipelineConfig &config) 
+      : config_(config), chains_(config.order), files_(config.order) {
+      config_.minimum_block = std::max(NGram::TotalSize(config_.order), config_.minimum_block);
+    }
+
+    const PipelineConfig &Config() const { return config_; }
+
+    Chains &MutableChains() { return chains_; }
+
+    template <class T> Master &operator>>(const T &worker) {
+      chains_ >> worker;
+      return *this;
+    }
+
+    // This takes the (partially) sorted ngrams and sets up for adjusted counts.
+    void InitForAdjust(util::stream::Sort<SuffixOrder, AddCombiner> &ngrams, WordIndex types) {
+      const std::size_t each_order_min = config_.minimum_block * config_.block_count;
+      // We know how many unigrams there are.  Don't allocate more than needed to them.
+      const std::size_t min_chains = (config_.order - 1) * each_order_min +
+        std::min(types * NGram::TotalSize(1), each_order_min);
+      // Do merge sort with calculated laziness.
+      const std::size_t merge_using = ngrams.Merge(std::min(config_.TotalMemory() - min_chains, ngrams.DefaultLazy()));
+
+      std::vector<uint64_t> count_bounds(1, types);
+      CreateChains(config_.TotalMemory() - merge_using, count_bounds);
+      ngrams.Output(chains_.back(), merge_using);
+
+      // Setup unigram file.  
+      files_.push_back(util::MakeTemp(config_.TempPrefix()));
+    }
+
+    // For initial probabilities, but this is generic.
+    void SortAndReadTwice(const std::vector<uint64_t> &counts, Sorts<ContextOrder> &sorts, Chains &second, util::stream::ChainConfig second_config) {
+      // Do merge first before allocating chain memory.
+      for (std::size_t i = 1; i < config_.order; ++i) {
+        sorts[i - 1].Merge(0);
+      }
+      // There's no lazy merge, so just divide memory amongst the chains.
+      CreateChains(config_.TotalMemory(), counts);
+      chains_.back().ActivateProgress();
+      chains_[0] >> files_[0].Source();
+      second_config.entry_size = NGram::TotalSize(1);
+      second.push_back(second_config);
+      second.back() >> files_[0].Source();
+      for (std::size_t i = 1; i < config_.order; ++i) {
+        util::scoped_fd fd(sorts[i - 1].StealCompleted());
+        chains_[i].SetProgressTarget(util::SizeOrThrow(fd.get()));
+        chains_[i] >> util::stream::PRead(util::DupOrThrow(fd.get()), true);
+        second_config.entry_size = NGram::TotalSize(i + 1);
+        second.push_back(second_config);
+        second.back() >> util::stream::PRead(fd.release(), true);
+      }
+    }
+
+    // There is no sort after this, so go for broke on lazy merging.
+    template <class Compare> void MaximumLazyInput(const std::vector<uint64_t> &counts, Sorts<Compare> &sorts) {
+      // Determine the minimum we can use for all the chains.
+      std::size_t min_chains = 0;
+      for (std::size_t i = 0; i < config_.order; ++i) {
+        min_chains += std::min(counts[i] * NGram::TotalSize(i + 1), static_cast<uint64_t>(config_.minimum_block));
+      }
+      std::size_t for_merge = min_chains > config_.TotalMemory() ? 0 : (config_.TotalMemory() - min_chains);
+      std::vector<std::size_t> laziness;
+      // Prioritize longer n-grams.
+      for (util::stream::Sort<SuffixOrder> *i = sorts.end() - 1; i >= sorts.begin(); --i) {
+        laziness.push_back(i->Merge(for_merge));
+        assert(for_merge >= laziness.back());
+        for_merge -= laziness.back();
+      }
+      std::reverse(laziness.begin(), laziness.end());
+
+      CreateChains(for_merge + min_chains, counts);
+      chains_.back().ActivateProgress();
+      chains_[0] >> files_[0].Source();
+      for (std::size_t i = 1; i < config_.order; ++i) {
+        sorts[i - 1].Output(chains_[i], laziness[i - 1]);
+      }
+    }
+
+    void BufferFinal(const std::vector<uint64_t> &counts) {
+      chains_[0] >> files_[0].Sink();
+      for (std::size_t i = 1; i < config_.order; ++i) {
+        files_.push_back(util::MakeTemp(config_.TempPrefix()));
+        chains_[i] >> files_[i].Sink();
+      }
+      chains_.Wait(true);
+      // Use less memory.  Because we can.
+      CreateChains(std::min(config_.sort.buffer_size * config_.order, config_.TotalMemory()), counts);
+      for (std::size_t i = 0; i < config_.order; ++i) {
+        chains_[i] >> files_[i].Source();
+      }
+    }
+
+    template <class Compare> void SetupSorts(Sorts<Compare> &sorts) {
+      sorts.Init(config_.order - 1);
+      // Unigrams don't get sorted because their order is always the same.
+      chains_[0] >> files_[0].Sink();
+      for (std::size_t i = 1; i < config_.order; ++i) {
+        sorts.push_back(chains_[i], config_.sort, Compare(i + 1));
+      }
+      chains_.Wait(true);
+    }
+
+  private:
+    // Create chains, allocating memory to them.  Totally heuristic.  Count
+    // bounds are upper bounds on the counts or not present.
+    void CreateChains(std::size_t remaining_mem, const std::vector<uint64_t> &count_bounds) {
+      std::vector<std::size_t> assignments;
+      assignments.reserve(config_.order);
+      // Start by assigning maximum memory usage (to be refined later).
+      for (std::size_t i = 0; i < count_bounds.size(); ++i) {
+        assignments.push_back(static_cast<std::size_t>(std::min(
+            static_cast<uint64_t>(remaining_mem),
+            count_bounds[i] * static_cast<uint64_t>(NGram::TotalSize(i + 1)))));
+      }
+      assignments.resize(config_.order, remaining_mem);
+
+      // Now we know how much memory everybody wants.  How much will they get?
+      // Proportional to this.
+      std::vector<float> portions;
+      // Indices of orders that have yet to be assigned.
+      std::vector<std::size_t> unassigned;
+      for (std::size_t i = 0; i < config_.order; ++i) {
+        portions.push_back(static_cast<float>((i+1) * NGram::TotalSize(i+1)));
+        unassigned.push_back(i);
+      }
+      /*If somebody doesn't eat their full dinner, give it to the rest of the
+       * family.  Then somebody else might not eat their full dinner etc.  Ends
+       * when everybody unassigned is hungry.
+       */
+      float sum;
+      bool found_more;
+      std::vector<std::size_t> block_count(config_.order);
+      do {
+        sum = 0.0;
+        for (std::size_t i = 0; i < unassigned.size(); ++i) {
+          sum += portions[unassigned[i]];
+        }
+        found_more = false;
+        // If the proportional assignment is more than needed, give it just what it needs.
+        for (std::vector<std::size_t>::iterator i = unassigned.begin(); i != unassigned.end();) {
+          if (assignments[*i] <= remaining_mem * (portions[*i] / sum)) {
+            remaining_mem -= assignments[*i];
+            block_count[*i] = 1;
+            i = unassigned.erase(i);
+            found_more = true;
+          } else {
+            ++i;
+          }
+        }
+      } while (found_more);
+      for (std::vector<std::size_t>::iterator i = unassigned.begin(); i != unassigned.end(); ++i) {
+        assignments[*i] = remaining_mem * (portions[*i] / sum);
+        block_count[*i] = config_.block_count;
+      }
+      chains_.clear();
+      std::cerr << "Chain sizes:";
+      for (std::size_t i = 0; i < config_.order; ++i) {
+        std::cerr << ' ' << (i+1) << ":" << assignments[i];
+        chains_.push_back(util::stream::ChainConfig(NGram::TotalSize(i + 1), block_count[i], assignments[i]));
+      }
+      std::cerr << std::endl;
+    }
+
+    PipelineConfig config_;
+
+    Chains chains_;
+    // Often only unigrams, but sometimes all orders.  
+    FixedArray<util::stream::FileBuffer> files_;
+};
+
+void CountText(int text_file /* input */, int vocab_file /* output */, Master &master, uint64_t &token_count, std::string &text_file_name) {
+  const PipelineConfig &config = master.Config();
+  std::cerr << "=== 1/5 Counting and sorting n-grams ===" << std::endl;
+
+  UTIL_THROW_IF(config.TotalMemory() < config.assume_vocab_hash_size, util::Exception, "Vocab hash size estimate " << config.assume_vocab_hash_size << " exceeds total memory " << config.TotalMemory());
+  std::size_t memory_for_chain = 
+    // This much memory to work with after vocab hash table.
+    static_cast<float>(config.TotalMemory() - config.assume_vocab_hash_size) /
+    // Solve for block size including the dedupe multiplier for one block.
+    (static_cast<float>(config.block_count) + CorpusCount::DedupeMultiplier(config.order)) *
+    // Chain likes memory expressed in terms of total memory.
+    static_cast<float>(config.block_count);
+  util::stream::Chain chain(util::stream::ChainConfig(NGram::TotalSize(config.order), config.block_count, memory_for_chain));
+
+  WordIndex type_count;
+  util::FilePiece text(text_file, NULL, &std::cerr);
+  text_file_name = text.FileName();
+  CorpusCount counter(text, vocab_file, token_count, type_count, chain.BlockSize() / chain.EntrySize());
+  chain >> boost::ref(counter);
+
+  util::stream::Sort<SuffixOrder, AddCombiner> sorter(chain, config.sort, SuffixOrder(config.order), AddCombiner());
+  chain.Wait(true);
+  std::cerr << "=== 2/5 Calculating and sorting adjusted counts ===" << std::endl;
+  master.InitForAdjust(sorter, type_count);
+}
+
+void InitialProbabilities(const std::vector<uint64_t> &counts, const std::vector<Discount> &discounts, Master &master, Sorts<SuffixOrder> &primary, FixedArray<util::stream::FileBuffer> &gammas) {
+  const PipelineConfig &config = master.Config();
+  Chains second(config.order);
+
+  {
+    Sorts<ContextOrder> sorts;
+    master.SetupSorts(sorts);
+    PrintStatistics(counts, discounts);
+    lm::ngram::ShowSizes(counts);
+    std::cerr << "=== 3/5 Calculating and sorting initial probabilities ===" << std::endl;
+    master.SortAndReadTwice(counts, sorts, second, config.initial_probs.adder_in);
+  }
+
+  Chains gamma_chains(config.order);
+  InitialProbabilities(config.initial_probs, discounts, master.MutableChains(), second, gamma_chains);
+  // Don't care about gamma for 0.  
+  gamma_chains[0] >> util::stream::kRecycle;
+  gammas.Init(config.order - 1);
+  for (std::size_t i = 1; i < config.order; ++i) {
+    gammas.push_back(util::MakeTemp(config.TempPrefix()));
+    gamma_chains[i] >> gammas[i - 1].Sink();
+  }
+  // Has to be done here due to gamma_chains scope.
+  master.SetupSorts(primary);
+}
+
+void InterpolateProbabilities(const std::vector<uint64_t> &counts, Master &master, Sorts<SuffixOrder> &primary, FixedArray<util::stream::FileBuffer> &gammas) {
+  std::cerr << "=== 4/5 Calculating and writing order-interpolated probabilities ===" << std::endl;
+  const PipelineConfig &config = master.Config();
+  master.MaximumLazyInput(counts, primary);
+
+  Chains gamma_chains(config.order - 1);
+  util::stream::ChainConfig read_backoffs(config.read_backoffs);
+  read_backoffs.entry_size = sizeof(float);
+  for (std::size_t i = 0; i < config.order - 1; ++i) {
+    gamma_chains.push_back(read_backoffs);
+    gamma_chains.back() >> gammas[i].Source();
+  }
+  master >> Interpolate(counts[0], ChainPositions(gamma_chains));
+  gamma_chains >> util::stream::kRecycle;
+  master.BufferFinal(counts);
+}
+
+} // namespace
+
+void Pipeline(PipelineConfig config, int text_file, int out_arpa) {
+  // Some fail-fast sanity checks.
+  if (config.sort.buffer_size * 4 > config.TotalMemory()) {
+    config.sort.buffer_size = config.TotalMemory() / 4;
+    std::cerr << "Warning: changing sort block size to " << config.sort.buffer_size << " bytes due to low total memory." << std::endl;
+  }
+  if (config.minimum_block < NGram::TotalSize(config.order)) {
+    config.minimum_block = NGram::TotalSize(config.order);
+    std::cerr << "Warning: raising minimum block to " << config.minimum_block << " to fit an ngram in every block." << std::endl;
+  }
+  UTIL_THROW_IF(config.sort.buffer_size < config.minimum_block, util::Exception, "Sort block size " << config.sort.buffer_size << " is below the minimum block size " << config.minimum_block << ".");
+  UTIL_THROW_IF(config.TotalMemory() < config.minimum_block * config.order * config.block_count, util::Exception,
+      "Not enough memory to fit " << (config.order * config.block_count) << " blocks with minimum size " << config.minimum_block << ".  Increase memory to " << (config.minimum_block * config.order * config.block_count) << " bytes or decrease the minimum block size.");
+
+  UTIL_TIMER("(%w s) Total wall time elapsed\n");
+  Master master(config);
+
+  util::scoped_fd vocab_file(config.vocab_file.empty() ? 
+      util::MakeTemp(config.TempPrefix()) : 
+      util::CreateOrThrow(config.vocab_file.c_str()));
+  uint64_t token_count;
+  std::string text_file_name;
+  CountText(text_file, vocab_file.get(), master, token_count, text_file_name);
+
+  std::vector<uint64_t> counts;
+  std::vector<Discount> discounts;
+  master >> AdjustCounts(counts, discounts);
+
+  {
+    FixedArray<util::stream::FileBuffer> gammas;
+    Sorts<SuffixOrder> primary;
+    InitialProbabilities(counts, discounts, master, primary, gammas);
+    InterpolateProbabilities(counts, master, primary, gammas);
+  }
+
+  std::cerr << "=== 5/5 Writing ARPA model ===" << std::endl;
+  VocabReconstitute vocab(vocab_file.get());
+  UTIL_THROW_IF(vocab.Size() != counts[0], util::Exception, "Vocab words don't match up.  Is there a null byte in the input?");
+  HeaderInfo header_info(text_file_name, token_count);
+  master >> PrintARPA(vocab, counts, (config.verbose_header ? &header_info : NULL), out_arpa) >> util::stream::kRecycle;
+  master.MutableChains().Wait(true);
+}
+
+}} // namespaces