add nplm wrapper from Kenneth's kenlm repo

lm/wrappers/README

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+This directory is for wrappers around other people's LMs, presenting an interface similar to KenLM's.  You will need to have their LM installed.
+
+NPLM is a work in progress.  

lm/wrappers/nplm.cc

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+#include "lm/wrappers/nplm.hh"
+#include "util/exception.hh"
+#include "util/file.hh"
+
+#include <algorithm>
+
+#include <string.h>
+
+#include "neuralLM.h"
+
+namespace lm {
+namespace np {
+
+Vocabulary::Vocabulary(const nplm::vocabulary &vocab) 
+  : base::Vocabulary(vocab.lookup_word("<s>"), vocab.lookup_word("</s>"), vocab.lookup_word("<unk>")),
+    vocab_(vocab), null_word_(vocab.lookup_word("<null>")) {}
+
+Vocabulary::~Vocabulary() {}
+
+WordIndex Vocabulary::Index(const std::string &str) const {
+  return vocab_.lookup_word(str);
+}
+
+bool Model::Recognize(const std::string &name) {
+  try {
+    util::scoped_fd file(util::OpenReadOrThrow(name.c_str()));
+    char magic_check[16];
+    util::ReadOrThrow(file.get(), magic_check, sizeof(magic_check));
+    const char nnlm_magic[] = "\\config\nversion ";
+    return !memcmp(magic_check, nnlm_magic, 16);
+  } catch (const util::Exception &) {
+    return false;
+  }
+} 
+
+Model::Model(const std::string &file, std::size_t cache) 
+  : base_instance_(new nplm::neuralLM(file)), vocab_(base_instance_->get_vocabulary()), cache_size_(cache) {
+  UTIL_THROW_IF(base_instance_->get_order() > NPLM_MAX_ORDER, util::Exception, "This NPLM has order " << (unsigned int)base_instance_->get_order() << " but the KenLM wrapper was compiled with " << NPLM_MAX_ORDER << ".  Change the defintion of NPLM_MAX_ORDER and recompile.");
+  // log10 compatible with backoff models.
+  base_instance_->set_log_base(10.0);
+  State begin_sentence, null_context;
+  std::fill(begin_sentence.words, begin_sentence.words + NPLM_MAX_ORDER - 1, base_instance_->lookup_word("<s>"));
+  null_word_ = base_instance_->lookup_word("<null>");
+  std::fill(null_context.words, null_context.words + NPLM_MAX_ORDER - 1, null_word_);
+
+  Init(begin_sentence, null_context, vocab_, base_instance_->get_order());
+}
+
+Model::~Model() {}
+
+FullScoreReturn Model::FullScore(const State &from, const WordIndex new_word, State &out_state) const {
+  nplm::neuralLM *lm = backend_.get();
+  if (!lm) {
+    lm = new nplm::neuralLM(*base_instance_);
+    backend_.reset(lm);
+    lm->set_cache(cache_size_);
+  }
+  // State is in natural word order.
+  FullScoreReturn ret;
+  for (int i = 0; i < lm->get_order() - 1; ++i) {
+    lm->staging_ngram()(i) = from.words[i];
+  }
+  lm->staging_ngram()(lm->get_order() - 1) = new_word;
+  ret.prob = lm->lookup_from_staging();
+  // Always say full order.
+  ret.ngram_length = lm->get_order();
+  // Shift everything down by one.
+  memcpy(out_state.words, from.words + 1, sizeof(WordIndex) * (lm->get_order() - 2));
+  out_state.words[lm->get_order() - 2] = new_word;
+  // Fill in trailing words with zeros so state comparison works.
+  memset(out_state.words + lm->get_order() - 1, 0, sizeof(WordIndex) * (NPLM_MAX_ORDER - lm->get_order()));
+  return ret;
+}
+
+// TODO: optimize with direct call?
+FullScoreReturn Model::FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const {
+  // State is in natural word order.  The API here specifies reverse order.
+  std::size_t state_length = std::min<std::size_t>(Order() - 1, context_rend - context_rbegin);
+  State state;
+  // Pad with null words.
+  for (lm::WordIndex *i = state.words; i < state.words + Order() - 1 - state_length; ++i) {
+    *i = null_word_;
+  }
+  // Put new words at the end.
+  std::reverse_copy(context_rbegin, context_rbegin + state_length, state.words + Order() - 1 - state_length);
+  return FullScore(state, new_word, out_state);
+}
+
+} // namespace np
+} // namespace lm

lm/wrappers/nplm.hh

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+#ifndef LM_WRAPPERS_NPLM_H
+#define LM_WRAPPERS_NPLM_H
+
+#include "lm/facade.hh"
+#include "lm/max_order.hh"
+#include "util/string_piece.hh"
+
+#include <boost/thread/tss.hpp>
+#include <boost/scoped_ptr.hpp>
+
+/* Wrapper to NPLM "by Ashish Vaswani, with contributions from David Chiang
+ * and Victoria Fossum."  
+ * http://nlg.isi.edu/software/nplm/
+ */
+
+namespace nplm {
+class vocabulary;
+class neuralLM;
+} // namespace nplm
+
+namespace lm {
+namespace np {
+
+class Vocabulary : public base::Vocabulary {
+  public:
+    Vocabulary(const nplm::vocabulary &vocab);
+
+    ~Vocabulary();
+
+    WordIndex Index(const std::string &str) const;
+
+    // TODO: lobby them to support StringPiece
+    WordIndex Index(const StringPiece &str) const {
+      return Index(std::string(str.data(), str.size()));
+    }
+
+    lm::WordIndex NullWord() const { return null_word_; }
+
+  private:
+    const nplm::vocabulary &vocab_;
+
+    const lm::WordIndex null_word_;
+};
+
+// Sorry for imposing my limitations on your code.
+#define NPLM_MAX_ORDER 7
+
+struct State {
+  WordIndex words[NPLM_MAX_ORDER - 1];
+};
+
+class Model : public lm::base::ModelFacade<Model, State, Vocabulary> {
+  private:
+    typedef lm::base::ModelFacade<Model, State, Vocabulary> P;
+
+  public:
+    // Does this look like an NPLM?
+    static bool Recognize(const std::string &file);
+
+    explicit Model(const std::string &file, std::size_t cache_size = 1 << 20);
+
+    ~Model();
+
+    FullScoreReturn FullScore(const State &from, const WordIndex new_word, State &out_state) const;
+
+    FullScoreReturn FullScoreForgotState(const WordIndex *context_rbegin, const WordIndex *context_rend, const WordIndex new_word, State &out_state) const;
+
+  private:
+    boost::scoped_ptr<nplm::neuralLM> base_instance_;
+
+    mutable boost::thread_specific_ptr<nplm::neuralLM> backend_;
+
+    Vocabulary vocab_;
+
+    lm::WordIndex null_word_;
+
+    const std::size_t cache_size_;
+};
+
+} // namespace np
+} // namespace lm
+
+#endif // LM_WRAPPERS_NPLM_H