initial checkin

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@2 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 454 insertions, 0 deletions

diff --git a/decoder/ff_lm.cc b/decoder/ff_lm.cc
new file mode 100644
index 00000000..a1dc8b81
--- /dev/null
+++ b/decoder/ff_lm.cc
@@ -0,0 +1,454 @@
+#include "ff_lm.h"
+
+#include <sstream>
+#include <unistd.h>
+#include <sys/socket.h>
+#include <sys/types.h>
+#include <netinet/in.h>
+#include <netdb.h>
+
+#include <boost/shared_ptr.hpp>
+
+#include "tdict.h"
+#include "Vocab.h"
+#include "Ngram.h"
+#include "hg.h"
+#include "stringlib.h"
+
+#ifdef HAVE_RANDLM
+#include "RandLM.h"
+#endif
+
+using namespace std;
+
+namespace NgramCache {
+  struct Cache {
+    map<WordID, Cache> tree;
+    float prob;
+    Cache() : prob() {}
+  };
+  static Cache cache_;
+  void Clear() { cache_.tree.clear(); }
+}
+
+struct LMClient {
+
+  LMClient(const char* host) : port(6666) {
+    strcpy(request_buffer, "prob ");
+    s = const_cast<char*>(strchr(host, ':'));  // TODO fix const_cast
+    if (s != NULL) {
+      *s = '\0';
+      ++s;
+      port = atoi(s);
+    }
+    sock = socket(AF_INET, SOCK_STREAM, 0);
+    hp = gethostbyname(host);
+    if (hp == NULL) {
+      cerr << "unknown host " << host << endl;
+      abort();
+    }
+    bzero((char *)&server, sizeof(server));
+    bcopy(hp->h_addr, (char *)&server.sin_addr, hp->h_length);
+    server.sin_family = hp->h_addrtype;
+    server.sin_port = htons(port);
+
+    int errors = 0;
+    while (connect(sock, (struct sockaddr *)&server, sizeof(server)) < 0) {
+      cerr << "Error: connect()\n";
+      sleep(1);
+      errors++;
+      if (errors > 3) exit(1);
+    }
+    cerr << "Connected to LM on " << host << " on port " << port << endl;
+  }
+
+  float wordProb(int word, int* context) {
+    NgramCache::Cache* cur = &NgramCache::cache_;
+    int i = 0;
+    while (context[i] > 0) {
+      cur = &cur->tree[context[i++]];
+    }
+    cur = &cur->tree[word];
+    if (cur->prob) { return cur->prob; }
+
+    i = 0;
+    int pos = TD::AppendString(word, 5, 16000, request_buffer);
+    while (context[i] > 0) {
+      assert(pos < 15995);
+      request_buffer[pos] = ' ';
+      ++pos;
+      pos = TD::AppendString(context[i], pos, 16000, request_buffer);
+      ++i;
+    }
+    assert(pos < 15999);
+    request_buffer[pos] = '\n';
+    ++pos;
+    request_buffer[pos] = 0;
+    write(sock, request_buffer, pos);
+    int r = read(sock, res, 6);
+    int errors = 0;
+    int cnt = 0;
+    while (1) {
+      if (r < 0) {
+        errors++; sleep(1);
+        cerr << "Error: read()\n";
+        if (errors > 5) exit(1);
+      } else if (r==0 || res[cnt] == '\n') { break; }
+      else {
+        cnt += r;
+        if (cnt==6) break;
+        read(sock, &res[cnt], 6-cnt);
+      }
+    }
+    cur->prob = *reinterpret_cast<float*>(res);
+    return cur->prob;
+  }
+
+ private:
+  int sock, port;
+  char *s;
+  struct hostent *hp;
+  struct sockaddr_in server;
+  char res[8];
+  char request_buffer[16000];
+};
+
+class LanguageModelImpl {
+ public:
+  explicit LanguageModelImpl(int order) :
+      ngram_(*TD::dict_, order), buffer_(), order_(order), state_size_(OrderToStateSize(order) - 1),
+      floor_(-100.0),
+      client_(),
+      kSTART(TD::Convert("<s>")),
+      kSTOP(TD::Convert("</s>")),
+      kUNKNOWN(TD::Convert("<unk>")),
+      kNONE(-1),
+      kSTAR(TD::Convert("<{STAR}>")) {}
+
+  LanguageModelImpl(int order, const string& f) :
+      ngram_(*TD::dict_, order), buffer_(), order_(order), state_size_(OrderToStateSize(order) - 1),
+      floor_(-100.0),
+      client_(NULL),
+      kSTART(TD::Convert("<s>")),
+      kSTOP(TD::Convert("</s>")),
+      kUNKNOWN(TD::Convert("<unk>")),
+      kNONE(-1),
+      kSTAR(TD::Convert("<{STAR}>")) {
+    if (f.find("lm://") == 0) {
+      client_ = new LMClient(f.substr(5).c_str());
+    } else {
+      File file(f.c_str(), "r", 0);
+      assert(file);
+      cerr << "Reading " << order_ << "-gram LM from " << f << endl;
+      ngram_.read(file, false);
+    }
+  }
+
+  virtual ~LanguageModelImpl() {
+    delete client_;
+  }
+
+  inline int StateSize(const void* state) const {
+    return *(static_cast<const char*>(state) + state_size_);
+  }
+
+  inline void SetStateSize(int size, void* state) const {
+    *(static_cast<char*>(state) + state_size_) = size;
+  }
+
+  virtual double WordProb(int word, int* context) {
+    return client_ ?
+          client_->wordProb(word, context)
+        : ngram_.wordProb(word, (VocabIndex*)context);
+  }
+
+  inline double LookupProbForBufferContents(int i) {
+//    int k = i; cerr << "P("; while(buffer_[k] > 0) { std::cerr << TD::Convert(buffer_[k++]) << " "; }
+    double p = WordProb(buffer_[i], &buffer_[i+1]);
+    if (p < floor_) p = floor_;
+//    cerr << ")=" << p << endl;
+    return p;
+  }
+
+  string DebugStateToString(const void* state) const {
+    int len = StateSize(state);
+    const int* astate = reinterpret_cast<const int*>(state);
+    string res = "[";
+    for (int i = 0; i < len; ++i) {
+      res += " ";
+      res += TD::Convert(astate[i]);
+    }
+    res += " ]";
+    return res;
+  }
+
+  inline double ProbNoRemnant(int i, int len) {
+    int edge = len;
+    bool flag = true;
+    double sum = 0.0;
+    while (i >= 0) {
+      if (buffer_[i] == kSTAR) {
+        edge = i;
+        flag = false;
+      } else if (buffer_[i] <= 0) {
+        edge = i;
+        flag = true;
+      } else {
+        if ((edge-i >= order_) || (flag && !(i == (len-1) && buffer_[i] == kSTART)))
+          sum += LookupProbForBufferContents(i);
+      }
+      --i;
+    }
+    return sum;
+  }
+
+  double EstimateProb(const vector<WordID>& phrase) {
+    int len = phrase.size();
+    buffer_.resize(len + 1);
+    buffer_[len] = kNONE;
+    int i = len - 1;
+    for (int j = 0; j < len; ++j,--i)
+      buffer_[i] = phrase[j];
+    return ProbNoRemnant(len - 1, len);
+  }
+
+  double EstimateProb(const void* state) {
+    int len = StateSize(state);
+    // cerr << "residual len: " << len << endl;
+    buffer_.resize(len + 1);
+    buffer_[len] = kNONE;
+    const int* astate = reinterpret_cast<const int*>(state);
+    int i = len - 1;
+    for (int j = 0; j < len; ++j,--i)
+      buffer_[i] = astate[j];
+    return ProbNoRemnant(len - 1, len);
+  }
+
+  double FinalTraversalCost(const void* state) {
+    int slen = StateSize(state);
+    int len = slen + 2;
+    // cerr << "residual len: " << len << endl;
+    buffer_.resize(len + 1);
+    buffer_[len] = kNONE;
+    buffer_[len-1] = kSTART;
+    const int* astate = reinterpret_cast<const int*>(state);
+    int i = len - 2;
+    for (int j = 0; j < slen; ++j,--i)
+      buffer_[i] = astate[j];
+    buffer_[i] = kSTOP;
+    assert(i == 0);
+    return ProbNoRemnant(len - 1, len);
+  }
+
+  double LookupWords(const TRule& rule, const vector<const void*>& ant_states, void* vstate) {
+    int len = rule.ELength() - rule.Arity();
+    for (int i = 0; i < ant_states.size(); ++i)
+      len += StateSize(ant_states[i]);
+    buffer_.resize(len + 1);
+    buffer_[len] = kNONE;
+    int i = len - 1;
+    const vector<WordID>& e = rule.e();
+    for (int j = 0; j < e.size(); ++j) {
+      if (e[j] < 1) {
+        const int* astate = reinterpret_cast<const int*>(ant_states[-e[j]]);
+        int slen = StateSize(astate);
+        for (int k = 0; k < slen; ++k)
+          buffer_[i--] = astate[k];
+      } else {
+        buffer_[i--] = e[j];
+      }
+    }
+
+    double sum = 0.0;
+    int* remnant = reinterpret_cast<int*>(vstate);
+    int j = 0;
+    i = len - 1;
+    int edge = len;
+
+    while (i >= 0) {
+      if (buffer_[i] == kSTAR) {
+        edge = i;
+      } else if (edge-i >= order_) {
+        sum += LookupProbForBufferContents(i);
+      } else if (edge == len && remnant) {
+        remnant[j++] = buffer_[i];
+      }
+      --i;
+    }
+    if (!remnant) return sum;
+
+    if (edge != len || len >= order_) {
+      remnant[j++] = kSTAR;
+      if (order_-1 < edge) edge = order_-1;
+      for (int i = edge-1; i >= 0; --i)
+        remnant[j++] = buffer_[i];
+    }
+
+    SetStateSize(j, vstate);
+    return sum;
+  }
+
+  static int OrderToStateSize(int order) {
+    return ((order-1) * 2 + 1) * sizeof(WordID) + 1;
+  }
+
+ protected:
+  Ngram ngram_;
+  vector<WordID> buffer_;
+  const int order_;
+  const int state_size_;
+  const double floor_;
+ private:
+  LMClient* client_;
+
+ public:
+  const WordID kSTART;
+  const WordID kSTOP;
+  const WordID kUNKNOWN;
+  const WordID kNONE;
+  const WordID kSTAR;
+};
+
+LanguageModel::LanguageModel(const string& param) :
+    fid_(FD::Convert("LanguageModel")) {
+  vector<string> argv;
+  int argc = SplitOnWhitespace(param, &argv);
+  int order = 3;
+  // TODO add support for -n FeatureName
+  string filename;
+  if (argc < 1) { cerr << "LanguageModel requires a filename, minimally!\n"; abort(); }
+  else if (argc == 1) { filename = argv[0]; }
+  else if (argc == 2 || argc > 3) { cerr << "Don't understand 'LanguageModel " << param << "'\n"; }
+  else if (argc == 3) {
+    if (argv[0] == "-o") {
+      order = atoi(argv[1].c_str());
+      filename = argv[2];
+    } else if (argv[1] == "-o") {
+      order = atoi(argv[2].c_str());
+      filename = argv[0];
+    }
+  }
+  SetStateSize(LanguageModelImpl::OrderToStateSize(order));
+  pimpl_ = new LanguageModelImpl(order, filename);
+}
+
+LanguageModel::~LanguageModel() {
+  delete pimpl_;
+}
+
+string LanguageModel::DebugStateToString(const void* state) const{
+  return pimpl_->DebugStateToString(state);
+}
+
+void LanguageModel::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                          const Hypergraph::Edge& edge,
+                                          const vector<const void*>& ant_states,
+                                          SparseVector<double>* features,
+                                          SparseVector<double>* estimated_features,
+                                          void* state) const {
+  (void) smeta;
+  features->set_value(fid_, pimpl_->LookupWords(*edge.rule_, ant_states, state));
+  estimated_features->set_value(fid_, pimpl_->EstimateProb(state));
+}
+
+void LanguageModel::FinalTraversalFeatures(const void* ant_state,
+                                           SparseVector<double>* features) const {
+  features->set_value(fid_, pimpl_->FinalTraversalCost(ant_state));
+}
+
+#ifdef HAVE_RANDLM
+struct RandLMImpl : public LanguageModelImpl {
+  RandLMImpl(int order, randlm::RandLM* rlm) :
+      LanguageModelImpl(order),
+      rlm_(rlm),
+      oov_(rlm->getWordID(rlm->getOOV())),
+      rb_(1000, oov_) {
+    map<int, randlm::WordID> map_cdec2randlm;
+    int max_wordid = 0;
+    for(map<randlm::Word, randlm::WordID>::const_iterator it = rlm->vocabStart();
+        it != rlm->vocabEnd(); ++it) {
+      const int cur = TD::Convert(it->first);
+      map_cdec2randlm[TD::Convert(it->first)] = it->second;
+      if (cur > max_wordid) max_wordid = cur;
+    }
+    cdec2randlm_.resize(max_wordid + 1, oov_);
+    for (map<int, randlm::WordID>::iterator it = map_cdec2randlm.begin();
+         it != map_cdec2randlm.end(); ++it)
+      cdec2randlm_[it->first] = it->second;
+    map_cdec2randlm.clear();
+  }
+
+  inline randlm::WordID Convert2RandLM(int w) {
+    return (w < cdec2randlm_.size() ? cdec2randlm_[w] : oov_);
+  }
+
+  virtual double WordProb(int word, int* context) {
+    int i = order_;
+    int c = 1;
+    rb_[i] = Convert2RandLM(word);
+    while (i > 1 && *context > 0) {
+      --i;
+      rb_[i] = Convert2RandLM(*context);
+      ++context;
+      ++c;
+    }
+    const void* finalState = 0;
+    int found;
+    //cerr << "I = " << i << endl;
+    return rlm_->getProb(&rb_[i], c, &found, &finalState);
+  }
+ private:
+  boost::shared_ptr<randlm::RandLM> rlm_;
+  randlm::WordID oov_;
+  vector<randlm::WordID> cdec2randlm_;
+  vector<randlm::WordID> rb_;
+};
+
+LanguageModelRandLM::LanguageModelRandLM(const string& param) :
+    fid_(FD::Convert("RandLM")) {
+  vector<string> argv;
+  int argc = SplitOnWhitespace(param, &argv);
+  int order = 3;
+  // TODO add support for -n FeatureName
+  string filename;
+  if (argc < 1) { cerr << "RandLM requires a filename, minimally!\n"; abort(); }
+  else if (argc == 1) { filename = argv[0]; }
+  else if (argc == 2 || argc > 3) { cerr << "Don't understand 'RandLM " << param << "'\n"; }
+  else if (argc == 3) {
+    if (argv[0] == "-o") {
+      order = atoi(argv[1].c_str());
+      filename = argv[2];
+    } else if (argv[1] == "-o") {
+      order = atoi(argv[2].c_str());
+      filename = argv[0];
+    }
+  }
+  SetStateSize(LanguageModelImpl::OrderToStateSize(order));
+  int cache_MB = 200; // increase cache size
+  randlm::RandLM* rlm = randlm::RandLM::initRandLM(filename, order, cache_MB);
+  assert(rlm != NULL);
+  pimpl_ = new RandLMImpl(order, rlm);
+}
+
+LanguageModelRandLM::~LanguageModelRandLM() {
+  delete pimpl_;
+}
+
+void LanguageModelRandLM::TraversalFeaturesImpl(const SentenceMetadata& smeta,
+                                          const Hypergraph::Edge& edge,
+                                          const vector<const void*>& ant_states,
+                                          SparseVector<double>* features,
+                                          SparseVector<double>* estimated_features,
+                                          void* state) const {
+  (void) smeta;
+  features->set_value(fid_, pimpl_->LookupWords(*edge.rule_, ant_states, state));
+  estimated_features->set_value(fid_, pimpl_->EstimateProb(state));
+}
+
+void LanguageModelRandLM::FinalTraversalFeatures(const void* ant_state,
+                                           SparseVector<double>* features) const {
+  features->set_value(fid_, pimpl_->FinalTraversalCost(ant_state));
+}
+
+#endif
+