optional support for doing perfect hashing of feature strings to save lots of memory

1 files changed, 89 insertions, 43 deletions

diff --git a/utils/weights.cc b/utils/weights.cc
index b994a2fe..0916b72a 100644
--- a/utils/weights.cc
+++ b/utils/weights.cc
@@ -13,40 +13,75 @@ void Weights::InitFromFile(const std::string& filename, vector<string>* feature_
   ReadFile in_file(filename);
   istream& in = *in_file.stream();
   assert(in);
-  int weight_count = 0;
-  bool fl = false;
-  string buf;
-  double val = 0;
-  while (in) {
-    getline(in, buf);
-    if (buf.size() == 0) continue;
-    if (buf[0] == '#') continue;
-    for (int i = 0; i < buf.size(); ++i)
-      if (buf[i] == '=') buf[i] = ' ';
-    int start = 0;
-    while(start < buf.size() && buf[start] == ' ') ++start;
-    int end = 0;
-    while(end < buf.size() && buf[end] != ' ') ++end;
-    const int fid = FD::Convert(buf.substr(start, end - start));
-    while(end < buf.size() && buf[end] == ' ') ++end;
-    val = strtod(&buf.c_str()[end], NULL);
-    if (isnan(val)) {
-      cerr << FD::Convert(fid) << " has weight NaN!\n";
-     abort();
+  
+  bool read_text = true;
+  if (1) {
+    ReadFile hdrrf(filename);
+    istream& hi = *hdrrf.stream();
+    assert(hi);
+    char buf[10];
+    hi.get(buf, 6);
+    assert(hi.good());
+    if (strncmp(buf, "_PHWf", 5) == 0) {
+      read_text = false;
+    }
+  }
+
+  if (read_text) {
+    int weight_count = 0;
+    bool fl = false;
+    string buf;
+    weight_t val = 0;
+    while (in) {
+      getline(in, buf);
+      if (buf.size() == 0) continue;
+      if (buf[0] == '#') continue;
+      if (buf[0] == ' ') {
+        cerr << "Weights file lines may not start with whitespace.\n" << buf << endl;
+        abort();
+      }
+      for (int i = buf.size() - 1; i > 0; --i)
+        if (buf[i] == '=' || buf[i] == '\t') { buf[i] = ' '; break; }
+      int start = 0;
+      while(start < buf.size() && buf[start] == ' ') ++start;
+      int end = 0;
+      while(end < buf.size() && buf[end] != ' ') ++end;
+      const int fid = FD::Convert(buf.substr(start, end - start));
+      while(end < buf.size() && buf[end] == ' ') ++end;
+      val = strtod(&buf.c_str()[end], NULL);
+      if (isnan(val)) {
+        cerr << FD::Convert(fid) << " has weight NaN!\n";
+        abort();
+      }
+      if (wv_.size() <= fid)
+        wv_.resize(fid + 1);
+      wv_[fid] = val;
+      if (feature_list) { feature_list->push_back(FD::Convert(fid)); }
+      ++weight_count;
+      if (!SILENT) {
+        if (weight_count %   50000 == 0) { cerr << '.' << flush; fl = true; }
+        if (weight_count % 2000000 == 0) { cerr << " [" << weight_count << "]\n"; fl = false; }
+      }
     }
-    if (wv_.size() <= fid)
-      wv_.resize(fid + 1);
-    wv_[fid] = val;
-    if (feature_list) { feature_list->push_back(FD::Convert(fid)); }
-    ++weight_count;
     if (!SILENT) {
-      if (weight_count %   50000 == 0) { cerr << '.' << flush; fl = true; }
-      if (weight_count % 2000000 == 0) { cerr << " [" << weight_count << "]\n"; fl = false; }
+      if (fl) { cerr << endl; }
+      cerr << "Loaded " << weight_count << " feature weights\n";
+    }
+  } else {   // !read_text
+    char buf[6];
+    in.get(buf, 6);
+    size_t num_keys[2];
+    in.get(reinterpret_cast<char*>(&num_keys[0]), sizeof(size_t) + 1);
+    if (num_keys[0] != FD::NumFeats()) {
+      cerr << "Hash function reports " << FD::NumFeats() << " keys but weights file contains " << num_keys[0] << endl;
+      abort();
+    }
+    wv_.resize(num_keys[0]);
+    in.get(reinterpret_cast<char*>(&wv_[0]), num_keys[0] * sizeof(weight_t));
+    if (!in.good()) {
+      cerr << "Error loading weights!\n";
+      abort();
     }
-  }
-  if (!SILENT) {
-    if (fl) { cerr << endl; }
-    cerr << "Loaded " << weight_count << " feature weights\n";
   }
 }
 
@@ -54,37 +89,48 @@ void Weights::WriteToFile(const std::string& fname, bool hide_zero_value_feature
   WriteFile out(fname);
   ostream& o = *out.stream();
   assert(o);
-  if (extra) { o << "# " << *extra << endl; }
-  o.precision(17);
-  const int num_feats = FD::NumFeats();
-  for (int i = 1; i < num_feats; ++i) {
-    const double val = (i < wv_.size() ? wv_[i] : 0.0);
-    if (hide_zero_value_features && val == 0.0) continue;
-    o << FD::Convert(i) << ' ' << val << endl;
+  bool write_text = !FD::UsingPerfectHashFunction();
+
+  if (write_text) {
+    if (extra) { o << "# " << *extra << endl; }
+    o.precision(17);
+    const int num_feats = FD::NumFeats();
+    for (int i = 1; i < num_feats; ++i) {
+      const weight_t val = (i < wv_.size() ? wv_[i] : 0.0);
+      if (hide_zero_value_features && val == 0.0) continue;
+      o << FD::Convert(i) << ' ' << val << endl;
+    }
+  } else {
+    o.write("_PHWf", 5);
+    const size_t keys = FD::NumFeats();
+    assert(keys <= wv_.size());
+    o.write(reinterpret_cast<const char*>(&keys), sizeof(keys));
+    o.write(reinterpret_cast<const char*>(&wv_[0]), keys * sizeof(weight_t));
   }
 }
 
-void Weights::InitVector(std::vector<double>* w) const {
+void Weights::InitVector(std::vector<weight_t>* w) const {
   *w = wv_;
 }
 
-void Weights::InitSparseVector(SparseVector<double>* w) const {
+void Weights::InitSparseVector(SparseVector<weight_t>* w) const {
   for (int i = 1; i < wv_.size(); ++i) {
-    const double& weight = wv_[i];
+    const weight_t& weight = wv_[i];
     if (weight) w->set_value(i, weight);
   }
 }
 
-void Weights::InitFromVector(const std::vector<double>& w) {
+void Weights::InitFromVector(const std::vector<weight_t>& w) {
   wv_ = w;
   if (wv_.size() > FD::NumFeats())
     cerr << "WARNING: initializing weight vector has more features than the global feature dictionary!\n";
   wv_.resize(FD::NumFeats(), 0);
 }
 
-void Weights::InitFromVector(const SparseVector<double>& w) {
+void Weights::InitFromVector(const SparseVector<weight_t>& w) {
   wv_.clear();
   wv_.resize(FD::NumFeats(), 0.0);
   for (int i = 1; i < FD::NumFeats(); ++i)
     wv_[i] = w.value(i);
 }
+