Merge branch 'net' of github.com:pks/cdec-dtrain into net

5 files changed, 222 insertions, 11 deletions

diff --git a/training/dtrain/Makefile.am b/training/dtrain/Makefile.am
index 590218be..d93597c9 100644
--- a/training/dtrain/Makefile.am
+++ b/training/dtrain/Makefile.am
@@ -1,4 +1,4 @@
-bin_PROGRAMS = dtrain dtrain_net
+bin_PROGRAMS = dtrain dtrain_net dtrain_net_interface
 
 dtrain_SOURCES = dtrain.cc dtrain.h sample.h score.h update.h
 dtrain_LDADD   = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a
@@ -6,5 +6,8 @@ dtrain_LDADD   = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mte
 dtrain_net_SOURCES = dtrain_net.cc dtrain_net.h dtrain.h sample.h score.h update.h
 dtrain_net_LDADD   = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a /usr/lib64/libnanomsg.so
 
+dtrain_net_interface_SOURCES = dtrain_net_interface.cc dtrain_net.h dtrain.h sample_net.h score.h update.h
+dtrain_net_interface_LDADD   = ../../decoder/libcdec.a ../../klm/search/libksearch.a ../../mteval/libmteval.a ../../utils/libutils.a ../../klm/lm/libklm.a ../../klm/util/libklm_util.a ../../klm/util/double-conversion/libklm_util_double.a /usr/lib64/libnanomsg.so
+
 AM_CPPFLAGS = -W -Wall -Wno-sign-compare -I$(top_srcdir)/utils -I$(top_srcdir)/decoder -I$(top_srcdir)/mteval
 
diff --git a/training/dtrain/dtrain_net.cc b/training/dtrain/dtrain_net.cc
index 946b7587..306da957 100644
--- a/training/dtrain/dtrain_net.cc
+++ b/training/dtrain/dtrain_net.cc
@@ -67,16 +67,19 @@ main(int argc, char** argv)
       } else {
         vector<string> parts;
         boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
-        source = parts[0];
-        parts.erase(parts.begin());
-        for (auto s: parts) {
-          vector<WordID> r;
-          vector<string> toks;
-          boost::split(toks, s, boost::is_any_of(" "));
-          for (auto tok: toks)
-            r.push_back(TD::Convert(tok));
-          refs.emplace_back(MakeNgrams(r, N));
-          rsz.push_back(r.size());
+        if (parts[0] == "act:translate") {
+        } else {
+          source = parts[0];
+          parts.erase(parts.begin());
+          for (auto s: parts) {
+            vector<WordID> r;
+            vector<string> toks;
+            boost::split(toks, s, boost::is_any_of(" "));
+            for (auto tok: toks)
+              r.push_back(TD::Convert(tok));
+            refs.emplace_back(MakeNgrams(r, N));
+            rsz.push_back(r.size());
+          }
         }
       }
     }
diff --git a/training/dtrain/dtrain_net_interface.cc b/training/dtrain/dtrain_net_interface.cc
new file mode 100644
index 00000000..f484b56b
--- /dev/null
+++ b/training/dtrain/dtrain_net_interface.cc
@@ -0,0 +1,120 @@
+#include "dtrain_net.h"
+#include "sample_net.h"
+#include "score.h"
+#include "update.h"
+
+#include <nanomsg/nn.h>
+#include <nanomsg/pair.h>
+#include "nn.hpp"
+
+using namespace dtrain;
+
+int
+main(int argc, char** argv)
+{
+  // get configuration
+  po::variables_map conf;
+  if (!dtrain_net_init(argc, argv, &conf))
+    exit(1); // something is wrong
+  const size_t k              = conf["k"].as<size_t>();
+  const size_t N              = conf["N"].as<size_t>();
+  const weight_t margin       = conf["margin"].as<weight_t>();
+  const string master_addr    = conf["addr"].as<string>();
+
+  // setup decoder
+  register_feature_functions();
+  SetSilent(true);
+  ReadFile f(conf["decoder_conf"].as<string>());
+  Decoder decoder(f.stream());
+  ScoredKbest* observer = new ScoredKbest(k, new PerSentenceBleuScorer(N));
+
+  // weights
+  vector<weight_t>& decoder_weights = decoder.CurrentWeightVector();
+  SparseVector<weight_t> lambdas, w_average;
+  if (conf.count("input_weights")) {
+    Weights::InitFromFile(conf["input_weights"].as<string>(), &decoder_weights);
+    Weights::InitSparseVector(decoder_weights, &lambdas);
+  }
+
+  cerr << _p4;
+  // output configuration
+  cerr << "dtrain_net" << endl << "Parameters:" << endl;
+  cerr << setw(25) << "k " << k << endl;
+  cerr << setw(25) << "N " << N << endl;
+  cerr << setw(25) << "margin " << margin << endl;
+  cerr << setw(25) << "decoder conf " << "'"
+       << conf["decoder_conf"].as<string>() << "'" << endl;
+
+  // socket
+  nn::socket sock(AF_SP, NN_PAIR);
+  sock.connect(master_addr.c_str());
+
+  size_t i = 0;
+  while(true)
+  {
+    char *buf = NULL;
+    string source;
+    vector<Ngrams> refs;
+    vector<size_t> rsz;
+    bool next = true;
+    size_t sz = sock.recv(&buf, NN_MSG, 0);
+    if (buf) {
+      const string in(buf, buf+sz);
+      nn::freemsg(buf);
+      if (in == "shutdown") {
+        next = false;
+      } else {
+        vector<string> parts;
+        boost::algorithm::split_regex(parts, in, boost::regex(" \\|\\|\\| "));
+        if (parts[0] == "act:translate") {
+          cerr << "translating ..." << endl;
+          lambdas.init_vector(&decoder_weights);
+          observer->dont_score = true;
+          decoder.Decode(parts[1], observer);
+          observer->dont_score = false;
+          vector<ScoredHyp>* samples = observer->GetSamples();
+          ostringstream os;
+          PrintWordIDVec((*samples)[0].w, os);
+          sock.send(os.str().c_str(), os.str().size()+1, 0);
+          cerr << "done" << endl;
+          continue;
+        } else {
+          cerr << "learning ..." << endl;
+          source = parts[0];
+          parts.erase(parts.begin());
+          for (auto s: parts) {
+            vector<WordID> r;
+            vector<string> toks;
+            boost::split(toks, s, boost::is_any_of(" "));
+            for (auto tok: toks)
+              r.push_back(TD::Convert(tok));
+            refs.emplace_back(MakeNgrams(r, N));
+            rsz.push_back(r.size());
+          }
+        }
+      }
+    }
+    
+    if (!next)
+      break;
+
+    // decode
+    lambdas.init_vector(&decoder_weights);
+    observer->SetReference(refs, rsz);
+    decoder.Decode(source, observer);
+    vector<ScoredHyp>* samples = observer->GetSamples();
+
+    // get pairs and update
+    SparseVector<weight_t> updates;
+    CollectUpdates(samples, updates, margin);
+    lambdas.plus_eq_v_times_s(updates, 1.0); // fixme
+    string s = "x";
+    sock.send(s.c_str(), s.size()+1, 0);
+    i++;
+
+    cerr << "done" << endl;
+  } // input loop
+
+  return 0;
+}
+
diff --git a/training/dtrain/feed1.rb b/training/dtrain/feed1.rb
new file mode 100755
index 00000000..a76e4c1e
--- /dev/null
+++ b/training/dtrain/feed1.rb
@@ -0,0 +1,24 @@
+#!/usr/bin/env ruby
+
+require 'nanomsg'
+
+#port = ARGV[0]
+port = 60667
+sock = NanoMsg::PairSocket.new
+addr = "tcp://127.0.0.1:#{port}"
+#addr = "ipc:///tmp/xxx.ipc"
+sock.bind addr
+
+#puts sock.recv
+while true
+  line = STDIN.gets
+  if !line
+    sock.send 'shutdown'
+    break
+  end
+  sock.send line.strip
+  sleep 1
+  puts "got translation: #{sock.recv}\n\n"
+  #sock.send "a=1 b=2"
+end
+
diff --git a/training/dtrain/sample_net.h b/training/dtrain/sample_net.h
new file mode 100644
index 00000000..497149d9
--- /dev/null
+++ b/training/dtrain/sample_net.h
@@ -0,0 +1,61 @@
+#ifndef _DTRAIN_SAMPLE_NET_H_
+#define _DTRAIN_SAMPLE_NET_H_
+
+#include "kbest.h"
+
+#include "score.h"
+
+namespace dtrain
+{
+
+struct ScoredKbest : public DecoderObserver
+{
+  const size_t k_;
+  size_t feature_count_, effective_sz_;
+  vector<ScoredHyp> samples_;
+  PerSentenceBleuScorer* scorer_;
+  vector<Ngrams>* ref_ngs_;
+  vector<size_t>* ref_ls_;
+  bool dont_score;
+
+  ScoredKbest(const size_t k, PerSentenceBleuScorer* scorer) :
+    k_(k), scorer_(scorer), dont_score(false) {}
+
+  virtual void
+  NotifyTranslationForest(const SentenceMetadata& smeta, Hypergraph* hg)
+  {
+    samples_.clear(); effective_sz_ = feature_count_ = 0;
+    KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
+      KBest::FilterUnique, prob_t, EdgeProb> kbest(*hg, k_);
+    for (size_t i = 0; i < k_; ++i) {
+      const KBest::KBestDerivations<vector<WordID>, ESentenceTraversal,
+            KBest::FilterUnique, prob_t, EdgeProb>::Derivation* d =
+              kbest.LazyKthBest(hg->nodes_.size() - 1, i);
+      if (!d) break;
+      ScoredHyp h;
+      h.w = d->yield;
+      h.f = d->feature_values;
+      h.model = log(d->score);
+      h.rank = i;
+      if (!dont_score)
+        h.gold = scorer_->Score(h.w, *ref_ngs_, *ref_ls_);
+      samples_.push_back(h);
+      effective_sz_++;
+      feature_count_ += h.f.size();
+    }
+  }
+
+  vector<ScoredHyp>* GetSamples() { return &samples_; }
+  inline void SetReference(vector<Ngrams>& ngs, vector<size_t>& ls)
+  {
+    ref_ngs_ = &ngs;
+    ref_ls_ = &ls;
+  }
+  inline size_t GetFeatureCount() { return feature_count_; }
+  inline size_t GetSize() { return effective_sz_; }
+};
+
+} // namespace
+
+#endif
+