starting an mpi version.

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@253 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 431 insertions, 0 deletions

diff --git a/gi/pyp-topics/src/mpi-pyp-topics.cc b/gi/pyp-topics/src/mpi-pyp-topics.cc
new file mode 100644
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--- /dev/null
+++ b/gi/pyp-topics/src/mpi-pyp-topics.cc
@@ -0,0 +1,431 @@
+#include "timing.h"
+#include "mpi-pyp-topics.hh"
+
+//#include <boost/date_time/posix_time/posix_time_types.hpp>
+void PYPTopics::sample_corpus(const Corpus& corpus, int samples,
+                              int freq_cutoff_start, int freq_cutoff_end,
+                              int freq_cutoff_interval,
+                              int max_contexts_per_document) {
+  Timer timer;
+
+  if (!m_backoff.get()) {
+    m_word_pyps.clear();
+    m_word_pyps.push_back(PYPs());
+  }
+
+  std::cerr << "\n Training with " << m_word_pyps.size()-1 << " backoff level"
+    << (m_word_pyps.size()==2 ? ":" : "s:") << std::endl;
+
+  for (int i=0; i<(int)m_word_pyps.size(); ++i)
+  {
+    m_word_pyps.at(i).reserve(m_num_topics);
+    for (int j=0; j<m_num_topics; ++j)
+      m_word_pyps.at(i).push_back(new PYP<int>(0.5, 1.0, m_seed));
+  }
+  std::cerr << std::endl;
+
+  m_document_pyps.reserve(corpus.num_documents());
+  for (int j=0; j<corpus.num_documents(); ++j)
+    m_document_pyps.push_back(new PYP<int>(0.5, 1.0, m_seed));
+
+  m_topic_p0 = 1.0/m_num_topics;
+  m_term_p0 = 1.0/corpus.num_types();
+  m_backoff_p0 = 1.0/corpus.num_documents();
+
+  std::cerr << " Documents: " << corpus.num_documents() << " Terms: "
+    << corpus.num_types() << std::endl;
+
+  int frequency_cutoff = freq_cutoff_start;
+  std::cerr << " Context frequency cutoff set to " << frequency_cutoff << std::endl;
+
+  timer.Reset();
+  // Initialisation pass
+  int document_id=0, topic_counter=0;
+  for (Corpus::const_iterator corpusIt=corpus.begin();
+       corpusIt != corpus.end(); ++corpusIt, ++document_id) {
+    m_corpus_topics.push_back(DocumentTopics(corpusIt->size(), 0));
+
+    int term_index=0;
+    for (Document::const_iterator docIt=corpusIt->begin();
+         docIt != corpusIt->end(); ++docIt, ++term_index) {
+      topic_counter++;
+      Term term = *docIt;
+
+      // sample a new_topic
+      //int new_topic = (topic_counter % m_num_topics);
+      int freq = corpus.context_count(term);
+      int new_topic = -1;
+      if (freq > frequency_cutoff
+          && (!max_contexts_per_document || term_index < max_contexts_per_document)) {
+        new_topic = document_id % m_num_topics;
+
+        // add the new topic to the PYPs
+        increment(term, new_topic);
+
+        if (m_use_topic_pyp) {
+          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
+          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
+          if (table_delta)
+            m_topic_pyp.increment(new_topic, m_topic_p0);
+        }
+        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
+      }
+
+      m_corpus_topics[document_id][term_index] = new_topic;
+    }
+  }
+  std::cerr << "  Initialized in " << timer.Elapsed() << " seconds\n";
+
+  int* randomDocIndices = new int[corpus.num_documents()];
+  for (int i = 0; i < corpus.num_documents(); ++i)
+	  randomDocIndices[i] = i;
+
+  // Sampling phase
+  for (int curr_sample=0; curr_sample < samples; ++curr_sample) {
+    if (freq_cutoff_interval > 0 && curr_sample != 1
+        && curr_sample % freq_cutoff_interval == 1
+        && frequency_cutoff > freq_cutoff_end) {
+      frequency_cutoff--;
+      std::cerr << "\n Context frequency cutoff set to " << frequency_cutoff << std::endl;
+    }
+
+    std::cerr << "\n  -- Sample " << curr_sample << " "; std::cerr.flush();
+
+    // Randomize the corpus indexing array
+    int tmp;
+    int processed_terms=0;
+    for (int i = corpus.num_documents()-1; i > 0; --i)
+    {
+        //i+1 since j \in [0,i] but rnd() \in [0,1)
+    	int j = (int)(rnd() * (i+1));
+      assert(j >= 0 && j <= i);
+     	tmp = randomDocIndices[i];
+    	randomDocIndices[i] = randomDocIndices[j];
+    	randomDocIndices[j] = tmp;
+    }
+
+    // for each document in the corpus
+    int document_id;
+    for (int i=0; i<corpus.num_documents(); ++i) {
+    	document_id = randomDocIndices[i];
+
+      // for each term in the document
+      int term_index=0;
+      Document::const_iterator docEnd = corpus.at(document_id).end();
+      for (Document::const_iterator docIt=corpus.at(document_id).begin();
+           docIt != docEnd; ++docIt, ++term_index) {
+        if (max_contexts_per_document && term_index > max_contexts_per_document)
+          break;
+        
+        Term term = *docIt;
+        int freq = corpus.context_count(term);
+        if (freq < frequency_cutoff)
+          continue;
+
+        processed_terms++;
+
+        // remove the prevous topic from the PYPs
+        int current_topic = m_corpus_topics[document_id][term_index];
+        // a negative label mean that term hasn't been sampled yet
+        if (current_topic >= 0) {
+          decrement(term, current_topic);
+
+          int table_delta = m_document_pyps[document_id].decrement(current_topic);
+          if (m_use_topic_pyp && table_delta < 0)
+            m_topic_pyp.decrement(current_topic);
+        }
+
+        // sample a new_topic
+        int new_topic = sample(document_id, term);
+
+        // add the new topic to the PYPs
+        m_corpus_topics[document_id][term_index] = new_topic;
+        increment(term, new_topic);
+
+        if (m_use_topic_pyp) {
+          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
+          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
+          if (table_delta)
+            m_topic_pyp.increment(new_topic, m_topic_p0);
+        }
+        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
+      }
+      if (document_id && document_id % 10000 == 0) {
+        std::cerr << "."; std::cerr.flush();
+      }
+    }
+    std::cerr << " ||| sampled " << processed_terms << " terms.";
+
+    if (curr_sample != 0 && curr_sample % 10 == 0) {
+      std::cerr << " ||| time=" << (timer.Elapsed() / 10.0) << " sec/sample" << std::endl;
+      timer.Reset();
+      std::cerr << "     ... Resampling hyperparameters (" << max_threads << " threads)"; std::cerr.flush();
+
+      // resample the hyperparamters
+      F log_p=0.0;
+      for (std::vector<PYPs>::iterator levelIt=m_word_pyps.begin();
+           levelIt != m_word_pyps.end(); ++levelIt) {
+        for (PYPs::iterator pypIt=levelIt->begin();
+             pypIt != levelIt->end(); ++pypIt) {
+          pypIt->resample_prior();
+          log_p += pypIt->log_restaurant_prob();
+        }
+      }
+
+      WorkerPtrVect workers;
+      for (int i = 0; i < max_threads; ++i)
+      {
+        JobReturnsF job = boost::bind(&PYPTopics::hresample_docs, this, max_threads, i);
+        workers.push_back(new SimpleResampleWorker(job));
+      }
+
+      WorkerPtrVect::iterator workerIt;
+      for (workerIt = workers.begin(); workerIt != workers.end(); ++workerIt)
+      {
+        //std::cerr << "Retrieving worker result.."; std::cerr.flush();
+        F wresult = workerIt->getResult(); //blocks until worker done
+        log_p += wresult;
+        //std::cerr << ".. got " << wresult << std::endl; std::cerr.flush();
+
+      }
+
+      if (m_use_topic_pyp) {
+        m_topic_pyp.resample_prior();
+        log_p += m_topic_pyp.log_restaurant_prob();
+      }
+
+      std::cerr.precision(10);
+      std::cerr << " ||| LLH=" << log_p << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl;
+      timer.Reset();
+
+      int k=0;
+      std::cerr << "Topics distribution: ";
+      std::cerr.precision(2);
+      for (PYPs::iterator pypIt=m_word_pyps.front().begin();
+           pypIt != m_word_pyps.front().end(); ++pypIt, ++k) {
+        if (k % 5 == 0) std::cerr << std::endl << '\t';
+        std::cerr << "<" << k << ":" << pypIt->num_customers() << ","
+          << pypIt->num_types() << "," << m_topic_pyp.prob(k, m_topic_p0) << "> ";
+      }
+      std::cerr.precision(4);
+      std::cerr << std::endl;
+    }
+  }
+  delete [] randomDocIndices;
+}
+
+PYPTopics::F PYPTopics::hresample_docs(int num_threads, int thread_id)
+{
+  int resample_counter=0;
+  F log_p = 0.0;
+  PYPs::iterator pypIt = m_document_pyps.begin();
+  PYPs::iterator end = m_document_pyps.end();
+  pypIt += thread_id;
+//  std::cerr << thread_id << " started " << std::endl; std::cerr.flush();
+
+  while (pypIt < end)
+  {
+    pypIt->resample_prior();
+    log_p += pypIt->log_restaurant_prob();
+    if (resample_counter++ % 5000 == 0) {
+      std::cerr << "."; std::cerr.flush();
+    }
+    pypIt += num_threads;
+  }
+//  std::cerr << thread_id << " did " << resample_counter << " with answer " << log_p << std::endl; std::cerr.flush();
+
+  return log_p;
+}
+
+//PYPTopics::F PYPTopics::hresample_topics()
+//{
+//  F log_p = 0.0;
+//  for (std::vector<PYPs>::iterator levelIt=m_word_pyps.begin();
+//      levelIt != m_word_pyps.end(); ++levelIt) {
+//    for (PYPs::iterator pypIt=levelIt->begin();
+//        pypIt != levelIt->end(); ++pypIt) {
+//
+//      pypIt->resample_prior();
+//      log_p += pypIt->log_restaurant_prob();
+//    }
+//  }
+//  //std::cerr << "topicworker has answer " << log_p << std::endl; std::cerr.flush();
+//
+// return log_p;
+//}
+
+void PYPTopics::decrement(const Term& term, int topic, int level) {
+  //std::cerr << "PYPTopics::decrement(" << term << "," << topic << "," << level << ")" << std::endl;
+  m_word_pyps.at(level).at(topic).decrement(term);
+  if (m_backoff.get()) {
+    Term backoff_term = (*m_backoff)[term];
+    if (!m_backoff->is_null(backoff_term))
+      decrement(backoff_term, topic, level+1);
+  }
+}
+
+void PYPTopics::increment(const Term& term, int topic, int level) {
+  //std::cerr << "PYPTopics::increment(" << term << "," << topic << "," << level << ")" << std::endl;
+  m_word_pyps.at(level).at(topic).increment(term, word_pyps_p0(term, topic, level));
+
+  if (m_backoff.get()) {
+    Term backoff_term = (*m_backoff)[term];
+    if (!m_backoff->is_null(backoff_term))
+      increment(backoff_term, topic, level+1);
+  }
+}
+
+int PYPTopics::sample(const DocumentId& doc, const Term& term) {
+  // First pass: collect probs
+  F sum=0.0;
+  std::vector<F> sums;
+  for (int k=0; k<m_num_topics; ++k) {
+    F p_w_k = prob(term, k);
+
+    F topic_prob = m_topic_p0;
+    if (m_use_topic_pyp) topic_prob = m_topic_pyp.prob(k, m_topic_p0);
+
+    //F p_k_d = m_document_pyps[doc].prob(k, topic_prob);
+    F p_k_d = m_document_pyps[doc].unnormalised_prob(k, topic_prob);
+
+    sum += (p_w_k*p_k_d);
+    sums.push_back(sum);
+  }
+  // Second pass: sample a topic
+  F cutoff = rnd() * sum;
+  for (int k=0; k<m_num_topics; ++k) {
+    if (cutoff <= sums[k])
+      return k;
+  }
+  assert(false);
+}
+
+PYPTopics::F PYPTopics::word_pyps_p0(const Term& term, int topic, int level) const {
+  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
+  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ")" << std::endl;
+
+  F p0 = m_term_p0;
+  if (m_backoff.get()) {
+    //static F fudge=m_backoff_p0; // TODO
+
+    Term backoff_term = (*m_backoff)[term];
+    if (!m_backoff->is_null(backoff_term)) {
+      assert (level < m_backoff->order());
+      p0 = (1.0/(double)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1);
+    }
+    else
+      p0 = m_term_p0;
+  }
+  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
+  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ") = " << p0 << std::endl;
+  return p0;
+}
+
+PYPTopics::F PYPTopics::prob(const Term& term, int topic, int level) const {
+  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
+  //std::cerr << "PYPTopics::prob(" << term << "," << topic << "," << level << " " << factor << ")" << std::endl;
+
+  F p0 = word_pyps_p0(term, topic, level);
+  F p_w_k = m_word_pyps.at(level).at(topic).prob(term, p0);
+
+  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
+  //std::cerr << "PYPTopics::prob(" << term << "," << topic << "," << level << ") = " << p_w_k << std::endl;
+
+  return p_w_k;
+}
+
+int PYPTopics::max_topic() const {
+  if (!m_use_topic_pyp)
+    return -1;
+
+  F current_max=0.0;
+  int current_topic=-1;
+  for (int k=0; k<m_num_topics; ++k) {
+    F prob = m_topic_pyp.prob(k, m_topic_p0);
+    if (prob > current_max) {
+      current_max = prob;
+      current_topic = k;
+    }
+  }
+  assert(current_topic >= 0);
+  return current_topic;
+}
+
+int PYPTopics::max(const DocumentId& doc) const {
+  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
+  // collect probs
+  F current_max=0.0;
+  int current_topic=-1;
+  for (int k=0; k<m_num_topics; ++k) {
+    //F p_w_k = prob(term, k);
+
+    F topic_prob = m_topic_p0;
+    if (m_use_topic_pyp)
+      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
+
+    F prob = 0;
+    if (doc < 0) prob = topic_prob;
+    else         prob = m_document_pyps[doc].prob(k, topic_prob);
+
+    if (prob > current_max) {
+      current_max = prob;
+      current_topic = k;
+    }
+  }
+  assert(current_topic >= 0);
+  return current_topic;
+}
+
+int PYPTopics::max(const DocumentId& doc, const Term& term) const {
+  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
+  // collect probs
+  F current_max=0.0;
+  int current_topic=-1;
+  for (int k=0; k<m_num_topics; ++k) {
+    F p_w_k = prob(term, k);
+
+    F topic_prob = m_topic_p0;
+    if (m_use_topic_pyp)
+      topic_prob = m_topic_pyp.prob(k, m_topic_p0);
+
+    F p_k_d = 0;
+    if (doc < 0) p_k_d = topic_prob;
+    else         p_k_d = m_document_pyps[doc].prob(k, topic_prob);
+
+    F prob = (p_w_k*p_k_d);
+    if (prob > current_max) {
+      current_max = prob;
+      current_topic = k;
+    }
+  }
+  assert(current_topic >= 0);
+  return current_topic;
+}
+
+std::ostream& PYPTopics::print_document_topics(std::ostream& out) const {
+  for (CorpusTopics::const_iterator corpusIt=m_corpus_topics.begin();
+       corpusIt != m_corpus_topics.end(); ++corpusIt) {
+    int term_index=0;
+    for (DocumentTopics::const_iterator docIt=corpusIt->begin();
+         docIt != corpusIt->end(); ++docIt, ++term_index) {
+      if (term_index) out << " ";
+      out << *docIt;
+    }
+    out << std::endl;
+  }
+  return out;
+}
+
+std::ostream& PYPTopics::print_topic_terms(std::ostream& out) const {
+  for (PYPs::const_iterator pypsIt=m_word_pyps.front().begin();
+       pypsIt != m_word_pyps.front().end(); ++pypsIt) {
+    int term_index=0;
+    for (PYP<int>::const_iterator termIt=pypsIt->begin();
+         termIt != pypsIt->end(); ++termIt, ++term_index) {
+      if (term_index) out << " ";
+      out << termIt->first << ":" << termIt->second;
+    }
+    out << std::endl;
+  }
+  return out;
+}