use broadcast version of vocab in aggregate

mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala

@@ -29,7 +29,7 @@ import org.apache.spark.rdd._
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.HashPartitioner
-
+import org.apache.spark.storage.StorageLevel
 /**
  *  Entry in vocabulary 
  */
@@ -215,18 +215,18 @@ class Word2Vec(
     val sc = dataset.context
 
     val expTable = sc.broadcast(createExpTable())
-    val V = sc.broadcast(vocab)
-    val VHash = sc.broadcast(vocabHash)
+    val bcVocab = sc.broadcast(vocab)
+    val bcVocabHash = sc.broadcast(vocabHash)
 
-    val sentences = words.mapPartitions {
+    val sentences: RDD[Array[Int]] = words.mapPartitions {
       iter => { new Iterator[Array[Int]] {
           def hasNext = iter.hasNext
 
           def next = {
             var sentence = new ArrayBuffer[Int]
             var sentenceLength = 0
             while (iter.hasNext && sentenceLength < MAX_SENTENCE_LENGTH) {
-              val word = VHash.value.get(iter.next)
+              val word = bcVocabHash.value.get(iter.next)
               word match {
                 case Some(w) => {
                   sentence += w
@@ -278,14 +278,14 @@ class Word2Vec(
                     val neu1e = new Array[Double](layer1Size)
                     // Hierarchical softmax 
                     var d = 0
-                    while (d < vocab(word).codeLen) {
-                      val l2 = vocab(word).point(d) * layer1Size
+                    while (d < bcVocab.value(word).codeLen) {
+                      val l2 = bcVocab.value(word).point(d) * layer1Size
                       // Propagate hidden -> output
                       var f = blas.ddot(layer1Size, syn0, l1, 1, syn1, l2, 1)
                       if (f > -MAX_EXP && f < MAX_EXP) {
                         val ind = ((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2.0)).toInt
                         f = expTable.value(ind)
-                        val g = (1 - vocab(word).code(d) - f) * alpha
+                        val g = (1 - bcVocab.value(word).code(d) - f) * alpha
                         blas.daxpy(layer1Size, g, syn1, l2, 1, neu1e, 0, 1)
                         blas.daxpy(layer1Size, g, syn0, l1, 1, syn1, l2, 1)
                       }
@@ -310,17 +310,21 @@ class Word2Vec(
       syn0Global = aggSyn0
       syn1Global = aggSyn1
     }
+    newSentences.unpersist()
+
     val wordMap = new Array[(String, Array[Double])](vocabSize)
     var i = 0
     while (i < vocabSize) {
-      val word = vocab(i).word
+      val word = bcVocab.value(i).word
       val vector = new Array[Double](layer1Size)
       Array.copy(syn0Global, i * layer1Size, vector, 0, layer1Size)
       wordMap(i) = (word, vector)
       i += 1
     }
     val modelRDD = sc.parallelize(wordMap, modelPartitionNum)
-      .partitionBy(new HashPartitioner(modelPartitionNum)).cache()
+      .partitionBy(new HashPartitioner(modelPartitionNum))
+      .persist(StorageLevel.MEMORY_AND_DISK)
+
     new Word2VecModel(modelRDD)
   }
 }