[GitHub] [spark] srowen commented on a change in pull request #30548: [SPARK-33609][ML] word2vec reduce broadcast size

[GitBox <gi...@apache.org>]

srowen commented on a change in pull request #30548:
URL: https://github.com/apache/spark/pull/30548#discussion_r532684049



##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
##########
@@ -278,34 +279,45 @@ class Word2VecModel private[ml] (
   @Since("1.4.0")
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
+  private var bcModel: Broadcast[Word2VecModel] = _

Review comment:
       I don't suppose we have a way to clean this up after use - will just have to get GCed?

##########
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##########
@@ -538,9 +538,13 @@ class Word2VecModel private[spark] (
   @Since("1.1.0")
   def transform(word: String): Vector = {
     wordIndex.get(word) match {
-      case Some(ind) =>
-        val vec = wordVectors.slice(ind * vectorSize, ind * vectorSize + vectorSize)
-        Vectors.dense(vec.map(_.toDouble))
+      case Some(index) =>
+        val size = vectorSize
+        val offset = index * size
+        val array = Array.ofDim[Double](size)
+        var i = 0
+        while (i < size) { array(i) = wordVectors(offset + i); i += 1 }

Review comment:
       Is this actually more efficient than slice? Likewise above.

##########
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##########
@@ -502,19 +502,19 @@ class Word2VecModel private[spark] (
   private val vectorSize = wordVectors.length / numWords
 
   // wordList: Ordered list of words obtained from wordIndex.
-  private val wordList: Array[String] = {
-    val (wl, _) = wordIndex.toSeq.sortBy(_._2).unzip
-    wl.toArray
+  private lazy val wordList: Array[String] = {
+    wordIndex.toSeq.sortBy(_._2).iterator.map(_._1).toArray
   }
 
   // wordVecNorms: Array of length numWords, each value being the Euclidean norm
   //               of the wordVector.
-  private val wordVecNorms: Array[Float] = {
-    val wordVecNorms = new Array[Float](numWords)
+  private lazy val wordVecNorms: Array[Float] = {

Review comment:
       How much does this save, if it only happens once and has to happen to use the model?

##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
##########
@@ -278,34 +279,45 @@ class Word2VecModel private[ml] (
   @Since("1.4.0")
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
+  private var bcModel: Broadcast[Word2VecModel] = _
+
   /**
    * Transform a sentence column to a vector column to represent the whole sentence. The transform
    * is performed by averaging all word vectors it contains.
    */
   @Since("2.0.0")
   override def transform(dataset: Dataset[_]): DataFrame = {
     val outputSchema = transformSchema(dataset.schema, logging = true)
-    val vectors = wordVectors.getVectors
-      .mapValues(vv => Vectors.dense(vv.map(_.toDouble)))
-      .map(identity).toMap // mapValues doesn't return a serializable map (SI-7005)
-    val bVectors = dataset.sparkSession.sparkContext.broadcast(vectors)
-    val d = $(vectorSize)
-    val emptyVec = Vectors.sparse(d, Array.emptyIntArray, Array.emptyDoubleArray)
-    val word2Vec = udf { sentence: Seq[String] =>
+
+    if (bcModel == null) {
+      bcModel = dataset.sparkSession.sparkContext.broadcast(this)

Review comment:
       Looks like you only use this.wordVectors below? maybe just broadcast that




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