srowen commented on a change in pull request #30548:
URL: https://github.com/apache/spark/pull/30548#discussion_r534626342
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
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@@ -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:
Oops, right, I _think_ I meant to say that you only use those two. is
there any savings from just broadcasting those rather than the whole model? if
not that's fine.
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