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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