viirya commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r353396038
##########
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
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@@ -439,9 +439,21 @@ class Word2Vec extends Serializable with Logging {
}
}.flatten
}
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val synAgg = partial.mapPartitions { iter =>
+ iter.map { case (id, vec) =>
+ (id, (vec, 1))
+ }
+ }.reduceByKey { case ((v1, count1), (v2, count2)) =>
+ blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
+ (v1, count1 + count2)
+ }.map { case (id, (vec, count)) =>
+ val averagedVec = Array.fill[Float](vectorSize)(0.0f)
+ blas.saxpy(vectorSize, 1.0f / count, vec, 1, averagedVec, 1)
Review comment:
sscal will scales the vector in place.
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