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_r352937085
##########
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
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@@ -438,11 +438,23 @@ class Word2Vec extends Serializable with Logging {
None
}
}.flatten
- }
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ }.persist()
+ // 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 keyCounts = partial.countByKey()
+ val synAgg = partial.mapPartitions { iter =>
+ iter.map { case (id, vec) =>
+ val v1 = Array.fill[Float](vectorSize)(0.0f)
+ blas.saxpy(vectorSize, 1.0f / keyCounts(id), vec, 1, v1, 1)
+ (id, v1)
+ }
+ }.reduceByKey { case (v1, v2) =>
Review comment:
During `reduceByKey`, we already do sum up and can lead to infinity? Once it
is done, i think it does not make sense anymore to divide?
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