[GitHub] [spark] viirya commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large

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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_r352929865
 
 

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 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:
   I can only do averaging like this. The group key can not be accessed in `reduceByKey`.

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