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https://issues.apache.org/jira/browse/SPARK-15720?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15311647#comment-15311647
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yuhao yang commented on SPARK-15720:
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This can only happen when creating a Word2VecModel from pre-trained vectors, as
currently Word2Vec in MLlib cannot support that scope. Current upper limit is
vocab * vectorSize < Max Array Size (approximately (Int.Max - 8) depending on
different platforms).
This will be a fundamental change to Word2Vec if we want to extend the scope.
[~rohangpatil] What's the target scope of vocabSize and vectorLength for your
application. I'm not sure if larger scope of word2vec is a popular requirement,
appreciate if you can provide some supporting examples.
> MLLIB Word2Vec loading large number of vectors in the model results in
> java.lang.NegativeArraySizeException
> -----------------------------------------------------------------------------------------------------------
>
> Key: SPARK-15720
> URL: https://issues.apache.org/jira/browse/SPARK-15720
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.6.1
> Environment: Linux
> Reporter: Rohan G Patil
>
> While loading a large number of pre-trained vectors into Spark MLLIB's
> Word2Vec model, will result in java.lang.NegativeArraySizeException.
> Code -
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala#L597
> Test with number of vectors greater than 16777215 with size of each vector
> 128 or more.
> there is Integer Overflow happening here. Should be an easy fix.
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