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Fix canonicalization regression with Spark 3.2 #3403

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Sep 9, 2021
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Fix canonicalization regression with Spark 3.2 #3403

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9 changes: 5 additions & 4 deletions sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -149,12 +149,13 @@ case class GpuAggregateExpression(origAggregateFunction: GpuAggregateFunction,
// We compute the same thing regardless of our final result.
override lazy val canonicalized: Expression = {
val normalizedAggFunc = mode match {
// For PartialMerge or Final mode, the input to the `aggregateFunction` is aggregate buffers,
// and the actual children of `aggregateFunction` is not used, here we normalize the expr id.
case PartialMerge | Final => aggregateFunction.transform {
// For Partial, PartialMerge, or Final mode, the input to the `aggregateFunction` is
// aggregate buffers, and the actual children of `aggregateFunction` is not used,
// here we normalize the expr id.
case Partial | PartialMerge | Final => aggregateFunction.transform {
case a: AttributeReference => a.withExprId(ExprId(0))
}
case Partial | Complete => aggregateFunction
case Complete => aggregateFunction
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What happens if we normalize all? (i.e. no match).

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I did wonder about that as well but I really don't know. Prior to this PR, our code matches Spark, so there seems to be a reason not to apply this in all cases.

}

GpuAggregateExpression(
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