Fix a test error for DB13.3 [databricks] (#10816)

Fix #10797 This PR uses a new config relevant to arrow batch slicing for the arrow python runner pick, and applies the pick rule of the arrow python runner to GpuAggreagteInPandasExec in addition to GpuFlatMapGroupInPandasExec. --------- Signed-off-by: Firestarman <firestarmanllc@gmail.com>
NVIDIA · May 15, 2024 · 95067fe · 95067fe
1 parent 4b3366f
commit 95067fe
Show file tree

Hide file tree

Showing 5 changed files with 32 additions and 35 deletions.
diff --git a/...rc/main/scala/org/apache/spark/sql/rapids/execution/python/GpuAggregateInPandasExec.scala b/...rc/main/scala/org/apache/spark/sql/rapids/execution/python/GpuAggregateInPandasExec.scala
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -31,8 +31,8 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning}
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.rapids.execution.python.shims.GpuArrowPythonRunner
-import org.apache.spark.sql.rapids.shims.{ArrowUtilsShim, DataTypeUtilsShim}
+import org.apache.spark.sql.rapids.execution.python.shims.GpuGroupedPythonRunnerFactory
+import org.apache.spark.sql.rapids.shims.DataTypeUtilsShim
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
@@ -109,8 +109,6 @@ case class GpuAggregateInPandasExec(
     val (mNumInputRows, mNumInputBatches, mNumOutputRows, mNumOutputBatches) = commonGpuMetrics()
 
     lazy val isPythonOnGpuEnabled = GpuPythonHelper.isPythonOnGpuEnabled(conf)
-    val sessionLocalTimeZone = conf.sessionLocalTimeZone
-    val pythonRunnerConf = ArrowUtilsShim.getPythonRunnerConfMap(conf)
     val childOutput = child.output
     val resultExprs = resultExpressions
 
@@ -204,27 +202,22 @@ case class GpuAggregateInPandasExec(
         }
       }
 
+      val runnerFactory = GpuGroupedPythonRunnerFactory(conf, pyFuncs, argOffsets,
+        aggInputSchema, DataTypeUtilsShim.fromAttributes(pyOutAttributes),
+        PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF)
+
       // Third, sends to Python to execute the aggregate and returns the result.
       if (pyInputIter.hasNext) {
         // Launch Python workers only when the data is not empty.
-        val pyRunner = new GpuArrowPythonRunner(
-          pyFuncs,
-          PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
-          argOffsets,
-          aggInputSchema,
-          sessionLocalTimeZone,
-          pythonRunnerConf,
-          // The whole group data should be written in a single call, so here is unlimited
-          Int.MaxValue,
-          DataTypeUtilsShim.fromAttributes(pyOutAttributes))
-
+        val pyRunner = runnerFactory.getRunner()
         val pyOutputIterator = pyRunner.compute(pyInputIter, context.partitionId(), context)
 
         val combinedAttrs = gpuGroupingExpressions.map(_.toAttribute) ++ pyOutAttributes
         val resultRefs = GpuBindReferences.bindGpuReferences(resultExprs, combinedAttrs)
         // Gets the combined batch for each group and projects for the output.
-        new CombiningIterator(batchProducer.getBatchQueue, pyOutputIterator, pyRunner,
-            mNumOutputRows, mNumOutputBatches).map { combinedBatch =>
+        new CombiningIterator(batchProducer.getBatchQueue, pyOutputIterator,
+            pyRunner.asInstanceOf[GpuArrowOutput], mNumOutputRows,
+            mNumOutputBatches).map { combinedBatch =>
           withResource(combinedBatch) { batch =>
             GpuProjectExec.project(batch, resultRefs)
           }

diff --git a/...ain/scala/org/apache/spark/sql/rapids/execution/python/GpuFlatMapGroupsInPandasExec.scala b/...ain/scala/org/apache/spark/sql/rapids/execution/python/GpuFlatMapGroupsInPandasExec.scala
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2020-2023, NVIDIA CORPORATION.
+ * Copyright (c) 2020-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -21,7 +21,7 @@ import com.nvidia.spark.rapids.python.PythonWorkerSemaphore
 import com.nvidia.spark.rapids.shims.ShimUnaryExecNode
 
 import org.apache.spark.TaskContext
-import org.apache.spark.api.python.ChainedPythonFunctions
+import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{AllTuples, ClusteredDistribution, Distribution, Partitioning}
@@ -123,7 +123,8 @@ case class GpuFlatMapGroupsInPandasExec(
         resolveArgOffsets(child, groupingAttributes)
 
     val runnerFactory = GpuGroupedPythonRunnerFactory(conf, chainedFunc, Array(argOffsets),
-        DataTypeUtilsShim.fromAttributes(dedupAttrs), pythonOutputSchema)
+        DataTypeUtilsShim.fromAttributes(dedupAttrs), pythonOutputSchema,
+        PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF)
 
     // Start processing. Map grouped batches to ArrowPythonRunner results.
     child.executeColumnar().mapPartitionsInternal { inputIter =>

diff --git a/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala b/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2023, NVIDIA CORPORATION.
+ * Copyright (c) 2023-2024, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -39,7 +39,7 @@
 spark-rapids-shim-json-lines ***/
 package org.apache.spark.sql.rapids.execution.python.shims
 
-import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.api.python.ChainedPythonFunctions
 import org.apache.spark.sql.rapids.shims.ArrowUtilsShim
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.vectorized.ColumnarBatch
@@ -49,14 +49,15 @@ case class GpuGroupedPythonRunnerFactory(
     chainedFunc: Seq[ChainedPythonFunctions],
     argOffsets: Array[Array[Int]],
     dedupAttrs: StructType,
-    pythonOutputSchema: StructType) {
+    pythonOutputSchema: StructType,
+    evalType: Int) {
   val sessionLocalTimeZone = conf.sessionLocalTimeZone
   val pythonRunnerConf = ArrowUtilsShim.getPythonRunnerConfMap(conf)
 
   def getRunner(): GpuBasePythonRunner[ColumnarBatch] = {
     new GpuArrowPythonRunner(
       chainedFunc,
-      PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
+      evalType,
       argOffsets,
       dedupAttrs,
       sessionLocalTimeZone,
@@ -65,4 +66,4 @@ case class GpuGroupedPythonRunnerFactory(
       Int.MaxValue,
       pythonOutputSchema)
   }
-}
+}
diff --git a/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala b/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala
@@ -20,7 +20,7 @@
 spark-rapids-shim-json-lines ***/
 package org.apache.spark.sql.rapids.execution.python.shims
 
-import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
+import org.apache.spark.api.python.ChainedPythonFunctions
 import org.apache.spark.sql.rapids.shims.ArrowUtilsShim
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.vectorized.ColumnarBatch
@@ -30,7 +30,8 @@ case class GpuGroupedPythonRunnerFactory(
   chainedFunc: Seq[ChainedPythonFunctions],
   argOffsets: Array[Array[Int]],
   dedupAttrs: StructType,
-  pythonOutputSchema: StructType) {
+  pythonOutputSchema: StructType,
+  evalType: Int) {
   // Configs from DB runtime
   val maxBytes = conf.pandasZeroConfConversionGroupbyApplyMaxBytesPerSlice
   val zeroConfEnabled = conf.pandasZeroConfConversionGroupbyApplyEnabled
@@ -41,7 +42,7 @@ case class GpuGroupedPythonRunnerFactory(
     if (zeroConfEnabled && maxBytes > 0L) {
       new GpuGroupUDFArrowPythonRunner(
         chainedFunc,
-        PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
+        evalType,
         argOffsets,
         dedupAttrs,
         sessionLocalTimeZone,
@@ -52,7 +53,7 @@ case class GpuGroupedPythonRunnerFactory(
     } else {
       new GpuArrowPythonRunner(
         chainedFunc,
-        PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
+        evalType,
         argOffsets,
         dedupAttrs,
         sessionLocalTimeZone,

diff --git a/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala b/...la/org/apache/spark/sql/rapids/execution/python/shims/GpuGroupedPythonRunnerFactory.scala
@@ -24,24 +24,25 @@ import org.apache.spark.sql.rapids.shims.ArrowUtilsShim
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.vectorized.ColumnarBatch
 
-//TODO is this needed? we already have a similar version in spark330db
 case class GpuGroupedPythonRunnerFactory(
   conf: org.apache.spark.sql.internal.SQLConf,
   chainedFunc: Seq[ChainedPythonFunctions],
   argOffsets: Array[Array[Int]],
   dedupAttrs: StructType,
-  pythonOutputSchema: StructType) {
+  pythonOutputSchema: StructType,
+  evalType: Int) {
   // Configs from DB runtime
   val maxBytes = conf.pandasZeroConfConversionGroupbyApplyMaxBytesPerSlice
   val zeroConfEnabled = conf.pandasZeroConfConversionGroupbyApplyEnabled
+  val isArrowBatchSlicingEnabled = conf.pythonArrowBatchSlicingEnabled
   val sessionLocalTimeZone = conf.sessionLocalTimeZone
   val pythonRunnerConf = ArrowUtilsShim.getPythonRunnerConfMap(conf)
 
   def getRunner(): GpuBasePythonRunner[ColumnarBatch] = {
-    if (zeroConfEnabled && maxBytes > 0L) {
+    if (isArrowBatchSlicingEnabled || (zeroConfEnabled && maxBytes > 0L)) {
       new GpuGroupUDFArrowPythonRunner(
         chainedFunc,
-        PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
+        evalType,
         argOffsets,
         dedupAttrs,
         sessionLocalTimeZone,
@@ -52,7 +53,7 @@ case class GpuGroupedPythonRunnerFactory(
     } else {
       new GpuArrowPythonRunner(
         chainedFunc,
-        PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
+        evalType,
         argOffsets,
         dedupAttrs,
         sessionLocalTimeZone,