org.apache.spark.sql.rapids.GpuScalaUDF.scala Maven / Gradle / Ivy
/*
* Copyright (c) 2021-2024, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.rapids
import java.lang.invoke.SerializedLambda
import com.nvidia.spark.RapidsUDF
import com.nvidia.spark.rapids._
import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
import org.apache.spark.sql.catalyst.expressions.{Expression, GenericInternalRow, ScalaUDF, SpecializedGetters}
import org.apache.spark.sql.rapids.execution.TrampolineUtil
import org.apache.spark.sql.types.{AbstractDataType, AnyDataType, ArrayType, DataType, MapType, StructType}
case class GpuScalaUDF(
function: RapidsUDF,
dataType: DataType,
children: Seq[Expression],
udfName: Option[String],
nullable: Boolean,
udfDeterministic: Boolean) extends GpuUserDefinedFunction {
override def toString: String = s"${udfName.getOrElse("UDF")}(${children.mkString(", ")})"
/** name of the UDF function */
override val name: String = udfName.getOrElse("???")
}
object GpuScalaUDFMeta {
def exprMeta: ExprRule[ScalaUDF] = GpuOverrides.expr[ScalaUDF](
"User Defined Function, the UDF can choose to implement a RAPIDS accelerated interface " +
"to get better performance.",
ExprChecks.projectOnly(
GpuUserDefinedFunction.udfTypeSig,
TypeSig.all,
repeatingParamCheck =
Some(RepeatingParamCheck("param", GpuUserDefinedFunction.udfTypeSig, TypeSig.all))),
(expr, conf, p, r) => new ExprMeta(expr, conf, p, r) {
lazy val opRapidsFunc = GpuScalaUDF.getRapidsUDFInstance(expr.function)
override def tagExprForGpu(): Unit = {
if (opRapidsFunc.isEmpty && !this.conf.isCpuBasedUDFEnabled) {
val udfName = expr.udfName.getOrElse("UDF")
val udfClass = expr.function.getClass
willNotWorkOnGpu(s"neither $udfName implemented by $udfClass provides " +
s"a GPU implementation, nor the conf `${RapidsConf.ENABLE_CPU_BASED_UDF.key}` " +
s"is enabled")
}
}
override def convertToGpu(): GpuExpression = {
// It can come here only when at least one option as below is true.
// 1. UDF implements a RAPIDS accelerated interface.
// 2. The conf "spark.rapids.sql.rowBasedUDF.enabled" is enabled.
opRapidsFunc.map { rapidsFunc =>
GpuScalaUDF(
rapidsFunc,
expr.dataType,
childExprs.map(_.convertToGpu()),
expr.udfName,
expr.nullable,
expr.udfDeterministic)
}.getOrElse {
// This `require` is just for double check.
require(this.conf.isCpuBasedUDFEnabled)
GpuRowBasedScalaUDF(
expr.function,
expr.dataType,
childExprs.map(_.convertToGpu()),
expr.inputEncoders,
expr.outputEncoder,
expr.udfName,
expr.nullable,
expr.udfDeterministic)
}
}
})
}
case class GpuRowBasedScalaUDF(
sparkFunc: AnyRef,
dataType: DataType,
children: Seq[Expression],
inputEncoders: Seq[Option[ExpressionEncoder[_]]],
outputEncoder: Option[ExpressionEncoder[_]],
udfName: Option[String],
nullable: Boolean,
udfDeterministic: Boolean) extends GpuRowBasedUserDefinedFunction {
/**
* Create the converter which converts the catalyst data type to the scala data type.
* This converter will be used for the UDF input type conversion.
* We use `CatalystTypeConverters` to create the converter for:
* - UDF which doesn't provide inputEncoders, e.g., untyped Scala UDF and Java UDF
* - type which isn't supported by `ExpressionEncoder`, e.g., Any
* - primitive types, in order to use `identity` for better performance
* For other cases like case class, Option[T], we use `ExpressionEncoder` instead since
* `CatalystTypeConverters` doesn't support these data types.
*
* @param i the index of the child
* @param dataType the output data type of the i-th child
* @return the converter
*/
def createInputConverter(i: Int, dataType: DataType): Any => Any = {
val useEncoder =
!(inputEncoders.isEmpty || // for untyped Scala UDF and Java UDF
inputEncoders(i).isEmpty || // for types aren't supported by encoder, e.g. Any
inputPrimitives(i)) // for primitive types
if (useEncoder) {
val enc = inputEncoders(i).get
val fromRow = enc.createDeserializer()
if (enc.isSerializedAsStructForTopLevel) {
row: Any => fromRow(row.asInstanceOf[InternalRow])
} else {
val inputRow = new GenericInternalRow(1)
value: Any => inputRow.update(0, value); fromRow(inputRow)
}
} else { // use CatalystTypeConverters
CatalystTypeConverters.createToScalaConverter(dataType)
}
}
/**
* Need nulls check when there are array types with nulls in the input.
* This is for `https://github.com/NVIDIA/spark-rapids/issues/3942`.
*/
override val checkNull: Boolean = children.exists(child => hasArrayWithNulls(child.dataType))
private def hasArrayWithNulls(dt: DataType): Boolean = dt match {
case ArrayType(et, hasNull) => hasNull || hasArrayWithNulls(et)
case MapType(kt, vt, _) => hasArrayWithNulls(kt) || hasArrayWithNulls(vt)
case StructType(fields) => fields.exists(f => hasArrayWithNulls(f.dataType))
case _ => false
}
override def toString: String = s"$name(${children.mkString(", ")})"
/** name of the UDF function */
override val name: String = udfName.getOrElse(this.getClass.getSimpleName)
/** The input `row` consists of only child columns. */
override final protected def evaluateRow(childrenRow: InternalRow): Any =
catalystConverter(wrappedFunc(childrenRow))
/**
* The analyzer should be aware of Scala primitive types so as to make the
* UDF return null if there is any null input value of these types. On the
* other hand, Java UDFs can only have boxed types, thus this will return
* Nil(has same effect with all false) and analyzer will skip null-handling
* on them.
*/
lazy val inputPrimitives: Seq[Boolean] = {
inputEncoders.map { encoderOpt =>
// It's possible that some of the inputs don't have a specific encoder(e.g. `Any`)
if (encoderOpt.isDefined) {
val encoder = encoderOpt.get
if (encoder.isSerializedAsStruct) {
// struct type is not primitive
false
} else {
// `nullable` is false iff the type is primitive
!encoder.schema.head.nullable
}
} else {
// Any type is not primitive
false
}
}
}
/**
* The expected input types of this UDF, used to perform type coercion. If we do
* not want to perform coercion, simply use "Nil". Note that it would've been
* better to use Option of Seq[DataType] so we can use "None" as the case for no
* type coercion. However, that would require more refactoring of the codebase.
*/
def inputTypes: Seq[AbstractDataType] = {
inputEncoders.map { encoderOpt =>
if (encoderOpt.isDefined) {
val encoder = encoderOpt.get
if (encoder.isSerializedAsStruct) {
encoder.schema
} else {
encoder.schema.head.dataType
}
} else {
AnyDataType
}
}
}
/**
* Create the converter which converts the scala data type to the catalyst data type for
* the return data type of udf function. We'd use `ExpressionEncoder` to create the
* converter for typed ScalaUDF only, since its the only case where we know the type tag
* of the return data type of udf function.
*/
private[this] lazy val catalystConverter: Any => Any = outputEncoder.map { enc =>
val toRow = enc.createSerializer().asInstanceOf[Any => Any]
if (enc.isSerializedAsStructForTopLevel) {
value: Any =>
if (value == null) null else toRow(value).asInstanceOf[InternalRow]
} else {
value: Any =>
if (value == null) null else toRow(value).asInstanceOf[InternalRow].get(0, dataType)
}
}.getOrElse(CatalystTypeConverters.createToCatalystConverter(dataType))
/** A little refactor to simplify the Spark code of executing the function */
/** Build an accessor for each child to read the data from a row and do type conversion */
private[this] lazy val childAccessors: Array[SpecializedGetters => Any] =
children.zipWithIndex.map { case (child, i) =>
val accessor = InternalRow.getAccessor(child.dataType, child.nullable)
val converter = createInputConverter(i, child.dataType)
row: SpecializedGetters => converter(accessor(row, i))
}.toArray
// scalastyle:off line.size.limit
/**
* Spark Scala UDF supports at most 22 parameters.
*/
private lazy val wrappedFunc: InternalRow => Any = {
children.size match {
case 0 =>
val f = sparkFunc.asInstanceOf[() => Any]
(row: InternalRow) => f()
case 1 =>
val f = sparkFunc.asInstanceOf[(Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
f(a0)
}
case 2 =>
val f = sparkFunc.asInstanceOf[(Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
f(a0, a1)
}
case 3 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
f(a0, a1, a2)
}
case 4 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
f(a0, a1, a2, a3)
}
case 5 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
f(a0, a1, a2, a3, a4)
}
case 6 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
f(a0, a1, a2, a3, a4, a5)
}
case 7 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
f(a0, a1, a2, a3, a4, a5, a6)
}
case 8 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7)
}
case 9 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8)
}
case 10 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9)
}
case 11 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10)
}
case 12 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11)
}
case 13 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12)
}
case 14 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13)
}
case 15 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14)
}
case 16 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15)
}
case 17 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16)
}
case 18 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
val a17 = childAccessors(17)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17)
}
case 19 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
val a17 = childAccessors(17)(row)
val a18 = childAccessors(18)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18)
}
case 20 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
val a17 = childAccessors(17)(row)
val a18 = childAccessors(18)(row)
val a19 = childAccessors(19)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19)
}
case 21 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
val a17 = childAccessors(17)(row)
val a18 = childAccessors(18)(row)
val a19 = childAccessors(19)(row)
val a20 = childAccessors(20)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20)
}
case 22 =>
val f = sparkFunc.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]
(row: InternalRow) => {
val a0 = childAccessors(0)(row)
val a1 = childAccessors(1)(row)
val a2 = childAccessors(2)(row)
val a3 = childAccessors(3)(row)
val a4 = childAccessors(4)(row)
val a5 = childAccessors(5)(row)
val a6 = childAccessors(6)(row)
val a7 = childAccessors(7)(row)
val a8 = childAccessors(8)(row)
val a9 = childAccessors(9)(row)
val a10 = childAccessors(10)(row)
val a11 = childAccessors(11)(row)
val a12 = childAccessors(12)(row)
val a13 = childAccessors(13)(row)
val a14 = childAccessors(14)(row)
val a15 = childAccessors(15)(row)
val a16 = childAccessors(16)(row)
val a17 = childAccessors(17)(row)
val a18 = childAccessors(18)(row)
val a19 = childAccessors(19)(row)
val a20 = childAccessors(20)(row)
val a21 = childAccessors(21)(row)
f(a0, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21)
}
}
} // end of wrappedFunc
// scalastyle:on line.size.limit
}
object GpuScalaUDF {
/**
* Determine if the UDF function implements the [[com.nvidia.spark.RapidsUDF]] interface,
* returning the instance if it does. The lambda wrapper that Spark applies to Java UDFs will be
* inspected if necessary to locate the user's UDF instance.
*/
def getRapidsUDFInstance(function: AnyRef): Option[RapidsUDF] = {
function match {
case f: RapidsUDF => Some(f)
case f =>
try {
// This may be a lambda that Spark's UDFRegistration wrapped around a Java UDF instance.
val clazz = f.getClass
if (TrampolineUtil.getSimpleName(clazz).toLowerCase().contains("lambda")) {
// Try to find a `writeReplace` method, further indicating it is likely a lambda
// instance, and invoke it to serialize the lambda. Once serialized, captured arguments
// can be examine to locate the Java UDF instance.
// Note this relies on implementation details of Spark's UDFRegistration class.
val writeReplace = clazz.getDeclaredMethod("writeReplace")
writeReplace.setAccessible(true)
val serializedLambda = writeReplace.invoke(f).asInstanceOf[SerializedLambda]
if (serializedLambda.getCapturedArgCount == 1) {
serializedLambda.getCapturedArg(0) match {
case c: RapidsUDF => Some(c)
case _ => None
}
} else {
None
}
} else {
None
}
} catch {
case _: ClassCastException | _: NoSuchMethodException | _: SecurityException => None
}
}
}
}
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