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Creates the distribution package of the RAPIDS plugin for Apache Spark
/*
* 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.
* 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 com.nvidia.spark.rapids
import scala.collection.mutable.ArrayBuffer
import ai.rapids.cudf.{ColumnVector, DType, NvtxColor, NvtxRange, OrderByArg, Scalar, Table}
import com.nvidia.spark.rapids.Arm.{closeOnExcept, withResource}
import com.nvidia.spark.rapids.GpuOverrides.extractLit
import com.nvidia.spark.rapids.RapidsPluginImplicits.AutoCloseableProducingArray
import com.nvidia.spark.rapids.RmmRapidsRetryIterator.{splitSpillableInHalfByRows, withRetry}
import com.nvidia.spark.rapids.ScalableTaskCompletion.onTaskCompletion
import com.nvidia.spark.rapids.jni.GpuSplitAndRetryOOM
import com.nvidia.spark.rapids.shims.{ShimExpression, ShimUnaryExecNode}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression, Generator, ReplicateRows, Stack}
import org.apache.spark.sql.catalyst.plans.physical.Partitioning
import org.apache.spark.sql.execution.{GenerateExec, SparkPlan}
import org.apache.spark.sql.rapids.GpuCreateArray
import org.apache.spark.sql.types.{ArrayType, DataType, IntegerType, MapType, StructField, StructType}
import org.apache.spark.sql.vectorized.ColumnarBatch
class GpuGenerateExecSparkPlanMeta(
gen: GenerateExec,
conf: RapidsConf,
p: Option[RapidsMeta[_, _, _]],
r: DataFromReplacementRule) extends SparkPlanMeta[GenerateExec](gen, conf, p, r) {
override val childExprs: Seq[BaseExprMeta[_]] = {
(Seq(gen.generator) ++ gen.requiredChildOutput).map(
GpuOverrides.wrapExpr(_, conf, Some(this)))
}
override def tagPlanForGpu(): Unit = {
if (gen.outer && childExprs.head.isInstanceOf[GeneratorExprMeta[_]] &&
!childExprs.head.asInstanceOf[GeneratorExprMeta[Generator]].supportOuter) {
willNotWorkOnGpu(s"outer is not currently supported with ${gen.generator.nodeName}")
}
}
private def getExpandExecForStack(stackMeta: GpuStackMeta): GpuExec = {
val numRows = extractLit(stackMeta.childExprs.head.convertToCpu()) match {
case Some(lit) => lit.value.asInstanceOf[Int]
case _ => throw new IllegalStateException("First parameter of stack should be a literal Int")
}
val numFields = Math.ceil((stackMeta.childExprs.length - 1.0) / numRows).toInt
val projections: Seq[Seq[Expression]] = for (row <- 0 until numRows) yield {
val childExprsGpu = childExprs.tail.map(_.convertToGpu())
val otherProj: Seq[Expression] = for (req <- childExprsGpu) yield {
req
}
val stackProj: Seq[Expression] = for (col <- 0 until numFields) yield {
val index = row * numFields + col
val dataChildren = stackMeta.childExprs.tail
if (index >= dataChildren.length) {
val typeInfo = dataChildren(col).dataType
GpuLiteral(null, typeInfo)
} else {
dataChildren(index).convertToGpu()
}
}
otherProj ++ stackProj
}
val output: Seq[Attribute] = gen.requiredChildOutput ++ gen.generatorOutput.take(numFields)
GpuExpandExec(projections, output, childPlans.head.convertIfNeeded())(
preprojectEnabled = conf.isExpandPreprojectEnabled)
}
override def convertToGpu(): GpuExec = {
// if child expression contains Stack, use GpuExpandExec instead
childExprs.head match {
case stackMeta: GpuStackMeta =>
getExpandExecForStack(stackMeta)
case genMeta =>
GpuGenerateExec(
genMeta.convertToGpu().asInstanceOf[GpuGenerator],
gen.requiredChildOutput,
gen.outer,
gen.generatorOutput,
childPlans.head.convertIfNeeded())
}
}
}
class GpuStackMeta(
stack: Stack,
conf: RapidsConf,
parent: Option[RapidsMeta[_, _, _]],
rule: DataFromReplacementRule)
extends BaseExprMeta[Stack](stack, conf, parent, rule) {
override val childExprs: Seq[BaseExprMeta[_]] = stack.children
.map(GpuOverrides.wrapExpr(_, conf, Some(this)))
override def convertToGpu(): GpuExpression = {
// There is no need to implement convertToGpu() here, because GpuGenerateExec will handle
// stack logic in terms of GpuExpandExec, no convertToGpu() will be called during the process
throw new UnsupportedOperationException(s"Should not be here: $this")
}
}
abstract class GeneratorExprMeta[INPUT <: Generator](
gen: INPUT,
conf: RapidsConf,
p: Option[RapidsMeta[_, _, _]],
r: DataFromReplacementRule) extends ExprMeta[INPUT](gen, conf, p, r) {
/* whether supporting outer generate or not */
val supportOuter: Boolean = false
}
/**
* Base class for metadata around GeneratorExprMeta.
*/
abstract class ReplicateRowsExprMeta[INPUT <: ReplicateRows](
gen: INPUT,
conf: RapidsConf,
parent: Option[RapidsMeta[_, _, _]],
rule: DataFromReplacementRule)
extends GeneratorExprMeta[INPUT](gen, conf, parent, rule) {
override final def convertToGpu(): GpuExpression =
convertToGpu(childExprs.map(_.convertToGpu()))
def convertToGpu(childExprs: Seq[Expression]): GpuExpression
}
/**
* GPU overrides of `Generator`, corporate with `GpuGenerateExec`.
*/
trait GpuGenerator extends GpuUnevaluable {
override def dataType: DataType = ArrayType(elementSchema)
override def foldable: Boolean = false
override def nullable: Boolean = false
/**
* The output element schema.
*/
def elementSchema: StructType
/**
* Apply generator to produce result ColumnarBatch from input batch.
*
* This is a specialized method for GPU runtime, which is called by GpuGenerateExec who owns
* the generator. The reason of creating a new method rather than implementing columnarEval is
* that generator is an integrated Table transformer instead of column transformer in terms of
* cuDF.
*
* @param batches iterator of spillable batches
* @param generatorOffset column offset of generator's input columns in `inputBatch`
* @param outer when true, each input row will be output at least once, even if the
* output of the given `generator` is empty.
* @return result ColumnarBatch
*/
def generate(
batches: Iterator[SpillableColumnarBatch],
generatorOffset: Int,
outer: Boolean): Iterator[ColumnarBatch]
/**
* Compute split indices for generator's input batches.
*
* This is a specialized method for GPU runtime, which is called by GpuGenerateExec to split up
* input batches to reduce total memory cost during generating. It is necessary because most of
* generators may produce multiple records for each input record, which make output batch size
* much larger than input size.
*
* @param inputBatch projected input data, which ensures appending columns are ahead of
* generators' inputs. So, generators can distinguish them with an offset.
* @param generatorOffset column offset of generator's input columns in `inputBatch`
* @param outer when true, each input row will be output at least once, even if the
* output of the given `generator` is empty.
* @param targetSizeBytes the target number of bytes for a GPU batch, one of `RapidsConf`
* @param maxRows the target number of rows for a GPU batch, exposed for testing purposes.
* @return split indices of input batch
*/
def inputSplitIndices(inputBatch: ColumnarBatch,
generatorOffset: Int,
outer: Boolean,
targetSizeBytes: Long,
maxRows: Int = Int.MaxValue): Array[Int]
/**
* Extract lazy expressions from generator if exists.
*
* This is a specialized method for GPU runtime, which is called by GpuGenerateExec to determine
* whether current generation plan can be executed with optimized lazy array generation or not.
*
* @return fixed length lazy expressions for generation. Nil value means no lazy expressions to
* extract, which indicates fixed length lazy array generation is unavailable.
*/
def fixedLenLazyExpressions: Seq[Expression] = Nil
/**
* Optimized lazy generation interface which is specialized for fixed length array input.
*
* For some generators (like explode), it is possible to improve performance through lazy
* evaluation when input schema is fixed length array.
*
* @param inputIterator input iterator from child plan
* @param boundLazyProjectList lazy expressions bounded with child outputs
* @param boundOthersProjectList other required expressions bounded with child outputs
* @param outputSchema result schema of GpuGenerateExec
* @param outer when true, each input row will be output at least once, even if
* the output of the given `generator` is empty.
* @param numOutputRows Gpu spark metric of output rows
* @param numOutputBatches Gpu spark metric of output batches
* @param opTime Gpu spark metric of time on GPU by GpuGenerateExec
* @return result iterator
*/
def fixedLenLazyArrayGenerate(inputIterator: Iterator[ColumnarBatch],
boundLazyProjectList: Seq[Expression],
boundOthersProjectList: Seq[Expression],
outputSchema: Array[DataType],
outer: Boolean,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
opTime: GpuMetric): Iterator[ColumnarBatch] = {
throw new NotImplementedError("The method should be implemented by specific generators.")
}
}
case class GpuReplicateRows(children: Seq[Expression]) extends GpuGenerator with ShimExpression {
override def elementSchema: StructType =
StructType(children.tail.zipWithIndex.map {
case (e, index) => StructField(s"col$index", e.dataType)
})
override def generate(
inputBatches: Iterator[SpillableColumnarBatch],
generatorOffset: Int,
outer: Boolean): Iterator[ColumnarBatch] = {
withRetry(inputBatches, splitSpillableInHalfByRows) { attempt =>
withResource(attempt.getColumnarBatch()) { inputBatch =>
val schema = GpuColumnVector.extractTypes(inputBatch)
withResource(GpuColumnVector.from(inputBatch)) { table =>
val replicateVector = table.getColumn(generatorOffset)
withResource(table.repeat(replicateVector)) { replicatedTable =>
GpuColumnVector.from(replicatedTable, schema)
}
}
}
}
}
override def inputSplitIndices(inputBatch: ColumnarBatch,
generatorOffset: Int,
outer: Boolean,
targetSizeBytes: Long,
maxRows: Int = Int.MaxValue): Array[Int] = {
val vectors = GpuColumnVector.extractBases(inputBatch)
val inputRows = inputBatch.numRows()
if (inputRows == 0) return Array()
// Calculate the number of rows that needs to be replicated. Here we find the mean of the
// generator column. Multiplying the mean with size of projected columns would give us the
// approximate memory required.
val meanOutputRows = math.ceil(withResource(vectors(generatorOffset).mean()) {
_.getDouble
})
val estimatedOutputRows = meanOutputRows * inputRows
// input size of columns to be repeated
val repeatColsInputSize = vectors.slice(0, generatorOffset).map(_.getDeviceMemorySize).sum
// estimated total output size
val estimatedOutputSizeBytes = repeatColsInputSize * estimatedOutputRows / inputRows
// how may splits will we need to keep the output size under the target size
val numSplitsForTargetSize = math.ceil(estimatedOutputSizeBytes / targetSizeBytes).toInt
// how may splits will we need to keep the output rows under max value
val numSplitsForTargetRow = math.ceil(estimatedOutputRows / maxRows).toInt
// how may splits will we need to keep replicateRows working safely
val numSplits = numSplitsForTargetSize max numSplitsForTargetRow
if (numSplits == 0) {
Array()
} else {
GpuBatchUtils.generateSplitIndices(inputRows, numSplits)
}
}
}
abstract class GpuExplodeBase extends GpuUnevaluableUnaryExpression with GpuGenerator {
/** The position of an element within the collection should also be returned. */
def position: Boolean
// hive-compatible default alias for explode function ("col" for array, "key", "value" for map)
override def elementSchema: StructType = child.dataType match {
case ArrayType(et, containsNull) =>
if (position) {
new StructType()
.add("pos", IntegerType, nullable = false)
.add("col", et, containsNull)
} else {
new StructType()
.add("col", et, containsNull)
}
case MapType(kt, vt, valueContainsNull) =>
if (position) {
new StructType()
.add("pos", IntegerType, nullable = false)
.add("key", kt, nullable = false)
.add("value", vt, valueContainsNull)
} else {
new StructType()
.add("key", kt, nullable = false)
.add("value", vt, valueContainsNull)
}
}
private def getPerRowRepetition(explodingColumn: ColumnVector, outer: Boolean): ColumnVector = {
withResource(explodingColumn.countElements()) { arrayLengths =>
if (outer) {
// for outer, empty arrays and null arrays will produce a row
withResource(GpuScalar.from(1, IntegerType)) { one =>
val noNulls = withResource(arrayLengths.isNotNull) { isLhsNotNull =>
isLhsNotNull.ifElse(arrayLengths, one)
}
withResource(noNulls) { _ =>
withResource(noNulls.greaterOrEqualTo(one)) { isGeOne =>
isGeOne.ifElse(noNulls, one)
}
}
}
} else {
// ensure no nulls in this output
withResource(GpuScalar.from(0, IntegerType)) { zero =>
GpuNvl(arrayLengths, zero)
}
}
}
}
private def getRowByteCount(column: Seq[ColumnVector]): ColumnVector = {
withResource(new NvtxRange("getRowByteCount", NvtxColor.GREEN)) { _ =>
val bits = withResource(new Table(column: _*)) { tbl =>
tbl.rowBitCount()
}
withResource(bits) { _ =>
withResource(Scalar.fromLong(8)) { toBytes =>
bits.trueDiv(toBytes, DType.INT64)
}
}
}
}
override def inputSplitIndices(inputBatch: ColumnarBatch,
generatorOffset: Int, outer: Boolean,
targetSizeBytes: Long,
maxRows: Int = Int.MaxValue): Array[Int] = {
val inputRows = inputBatch.numRows()
// if the number of input rows is 1 or less, cannot split
if (inputRows <= 1) return Array()
val vectors = GpuColumnVector.extractBases(inputBatch)
val explodingColumn = vectors(generatorOffset)
// Get the output size in bytes of the column that we are going to explode
// along with an estimate of how many output rows produced by the explode
val (explodeColOutputSize, estimatedOutputRows) =
withResource(explodingColumn.getChildColumnView(0)) { listValues =>
val totalSize = listValues.getDeviceMemorySize
// get the number of elements in the array child
var totalCount = listValues.getRowCount
// when we are calculating an explode_outer, we need to add to the row count
// the cases where the parent element has a null array, as we are going to produce
// these rows.
if (outer) {
totalCount += explodingColumn.getNullCount
}
(totalSize.toDouble, totalCount.toDouble)
}
// we know we are going to output at least this much
var estimatedOutputSizeBytes = explodeColOutputSize
val splitIndices = if (generatorOffset == 0) {
// this explodes the array column, and the table has no other columns to go
// along with it
val numSplitsForTargetSize =
math.min(inputRows,
math.ceil(estimatedOutputSizeBytes / targetSizeBytes).toInt)
GpuBatchUtils.generateSplitIndices(inputRows, numSplitsForTargetSize).distinct
} else {
withResource(new NvtxRange("EstimateRepetition", NvtxColor.BLUE)) { _ =>
// get the # of repetitions per row of the input for this explode
val perRowRepetition = getPerRowRepetition(explodingColumn, outer)
val repeatingColumns = vectors.slice(0, generatorOffset)
// get per row byte count of every column, except the exploding one
// NOTE: in the future, we may want to factor in the exploding column size
// into this math, if there is skew in the column to explode.
val repeatingByteCount = withResource(perRowRepetition) { _ =>
withResource(getRowByteCount(repeatingColumns)) { byteCountBeforeRepetition =>
byteCountBeforeRepetition.mul(perRowRepetition)
}
}
// compute prefix sum of byte sizes, this can be used to find input row
// split points at which point the output batch is estimated to be roughly
// prefixSum(row) bytes ( + exploding column size for `row`)
val prefixSum = withResource(repeatingByteCount) {
_.prefixSum
}
val splitIndices = withResource(prefixSum) { _ =>
// the last element of `repeatedSizeEstimate` is the overall sum
val repeatedSizeEstimate =
withResource(prefixSum.subVector((prefixSum.getRowCount - 1).toInt)) { lastRow =>
withResource(lastRow.copyToHost()) { hc =>
hc.getLong(0)
}
}
estimatedOutputSizeBytes += repeatedSizeEstimate
// how may splits will we need to keep the output size under the target size
val numSplitsForTargetSize =
math.min(inputRows,
math.ceil(estimatedOutputSizeBytes / targetSizeBytes).toInt)
val idealSplits = if (numSplitsForTargetSize == 0) {
Array.empty[Long]
} else {
// we need to apply the splits onto repeated size, because the prefixSum
// is only done for repeated sizes
val sizePerSplit =
math.ceil(repeatedSizeEstimate.toDouble / numSplitsForTargetSize).toLong
(1 until numSplitsForTargetSize).map { s =>
s * sizePerSplit
}.toArray
}
if (idealSplits.length == 0) {
Array.empty[Int]
} else {
val lowerBound =
withResource(new Table(prefixSum)) { prefixSumTable =>
withResource(ColumnVector.fromLongs(idealSplits: _*)) { idealBounds =>
withResource(new Table(idealBounds)) { idealBoundsTable =>
prefixSumTable.lowerBound(idealBoundsTable, OrderByArg.asc(0))
}
}
}
val largestSplitIndex = inputRows - 1
val splits = withResource(lowerBound) { _ =>
withResource(lowerBound.copyToHost) { hostBounds =>
(0 until hostBounds.getRowCount.toInt).map { s =>
// add 1 to the bound because you get the row index of the last
// row at which was smaller or equal to the bound, for example:
// prefixSum=[8, 16, 24, 32]
// if you are looking for a bound of 16, you get index 1. That said, to
// split this, you want the index to be between 16 and 24, so that's index 2.
// Make sure that the maximum bound is inputRows - 1, otherwise we can trigger
// a cuDF exception.
math.min(hostBounds.getInt(s) + 1, largestSplitIndex)
}
}
}
// apply distinct in the case of extreme skew, where for example we have all nulls
// except for 1 row that has all the data.
splits.distinct.toArray
}
}
splitIndices
}
}
// how may splits will we need to keep the output rows under max value
val numSplitsForTargetRow = math.ceil(estimatedOutputRows / maxRows).toInt
// If the number of splits needed to keep the row limits for cuDF is higher than
// the splits we found by size, we need to use the row-based splits.
// Note, given skewed input, we could be left with batches split at bad places,
// e.g. all of the non nulls are in a single split. So we may need to re-split
// that row-based slice using the size approach.
if (numSplitsForTargetRow > splitIndices.length) {
GpuBatchUtils.generateSplitIndices(inputRows, numSplitsForTargetRow)
} else {
splitIndices
}
}
// Infer result schema of GenerateExec from input schema
protected def resultSchema(inputSchema: Array[DataType],
genOffset: Int): Array[DataType] = {
val outputSchema = ArrayBuffer[DataType]()
inputSchema.zipWithIndex.foreach {
// extract output type of explode from input ArrayData
case (dataType, index) if index == genOffset =>
if (position) {
outputSchema += IntegerType
}
dataType match {
case ArrayType(elementType, _) =>
outputSchema += elementType
case MapType(keyType, valueType, _) =>
outputSchema += keyType
outputSchema += valueType
}
// map types of other required columns
case (dataType, _) =>
outputSchema += dataType
}
outputSchema.toArray
}
/**
* A function that will do the explode or position explode
*/
private[this] def explodeFun(
inputTable: Table,
genOffset: Int,
outer: Boolean,
fixUpOffset: Long): Table = {
if (position) {
val toFixUp = if (outer) {
inputTable.explodeOuterPosition(genOffset)
} else {
inputTable.explodePosition(genOffset)
}
// the position column is at genOffset and the exploded column at genOffset + 1
withResource(toFixUp) { _ =>
val posColToFix = toFixUp.getColumn(genOffset)
val fixedUpPosCol = withResource(Scalar.fromLong(fixUpOffset)) { offset =>
posColToFix.add(offset, posColToFix.getType)
}
withResource(fixedUpPosCol) { _ =>
val newCols = (0 until toFixUp.getNumberOfColumns).map { i =>
if (i == genOffset) {
fixedUpPosCol
} else {
toFixUp.getColumn(i)
}
}.toArray
new Table(newCols: _*)
}
}
} else {
if (outer) {
inputTable.explodeOuter(genOffset)
} else {
inputTable.explode(genOffset)
}
}
}
def generateSplitSpillableInHalfByRows(
genOffset: Int): BatchToGenerate => Seq[BatchToGenerate] = {
(batchToGenerate: BatchToGenerate) => {
withResource(batchToGenerate) { _ =>
val spillable = batchToGenerate.spillable
val toSplitRows = spillable.numRows()
if (toSplitRows == 1) {
// single row, we need to actually add row duplicates, then split
val tbl = withResource(spillable.getColumnarBatch()) { src =>
GpuColumnVector.from(src)
}
withResource(tbl) { _ =>
if (tbl.getColumn(genOffset).getNullCount == 1) {
// 1 row, and the element to explode is null, we cannot
// handle this case, and should never reach it either.
throw new GpuSplitAndRetryOOM(
"GPU OutOfMemory: cannot split a batch of 1 rows when " +
"the list to explode is null!")
}
val explodingColList = tbl.getColumn(genOffset)
val explodedTbl = {
withResource(explodingColList.getChildColumnView(0)) {
explodingColElements =>
if (explodingColElements.getRowCount < 2) {
throw new GpuSplitAndRetryOOM(
"GPU OutOfMemory: cannot split a batch of 1 rows when " +
"the list has a single element!")
}
withResource(explodingColElements.copyToColumnVector()) { col =>
new Table(col)
}
}
}
// Because we are likely to be here solving a cuDF column length limit
// we are just going to split in half blindly until we can fit it.
val splits = withResource(explodedTbl) { _ =>
explodedTbl.contiguousSplit(explodedTbl.getRowCount.toInt / 2)
}
val result = new Array[BatchToGenerate](splits.size)
closeOnExcept(result) { _ =>
closeOnExcept(splits) { _ =>
// start our fixup offset to the one previously computed
// in the last retry. We will need to start again at this offset,
// and sub-lists we generate need to account for it if `pos_explode`.
var offset = batchToGenerate.fixUpOffset
(0 until splits.length).foreach { split =>
val explodedTblToConvertBack = splits(split)
val cols = new Array[ColumnVector](tbl.getNumberOfColumns)
withResource(explodedTblToConvertBack) { _ =>
val colToReconstitute = explodedTblToConvertBack.getTable.getColumn(0)
closeOnExcept(cols) { _ =>
(0 until tbl.getNumberOfColumns).foreach { col =>
cols(col) = if (col == genOffset) {
// turn this column back onto a list, by pairing it with an offset
// column that is the length of the list.
require(colToReconstitute.getRowCount < Int.MaxValue,
s"The number of elements in the list to explode " +
s"${colToReconstitute.getRowCount} is larger than " +
"cuDF column row limits.")
withResource(
ColumnVector.fromInts(0, colToReconstitute.getRowCount.toInt)) {
offsets =>
colToReconstitute.makeListFromOffsets(1, offsets)
}
} else {
tbl.getColumn(col).incRefCount()
}
}
}
}
val newOffset = splits(split).getRowCount
splits(split) = null
val finalTbl = withResource(cols) { _ =>
new Table(cols: _*)
}
withResource(finalTbl) { _ =>
result(split) = new BatchToGenerate(offset, SpillableColumnarBatch(
GpuColumnVector.from(finalTbl, spillable.dataTypes),
SpillPriorities.ACTIVE_BATCHING_PRIORITY))
// we update our fixup offset for future batches, as they will
// need to add the number of rows seen so far to the position column
// if this is a `pos_explode`
offset += newOffset
}
}
}
}
result
}
} else {
// more than 1 rows, we just do the regular split-by-rows,
// we need to keep track of the fixup offset
RmmRapidsRetryIterator.splitSpillableInHalfByRows(spillable)
.map(new BatchToGenerate(batchToGenerate.fixUpOffset, _))
}
}
}
}
override def generate(
inputSpillables: Iterator[SpillableColumnarBatch],
generatorOffset: Int,
outer: Boolean): Iterator[ColumnarBatch] = {
val batchesToGenerate = inputSpillables.map(new BatchToGenerate(0, _))
withRetry(batchesToGenerate, generateSplitSpillableInHalfByRows(generatorOffset)) { attempt =>
withResource(attempt.spillable.getColumnarBatch()) { inputBatch =>
require(inputBatch.numCols() - 1 == generatorOffset,
s"Internal Error ${getClass.getSimpleName} supports one and only one input attribute.")
val schema = resultSchema(GpuColumnVector.extractTypes(inputBatch), generatorOffset)
withResource(GpuColumnVector.from(inputBatch)) { table =>
withResource(
explodeFun(table, generatorOffset, outer, attempt.fixUpOffset)) { exploded =>
child.dataType match {
case _: ArrayType =>
GpuColumnVector.from(exploded, schema)
case MapType(kt, vt, _) =>
// We need to pull the key and value of of the struct column
withResource(convertMapOutput(exploded, generatorOffset, kt, vt, outer)) { fixed =>
GpuColumnVector.from(fixed, schema)
}
case other =>
throw new IllegalArgumentException(
s"$other is not supported as explode input right now")
}
}
}
}
}
}
private[this] def convertMapOutput(exploded: Table,
genOffset: Int,
kt: DataType,
vt: DataType,
fixChildValidity: Boolean): Table = {
val numPos = if (position) 1 else 0
// scalastyle:off line.size.limit
// The input will look like the following, and we just want to expand the key, value in the
// struct into separate columns
// INDEX [0, genOffset)| genOffset | genOffset + numPos | [genOffset + numPos + 1, exploded.getNumberOfColumns)
// SOME INPUT COLUMNS | POS COLUMN? | STRUCT(KEY, VALUE) | MORE INPUT COLUMNS
// scalastyle:on line.size.limit
val structPos = genOffset + numPos
withResource(ArrayBuffer.empty[ColumnVector]) { newColumns =>
(0 until exploded.getNumberOfColumns).foreach { index =>
if (index == structPos) {
val kvStructCol = exploded.getColumn(index)
if (fixChildValidity) {
// TODO once explode outer is fixed remove the following workaround
// https://github.com/rapidsai/cudf/issues/9003
withResource(kvStructCol.isNull) { isNull =>
newColumns += withResource(kvStructCol.getChildColumnView(0)) { keyView =>
withResource(GpuScalar.from(null, kt)) { nullKey =>
isNull.ifElse(nullKey, keyView)
}
}
newColumns += withResource(kvStructCol.getChildColumnView(1)) { valueView =>
withResource(GpuScalar.from(null, vt)) { nullValue =>
isNull.ifElse(nullValue, valueView)
}
}
}
} else {
newColumns += withResource(kvStructCol.getChildColumnView(0)) { keyView =>
keyView.copyToColumnVector()
}
newColumns += withResource(kvStructCol.getChildColumnView(1)) { valueView =>
valueView.copyToColumnVector()
}
}
} else {
newColumns += exploded.getColumn(index).incRefCount()
}
}
new Table(newColumns.toSeq: _*)
}
}
override def fixedLenLazyExpressions: Seq[Expression] = child match {
// GpuLiteral of ArrayData will be converted to GpuCreateArray with GpuLiterals
case GpuCreateArray(expressions, _) => expressions
case GpuAlias(GpuCreateArray(expressions, _), _) => expressions
case _ => Nil
}
override def fixedLenLazyArrayGenerate(inputIterator: Iterator[ColumnarBatch],
boundLazyProjectList: Seq[Expression],
boundOthersProjectList: Seq[Expression],
outputSchema: Array[DataType],
outer: Boolean,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
opTime: GpuMetric): Iterator[ColumnarBatch] = {
val numArrayColumns = boundLazyProjectList.length
new Iterator[ColumnarBatch] {
var spillableCurrentBatch: SpillableColumnarBatch = _
var indexIntoData = 0
private def closeCurrent(): Unit = if (spillableCurrentBatch != null) {
spillableCurrentBatch.close()
spillableCurrentBatch = null
}
onTaskCompletion(closeCurrent())
def fetchNextBatch(): Unit = {
indexIntoData = 0
closeCurrent()
if (inputIterator.hasNext) {
spillableCurrentBatch = SpillableColumnarBatch(inputIterator.next(),
SpillPriorities.ACTIVE_BATCHING_PRIORITY)
}
}
override def hasNext: Boolean = {
if (spillableCurrentBatch == null || indexIntoData >= numArrayColumns) {
fetchNextBatch()
}
spillableCurrentBatch != null
}
override def next(): ColumnarBatch = {
withResource(new NvtxWithMetrics("GpuGenerateExec", NvtxColor.PURPLE, opTime)) { _ =>
val boundExprs = if (position) {
boundOthersProjectList ++ Seq(GpuLiteral.create(indexIntoData, IntegerType),
boundLazyProjectList(indexIntoData))
} else {
boundOthersProjectList :+ boundLazyProjectList(indexIntoData)
}
// Do projection with bound expressions
val projectCb =
GpuProjectExec.projectWithRetrySingleBatch(spillableCurrentBatch, boundExprs)
indexIntoData += 1
numOutputBatches += 1
numOutputRows += projectCb.numRows()
projectCb
}
}
}
}
}
case class GpuExplode(child: Expression) extends GpuExplodeBase {
override val position: Boolean = false
}
case class GpuPosExplode(child: Expression) extends GpuExplodeBase {
override def position: Boolean = true
}
case class GpuGenerateExec(
generator: GpuGenerator,
requiredChildOutput: Seq[Attribute],
outer: Boolean,
generatorOutput: Seq[Attribute],
child: SparkPlan) extends ShimUnaryExecNode with GpuExec {
import GpuMetric._
override lazy val additionalMetrics: Map[String, GpuMetric] = Map(
OP_TIME -> createNanoTimingMetric(MODERATE_LEVEL, DESCRIPTION_OP_TIME)
)
override def output: Seq[Attribute] = requiredChildOutput ++ generatorOutput
override def producedAttributes: AttributeSet = AttributeSet(generatorOutput)
override def outputPartitioning: Partitioning = child.outputPartitioning
override protected val outputRowsLevel: MetricsLevel = ESSENTIAL_LEVEL
override protected val outputBatchesLevel: MetricsLevel = MODERATE_LEVEL
override def doExecute(): RDD[InternalRow] =
throw new IllegalStateException(s"Row-based execution should not occur for $this")
override def internalDoExecuteColumnar(): RDD[ColumnarBatch] = {
val numOutputRows = gpuLongMetric(NUM_OUTPUT_ROWS)
val numOutputBatches = gpuLongMetric(NUM_OUTPUT_BATCHES)
val opTime = gpuLongMetric(OP_TIME)
generator.fixedLenLazyExpressions match {
// If lazy expressions can be extracted from generator,
// perform optimized lazy generation via `generator.fixedLenLazyArrayGenerate`
case expressions if expressions.nonEmpty =>
val boundLazyProjectList =
GpuBindReferences.bindGpuReferences(expressions, child.output).toArray
val boundOthersProjectList =
GpuBindReferences.bindGpuReferences(requiredChildOutput, child.output).toArray
val outputSchema = output.map(_.dataType).toArray
child.executeColumnar().mapPartitions { iter =>
generator.fixedLenLazyArrayGenerate(iter,
boundLazyProjectList,
boundOthersProjectList,
outputSchema,
outer,
numOutputRows,
numOutputBatches,
opTime)
}
// Otherwise, perform common generation via `generator.generate`
case _ =>
val genProjectList: Seq[GpuExpression] =
GpuBindReferences.bindGpuReferences(generator.children, child.output)
val othersProjectList: Seq[GpuExpression] =
GpuBindReferences.bindGpuReferences(requiredChildOutput, child.output)
child.executeColumnar().flatMap { inputFromChild =>
doGenerateAndClose(inputFromChild, genProjectList, othersProjectList,
numOutputRows, numOutputBatches, opTime)
}
}
}
private def doGenerateAndClose(input: ColumnarBatch,
genProjectList: Seq[GpuExpression],
othersProjectList: Seq[GpuExpression],
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
opTime: GpuMetric): Iterator[ColumnarBatch] = {
val splits = withResource(new NvtxWithMetrics("GpuGenerate project split",
NvtxColor.PURPLE, opTime)) { _ =>
// Project input columns, setting other columns ahead of generator's input columns.
// With the projected batches and an offset, generators can extract input columns or
// other required columns separately.
val projectedInput = GpuProjectExec.projectAndCloseWithRetrySingleBatch(
SpillableColumnarBatch(input, SpillPriorities.ACTIVE_ON_DECK_PRIORITY),
othersProjectList ++ genProjectList)
getSplits(projectedInput, othersProjectList, new RapidsConf(conf).gpuTargetBatchSizeBytes)
}
new GpuGenerateIterator(splits, generator, othersProjectList.length, outer,
numOutputRows, numOutputBatches, opTime)
}
// Split up the input batch and call generate on each split.
private def getSplits(cb: ColumnarBatch,
othersProjectList: Seq[GpuExpression],
targetSize: Long): Seq[SpillableColumnarBatch] = {
withResource(cb) { _ =>
// compute split indices of input batch
val splitIndices = generator.inputSplitIndices(
cb, othersProjectList.length, outer, targetSize)
// split up input batch with indices
makeSplits(cb, splitIndices)
}
}
// Split a ColumnarBatch according to a list of indices.
// Returns a list of spillable batches corresponding to the splits.
private def makeSplits(input: ColumnarBatch,
splitIndices: Array[Int]): Array[SpillableColumnarBatch] = {
val schema = GpuColumnVector.extractTypes(input)
if (splitIndices.isEmpty) {
// The caller will close input, so we increment here to offset.
Array(SpillableColumnarBatch(GpuColumnVector.incRefCounts(input),
SpillPriorities.ACTIVE_BATCHING_PRIORITY))
} else {
val splitInput = withResource(GpuColumnVector.from(input)) { table =>
table.contiguousSplit(splitIndices: _*)
}
closeOnExcept(splitInput) { _ =>
splitInput.zipWithIndex.safeMap { case (ct, i) =>
splitInput(i) = null
SpillableColumnarBatch(ct, schema,
SpillPriorities.ACTIVE_BATCHING_PRIORITY)
}
}
}
}
}
class BatchToGenerate(val fixUpOffset: Long, val spillable: SpillableColumnarBatch)
extends AutoCloseable {
override def close(): Unit = {
spillable.close()
}
}
class GpuGenerateIterator(
inputs: Seq[SpillableColumnarBatch],
generator: GpuGenerator,
generatorOffset: Int,
outer: Boolean,
numOutputRows: GpuMetric,
numOutputBatches: GpuMetric,
opTime: GpuMetric) extends Iterator[ColumnarBatch] with TaskAutoCloseableResource {
// Need to ensure these are closed in case of failure.
inputs.foreach(scb => use(scb))
// apply generation on each (sub)batch
private val generateIter = {
generator.generate(inputs.iterator, generatorOffset, outer)
}
override def hasNext: Boolean = generateIter.hasNext
override def next(): ColumnarBatch = {
withResource(new NvtxWithMetrics("GpuGenerateIterator", NvtxColor.PURPLE, opTime)) { _ =>
val cb = generateIter.next()
numOutputBatches += 1
numOutputRows += cb.numRows()
cb
}
}
}