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Support executing collect_list on GPU with windowing. #1548

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merged 15 commits into from
Feb 5, 2021
Merged

Support executing collect_list on GPU with windowing. #1548

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838344d
Support collect_list on GPU for windowing.
firestarman Jan 19, 2021
54a9ed5
Let collect_list supports array of struct.
firestarman Jan 27, 2021
371b83f
Only allow array type for collect_list
firestarman Jan 27, 2021
42a5408
Restore the cast for non nested type.
firestarman Jan 27, 2021
63aa7a0
Updated the type check
firestarman Jan 28, 2021
bfef795
Add integration tests for collect_list.
firestarman Jan 28, 2021
127ff2e
Doc updates
firestarman Jan 28, 2021
971fcaa
Address some comments
firestarman Jan 29, 2021
171c495
Comment update
firestarman Jan 29, 2021
4592a81
Addressed some new comments
firestarman Feb 1, 2021
d8c2121
Add an empty line at the end
firestarman Feb 1, 2021
549f11d
Merge branch 'branch-0.4' into collect_list
firestarman Feb 2, 2021
24a864b
Merge branch 'branch-0.4' into collect_list
firestarman Feb 4, 2021
929602b
Merge branch 'branch-0.4' into collect_list
firestarman Feb 5, 2021
3a5f94e
Addressed some new comments again
firestarman Feb 5, 2021
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1 change: 1 addition & 0 deletions docs/configs.md
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Expand Up @@ -246,6 +246,7 @@ Name | SQL Function(s) | Description | Default Value | Notes
<a name="sql.expression.Year"></a>spark.rapids.sql.expression.Year|`year`|Returns the year from a date or timestamp|true|None|
<a name="sql.expression.AggregateExpression"></a>spark.rapids.sql.expression.AggregateExpression| |Aggregate expression|true|None|
<a name="sql.expression.Average"></a>spark.rapids.sql.expression.Average|`avg`, `mean`|Average aggregate operator|true|None|
<a name="sql.expression.CollectList"></a>spark.rapids.sql.expression.CollectList|`collect_list`|Collect a list of elements, now only supported by windowing.|false|This is disabled by default because for now the GPU collects null values to a list, but Spark does not. This will be fixed in future releases.|
<a name="sql.expression.Count"></a>spark.rapids.sql.expression.Count|`count`|Count aggregate operator|true|None|
<a name="sql.expression.First"></a>spark.rapids.sql.expression.First|`first_value`, `first`|first aggregate operator|true|None|
<a name="sql.expression.Last"></a>spark.rapids.sql.expression.Last|`last`, `last_value`|last aggregate operator|true|None|
Expand Down
67 changes: 57 additions & 10 deletions docs/supported_ops.md
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Expand Up @@ -745,9 +745,9 @@ Accelerator supports are described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
</tr>
</table>
Expand Down Expand Up @@ -15227,7 +15227,7 @@ Accelerator support is described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15269,7 +15269,7 @@ Accelerator support is described below.
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15453,7 +15453,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15495,7 +15495,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand All @@ -15517,7 +15517,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15559,7 +15559,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand All @@ -15581,7 +15581,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15623,7 +15623,7 @@ Accelerator support is described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -15762,6 +15762,53 @@ Accelerator support is described below.
<td> </td>
</tr>
<tr>
<td rowSpan="2">CollectList</td>
<td rowSpan="2">`collect_list`</td>
<td rowSpan="2">Collect a list of elements, now only supported by windowing.</td>
<td rowSpan="2">This is disabled by default because for now the GPU collects null values to a list, but Spark does not. This will be fixed in future releases.</td>
<td rowSpan="2">window</td>
<td>input</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S</td>
<td>S*</td>
<td>S</td>
<td>S*</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>result</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td rowSpan="6">Count</td>
<td rowSpan="6">`count`</td>
<td rowSpan="6">Count aggregate operator</td>
Expand Down
51 changes: 51 additions & 0 deletions integration_tests/src/main/python/window_function_test.py
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Expand Up @@ -213,3 +213,54 @@ def test_window_aggs_for_ranges_of_dates(data_gen):
' range between 1 preceding and 1 following) as sum_c_asc '
'from window_agg_table'
)


'''
Spark will drop nulls when collecting, but seems GPU does not, so exceptions come up.
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E Caused by: java.lang.AssertionError: value at 350 is null
E at ai.rapids.cudf.HostColumnVectorCore.assertsForGet(HostColumnVectorCore.java:228)
E at ai.rapids.cudf.HostColumnVectorCore.getInt(HostColumnVectorCore.java:254)
E at com.nvidia.spark.rapids.RapidsHostColumnVectorCore.getInt(RapidsHostColumnVectorCore.java:109)
E at org.apache.spark.sql.vectorized.ColumnarArray.getInt(ColumnarArray.java:128)

Now set nullable to false to pass the tests, once native supports dropping nulls, will set it to true.
'''
collect_data_gen = [
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('a', RepeatSeqGen(LongGen(), length=20)),
('b', IntegerGen()),
('c_int', IntegerGen(nullable=False)),
('c_long', LongGen(nullable=False)),
('c_time', DateGen(nullable=False)),
('c_string', StringGen(nullable=False)),
('c_float', FloatGen(nullable=False)),
('c_decimal', DecimalGen(nullable=False, precision=8, scale=3)),
('c_struct', StructGen(nullable=False, children = [
['child_int', IntegerGen()],
['child_time', DateGen()],
['child_string', StringGen()],
['child_decimal', DecimalGen(nullable=False, precision=8, scale=3)]]))]

# SortExec does not support array type, so sort the result locally.
@ignore_order(local=True)
@pytest.mark.parametrize('data_gen', [collect_data_gen], ids=idfn)
def test_window_aggs_for_rows_collect_list(data_gen):
assert_gpu_and_cpu_are_equal_sql(
lambda spark : gen_df(spark, data_gen, length=2048),
"window_collect_table",
'select '
' collect_list(c_int) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_int, '
' collect_list(c_long) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_long, '
' collect_list(c_time) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_time, '
' collect_list(c_string) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_string, '
' collect_list(c_float) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_float, '
' collect_list(c_decimal) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_decimal, '
' collect_list(c_struct) over '
' (partition by a order by b,c_int rows between UNBOUNDED preceding and CURRENT ROW) as collect_struct '
'from window_collect_table ',
{'spark.rapids.sql.expression.CollectList': 'true'})
34 changes: 26 additions & 8 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides.scala
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Expand Up @@ -734,12 +734,11 @@ object GpuOverrides {
"Calculates a return value for every input row of a table based on a group (or " +
"\"window\") of rows",
ExprChecks.windowOnly(
TypeSig.commonCudfTypes + TypeSig.DECIMAL +
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL),
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested + TypeSig.ARRAY.nested(TypeSig.STRUCT),
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TypeSig.all,
Seq(ParamCheck("windowFunction",
TypeSig.commonCudfTypes + TypeSig.DECIMAL +
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL),
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested +
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TypeSig.ARRAY.nested(TypeSig.STRUCT),
TypeSig.all),
ParamCheck("windowSpec",
TypeSig.CALENDAR + TypeSig.NULL + TypeSig.integral + TypeSig.DECIMAL,
Expand Down Expand Up @@ -1644,11 +1643,13 @@ object GpuOverrides {
expr[AggregateExpression](
"Aggregate expression",
ExprChecks.fullAgg(
TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL,
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.NULL +
TypeSig.ARRAY.nested(TypeSig.STRUCT),
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TypeSig.all,
Seq(ParamCheck(
"aggFunc",
TypeSig.commonCudfTypes + TypeSig.NULL + TypeSig.DECIMAL,
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.NULL +
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TypeSig.ARRAY.nested(TypeSig.STRUCT),
TypeSig.all)),
Some(RepeatingParamCheck("filter", TypeSig.BOOLEAN, TypeSig.BOOLEAN))),
(a, conf, p, r) => new ExprMeta[AggregateExpression](a, conf, p, r) {
Expand Down Expand Up @@ -2170,7 +2171,22 @@ object GpuOverrides {
(a, conf, p, r) => new UnaryExprMeta[MakeDecimal](a, conf, p, r) {
override def convertToGpu(child: Expression): GpuExpression =
GpuMakeDecimal(child, a.precision, a.scale, a.nullOnOverflow)
})
}),
expr[CollectList](
"Collect a list of elements, now only supported by windowing.",
/* It should be 'fullAgg' eventually but now only support windowing,
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so 'aggNotGroupByOrReduction' */
ExprChecks.aggNotGroupByOrReduction(
TypeSig.ARRAY.nested(TypeSig.commonCudfTypes + TypeSig.DECIMAL + TypeSig.STRUCT),
TypeSig.ARRAY.nested(TypeSig.all),
Seq(ParamCheck("input",
(TypeSig.commonCudfTypes + TypeSig.DECIMAL).nested() + TypeSig.STRUCT,
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again I think longer is clearer, but in this case because there is only one nested type I think it is okay. So we don't support doing a collect list on a struct that also contains a struct?

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This should be supported. If it's not working, it might be a bug in libcudf. I'd be happy to investigate.

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@firestarman firestarman Feb 5, 2021 •
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Updated it, and the struct of struct is not covered in integration tests, so do not add it here. Shall we need it ?

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if yes, i will add it in a following PR

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Sounds good to me

TypeSig.all))),
(c, conf, p, r) => new ExprMeta[CollectList](c, conf, p, r) {
override def convertToGpu(): GpuExpression = GpuCollectList(
childExprs.head.convertToGpu(), c.mutableAggBufferOffset, c.inputAggBufferOffset)
}).disabledByDefault("for now the GPU collects null values to a list, but Spark does not." +
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" This will be fixed in future releases.")
).map(r => (r.getClassFor.asSubclass(classOf[Expression]), r)).toMap

val expressions: Map[Class[_ <: Expression], ExprRule[_ <: Expression]] =
Expand Down Expand Up @@ -2499,7 +2515,9 @@ object GpuOverrides {
(expand, conf, p, r) => new GpuExpandExecMeta(expand, conf, p, r)),
exec[WindowExec](
"Window-operator backend",
ExecChecks(TypeSig.commonCudfTypes + TypeSig.DECIMAL, TypeSig.all),
ExecChecks(
(TypeSig.commonCudfTypes + TypeSig.DECIMAL + TypeSig.STRUCT).nested() + TypeSig.ARRAY,
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TypeSig.all),
(windowOp, conf, p, r) =>
new GpuWindowExecMeta(windowOp, conf, p, r)
),
Expand Down
32 changes: 18 additions & 14 deletions sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuWindowExpression.scala
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Expand Up @@ -199,13 +199,15 @@ case class GpuWindowExpression(windowFunction: Expression, windowSpec: GpuWindow
}
}
}
val expectedType = GpuColumnVector.getNonNestedRapidsType(windowFunc.dataType)
if (expectedType != aggColumn.getType) {
withResource(aggColumn) { aggColumn =>
GpuColumnVector.from(aggColumn.castTo(expectedType), windowFunc.dataType)
}
} else {
GpuColumnVector.from(aggColumn, windowFunc.dataType)
// For nested type, do not cast
aggColumn.getType match {
case dType if dType.isNestedType =>
GpuColumnVector.from(aggColumn, windowFunc.dataType)
case _ =>
val expectedType = GpuColumnVector.getNonNestedRapidsType(windowFunc.dataType)
withResource(aggColumn) { aggColumn =>
GpuColumnVector.from(aggColumn.castTo(expectedType), windowFunc.dataType)
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}
}
}

Expand All @@ -230,13 +232,15 @@ case class GpuWindowExpression(windowFunction: Expression, windowSpec: GpuWindow
}
}
}
val expectedType = GpuColumnVector.getNonNestedRapidsType(windowFunc.dataType)
if (expectedType != aggColumn.getType) {
withResource(aggColumn) { aggColumn =>
GpuColumnVector.from(aggColumn.castTo(expectedType), windowFunc.dataType)
}
} else {
GpuColumnVector.from(aggColumn, windowFunc.dataType)
// For nested type, do not cast
aggColumn.getType match {
case dType if dType.isNestedType =>
GpuColumnVector.from(aggColumn, windowFunc.dataType)
case _ =>
val expectedType = GpuColumnVector.getNonNestedRapidsType(windowFunc.dataType)
withResource(aggColumn) { aggColumn =>
GpuColumnVector.from(aggColumn.castTo(expectedType), windowFunc.dataType)
}
}
}
}
Expand Down
14 changes: 13 additions & 1 deletion sql-plugin/src/main/scala/com/nvidia/spark/rapids/TypeChecks.scala
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Expand Up @@ -985,7 +985,7 @@ object ExprChecks {
}

/**
* Window only operations. Spark does not support these operations as anythign but a window
* Window only operations. Spark does not support these operations as anything but a window
* operation.
*/
def windowOnly(
Expand All @@ -996,6 +996,18 @@ object ExprChecks {
ExprChecksImpl(Map(
(WindowAggExprContext,
ContextChecks(outputCheck, sparkOutputSig, paramCheck, repeatingParamCheck))))

/**
* An aggregation check where window operations are supported by the plugin, but Spark
* also supports group by and reduction on these.
* This is now really for 'collect_list' which is only supported by windowing.
*/
def aggNotGroupByOrReduction(
outputCheck: TypeSig,
sparkOutputSig: TypeSig,
paramCheck: Seq[ParamCheck] = Seq.empty,
repeatingParamCheck: Option[RepeatingParamCheck] = None): ExprChecks =
windowOnly(outputCheck, sparkOutputSig, paramCheck, repeatingParamCheck)
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}

/**
Expand Down
43 changes: 41 additions & 2 deletions sql-plugin/src/main/scala/org/apache/spark/sql/rapids/AggregateFunctions.scala
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Expand Up @@ -22,9 +22,9 @@ import com.nvidia.spark.rapids._

import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
import org.apache.spark.sql.catalyst.expressions.{AttributeReference, AttributeSet, Expression, ExprId, ImplicitCastInputTypes}
import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateMode, Complete, Final, Partial, PartialMerge}
import org.apache.spark.sql.catalyst.expressions.aggregate._
import org.apache.spark.sql.catalyst.util.TypeUtils
import org.apache.spark.sql.types.{AbstractDataType, AnyDataType, BooleanType, DataType, DoubleType, LongType, NumericType, StructType}
import org.apache.spark.sql.types._

trait GpuAggregateFunction extends GpuExpression {
// using the child reference, define the shape of the vectors sent to
Expand Down Expand Up @@ -529,3 +529,42 @@ abstract class GpuLastBase(child: Expression)
override lazy val deterministic: Boolean = false
override def toString: String = s"gpulast($child)${if (ignoreNulls) " ignore nulls"}"
}

/**
* Collects and returns a list of non-unique elements.
*
* FIXME Not sure whether GPU version requires the two offset parameters. Keep it here first.
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*/
case class GpuCollectList(child: Expression,
mutableAggBufferOffset: Int = 0,
inputAggBufferOffset: Int = 0)
extends GpuDeclarativeAggregate with GpuAggregateWindowFunction {

def this(child: Expression) = this(child, 0, 0)

override lazy val deterministic: Boolean = false
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override def nullable: Boolean = false

override def prettyName: String = "collect_list"

override def dataType: DataType = ArrayType(child.dataType, false)

override def children: Seq[Expression] = child :: Nil

// WINDOW FUNCTION
override val windowInputProjection: Seq[Expression] = Seq(child)
override def windowAggregation(inputs: Seq[(ColumnVector, Int)]): AggregationOnColumn =
Aggregation.collect().onColumn(inputs.head._2)

// Declarative aggregate. But for now 'CollectList' does not support it.
// The members as below should NOT be used yet, ensured by the
// "TypeCheck.aggNotGroupByOrReduction" when trying to override the expression.
private lazy val cudfList = AttributeReference("collect_list", dataType)()
override val initialValues: Seq[GpuExpression] = Seq.empty
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override val updateExpressions: Seq[Expression] = Seq.empty
override val mergeExpressions: Seq[GpuExpression] = Seq.empty
override val evaluateExpression: Expression = null
override val inputProjection: Seq[Expression] = Seq(child)
override def aggBufferAttributes: Seq[AttributeReference] = cudfList :: Nil
}