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Support GpuCollectList and GpuCollectSet as TypedImperativeAggregate #2971

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merged 17 commits into from
Aug 3, 2021
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Support GpuCollectList and GpuCollectSet as TypedImperativeAggregate #2971

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e0bdf54
support CollectOps as TypedImperativeAggregate functions
sperlingxx Jul 20, 2021
2cc6ea0
add GpuColumnVector.getRapidsType
sperlingxx Jul 20, 2021
17958ec
add some comments
sperlingxx Jul 20, 2021
6f9c4e4
fix scala code style
sperlingxx Jul 20, 2021
108d122
support associated fallback on TypedImperativeAggregate
sperlingxx Jul 21, 2021
099fc5f
add AQE support
sperlingxx Jul 21, 2021
8a5ae94
Update sql-plugin/src/main/scala/com/nvidia/spark/rapids/GpuOverrides…
sperlingxx Jul 23, 2021
9b469be
Apply suggestions from code review
sperlingxx Jul 23, 2021
246fbf0
Merge remote-tracking branch 'origin/branch-21.10' into gpu_collect_ops
sperlingxx Jul 23, 2021
8c46a93
rework associated fallback
sperlingxx Jul 23, 2021
edadc21
add some comment
sperlingxx Jul 23, 2021
0118cd3
remove ENABLE_TYPED_IMPERATIVE_AGGREGATE
sperlingxx Jul 28, 2021
dadd585
refine
sperlingxx Jul 28, 2021
41a1613
refine
sperlingxx Jul 28, 2021
3bbc9aa
update
sperlingxx Jul 30, 2021
66084d4
test enhencement
sperlingxx Aug 2, 2021
28d3fda
make up some comments
sperlingxx Aug 3, 2021
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5 changes: 3 additions & 2 deletions docs/configs.md
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Original file line number Diff line number Diff line change
Expand Up @@ -294,8 +294,8 @@ 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 non-unique elements, only supported in rolling window in current.|true|None|
<a name="sql.expression.CollectSet"></a>spark.rapids.sql.expression.CollectSet|`collect_set`|Collect a set of unique elements, only supported in rolling window in current.|true|None|
<a name="sql.expression.CollectList"></a>spark.rapids.sql.expression.CollectList|`collect_list`|Collect a list of non-unique elements, not supported in reduction.|true|None|
<a name="sql.expression.CollectSet"></a>spark.rapids.sql.expression.CollectSet|`collect_set`|Collect a set of unique elements, not supported in reduction.|true|None|
<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 Expand Up @@ -327,6 +327,7 @@ Name | Description | Default Value | Notes
<a name="sql.exec.UnionExec"></a>spark.rapids.sql.exec.UnionExec|The backend for the union operator|true|None|
<a name="sql.exec.CustomShuffleReaderExec"></a>spark.rapids.sql.exec.CustomShuffleReaderExec|A wrapper of shuffle query stage|true|None|
<a name="sql.exec.HashAggregateExec"></a>spark.rapids.sql.exec.HashAggregateExec|The backend for hash based aggregations|true|None|
<a name="sql.exec.ObjectHashAggregateExec"></a>spark.rapids.sql.exec.ObjectHashAggregateExec|The backend for hash based aggregations supporting TypedImperativeAggregate functions|true|None|
<a name="sql.exec.SortAggregateExec"></a>spark.rapids.sql.exec.SortAggregateExec|The backend for sort based aggregations|true|None|
<a name="sql.exec.DataWritingCommandExec"></a>spark.rapids.sql.exec.DataWritingCommandExec|Writing data|true|None|
<a name="sql.exec.BatchScanExec"></a>spark.rapids.sql.exec.BatchScanExec|The backend for most file input|true|None|
Expand Down
137 changes: 80 additions & 57 deletions docs/supported_ops.md
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Expand Up @@ -495,8 +495,8 @@ Accelerator supports are described below.
<th>UDT</th>
</tr>
<tr>
<td>SortAggregateExec</td>
<td>The backend for sort based aggregations</td>
<td>ObjectHashAggregateExec</td>
<td>The backend for hash based aggregations supporting TypedImperativeAggregate functions</td>
<td>None</td>
<td>S</td>
<td>S</td>
Expand All @@ -512,10 +512,33 @@ Accelerator supports are described below.
<td>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
<td><em>PS (missing nested BOOLEAN, BYTE, SHORT, INT, LONG, FLOAT, DOUBLE, DATE, TIMESTAMP, DECIMAL, NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
</tr>
<tr>
<td>SortAggregateExec</td>
<td>The backend for sort based aggregations</td>
<td>None</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>S</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><em>PS* (not allowed for grouping expressions; missing nested BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>DataWritingCommandExec</td>
Expand Down Expand Up @@ -817,29 +840,6 @@ Accelerator supports are described below.
<td><b>NS</b></td>
</tr>
<tr>
<td>WindowInPandasExec</td>
<td>The backend for Window Aggregation Pandas UDF, Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled. For now it only supports row based window frame.</td>
<td>This is disabled by default because it only supports row based frame for now</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><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
</tr>
<tr>
<th>Executor</th>
<th>Description</th>
<th>Notes</th>
Expand All @@ -863,6 +863,29 @@ Accelerator supports are described below.
<th>UDT</th>
</tr>
<tr>
<td>WindowInPandasExec</td>
<td>The backend for Window Aggregation Pandas UDF, Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled. For now it only supports row based window frame.</td>
<td>This is disabled by default because it only supports row based frame for now</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><em>PS* (missing nested DECIMAL, NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
<td><b>NS</b></td>
</tr>
<tr>
<td>WindowExec</td>
<td>Window-operator backend</td>
<td>None</td>
Expand Down Expand Up @@ -18305,9 +18328,9 @@ Accelerator support is described below.
<tr>
<td rowSpan="6">CollectList</td>
<td rowSpan="6">`collect_list`</td>
<td rowSpan="6">Collect a list of non-unique elements, only supported in rolling window in current.</td>
<td rowSpan="6">Collect a list of non-unique elements, not supported in reduction.</td>
<td rowSpan="6">None</td>
<td rowSpan="2">aggregation</td>
<td rowSpan="2">reduction</td>
<td>input</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -18350,25 +18373,25 @@ Accelerator support is described below.
<td> </td>
</tr>
<tr>
<td rowSpan="2">reduction</td>
<td rowSpan="2">aggregation</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><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><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><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>
Expand All @@ -18387,7 +18410,7 @@ Accelerator support is described below.
<td> </td>
<td> </td>
<td> </td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, UDT)</em></td>
<td> </td>
<td> </td>
<td> </td>
Expand Down Expand Up @@ -18438,9 +18461,9 @@ Accelerator support is described below.
<tr>
<td rowSpan="6">CollectSet</td>
<td rowSpan="6">`collect_set`</td>
<td rowSpan="6">Collect a set of unique elements, only supported in rolling window in current.</td>
<td rowSpan="6">Collect a set of unique elements, not supported in reduction.</td>
<td rowSpan="6">None</td>
<td rowSpan="2">aggregation</td>
<td rowSpan="2">reduction</td>
<td>input</td>
<td><b>NS</b></td>
<td><b>NS</b></td>
Expand Down Expand Up @@ -18483,19 +18506,19 @@ Accelerator support is described below.
<td> </td>
</tr>
<tr>
<td rowSpan="2">reduction</td>
<td rowSpan="2">aggregation</td>
<td>input</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><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><b>NS</b></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>
Expand All @@ -18520,7 +18543,7 @@ Accelerator support is described below.
<td> </td>
<td> </td>
<td> </td>
<td><b>NS</b></td>
<td><em>PS* (missing nested NULL, BINARY, CALENDAR, ARRAY, MAP, STRUCT, UDT)</em></td>
<td> </td>
<td> </td>
<td> </td>
Expand Down
119 changes: 119 additions & 0 deletions integration_tests/src/main/python/hash_aggregate_test.py
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Expand Up @@ -375,6 +375,125 @@ def test_hash_reduction_pivot_without_nans(data_gen, conf):
.agg(f.sum('c')),
conf=conf)

_repeat_agg_column_for_collect_op = [
RepeatSeqGen(BooleanGen(), length=15),
RepeatSeqGen(IntegerGen(), length=15),
RepeatSeqGen(LongGen(), length=15),
RepeatSeqGen(ShortGen(), length=15),
RepeatSeqGen(DateGen(), length=15),
RepeatSeqGen(TimestampGen(), length=15),
RepeatSeqGen(ByteGen(), length=15),
RepeatSeqGen(StringGen(), length=15),
RepeatSeqGen(FloatGen(), length=15),
RepeatSeqGen(DoubleGen(), length=15),
RepeatSeqGen(DecimalGen(precision=8, scale=3), length=15),
# case to verify the NAN_UNEQUAL strategy
RepeatSeqGen(FloatGen().with_special_case(math.nan, 200.0), length=5),
]

_gen_data_for_collect_op = [[
('a', RepeatSeqGen(LongGen(), length=20)),
('b', value_gen),
('c', LongRangeGen())] for value_gen in _repeat_agg_column_for_collect_op
]

@approximate_float
@ignore_order(local=True)
@incompat
@pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
@pytest.mark.parametrize('use_obj_hash_agg', [True, False], ids=idfn)
def test_hash_groupby_collect_list(data_gen, use_obj_hash_agg):
assert_gpu_and_cpu_are_equal_collect(
lambda spark: gen_df(spark, data_gen, length=100)
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.groupby('a')
.agg(f.sort_array(f.collect_list('b')), f.count('b')),
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It seems every collect_* function is wrapped in a sort_array. Is that on purpose? Could a comment be added somewhere on why? Especially because we have @ignore_order so I was curious.

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It is because @ignore_order only ensures the order between rows, while in these cases we also need to take care of the orders of each Array produced by collect ops.
And I added this comment to the test file.

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Makes sense. I’d extend ignore_order to do the sorting after the collect for the array case. The reason being you would be able to test the aggregate in another way a user is likely to invoke.

I’m ok if you want to do that as a follow up also.

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I would be fine with an @ignore_array_order or something like that. I'd rather not have @ignore_order cover both.

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But do it as a follow on issue if we do it at all.

conf={'spark.sql.execution.useObjectHashAggregateExec': str(use_obj_hash_agg).lower()})

@approximate_float
@ignore_order(local=True)
@incompat
@pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
def test_hash_groupby_collect_set(data_gen):
assert_gpu_and_cpu_are_equal_collect(
lambda spark: gen_df(spark, data_gen, length=100)
.groupby('a')
.agg(f.sort_array(f.collect_set('b')), f.count('b')))

@approximate_float
@ignore_order(local=True)
@incompat
@pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
def test_hash_groupby_collect_with_single_distinct(data_gen):
# test collect_ops with other distinct aggregations
assert_gpu_and_cpu_are_equal_collect(
lambda spark: gen_df(spark, data_gen, length=100)
.groupby('a')
.agg(f.sort_array(f.collect_list('b')),
f.sort_array(f.collect_set('b')),
f.countDistinct('c'),
f.count('c')))

# test distinct collect with other aggregations
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sql = """select a,
sort_array(collect_list(distinct b)),
sort_array(collect_set(b)),
count(distinct b),
count(c)
from tbl group by a"""
assert_gpu_and_cpu_are_equal_sql(
df_fun=lambda spark: gen_df(spark, data_gen, length=100),
table_name="tbl", sql=sql)

# Queries with multiple distinct aggregations will fallback to CPU if they also contain
# collect aggregations. Because Spark optimizer will insert expressions like `If` and `First`
# when rewriting distinct aggregates, while `GpuIf` and `GpuFirst` doesn't support the datatype
# of collect aggregations (ArrayType).
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Can you dd in references to the issue to support these for GpuIF and GpuFirst? If they do not exist, then could you please file them?

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Yes, I filed the issue.

@approximate_float
@ignore_order(local=True)
@allow_non_gpu('SortAggregateExec',
'SortArray', 'Alias', 'Literal', 'First', 'If', 'EqualTo', 'Count',
'CollectList', 'CollectSet', 'AggregateExpression')
@incompat
@pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
def test_hash_groupby_collect_with_multi_distinct_fallback(data_gen):
sql = """select a,
sort_array(collect_list(b)),
sort_array(collect_set(b)),
count(distinct b),
count(distinct c)
from tbl group by a"""
assert_gpu_and_cpu_are_equal_sql(
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Typically when we have a fallback test we want an assertion that verifies part of the code actually did fall back like assert_gpu_sql_fallback_collect

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I added the check of fallback capture.

df_fun=lambda spark: gen_df(spark, data_gen, length=100),
table_name="tbl",
sql=sql)

@approximate_float
@ignore_order(local=True)
@allow_non_gpu('ObjectHashAggregateExec', 'ShuffleExchangeExec',
'HashPartitioning', 'SortArray', 'Alias', 'Literal',
'Count', 'CollectList', 'CollectSet', 'AggregateExpression')
@incompat
@pytest.mark.parametrize('data_gen', _gen_data_for_collect_op, ids=idfn)
@pytest.mark.parametrize('conf', [_nans_float_conf_partial, _nans_float_conf_final], ids=idfn)
@pytest.mark.parametrize('aqe_enabled', ['true', 'false'], ids=idfn)
def test_hash_groupby_collect_partial_replace_fallback(data_gen, conf, aqe_enabled):
conf.update({'spark.sql.adaptive.enabled': aqe_enabled})
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Please always copy conf before doing an update. We have seen issues with global values being modified by tests doing this and it is just good practice.

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Fixed.

# test without Distinct
assert_gpu_and_cpu_are_equal_collect(
lambda spark: gen_df(spark, data_gen, length=100)
.groupby('a')
.agg(f.sort_array(f.collect_list('b')), f.sort_array(f.collect_set('b'))),
conf=conf)
# test with single Distinct
assert_gpu_and_cpu_are_equal_collect(
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Here too if the tests are slightly different, then lets have a different test function for each test case.

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Refined.

lambda spark: gen_df(spark, data_gen, length=100)
.groupby('a')
.agg(f.sort_array(f.collect_list('b')),
f.sort_array(f.collect_set('b')),
f.countDistinct('c'),
f.count('c')),
conf=conf)

@approximate_float
@ignore_order
@incompat
Expand Down
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