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https://issues.apache.org/jira/browse/SPARK-60417?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Anna Liang updated SPARK-60417:
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Description:
`wrapped_train_fn` in `TorchDistributor._get_spark_task_function` is passed to
`DataFrame.mapInArrow`, but it is unannotated and marked `# type:
ignore[no-untyped-def]`. Removing only the comment makes mypy report "Function
is missing a type annotation [no-untyped-def]", because python/mypy.ini sets
`disallow_untyped_defs = True`. While the function is unannotated, mypy also
skips its body.
Annotate it with the `mapInArrow` contract (`Iterator[pyarrow.RecordBatch]` in
and out, see `ArrowMapIterFunction` in `pyspark/sql/connect/_typing.py`).
`pyarrow` is only imported inside the function. So the annotation is a string
and `pyarrow` is imported under `TYPE_CHECKING`
Once the body is checked, mypy reports that the 2nd argument passed to
"_setup_spark_partition_data" has type "dict[str, Any] | None" but the parameter
(input_schema_json) expects "dict[str, Any]". The argument is None when no
input DataFrame is given, and _setup_spark_partition_data already returns early
in that case ("if input_schema_json is None: yield; return"), so its
annotation is widened to Optional.
was:
`wrapped_train_fn` in `TorchDistributor._get_task_function` is passed to
`DataFrame.mapInArrow`, but it is unannotated and marked `# type:
ignore[no-untyped-def]`. Removing only the comment makes mypy report "Function
is missing a type annotation [no-untyped-def]", because python/mypy.ini sets
`disallow_untyped_defs = True`. While the function is unannotated, mypy also
skips its body.
Annotate it with the `mapInArrow` contract (`Iterator[pyarrow.RecordBatch]` in
and out, see `ArrowMapIterFunction` in `pyspark/sql/connect/_typing.py`).
`pyarrow` is only imported inside the function. So the annotation is a string
and `pyarrow` is imported under `TYPE_CHECKING`
Once the body is checked, mypy reports that the 2nd argument passed to
"_setup_spark_partition_data" has type "dict[str, Any] | None" but the parameter
(input_schema_json) expects "dict[str, Any]". The argument is None when no
input DataFrame is given, and _setup_spark_partition_data already returns early
in that case ("if input_schema_json is None: yield; return"), so its
annotation is widened to Optional.
> Remove type: ignore[no-untyped-def] in pyspark.ml.torch.distributor
> -------------------------------------------------------------------
>
> Key: SPARK-60417
> URL: https://issues.apache.org/jira/browse/SPARK-60417
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Affects Versions: 4.4.0
> Reporter: Anna Liang
> Priority: Minor
>
> `wrapped_train_fn` in `TorchDistributor._get_spark_task_function` is passed
> to `DataFrame.mapInArrow`, but it is unannotated and marked `# type:
> ignore[no-untyped-def]`. Removing only the comment makes mypy report
> "Function is missing a type annotation [no-untyped-def]", because
> python/mypy.ini sets `disallow_untyped_defs = True`. While the function is
> unannotated, mypy also skips its body.
>
> Annotate it with the `mapInArrow` contract (`Iterator[pyarrow.RecordBatch]`
> in and out, see `ArrowMapIterFunction` in `pyspark/sql/connect/_typing.py`).
> `pyarrow` is only imported inside the function. So the annotation is a string
> and `pyarrow` is imported under `TYPE_CHECKING`
>
> Once the body is checked, mypy reports that the 2nd argument passed to
> "_setup_spark_partition_data" has type "dict[str, Any] | None" but the
> parameter
> (input_schema_json) expects "dict[str, Any]". The argument is None when no
> input DataFrame is given, and _setup_spark_partition_data already returns
> early
> in that case ("if input_schema_json is None: yield; return"), so its
> annotation is widened to Optional.
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