[jira] [Work logged] (BEAM-4091) Typehint annotations don't work with @ptransform_fn annotation

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     [ https://issues.apache.org/jira/browse/BEAM-4091?focusedWorklogId=342839&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-342839 ]

ASF GitHub Bot logged work on BEAM-4091:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 13/Nov/19 19:51
            Start Date: 13/Nov/19 19:51
    Worklog Time Spent: 10m 
      Work Description: aaltay commented on issue #9907: [BEAM-4091] Pass type hints in ptransform_fn
URL: https://github.com/apache/beam/pull/9907#issuecomment-553572760
 
 
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Issue Time Tracking
-------------------

            Worklog Id:     (was: 342839)
    Remaining Estimate: 0h
            Time Spent: 10m

> Typehint annotations don't work with @ptransform_fn annotation
> --------------------------------------------------------------
>
>                 Key: BEAM-4091
>                 URL: https://issues.apache.org/jira/browse/BEAM-4091
>             Project: Beam
>          Issue Type: Bug
>          Components: sdk-py-core
>    Affects Versions: 2.4.0
>            Reporter: Chuan Yu Foo
>            Assignee: Udi Meiri
>            Priority: Major
>          Time Spent: 10m
>  Remaining Estimate: 0h
>
> Typehint annotations don't work with functions annotated with {{@ptransform_fn}}, but they do work with the equivalent classes.
> The following is a minimal example illustrating this:
> {code:python}
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> @beam.ptransform_fn
> def _DoStuffFn(pcoll):
>   return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> class _DoStuffClass(beam.PTransform):
>   def expand(self, pcoll):
>     return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> {code}
> With definitions as above, the class correctly fails the typecheck:
> {code:python}
> def class_correctly_fails():
>   p = beam.Pipeline(options=PipelineOptions(runtime_type_check=True))
>   _ = (p
>        | 'Create' >> beam.Create([1, 2, 3, 4, 5])
>        | 'DoStuff1' >> _DoStuffClass()
>        | 'DoStuff2' >> _DoStuffClass()
>        | 'Write' >> beam.io.WriteToText('/tmp/output'))
>   p.run().wait_until_finish()
> # apache_beam.typehints.decorators.TypeCheckError: Input type hint violation at DoStuff1: expected <type 'float'>, got <type 'int'>
> {code}
> But the {{ptransform_fn}} incorrectly passes the typecheck:
> {code:python}
> def ptransform_incorrectly_passes():
>   p = beam.Pipeline(options=PipelineOptions(runtime_type_check=True))
>   _ = (p
>        | 'Create' >> beam.Create([1, 2, 3, 4, 5])
>        | 'DoStuff1' >> _DoStuffFn()
>        | 'DoStuff2' >> _DoStuffFn()
>        | 'Write' >> beam.io.WriteToText('/tmp/output'))
>   p.run().wait_until_finish()
> # No error
> {code}
> Note that changing the order of the {{@ptransform_fn}} and type hint annotations doesn't change the result, i.e. changing {{_DoStuffFn}} to the following still results in it incorrectly passing the typecheck:
> {code:python}
> @beam.ptransform_fn
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> def _DoStuffFn(pcoll):
>   return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> {code}



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