[jira] [Work logged] (BEAM-4132) Element type inference doesn't work for multi-output DoFns

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ASF GitHub Bot logged work on BEAM-4132:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 24/Oct/19 20:01
            Start Date: 24/Oct/19 20:01
    Worklog Time Spent: 10m 
      Work Description: robertwb commented on pull request #9810: [BEAM-4132] 
Support multi-output type inference
URL: https://github.com/apache/beam/pull/9810#discussion_r338765251
 
 

 ##########
 File path: sdks/python/apache_beam/pipeline.py
 ##########
 @@ -571,6 +573,10 @@ def _infer_result_type(self, transform, inputs, 
result_pcollection):
       else:
         result_pcollection.element_type = transform.infer_output_type(
             input_element_type)
+    elif isinstance(result_pcollection, pvalue.DoOutputsTuple):
+      # Single-input, multi-output inference.
+      for pcoll in result_pcollection:
+        self._infer_result_type(transform, inputs, pcoll)
 
 Review comment:
   Simply calling this recursively seems to be the wrong thing to do, 
especially calling `type_hints.simple_output_type`. We should accept a 
IOTypeHints whose output has keywords (and possibly a single positional 
argument for the main output) and match them up, e.g.
   
   ```
   @with_output_types(main_type, tag1=int, tag2=str, ...)
   def process(element):
     yield main_type()
     yield TaggedOutput("tag1", 100)
     ...
   ```
   
   We can drop a TODO here to properly update the inference to understand 
TaggedOutput. 
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 333694)
    Time Spent: 1.5h  (was: 1h 20m)

> Element type inference doesn't work for multi-output DoFns
> ----------------------------------------------------------
>
>                 Key: BEAM-4132
>                 URL: https://issues.apache.org/jira/browse/BEAM-4132
>             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: 1.5h
>  Remaining Estimate: 0h
>
> TLDR: if you have a multi-output DoFn, then the non-main PCollections with 
> incorrectly have their element types set to None. This affects type checking 
> for pipelines involving these PCollections.
> Minimal example:
> {code}
> import apache_beam as beam
> class TripleDoFn(beam.DoFn):
>   def process(self, elem):
>     yield_elem
>     if elem % 2 == 0:
>       yield beam.pvalue.TaggedOutput('ten_times', elem * 10)
>     if elem % 3 == 0:
>       yield beam.pvalue.TaggedOutput('hundred_times', elem * 100)
>       
> @beam.typehints.with_input_types(int)
> @beam.typehints.with_output_types(int)
> class MultiplyBy(beam.DoFn):
>   def __init__(self, multiplier):
>     self._multiplier = multiplier
>   def process(self, elem):
>     return elem * self._multiplier
>   
> def main():
>   with beam.Pipeline() as p:
>     x, a, b = (
>       p
>       | 'Create' >> beam.Create([1, 2, 3])
>       | 'TripleDo' >> beam.ParDo(TripleDoFn()).with_outputs(
>         'ten_times', 'hundred_times', main='main_output'))
>     _ = a | 'MultiplyBy2' >> beam.ParDo(MultiplyBy(2))
> if __name__ == '__main__':
>   main()    
> {code}
> Running this yields the following error:
> {noformat}
> apache_beam.typehints.decorators.TypeCheckError: Type hint violation for 
> 'MultiplyBy2': requires <type 'int'> but got None for elem
> {noformat}
> Replacing {{a}} with {{b}} yields the same error. Replacing {{a}} with {{x}} 
> instead yields the following error:
> {noformat}
> apache_beam.typehints.decorators.TypeCheckError: Type hint violation for 
> 'MultiplyBy2': requires <type 'int'> but got Union[TaggedOutput, int] for elem
> {noformat}
> I would expect Beam to correctly infer that {{a}} and {{b}} have element 
> types of {{int}} rather than {{None}}, and I would also expect Beam to 
> correctly figure out that the element types of {{x}} are compatible with 
> {{int}}.



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