Make the Parallelizer class work for inference #715
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JingyaHuang
reviewed
Oct 22, 2024
| @@ -111,6 +111,7 @@ def transform( | |||
| sequence_parallel_enabled: bool = False, | |||
| device: Optional[torch.device] = None, | |||
| should_parallelize_layer_predicate_func: Optional[Callable[[torch.nn.Module], bool]] = None, | |||
| **parallel_layer_specific_kwargs, | |||
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as per my experience with T5, the majority of args in parallel_layer_specific_kwargs sent to the transform() functions in T5's parallel modules raised errors, eg:
TypeError: transform() got an unexpected keyword argument 'skip_linear_weight_load'
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I think it's just a matter of adding a missing argument here.
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What does this PR do?
As per title.