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https://issues.apache.org/jira/browse/BEAM-14337?focusedWorklogId=772797&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-772797
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ASF GitHub Bot logged work on BEAM-14337:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 20/May/22 12:20
Start Date: 20/May/22 12:20
Worklog Time Spent: 10m
Work Description: yeandy commented on code in PR #17470:
URL: https://github.com/apache/beam/pull/17470#discussion_r878085542
##########
sdks/python/apache_beam/ml/inference/pytorch.py:
##########
@@ -39,25 +42,47 @@ class PytorchInferenceRunner(InferenceRunner):
def __init__(self, device: torch.device):
self._device = device
- def run_inference(self, batch: List[torch.Tensor],
- model: torch.nn.Module) -> Iterable[PredictionResult]:
+ def run_inference(
+ self,
+ batch: List[Union[torch.Tensor, Dict[str, torch.Tensor]]],
+ model: torch.nn.Module,
+ prediction_params: Optional[Dict[str, Any]] = None,
Review Comment:
Unfortunately, I get this lint warning `Dangerous default value {} as
argument (dangerous-default-value)`.
Some explanations on why setting to `{}` is not good practice:
https://stackoverflow.com/questions/26320899/why-is-the-empty-dictionary-a-dangerous-default-value-in-python
https://stackoverflow.com/questions/51710037/how-should-i-use-the-optional-type-hint/51710151#51710151
Issue Time Tracking
-------------------
Worklog Id: (was: 772797)
Time Spent: 3h (was: 2h 50m)
> Support **kwargs for PyTorch models.
> ------------------------------------
>
> Key: BEAM-14337
> URL: https://issues.apache.org/jira/browse/BEAM-14337
> Project: Beam
> Issue Type: Sub-task
> Components: sdk-py-core
> Reporter: Anand Inguva
> Assignee: Andy Ye
> Priority: P2
> Time Spent: 3h
> Remaining Estimate: 0h
>
> Some models in Pytorch instantiating from torch.nn.Module, has extra
> parameters in the forward function call. These extra parameters can be passed
> as Dict or as positional arguments.
> Example of PyTorch models supported by Hugging Face ->
> [https://huggingface.co/bert-base-uncased]
> [Some torch models on Hugging
> face|https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py]
> Eg:
> [https://huggingface.co/docs/transformers/model_doc/bert#transformers.BertModel]
> {code:java}
> inputs = {
> input_ids: Tensor1,
> attention_mask: Tensor2,
> token_type_ids: Tensor3,
> }
> model = BertModel.from_pretrained("bert-base-uncased") # which is a
> # subclass of torch.nn.Module
> outputs = model(**inputs) # model forward method should be expecting the keys
> in the inputs as the positional arguments.{code}
>
> [Transformers|https://pytorch.org/hub/huggingface_pytorch-transformers/]
> integrated in Pytorch is supported by Hugging Face as well.
>
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