Integrating Huggingface models in Deepchem #3362
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arunppsg
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rbharath
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May 2, 2023
| inputs, labels, weights = self._prepare_batch(batch) | ||
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| optimizer.zero_grad() | ||
| outputs = self.model(**inputs) |
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Let's consider whether we should generalize TorchModel instead of reimplementing fit here since a lot of this code is identical to TorchModel
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There are a couple of ways we can generalize TorchModel here maintaining backward compatability.
- check type of inputs and if it is an instance of
Dict, then invoke asmodel(**inputs)else the old style -model(inputs) - Delegate
model(inputs)call to a function - sayforward()which can be implemented in child classes as well.
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Documenting offline discussion, it's ok to leave this code as-is for now, but we should come back and consider refactoring for maintainability
| logger.info("TIMING: model fitting took %0.3f s" % (time2 - time1)) | ||
| return last_avg_loss | ||
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| def _predict(self, generator: Iterable[Tuple[Any, Any, Any]], |
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Same point here; we may want to consider generalizing TorchModel
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Same comment as above from offline discussion
rbharath
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May 4, 2023
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Description
Integrating huggingface models in DeepChem.
Type of change
Please check the option that is related to your PR.
Checklist
yapf -i <modified file>and check no errors (yapf version must be 0.32.0)mypy -p deepchemand check no errorsflake8 <modified file> --countand check no errorspython -m doctest <modified file>and check no errors