Fix gradient requirements for layer methods #647
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vivekmig
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@vivekmig has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator. |
NarineK
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Apr 8, 2021
captum/_utils/gradient.py
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| @@ -23,7 +23,9 @@ | |||
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| def apply_gradient_requirements(inputs: Tuple[Tensor, ...]) -> List[bool]: | |||
| def apply_gradient_requirements( | |||
| inputs: Tuple[Tensor, ...], warn: bool = False | |||
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nit: In order to support original behavior don't we want to not warn only when we know that warning is not necessary in case of layer approaches ?
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Great catch, thanks! Yes, you're definitely right, meant to set the default to True
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This updates gradient requirements to be set on layer inputs / outputs rather than original inputs, which ensures that gradient requirements are set when inputs are non-floating point (e.g. token indices). This also avoids unnecessarily requiring gradients between the input and target layer, when only layer gradients are required.