experiment(backend): autocast dtype in CustomLinear #7843
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This resolves an issue where specifying `float32` precision causes FLUX Fill to error. I noticed that our other customized torch modules do some dtype casting themselves, so maybe this is a fine place to do this? Maybe this could break things... See #7836
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invokeai/backend/model_manager/load/model_cache/torch_module_autocast/cast_to_dtype.py
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| @@ -73,6 +74,10 @@ def _autocast_forward_with_patches(self, input: torch.Tensor) -> torch.Tensor: | |||
| def _autocast_forward(self, input: torch.Tensor) -> torch.Tensor: | |||
| weight = cast_to_device(self.weight, input.device) | |||
| bias = cast_to_device(self.bias, input.device) | |||
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| weight = cast_to_dtype(weight, input.dtype) | |||
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This is probably fine, but some models may act weirdly due to potential precision loss if we provide inputs with less precision than the model 🤔 In an ideal world I'd think we'd want to ensure the precision of the inputs are compatible with the model before calling it
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Summary
This resolves an issue where specifying
float32precision causes FLUX Fill to error.I noticed that our other customized torch modules do some dtype casting themselves, so maybe this is a fine place to do this? Maybe this could break things...
See #7836
Related Issues / Discussions
Closes #7836
QA Instructions
Try various model combos. I don't know what I'm doing and this could be a Bad Idea™️.
To reproduce the problem in the linked issue, set
precision: float32ininvokeai.yaml, then try to use FLUX Fill.Merge Plan
n/a
Checklist
What's Newcopy (if doing a release after this PR)