fix(modeling_utils): correct initialization of missing and mismatched… #40093
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… keys - Update the _initialize_missing_keys method to handle both missing and mismatched keys - Ensure proper initialization of weights when loading pretrained models
src/transformers/generation/utils.py
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| # Handle potential NaN values in accumulated_log_probs | ||
| probs = nn.functional.softmax(accumulated_log_probs, dim=-1) | ||
| # Replace NaN values with uniform distribution | ||
| if torch.isnan(probs).any(): | ||
| # Create a mask for NaN positions | ||
| nan_mask = torch.isnan(probs) | ||
| # Replace NaN with a small uniform probability | ||
| probs = torch.where(nan_mask, torch.ones_like(probs) / probs.shape[-1], probs) | ||
| # Renormalize to ensure probabilities sum to 1 | ||
| probs = probs / probs.sum(dim=-1, keepdim=True) | ||
| topk_indices = torch.multinomial(probs, num_samples=beams_to_keep) |
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I like that, but it needs a separate issue!
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I see it, but I do this because of seeing the circle ci failed and I try to solve it.
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I get you, the test failing is probably unrelated to your changes no?
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cc @Cyrilvallez as I don't have time to check! |
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Hey! The argument given is all keys present in the checkpoints - we then take the set inverse of that to init the other keys. So everything is initialized correctly 🙂🤗 So I believe this PR is wrong! Let me know if you have any repro that could make you think otherwise! |
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Fixes #40001
@ArthurZucker