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zucchini-nlp
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Feb 17, 2026
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Thanks, that works much better tbh
src/transformers/modeling_utils.py
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| # This check is for remote code that does NOT use either `torch.init` or `transformers.initialization` in `_init_weights` | ||
| # which allow to check the flag directly on param. As they don't and write the params in-place, params would be reinitialized | ||
| # otherwise | ||
| if all(getattr(param, "_is_hf_initialized", False) for param in module.parameters(recurse=False)) and all( | ||
| getattr(buffer, "_is_hf_initialized", False) | ||
| for buffer in module.buffers(recurse=False) | ||
| if buffer is not None |
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nice, forgot that we can get bigger modules
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What does this PR do?
As per the title. The check that was added in #43768 is wrong, as a missing weight would NOT be reinitialized in some cases!
As for the pointers check, it is actually not needed at all since when modules (modules, not parameters) are shared (tied), loading only 1 param from the checkpoints will load both weights correctly as it calls
setattron the module which is the object shared already. So we simply have to remove them from missing keys, instead of an expensive (and sometimes wrong) check of the pointers.This change fixes #44060