Fix convert to original state dict for VLMs

[hiyouga]

What does this PR do?

#37033 introduces the base models for all VLMs. The model weights will be converted by mapping the original keys according to

transformers/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py

Lines 1735 to 1738 in 701caef

	_checkpoint_conversion_mapping = {
	"^visual": "model.visual",
	r"^model(?!\.(language_model|visual))": "model.language_model",
	}

However, the previous implementation of Transformers could not properly convert the weights back due to existing bugs (replacement = re.sub(r"\(.*?\)", "", pattern) -> replacement = re.sub(r"\(.*?\)", "", replacement)) and the lack of support for nested parentheses

transformers/src/transformers/modeling_utils.py

Lines 3644 to 3657 in 701caef

	if any(allowed_name in self.__class__.__name__.lower() for allowed_name in VLMS):
	reverse_key_mapping = {v: k for k, v in self._checkpoint_conversion_mapping.items()}
	original_state_dict = {}
	for key, value in state_dict.items():
	for pattern, replacement in reverse_key_mapping.items():
	replacement = replacement.lstrip("^") # strip off un-needed chars and patterns
	replacement = re.sub(r"\(.*?\)", "", pattern)
	key, n_replace = re.subn(pattern, replacement, key)
	# Early exit of the loop
	if n_replace > 0:
	break
	original_state_dict[key] = value
	state_dict = original_state_dict

We want to provide a more accurate weight conversion implementation to prevent issues with third-party apps.
hiyouga/LlamaFactory#8147

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• Did you write any new necessary tests?

Who can review?

@ArthurZucker @zucchini-nlp

[zucchini-nlp]

thanks

[HuggingFaceDocBuilderDev]

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[hiyouga]

@zucchini-nlp Thanks for the merging. Could we have a unittest for this function?

[zucchini-nlp]

I don't think we need a test, since it's fixed already and we probably won't touch that part anymore. We have a test for loading the ckpt and for creating a new dummy model, loading-saving-loading-saving it back. That is enough imo

[hiyouga]

@zucchini-nlp Okay. By the way, do you think we should override the state_dict and named_parameters method to ensure consistency between the output and the original model weights? For example, OpenR1 uses the named_parameters method to synchronize the model weights with the inference engine.

https://github.com/huggingface/trl/blob/fef915e36f12f759b384e4ab6f650208130aa232/trl/trainer/grpo_trainer.py#L872-L875