Fix `resize_token_embeddings` (#26861)
Author: czy-orange
Pull request: yes

* Fix `resize_token_embeddings` about `requires_grad`

The method `resize_token_embeddings` should keep `requires_grad` unchanged for all parameters in embeddings.

Previously, `resize_token_embeddings` always set `requires_grad` to `True`. After fixed, `resize_token_embeddings` copy the `requires_grad` attribute in the old embeddings.

# What does this PR do?

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Fixes #26861


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case. 
  - issue: #26861 
  - forum: [forum discussion](https://discuss.huggingface.co/t/how-to-freez-a-model/12819/2)
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


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@ArthurZucker 

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Comments:
This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.

Perhaps I should explain how I found this problem and why this fix looks necessary in my situation. 

Let's say there is a model, `model = AutoModel.from_pretrained('bert-base-uncased')`. The `model` is updated by ordinary `backward` and `step` methods in the training phase. At the same time, there is another momentum model `model_m`, whose parameters' `requires_grad` are set to `False`. The momentum model `model_m` is updated by weighted sum of its parameters and `model`’s parameters.  

When transferring `model` and `model_m` to other tasks (where updating two models is still needed), vocabulary tables may change and resizing word embeddings is needed. After applying `resize_token_embeddings` to `model_m`, `model_m.embeddings.word_embeddings.weight.requires_grad` is silently changed from `False` to `True` (as discussed in #26861 ).

I was able to find this problem only because an error says that “your module has parameters that were not used in producing loss”.