Repository: huggingface/transformers
Pull request: Fix BitsAndBytesConfig JSON serialization in TrainingArguments

# What does this PR do?

This makes sure the BitsAndBytesConfig object gets converted to dict before TrainingArguments are serialized to JSON.

This happens when using BitsAndBytes and the TensorBoard integration for example. 

<!-- Remove if not applicable -->

Fixes #37518

## Before submitting
- [ ] 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#create-a-pull-request),
      Pull Request section?
- [ ] 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.
- [ ] 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?

## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker
- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier

Library:

- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber

Documentation: @stevhliu

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->

@zach-huggingface @SunMarc @MekkCyber

Changed files:
filename: src/transformers/training_args.py
patch: @@ -2498,6 +2498,11 @@ def to_dict(self):
             # Handle the accelerator_config if passed
             if is_accelerate_available() and isinstance(v, AcceleratorConfig):
                 d[k] = v.to_dict()
+            # Handle the quantization_config if passed
+            if k == "model_init_kwargs" and isinstance(v, dict) and "quantization_config" in v:
+                quantization_config = v.get("quantization_config")
+                if quantization_config and not isinstance(quantization_config, dict):
+                    d[k]["quantization_config"] = quantization_config.to_dict()
         self._dict_torch_dtype_to_str(d)
 
         return d