Repository: huggingface/transformers
Pull request: Saving `Trainer.collator.tokenizer` in when `Trainer.processing_class` is `None`

# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

This intend to improve `Trainer`. When we update the tokenizer (e.g. add new tokens) and passed it to `collator` used by `Trainer`, if we forgot to pass `processing_class` to `Trainer`, then the updated `tokenizer` will not be saved by default, and it's very possible we'll meeting dimension mismatching problem when we loading the model. This PR is intend to solve this problem by saving `Trainer.collator.tokenizer` when `Trainer.processing_class` is `None`.


## 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).
- [x] 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: @ylacombe, @eustlb
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @SunMarc
- chat templates: @Rocketknight1

Integrations:

- deepspeed: HF Trainer/Accelerate: @muellerzr
- 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: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

 -->


Changed files:
filename: src/transformers/trainer.py
patch: @@ -3992,6 +3992,13 @@ def _save(self, output_dir: Optional[str] = None, state_dict=None):
 
         if self.processing_class is not None:
             self.processing_class.save_pretrained(output_dir)
+        elif (
+            self.data_collator is not None
+            and hasattr(self.data_collator, "tokenizer")
+            and self.data_collator.tokenizer is not None
+        ):
+            logger.info("Saving Trainer.data_collator.tokenizer by default as Trainer.processing_class is `None`")
+            self.data_collator.tokenizer.save_pretrained(output_dir)
 
         # Good practice: save your training arguments together with the trained model
         torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))

filename: tests/trainer/test_trainer.py
patch: @@ -28,7 +28,7 @@
 from functools import partial
 from itertools import product
 from pathlib import Path
-from typing import Dict, List
+from typing import Any, Dict, List
 from unittest.mock import Mock, patch
 
 import numpy as np
@@ -46,6 +46,7 @@
     PretrainedConfig,
     TrainerCallback,
     TrainingArguments,
+    default_data_collator,
     enable_full_determinism,
     get_polynomial_decay_schedule_with_warmup,
     is_torch_available,
@@ -2975,6 +2976,24 @@ def test_safe_checkpoints(self):
                 tmp_dir, 5, int(self.n_epochs * 64 / self.batch_size), False, safe_weights=save_safetensors
             )
 
+    def test_save_collator_tokenizer_by_default(self):
+        class FakeCollator:
+            def __init__(self):
+                self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+                self.tokenizer.add_tokens(["<NEW_TOKEN1>", "<NEW_TOKEN2>"])
+
+            def __call__(self, features: List[Any], return_tensors="pt") -> Dict[str, Any]:
+                return default_data_collator(features, return_tensors)
+
+        data_collator = FakeCollator()
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        trainer = get_regression_trainer(
+            output_dir=tmp_dir, save_steps=5, save_safetensors=True, data_collator=data_collator
+        )
+        trainer.train()
+        loaded_tokenizer = AutoTokenizer.from_pretrained(os.path.join(tmp_dir, os.listdir(tmp_dir)[0]))
+        assert len(loaded_tokenizer) == len(trainer.data_collator.tokenizer), "Failed to load updated tokenizer"
+
     def test_load_best_model_with_save(self):
         tmp_dir = self.get_auto_remove_tmp_dir()
         trainer = get_regression_trainer(