[WIP] TensorFlow variant of DataCollatorForLanguageModeling.
Co-authored-by: Dalton Walker <dalton_walker@icloud.com>

# What does this PR do?

We didn't see any support for TensorFlow within the DataCollatorForLanguageModeling data class. Integrating directly with TensorFlow seems useful for TensorFlow users and avoids the necessity for tensor conversion.

This PR adds a TFDataCollatorForLangaugeModeling data class that integrates directly with TensorFlow tensors and paves the way for further TFDataCollator conversions. 

(Reopened  PR #12179)

## Before submitting

- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
      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.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

@LysandreJik @Rocketknight1 @sgugger 

Anyone in the community is free to review the PR.

Thanks a lot for your PR!

Before I review more in detail, could you provide an example of use of this API? Data-collators are very PyTorch-ic so I want to make sure this is something that can actually be used in TensorFlow without too many contorsions.<|||||>> Thanks a lot for your PR!
> 
> Before I review more in detail, could you provide an example of use of this API? Data-collators are very PyTorch-ic so I want to make sure this is something that can actually be used in TensorFlow without too many contorsions.

Absolutely! We are currently in the process of pretraining Bert with a custom dataset in a domain specific language. We are going to make use of the TFBertForPreTraining Model to achieve this as well as a custom trained Tokenizer.  (https://huggingface.co/transformers/model_doc/bert.html#tfbertforpretraining)
Specifically we started with the collator for language modeling to make our training data consistent with MLM and NSP tasks. The collator provided that functionality along with batching but only for PyTorch. 
We wanted to provide the functionality that existed for PyTorch for TensorFlow users, and plan on completing the entire API for TensorFlow support if desired.
If you need specific implementation details we are willing to expand further. <|||||>Do you have an example of data preprocessing a bit similar to the [run_mlm](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py) script we have in PyTorch? That would be helpful to see this TF data collator in action.<|||||>> Do you have an example of data preprocessing a bit similar to the [run_mlm](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_mlm.py) script we have in PyTorch? That would be helpful to see this TF data collator in action.

We are going to move this PR into a WIP so we can address your question. <|||||>In answer to your question @sgugger, our objective is to integrate the collator with TFTrainer. Currently PyTorch users enjoy this functionality but TensorFlow users do not have the built-in functionality that deserves to be there (unless we are mistaken, and if so apologize). Our idea is to implement the following change in TFTrainer/get_train_tfdataset:

```
if tf_collate_fn is None:
    ds = (
        self.train_dataset.repeat()
        .shuffle(self.num_train_examples, seed=self.args.seed)
        .batch(self.total_train_batch_size, drop_remainder=self.args.dataloader_drop_last)
        .prefetch(tf.data.experimental.AUTOTUNE)
    )
else
    ds = (
        self.train_dataset.repeat()
        .shuffle(self.num_train_examples, seed=self.args.seed)
        .batch(self.total_train_batch_size, drop_remainder=self.args.dataloader_drop_last)
        .map(tf_collate_fn)
        .prefetch(tf.data.experimental.AUTOTUNE)
    )
```

or we could  implement the dataset conversion in the collator:

```
if not tf_collate_fn is None:
    ds = tf_collate_fn(ds)
else:
    ds = (
        self.train_dataset.repeat()
        .shuffle(self.num_train_examples, seed=self.args.seed)
        .batch(self.total_train_batch_size, drop_remainder=self.args.dataloader_drop_last)
        .prefetch(tf.data.experimental.AUTOTUNE)
    )
```
This would provide an avenue for TensorFlow users to train any models requiring collator functionality in TFTrainer.

Any advice or alternative solutions are welcome! <|||||>We plan to drop the TFTrainer pretty soon to the profit of using Keras, but this could still be useful as we will still rely on the datasets.
I think the best API would be to apply it to a TensorFlow dataset but @Rocketknight1 might have other views.<|||||>Our intention is to drop TFTrainer to do training through Keras instead, and as a result in TF we want the input to come from tf.data.Dataset objects rather than custom collators.

A lot of things like multi-GPU or TPU training in Keras expect tf.data.Dataset input, and will coerce the input into a Dataset if you don't supply it as one.<|||||>@Rocketknight1 Understood. So providing a collator that could be passed to Dataset.map is the way to go if we want the option. Or are you saying that such an operation should be performed before TFTrainer?

I just want to clarify before we continue with a PR. <|||||>We want to avoid TFTrainer entirely in future, so yeah - any kind of custom collator should return a Dataset, or should work through Dataset.map(). This is something we're in the process of updating through our library - there's still a lot of usages of TFTrainer that I'm cleaning up over time!<|||||>Thank you for your quick response! 

We will continue with the PR going down the .map route. Even though TFTrainer is depreciating, some may still find it beneficial in the meantime. 

Cheers!<|||||>@LysandreJik @Rocketknight1 @sgugger

@sdwalker62 and I have made our working commit for the data_tf_collator.py functioning with tf.data.Dataset. We had quite a few commits within our test-branch that has slightly cluttered the PR, so if you want us to make another PR to help focus in on the code that matters most let us know.
Otherwise, the two scripts to primarily look at are data_tf_collator.py and test_data_tf_collator.py. 

Let us know if you have any questions. <|||||>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/master/CONTRIBUTING.md) are likely to be ignored.<|||||>Hey! This isn't something I want to go stale, but I lost track of it when I saw you were still adding commits! Are you happy with it as-is, and ready for a review?<|||||>That is no problem! And we are ready for a review at your convenience. <|||||>Hi! I'm reviewing now. This is actually quite timely - we're planning a general revamp of all the data collators to support both Tensorflow and JAX, as well as support for our Dataset objects to automatically convert to `tf.data.Dataset`, which will almost certainly include the new data collation functions as part of the `tf.data` pipeline.

The downside is that we haven't decided how exactly to structure the code yet, so we might ask you to move or rename this class, but hopefully we can use almost all of the code here as part of the revamp!<|||||>> Hi! I'm reviewing now. This is actually quite timely - we're planning a general revamp of all the data collators to support both Tensorflow and JAX, as well as support for our Dataset objects to automatically convert to `tf.data.Dataset`, which will almost certainly include the new data collation functions as part of the `tf.data` pipeline.
> 
> The downside is that we haven't decided how exactly to structure the code yet, so we might ask you to move or rename this class, but hopefully we can use almost all of the code here as part of the revamp!

That is perfect, we are glad we could help out! We will happily move/rename/or restructure the code in any way that best suits your revamp and the rest of your codebase :smile:  
<|||||>So I've been thinking this over a bit more - my guess is that `tokenizer.pad` probably cannot/shouldn't be compiled with tf.function. It's effectively a totally arbitrary function, and every new model we add might have a different one, so we couldn't make any guarantee that AutoGraph will play nicely with it, even though in testing it seemed to work for me on a few common cases. For the same reasons, we shouldn't try to reimplement `tokenizer.pad` like you did with `tf_pad_tokens`, because at any moment a model could come along that would require a fresh rewrite of that. 

Given that we need to call a block of arbitrary Python code, that means we can't guarantee that the collation function will be compilable with `tf.function` or `Dataset.map()`, although we could still use it in a `tf.data` pipeline by either using it when the data is loaded with `from_generator`, or wrapping it in `py_function` to allow it to be used in `Dataset.map()`.

I think we should go for the following:

1. The function should take input as either tf.Tensor or nested (possibly variable-length) lists. It could optionally accept `np.ndarray` or `tf.ragged.RaggedTensor` too.
2. No `tf.function` anywhere - code is pure Python
3. We can possibly have some kind of 'master' function that takes an argument like `return_tensors` and will call the framework-specific collators based on the argument value, but this is something we can implement later.

That's a lot of changes, though I'm hopeful we could keep a lot of your code here as-is. Do you think it makes sense, or do you have any objections to any of it?<|||||>In the meantime, I'm going to be working on this too - I'll take a different `DataCollator` class and try to write a TF equivalent of it tomorrow. If I run into any issues there I'll let you know.<|||||>Hey, I've rewritten a few of the classes in our preferred style, but left the language modelling ones alone for now, you can see them here: https://github.com/huggingface/transformers/pull/13105

We'd like to push ahead with this fairly soon, so if you'd like, you can try adjusting this PR to a similar style. If not, we can close this PR and I'll add the rest to my PR tomorrow. Either way, thank you for the contribution - whether or not we use the code directly, this PR was helpful in drawing our attention to the problem and to possible approaches for writing data collators that support frameworks besides Torch!<|||||>> Hey, I've rewritten a few of the classes in our preferred style, but left the language modelling ones alone for now, you can see them here: #13105
> 
> We'd like to push ahead with this fairly soon, so if you'd like, you can try adjusting this PR to a similar style. If not, we can close this PR and I'll add the rest to my PR tomorrow. Either way, thank you for the contribution - whether or not we use the code directly, this PR was helpful in drawing our attention to the problem and to possible approaches for writing data collators that support frameworks besides Torch!

This afternoon we started finalizing and adding some of those changes you've suggested in another branch. Once done, we will also adjust the code to match your preferred style shown in your new PR. We can merge those changes into the this PR here and you can feel free to just use this code in your PR or as a starting point for your revisions. Either way, no hard feelings, and we are glad we could help out in any way!<|||||>I'm happy for you to submit your code, and I'll avoid any classes you're touching when I make my own PR! Which ones would you like to handle?<|||||>Hey! We'd like to push to get this in soon, so we can proceed with a general overhaul of our TF data pipelines. At the same time, I know you're contributing code for free, and the rush is mostly caused by my own disorganization, so I don't want to force deadlines on you or anything! 

We'd like to move on and merge everything by Monday, so if you want to add any code today or this weekend, I'll grab it at that point and pull it into my PR. If not, then don't worry - what you've added up to now will already be quite helpful for the final PR, and we'll make sure that both of you get correct author/contributor credits for it regardless!<|||||>Hey there! 😃    We just made some code changes to integrate more closely with your style and had all of our tests pass. We are finishing up lunch and then will go through a final review before updating the PR. <|||||>@sdwalker62 and I just pushed up our revisions based on your review and recent PR. We changed the name of the file to TFDataCollatorForMaskedLanguageModeling. Hopefully, this helps with your upcoming merge this Monday! Let us know if you need anything else, and we look forward to contributing to more things in the future! :smile: <|||||>Thank you! We're just finishing off an upstream PR to `Datasets`, at which point I'll be merging your code into the other DataCollator PR and getting the rest of the team to review it.<|||||>Hey, just to update you: The code has been incorporated into my local copy, and I'm working on adding some other methods we need before I push it all to the other PR. I'll tag you as soon as that commit is in!<|||||>Code is all in at #13105. I'm very likely to steal some of the test code from this PR too once we incorporate tests for all the classes, so I'll make sure you're acknowledged as contributors for that too!<|||||>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/master/CONTRIBUTING.md) are likely to be ignored.