Version 4.12.5 doesn't work on sagemaker
## Environment info
- `transformers` version: 4.12.5
- Platform: sagemaker
- Python version: 3.6
- PyTorch version (GPU?): 1.8.1 GPU
- Using GPU in script?: yes, using sagemaker instance 
- Using distributed or parallel set-up in script?: no

## Information

I have code that I am using to train on sagemaker. Previously this code has worked just fine but today I initiated a new training run with new data and no changes to the code. The training run on sagemaker fails when it hits the trainer.train() using transformers.Trainer. Code below:

```
    logging.info(f"Loading Tokenizer")
    tokenizer = T5Tokenizer.from_pretrained(args.model_base

    # Generate tokenized dataset
    logging.info(f"Tokenizing dataset")
    train_dataset = ParaphraseDataset(
        tokenizer=tokenizer,
        file_path=training_file,
    )
    eval_dataset = ParaphraseDataset(
        tokenizer=tokenizer,
        file_path=testing_file,
    )

    # Initialize model
    logging.info(f"Initializing model from {args.model_base}.")
    model = T5ForConditionalGeneration.from_pretrained(args.model_base)
    
    # Training arguments
    training_args = TrainingArguments(
        **_get_training_args(args),
        report_to="wandb"
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        data_collator=DataCollator(tokenizer),
        callbacks=[EarlyStoppingCallback(early_stopping_patience=3, early_stopping_threshold=0.01)],
    )
    logging.info(f"Beginning model training using the following params:\n{training_args}.")
    output = trainer.train()
```

The error occurs during the trainer.train(). The error is the following:

```
#015  0% 0/7680 [00:00<?, ?it/s]Traceback (most recent call last):#015
  File "sm_train_deploy.py", line 271, in <module>#015
    train(parser.parse_args())#015
  File "sm_train_deploy.py", line 210, in train#015
    output = trainer.train()#015
  File "/opt/conda/lib/python3.6/site-packages/transformers/trainer.py", line 1316, in train#015
    tr_loss_step = self.training_step(model, inputs)#015
  File "/opt/conda/lib/python3.6/site-packages/transformers/trainer.py", line 1849, in training_step#015
    loss = self.compute_loss(model, inputs)#015
  File "/opt/conda/lib/python3.6/site-packages/transformers/trainer.py", line 1881, in compute_loss#015
    outputs = model(**inputs)#015
  File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 903, in _call_impl#015
    self._forward_pre_hooks.values()):#015
RuntimeError: OrderedDict mutated during iteration#015
```

I'm calling this on sagemaker using the following command:

```
# Instantiate estimator with parameters
estimator = PyTorch(
    entry_point="sm_train_deploy.py",
    source_dir="code",
    role=role,
    framework_version="1.8.1",
    py_version="py3",
    instance_count=1,  # this script only support distributed training for GPU instances.
    instance_type="ml.p3.2xlarge",
    output_path=output_path,
    module_dir=output_path,
    code_location=output_path,
#     checkpoint_s3_uri=output_path,
    hyperparameters={
        **params
    },
    disable_profiler=True # disable debugger
)

estimator.fit({"training": training_file_s3, "testing": test_file_s3})
```

## Expected behavior

This behavior is not seen when I run the code locally on my own GPU. The training completes as expected.

If I revert to using transformers 4.12.2 (the last version of transformers where this code worked on sagemaer), the code runs as expected on sagemaker without changing anything.

Sorry if I missed anything important, first time submitting a bug report like this.

Hmm @philschmid <|||||>And @sgugger <|||||>+1, ran into the exact same issue yesterday on `4.12.5`. Didn't try other versions exhaustively, but `4.12.2` worked.<|||||>This is the same error as with DeepSpeed, I'm guessing they are using a similar thing with the hooks not being allowed to be mutated. This is fixed on master so we should just hurry up to do a release and clearly document that this patch release won't work on SageMaker.<|||||>@setu4993 We have a documented overview of all HuggingFace DLCs on Amazon SageMaker [here](https://huggingface.co/docs/sagemaker/reference#deep-learning-container). We made sure that those versions are compatible with `transformers`. 
@dylanpmorgan I suggest to move from the `PyTorch` to the `HuggingFace` estimator, whiches uses the DLCs we build, maintain and monitor<|||||>@sgugger : Yeah, could be a hook-related issue. Would appreciate a release soon.

@philschmid : Thanks for the links to the DLCs, didn't know they were so clearly documented and that's very useful.

Fair point that those are _well_ supported, but it would be unfortunate if those are the _only_ versions supported. There are cases when we want to use latest versions of libraries (with recent features, fixes, etc.) and not just a single version that's available on the DLC. I understand it takes more maintenance and so do not expect all permutations of DLCs being available (we considered building and maintaining our own inference images for all the models and versions of libraries we serve, so I get the pain 😉), but unexpected regressions over patch make it harder to debug and fix issues that occur only during training. Had it not been for this already open issue, I wouldn't have expected this to be a `transformers` error (I first thought it was a `datasets` error).

I'll also note that the HF DLCs cannot be used on SageMaker Studio which only have PyTorch DLCs, and this error didn't appear while training within a notebook, but a different error could.<|||||>Thank you for your response and great feedback! 
> I'll also note that the HF DLCs cannot be used on SageMaker Studio which only have PyTorch DLCs, and this error didn't appear while training within a notebook, but a different error could.

We are working on enabling them for SM Studio as well. 
<|||||>@setu4993 Next release will be this Wednesday normally.<|||||>@philschmid :

> We are working on enabling them for SM Studio as well.

That'll come in very handy, thank you!

@sgugger : Sounds good, will test once the next version is released.<|||||>@sgugger @philschmid : Confirming 4.13.0 fixed this for us.<|||||>Thanks for letting us know!<|||||>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.