For multiple GPUs: torch.cuda.empty_cache() stuck forever
Author: animeshkumarpaul
Pull request: no
Labels: name: trainer

### System Info

- `transformers` version: 4.41.0.dev0
- Platform: Linux-5.4.0-172-generic-x86_64-with-glibc2.31
- Python version: 3.11.0
- Huggingface_hub version: 0.23.0
- Safetensors version: 0.4.3
- Accelerate version: 0.30.0
- Accelerate config:    not found
- PyTorch version (GPU?): 2.2.2 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?:  yes
- Using distributed or parallel set-up in script?: yes

### Who can help?

@muellerzr  and @pacman10

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction

[https://github.com/huggingface/transformers/blob/835de4c8335f72a9c53178f54cc3b4c0688960ec/src/transformers/trainer.py#L3219](https://github.com/huggingface/transformers/blob/835de4c8335f72a9c53178f54cc3b4c0688960ec/src/transformers/trainer.py#L3219)


I am using the BartForConditionalGeneration model. 

### Expected behavior

> For the multiple GPUs: during the training time, the process stucks at this line forever - at that time there is no GPU usage, but there is CPU usage.

Comments:
I think I found the main reason.

This is not the cause due to `torch.cuda.empty_cache()` (At least in my case)

It is due to gather metrics in trainer.py. Since the labels in the trainer.py might be have different tensor size (e.g. number of boxes differs from each batch). Recursive strategy in [_gpu_gather](https://github.com/huggingface/accelerate/blob/1f7a79b428749f45187ec69485f2c966fe21926e/src/accelerate/utils/operations.py#L323) stucks in gather forever when it is inappropriate shape. 

for example tensor shape could 2-dimension for the bbox. [however, the shape of output_tensors are in the shape of `state.num_process * tensor.numel()` which is not expected](https://github.com/huggingface/accelerate/blob/1f7a79b428749f45187ec69485f2c966fe21926e/src/accelerate/utils/operations.py#L343)
```
output_tensors = torch.empty(
                state.num_processes * tensor.numel(),
                dtype=tensor.dtype,
                device=state.device,
            )
gather_op(output_tensors, tensor)
```
cc. @qubvel (This was the reason of stuck forever when we train OD in multiple GPU)

https://github.com/huggingface/transformers/blob/7c71b61daef4da1ec5dc41674718d895176af3be/src/transformers/trainer.py#L3846

https://github.com/huggingface/accelerate/blob/1f7a79b428749f45187ec69485f2c966fe21926e/src/accelerate/accelerator.py#L2394

To handle this issue we can add additional training_arguments for `use_gather_object` to set `True`. I can open this PR if you want. (brief PR might be like as below)
```
    labels = self.accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
    labels = self.gather_function((labels), use_gather_object=self.args.use_gather_object)
    if not self.args.batch_eval_metrics or description == "Prediction":
        all_labels.add(labels)
```

@muellerzr @SunMarc

@SangbumChoi thanks for investigating it. Some issues related to `use_gather_object`
https://github.com/huggingface/accelerate/issues/2643
https://github.com/huggingface/transformers/issues/30357

It seems like option was added to accelerate, but not to Trainer

@qubvel Yeah exactly, I think we should add into Trainer also :)