core dumped
### System Info

```shell
centos7
transformers==4.17.0
datasets==1.17.0
pytorch==1.6.0
GPUs==4*GTX 3090
```


### Information

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

### Tasks

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

### Reproduction

I modify the example code of text classification (run_glue.py) and train it on my own dataset. When I run this code on the CPU, it is well running (setting no_cuda true). But when I run it on GPUs, it quits when finishing loading data and generate the following information:

run_burebert_ft.sh: line 16: 37055 Aborted                 (core dumped) CUDA_LAUNCH_BLOCKING=1 python run_br_pred.py --model_name_or_path ../plm/BureBERT --train_file ../dataset/priority_pred_data/priority_train.csv --validation_file ../dataset/priority_pred_data/priority_valid.csv --test_file ../dataset/priority_pred_data/priority_test.csv --cache_dir ./cache_dir --do_train --do_eval --do_predict --no_cuda false --max_seq_length 128 --per_device_train_batch_size 32 --learning_rate 5e-6 --num_train_epochs 10 --save_steps 10000 --output_dir ./results_priority

I am very confused because it just gives limited information. Actually, I also run the same code on another server which contains one tesla V100 GPU, it can run well. So I guest whether It requires extra settings when training on multiple GPUs.

### Expected behavior

```shell
When I run the code, it should start to fine-tune BureBERT on my dataset for ten epochs.
```


### Checklist

- [X] I have read the migration guide in the readme. ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers); [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
- [X] I checked if a related official extension example runs on my machine.
I find that my problem is produced by cuda version. It seems that GTX 3090 is not compatible with cuda 10 and torch installed by pip has the default cuda version (10). So I re-install pytorch with cuda 11 and solve the problem.