[BUG] Getting different sentence embeddings when using model on CPU and GPU
### System Info


- `transformers` version: 4.21.2
- Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.13
- Huggingface_hub version: 0.9.1
- PyTorch version (GPU?): 1.12.1+cu113 (False)
- Tensorflow version (GPU?): 2.8.2 (False)
- 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?: No

### Who can help?

@LysandreJik @sg

### 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

```from transformers import RobertaConfig, RobertaModel, RobertaTokenizer
import torch
import numpy as np
device = ("cuda" if torch.cuda.is_available() else "cpu")
# Initializing tokenizer
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")

# Initializing a RoBERTa configuration
configuration = RobertaConfig()
configuration.vocab_size = tokenizer.vocab_size

# Initializing a model from the configuration
model = RobertaModel(configuration)
model = model.to(device)
model = model.eval()

with torch.no_grad():
    tokenized_task = tokenizer('random_sentence_check_v000', return_tensors="pt")
    outputs = model(**tokenized_task.to(device))
    embedding = outputs.pooler_output.squeeze(0).cpu().numpy().tolist()

### Expected behavior

I should get same sentence embedding either on CPU and GPU from a pretrained model
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/main/CONTRIBUTING.md) are likely to be ignored.<|||||>To fix this issue, we need to set the seed values which I forgot.