Bugfix/inv freq
Author: cyr0930
Pull request: yes

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Comments:
Hi, I don't see the bug in the original code! `self.inv_freq.to(x.device)` and `inv_freq = self.inv_freq.to(x.device)` are both equivalent - in either case, the original tensor is moved. 

@Rocketknight1 It is not due to any missing assignment, you can verify this with the following script:
```python
import torch     # v2.3.1

x = torch.randn(size=(2, 4, 8)).to(device='cpu')
print(x.device)  # cpu

x.to(device='cuda')
print(x.device)  # cpu
```

Hence, you need an assignment - be it with a new var or directly on `self.inv_freq`.

Edit: Maybe for more clarification. The in-place movement for a device only happens when we call it on a `nn.Module` for example, not on a tensor / parameter. You can can also verify this with the following script:
```python
import torch


class TestModel(torch.nn.Module):
    def __init__(self):
        super().__init__()

        inv_freq = torch.tensor(1.0)
        self.register_buffer("inv_freq", tensor=inv_freq, persistent=False)

    def forward(self, x):
        self.inv_freq.to('cuda')
        print(self.inv_freq.device)


model = TestModel() 

# you will see 2x cpu
print(model.inv_freq.device)
model('test')

# in-place movement 
model.to('cuda')

# you will see 2x cuda:x
print(model.inv_freq.device)
model('test')

```

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_34525). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

No worries! Happens to all of us :) and tbh, it is some niche behaviour on torch's side on how device movement is handled.

I think cc @ArthurZucker would like to review this

I'm closing the issue as v4.48.0 looks like solving this