"Dynamic" Issue in LlamaDynamicNTKScalingRotaryEmbedding - Long context inference will impact short context inference.
### System Info

- `transformers` version: 4.32.0.dev0
- Platform: Linux-5.15.109+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- Safetensors version: 0.3.1
- Accelerate version: 0.22.0.dev0
- Accelerate config: 	not found
- PyTorch version (GPU?): 2.0.1+cu118 (True)
- Tensorflow version (GPU?): 2.12.0 (True)
- Flax version (CPU?/GPU?/TPU?): 0.7.0 (gpu)
- Jax version: 0.4.13
- JaxLib version: 0.4.13
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help?

@sgugger 

### 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

Please see my colab code:  
https://colab.research.google.com/drive/1SnQQxW7WMHgSOvAwF_HIlIDrAuXZ4IKp?usp=sharing

I asked the same prompt twice, with a long-context prompt inserted in between. However, this intermediate long-context inference resulted in different answers for the same question before and after it.

### Expected behavior

Since the input length of the tested prompts is within the maximum input token capacity the model can handle, the significance of "Dynamic" lies in ensuring that the embeddings for the inputs before and after remain the same, and consequently, the output results should also be the same.


I reviewed the code of the class "[LlamaDynamicNTKScalingRotaryEmbedding](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L147C8-L147C8)" and I think that due to caching, when the model infers a long context, the cached values of `cos_cached` and `sin_cached` are updated to adapt to the longer context. This causes the issue when the model infers a shorter context again.