implement position encoding for shifted tokens

Pull Request resolved: https://github.com/pytorch/executorch/pull/6646

In AttentionSink, it uses tokens' positions in the KVCache instead of the actual text. When tokens get shifted in KVCache, it needs to update q and k's position embedding.

In the original [implementation](https://github.com/mit-han-lab/streaming-llm) of AttentionSink with Rope, it caches the original q and k in KVCache and apply position embedding during inference.

This PR adds `RopeWithAttentionSink`. It assumes that q and k are already encoded with their original position. When we shift tokens, we reapply the position delta. This has two benefits:
- minimize our code since our existing `llama_transformer` applies rope embedding before doing KVCache update
- avoid performance regression when tokens are not shifted because we don't need to reapply position encoding in KVCache for them
ghstack-source-id: 255579838

Differential Revision: [D65366440](https://our.internmc.facebook.com/intern/diff/D65366440/)

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Co-authored-by: Lunwen He <lwhecser@gmail.com>