Improvements of KVCache and refactoring of subclasses of classes in model.py

[mseeger]

Studying your codebase (trying to learn about transformers in depth), I noted a few things that can be improved:

• KVCache: Second dimension of buffers should always be n_query_groups. If 1 < n_query_groups < n_head, you are wasting memory. Easy to fix.
• KVCache: forward returns tensors with final dimension max_seq_length. This is wasteful for the subsequence dot production attention computation. Can shorten this to a length that just covers all positions in input_pos. Relatively easy to fix.
• Code in adapter.py, adapter_v2.py, lora.py does a lot of copy and paste, which makes changing anything in model.py hard. I'd refactor that, so that as much common code only lives in model.py.

Let me know if this makes sense.

Thanks for doing this project. I really understand the details of transformer models better now.