[WIP]Support shared cache
Author: LoserCheems
Pull request: yes

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Fixes #35876 


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Comments:
Very interesting PR @LoserCheems , thanks for working on it! Before we review it, can you share any evaluations you did to verify that `SharedCache` is more memory-efficient and how much memory is saved, and how much it hurts model performance. I believe there's a sweet spot on `shared_layer_groups` which gives the best trade-off 

@gante 

Hey @LoserCheems, thank you for opening the PR!

+1 to what @zucchini-nlp wrote -- some benchmarks are needed to back up the speedup and/or quality claims, along the obvious memory benefits 🤗 

Thank you @zucchini-nlp @gante, here are some evaluation tables in [this paper](https://arxiv.org/pdf/2411.13676).
![image](https://github.com/user-attachments/assets/9ae695a5-28bd-4407-9755-5e29d9f914ad)


@LoserCheems seems like the results you show are from a Hybrid Mamba-Transformer model. Could you write a small eval benchmark using key LLMs (e.g. llama, qwen) and test how the method applies to them

I’m a bit skeptical about how well the results from the paper transfer across different models while maintaining the same performance quality

Thank your suggestion @zucchini-nlp, in the current test, I used `Llama-2-7b-hf` to conduct a comparison of memory usage with dynamic cache. However, I'm not quite sure how to write a quality benchmark test in `transformers`. I only know how to use `lighteval`.

You can take inspiration from a script we used for Static Cache (https://gist.github.com/ArthurZucker/ae0a86ef8f841c0ef69aaa52ccbc0b03), and remove the `torch.compile` part.

In general there are no guidelines, as long as the script compares the memory consumption of the default generation vs generation with proposed cache, using different `shared groups`, `batch sizes` and `max lengths`. For quality test, a simple perplexity measure would be enough on a dataset of your choice, to verify that Shared Cache doesn't degrade too much compared to baseline. Feel free to use the [`1B` llama](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), if you don
t have much compute 🤗 

Thank you @zucchini-nlp, but the benchmark submission will be late, because I currently only have a laptop with only CPU, and I also need to apply for the permission to use llama.

@LoserCheems a model without gating can also be used, e.g. qwen 2.5 :)

In a nutshell, we need to validate any technique before considering adding it to `transformers` -- otherwise, the library quickly gets bloated, which is bad for everyone. `SharedCache` seems indeed promising, but its original paper trained a model with this cache structure in mind, i.e. we don't know whether it translates well to existing pre-trained models. If not, then we don't want it until we add a model with this cache.

A good benchmark would confirm that `SharedCache` doesn't lose a big amount of performance. Modifying `transformers` and evaluating the resulting code (e.g. with `lighteval`) is the strategy I recommend.