[Model] Support Mamba

[tlrmchlsmth]

This is closely based on vLLM's Jamba implementation. It also has several changes and fixes to deal with the fact that there is no KV cache at all.

Changes in this PR

• Added the Mamba model definition and integration tests.
• Factored the Mamba cache management used by both Mamba and Jamba into a mamba_cache.py
• Added a new "placeholder" attention backend with many noop methods, as Mamba's state needs to be managed differently. AFAICT, this is an expedient way to get Mamba working without a larger refactor. We didn't need to do this for Jamba because Jamba does have some attention layers and does have an attention implementation.
• Various other changes to wrestle with the fact that Mamba doesn't have attention.

Support for Mamba2, Codestral-Mamba, and FalconMamba will come in subsequent PRs.

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[uniartisan]

Thank you very much for your work, I want to implement the vllm feature for RWKV, your implementation gives me a great reference.
Can I work with you to bring RWKV, an RNN model? I have a native pytorch implementation here, but it still needs to be modified, and I don't know how to adapt to so many VLLM backends (CUDA, XPU, TPU, HIP, etc.)

[tlrmchlsmth]

Hey @uniartisan, sure I can work with you to implement support for RWKV. Perhaps you could you start a draft PR, and we can talk there. Could you link your native pytorch implementation?

I think it would be a good idea to start with CUDA support -- for Mamba, for instance, we depend on kernels that require CUDA, and you may run into that as well.

[uniartisan]

Hey @uniartisan, sure I can work with you to implement support for RWKV. Perhaps you could you start a draft PR, and we can talk there. Could you link your native pytorch implementation?

I think it would be a good idea to start with CUDA support -- for Mamba, for instance, we depend on kernels that require CUDA, and you may run into that as well.

I have submitted an implementation of the model code (without any adaptation for vllm; I may come back to do this in the coming week).
I will create a new draft and mention you. Thank you!
My draft is available here: #6749
It's a native PyTorch implementation. I will first try to adapt it for vllm, and then port the CUDA kernel and Triton kernel.

[anguswangrv]

This is closely based on vLLM's Jamba implementation. It also has several changes and fixes to deal with the fact that there is no KV cache at all. This PR adds a placeholder attention backend, and adapts and renames EmbeddingModelBlockManager to handle cases like Mamba as well. TODO before landing:

• Fix issues with enforce_eager=True (this issue simply vanished after merging)
• Factor out common code between Jamba and Mamba
• Added a unit test.

I will also try to get Codestral Mamba working as well. The transformers-compatible Mamba models seem to be working. However, the mamba2 models unfortunately are not transformers-compatible and don't work out of the box. Codestral https://huggingface.co/mistralai/mamba-codestral-7B-v0.1 has the same issue.

See also #6479

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Hi @tlrmchlsmth, thanks for your work! Does the current state of this PR support Codestral Mamba on vLLM yet? If so, how do you run it?

[learning-chip]

However, the mamba2 models unfortunately are not transformers-compatible and don't work out of the box.

Huggingface transformers 4.40.0 supports Mamba-2 (codestral mamba) huggingface/transformers#32080