llama : initial Mamba-2 support

[compilade]

Follow-up from #8519 (comment). This should fix #7727 and fix #8519.

I've implemented the fully recurrent mode of Mamba-2, because it's very similar to Mamba-1, and also because it seems like the most appropriate mode for text generation.

This does not implement the sequentially semistructured matrix mode, because I'm not yet sure how the block decomposition would fit within the batch and ubatch framework of llama.cpp, and how the chunk size should be chosen. If the recurrent mode is faster at single-user auto-regressive text generation, then I'm not sure how to keep the graph node structure constant when using the most appropriate technique for the batch size.

If the sequentially semistructured matrix mode is eventually implemented, it should help with prompt processing speed for large prompts.

What to expect

(mostly taken from #8519 (comment))

The state in Mamba-2 is bigger than I thought; Mamba-Codestral-7B-v0.1 takes 263.5 MiB (in F32) per sequence (e.g. with -np 1), compared to 38 MiB (also in F32) for Falcon-Mamba-7B (which is based on Mamba-1). But that remains constant whatever the context size. Mamba-2 is easier to implement efficiently, so the bigger state does not really impede inference speed.

However, a big downside right now with recurrent models in llama.cpp is the lack of state rollback (which is implemented through state checkpoints in #7531, but needs to be re-adapted to #8526), so the prompt will be reprocessed a lot if using llama-server. I think using llama-cli in conversation mode does not have this problem, however (or maybe only the bare interactive mode with --in-prefix and --in-suffix, not sure).

This initial implementation is CPU-only, but uses SIMD for the SSM scan, so even though the state is bigger than for Mamba-1 models, in my tests, the speed of Mamba2-130M is similar or better than Mamba-130M (but still not that fast compared to transformer-based models with an empty context), when both are run on CPU.

The speed of Mamba-2 models seems comparable to Transformer-based models when the latter have 2k to 4k tokens in their context.

Summary of changes

• Add support for Mamba2ForCausalLM (including the official Mamba-2 models, and Mamba-Codestral-7B-v0.1)
  • Note that config.json needs to contain "architectures": ["Mamba2ForCausalLM"], for the convert script to properly detect the architecture.
• View Mamba-1 as having d_inner (aka 2 * n_embd) heads of size 1.
  • This simplifies the handling of shapes in ggml_ssm_scan
• ggml
  • Implement Mamba-2's selective state update in ggml_ssm_scan.
    • Re-using the same operator as Mamba-1, because it's pretty much the same operation. (except for how ssm_a is broadcast)
  • Fuse the operation with ssm_d into ggml_ssm_scan
    • Otherwise it would need to be transposed, because the dot-products are done head-wise.
  • Implement Mamba-2's SSM scan with GGML_SIMD.
    • This is possible because there is no element-wise expf in the state update unlike with Mamba-1.
  • Avoid state copies for the SSM state (both for Mamba-1 and Mamba-2) by passing state ids to ggml_ssm_scan.
    • Mamba-2 states are huge. Otherwise masking and copying took close to 10% of the CPU time according to perf.

Other

Here's my favorite quote from Section 3.3 of https://arxiv.org/abs/2405.21060:

Furthermore—by a twist of fate—structured state space models and sequentially semiseparable matrices have the same acronyms, underscoring their equivalence! Conveniently we can use any of these acronyms SSM (state space model or semiseparable matrix), SSS (structured state space or sequentially semiseparable), or SS (state space or semiseparable) interchangeably to unambiguously refer to either concept.

TODO

• Rebase onto master after merging llama : simplify Mamba with advanced batch splits #8526.
• Avoid unnecessary moves of the state
• Adapt the Metal kernels and the tests from ggml : add SSM Metal kernels #8546 to the updated ggml_ssm_scan
• Remove the new GGML_MUL fast broadcast path because it's not used anymore to mask the states.
• Maybe use a new metadata key instead of {arch}.ssm.time_step_rank for the number of heads of Mamba-2, because it's not really the rank of the time step (well, maybe kind of).
  • The meaning of the number of heads and the time-step rank is overlapping enough in Mamba-2 that I think this is fine.
• Maybe not fuse the multiplication with ssm_d in ggml_ssm_scan?
• Maybe split ggml_ssm_scan to separate the implementations for Mamba-1 and Mamba-2, although they do have a lot in common.
  • Seems like they can be distinguished easily enough at the time of kernel dispatch.

---

• I have read the contributing guidelines
• Self-reported review complexity:
  • Medium

[ngxson]

Hey @compilade , thanks for implementing this!

I tried converting https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1 using convert_hf_to_gguf.py, but it gives error:

    with open(dir_model / "config.json", "r", encoding="utf-8") as f:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'config.json'

Nevertheless, I successfully converted a Mamba-Codestral transformers-compatible model: https://huggingface.co/Molbap/code2 (Need to comment out the line raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") in convert_hf_to_gguf.py)

Run it output model (remember to select the correct chat template, since the model does not come with one):

make llama-cli -j && ./llama-cli -m ../models/mcode-7.3B-Q8_0.gguf -cnv -p "You are a helpful assistant" --chat-template mistral -ngl 0

The result looks promising, but I have no idea why there are [UNK_BYTE_0x29681...]. It seems like the there is a problem with space character:

<<SYS>>Youareahelpfulassistant<</SYS>>
> hi
[UNK_BYTE_0xe29681▁Hello]Hello![UNK_BYTE_0xe29681▁How]How[UNK_BYTE_0xe29681▁can]can[UNK_BYTE_0xe29681▁I]I[UNK_BYTE_0xe29681▁assist]assist[UNK_BYTE_0xe29681▁you]you[UNK_BYTE_0xe29681▁today]today?

Link to download GGUF: https://huggingface.co/ngxson/codestral-mamba-llamacpp-test/tree/main

[compilade]

Hey @compilade , thanks for implementing this!

I tried converting https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1 using convert_hf_to_gguf.py, but it gives error:

    with open(dir_model / "config.json", "r", encoding="utf-8") as f:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'config.json'

@ngxson

The steps I took to convert Mamba-Codestral-7B-v0.1 are the following:

1. Rename consolidated.safetensors to model.safetensors
2. Rename params.json to config.json
3. Add the line "architectures": ["Mamba2ForCausalLM"], in config.json
4. Rename tokenizer.model.v3 to tokenizer.model
5. Use convert_hf_to_gguf.py as usual.

I did not have tokenization problems in my tests. Maybe because I was using the original SentencePiece tokenizer instead of a BPE tokenizer.

That tokenizer.json in the transformers-compatible version seems to have problematic spaces. It uses the SentencePiece space escaping instead of the BPE one. Its normalizer seems to revert the escaping, but that's not handled in llama.cpp.

There are probably still problems with the SentencePiece tokenizer too, like the lack of special tokens (control tokens seem to be identified correctly, the only difference seems to be with the 20 [REFERENCE_DOC_{n}] tokens (where n is 0 to 19), which tokenzier.json identifies as non-special added tokens (maps to USER_DEFINED for llama.cpp), while tokenizer.model identifies them as NORMAL tokens).

I think the SentencePiece tokenizer should be preferred for this model; it should be easier to handle without workarounds. I should change that in convert_hf_to_gguf.py. Meanwhile either not include tokenizer.json or rename it to something else.

[ngxson]

Thanks for the guide! I've successfully converted the original repository the gguf by following your steps.

For the transformers-compatible, I will try to contact the one who made it. Hopefully it will be fixed soon.

I'm wondering if convert_hf_to_gguf.py can automatically handle the renaming of params.json, consolidated.safetensors and tokenizer.model.v3? For now, my fear is that someone who use automated tools like gguf-my-repo will be stuck due to this issue.

(Also cc @Vaibhavs10 since he's the maintainer of gguf-my-repo.)

[Vaibhavs10]

Hey @compilade/ @ngxson - JFYI - the transformers weights are now merged in the main repo: https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1

If you face any issues with the conversion with this could you open an issue on the repo for us to track! 🤗

[isr431]

Any updates on when Codestral Mamba should be supported?

[learning-chip]

Nice work! Just a note on the ssm_scan kernel performance: a better fused implementation by the flash-linear-attention project can give the equivalent functionality as Mamba2's original kernel: sustcsonglin/flash-linear-attention#49 , and runs 2x faster: sustcsonglin/flash-linear-attention#50

[molbap]

Hi @compilade ! I worked on repo conversion for the transformers-compatible mamba2 version, let us know if you need anything from us to move forward with this PR :)

[HanClinto]

I'm wondering if convert_hf_to_gguf.py can automatically handle the renaming of params.json, consolidated.safetensors and tokenizer.model.v3? For now, my fear is that someone who use automated tools like gguf-my-repo will be stuck due to this issue.

(Also cc @Vaibhavs10 since he's the maintainer of gguf-my-repo.)

It sounds like having a simple fallback of expected filenames would be a reasonable thing to include here? I don't know that we want to maintain a ton of different ones, but adding a second layer of fallbacks for alternate filenames doesn't feel arduous.

[compilade]

It sounds like having a simple fallback of expected filenames would be a reasonable thing to include here? I don't know that we want to maintain a ton of different ones, but adding a second layer of fallbacks for alternate filenames doesn't feel arduous.

@HanClinto

That's not really a problem anymore (at least for Mamba-Codestral) since the official repo was updated in https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1/commit/88085f9cdfa832c3aca8a0315a4520cf7558c947 to use more standard names.

What is currently blocking this is that the Metal and CUDA kernels for ggml_ssm_scan need to be updated BUT before that, I want to refactor the operator to completely avoid copying Mamba-2 states (because otherwise the unnecessary copies use a non-negligible fraction of the memory bandwidth (10% of total text generation inference time on my laptop), since Mamba-2 states are big).

[hg0428]

Any updates on this?