HuggingFace Compatible Mamba2

Introduction

This is a highly experimental implementation of Mamba2 [1] that is compatible with the transformers library by Hugging Face [2]. It is only supporting the pure Mamba2 block which means the hybrid variants with Attention and/or MLP are not available.

NOTE: You can use this repo to use Mamba2 based models with all optimisation paths:

• Triton kernels and causal-conv1d ("fastest")
• Triton kernels only (default)
• Pure PyTorch

NOTE: I'm not affiliated with the original authors of Mamba2 or Hugging Face.

Why?

• Don't have much time to properly test everything.
• Wanted a HF compatible version.
• Wanted to use any optimisation without the cuda wheels required by the original mamba repo.
• Less interested in hybrid attention variant --> needs flash attention (due to RoPE embeds).

Installation

I won't distribute a pypi package, but you can use it as package by cloning the repo and installing it at root:

git clone https://github.com/vasqu/mamba2-torch.git
cd mamba2-torch
pip install .

To use the "fastest" path, you need to install the causal-conv1d package separately.

Usage

Basics

To use any pretrained Mamba2 model you need a compatible format of the respective model. You have two options:

• Download a converted model from the huggingface hub via this download script.

# example usage to download mamba2-130m
# 1st argument = parameter count, 2nd argument = directory to save the converted model to
./download_mamba2.sh 130m ../models

• Convert it yourself by using the conversion script.

# example usage to download and convert mamba2-130m
# 1st argument = parameter count, 2nd argument = directory to save the converted model to
./convert_mamba2.sh 130m ../models

Now you can use the converted model the following way.

from transformers import AutoTokenizer
from mamba2_torch import Mamba2Model, Mamba2ForCausalLM, Mamba2Config

device = "cuda"
mamba2_hf_path = "<path-to-converted-model>"

model = Mamba2ForCausalLM.from_pretrained(mamba2_hf_path, local_files_only=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(mamba2_hf_path, local_files_only=True)

input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(device)

# expected output (130m): `["Hey how are you doing?\n\nI'm in the middle of a project"]`
out = model.generate(input_ids, max_new_tokens=10)
print(tokenizer.batch_decode(out))

Advanced

Some optional features to give more control over the model:

• Disabling/Enabling Triton Kernels
• Outputting The Last SSM States
• Passing Initial States

Disabling/Enabling Triton Kernels

from transformers import AutoTokenizer
from mamba2_torch import Mamba2Model, Mamba2ForCausalLM, Mamba2Config

mamba2_hf_path = "<path-to-converted-model>"

# flag to enable / disable using triton kernels
# --> pure PyTorch implementation will be used instead
config = Mamba2Config.from_pretrained(mamba2_hf_path, local_files_only=True)
config.use_triton_kernels = False

model = Mamba2ForCausalLM.from_pretrained(mamba2_hf_path, config=config, local_files_only=True)
...

Outputting The Last SSM States

from transformers import AutoTokenizer
from mamba2_torch import Mamba2Model, Mamba2ForCausalLM, Mamba2Config

device = "cuda"
mamba2_hf_path = "<path-to-converted-model>"

# flag to enable / disable outputting last SSM states
config = Mamba2Config.from_pretrained(mamba2_hf_path, local_files_only=True)
config.output_last_ssm_states = True

model = Mamba2ForCausalLM.from_pretrained(mamba2_hf_path, config=config, local_files_only=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(mamba2_hf_path, local_files_only=True)

input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(device)

# or do it in the forward pass directly 
out = model(input_ids, output_last_ssm_states=True)

Passing Initial States

import torch
from transformers import AutoTokenizer
from mamba2_torch import Mamba2Model, Mamba2ForCausalLM, Mamba2Config

device = "cuda"
mamba2_hf_path = "<path-to-converted-model>"

model = Mamba2ForCausalLM.from_pretrained(mamba2_hf_path, local_files_only=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(mamba2_hf_path, local_files_only=True)

input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(device)

# creating random initial states
config = Mamba2Config.from_pretrained(mamba2_hf_path, local_files_only=True)
initial_states = [
   torch.randn(size=(input_ids.shape[0], config.num_heads, config.head_dim, config.state_size)).to("cuda") 
   for _ in range(config.num_hidden_layers)
]
# don't pass an initial state to the 5th block
initial_states[4] = None

# pass it in the forward call 
out = model(input_ids, initial_states=initial_states)

Some (Maybe Interesting) Notes

• Most work goes to the original mamba repo. They did the heavy work, give them your flowers.
• ssd_minimal is a small script based on the original script provided by Tri Dao and Albert Gu (see here) and modified to work on any sequence length and with the D residual connection. A small test that checks roughly equal outputs is over here.
• AMD support has been added as of v2.1.0 of mamba_ssm. It should work with the triton kernels here as well.
• To properly utilize caching, you will need (at least) the pinned version in the requirements.txt of the transformers library.
• Some optional parallelization options introduced in the original mamba2 repo have been left out:
  • Groups in Multi-input SSM
  • Parallelized linear layers
  • Imo insignificant kernels (e.g. RMSNorm)
• There are still some issues I'm not so sure of myself:
  • Compiling doesn't seem to work on my end which would boost the performance of triton kernels even more. Speed possibly picks up after the first iteration(s), see this comment.
  • NaN losses seem to be fixed but you have to make sure that ( (d_model * expand) / headdim ) % 8 == 0.
  • tie_embedding_weights flag in the config is probably enforced in any case. Not too interested in digging into this but open for PRs.

Work this is based on

[1] Mamba2
@inproceedings{mamba2,
 title={Transformers are {SSM}s: Generalized Models and Efficient Algorithms Through Structured State Space Duality},
 author={Dao, Tri and Gu, Albert},
 booktitle={International Conference on Machine Learning (ICML)},
 year={2024}
}

[2] Hugging Face
@inproceedings{wolf-etal-2020-transformers,
   title = "Transformers: State-of-the-Art Natural Language Processing",
   author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
   month = oct,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
   pages = "38--45"
}