🐍 The Hidden Attention of Mamba Models 🐍

Ameen Ali¹ *,Itamar Zimerman¹ * and Lior Wolf¹
ameenali023@gmail.com, itamarzimm@gmail.com, liorwolf@gmail.com
¹ Tel Aviv University (*) equal contribution

Official PyTorch Implementation of "The Hidden Attention of Mamba Models"

The Mamba layer offers an efficient state space model (SSM) that is highly effective in modeling multiple domains including long-range sequences and images. SSMs are viewed as dual models, in which one trains in parallel on the entire sequence using convolutions, and deploys in an autoregressive manner. We add a third view and show that such models can be viewed as attention-driven models. This new perspective enables us to compare the underlying mechanisms to that of the self-attention layers in transformers and allows us to peer inside the inner workings of the Mamba model with explainability methods.
You can access the paper through : The Hidden Attention of Mamba Models

Set Up Environment

• Python 3.10.13

  • conda create -n your_env_name python=3.10.13

• Activate Env

  • conda activate your_env_name

• CUDA TOOLKIT 11.8

  • conda install nvidia/label/cuda-11.8.0::cuda-toolkit

• torch 2.1.1 + cu118

  • pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

• Requirements: vim_requirements.txt

  • pip install -r vim/vim_requirements.txt

• Install jupyter

  • pip install jupyter

• Install causal_conv1d and mamba from our source

  • cd causal-conv1d
  • pip install --editable .
  • cd ..
  • pip install --editable mamba-1p1p1

Pre-Trained Weights

We have used the official weights provided by Vim, which can be downloaded from here:

Model	#param.	Top-1 Acc.	Top-5 Acc.	Hugginface Repo
Vim-tiny	7M	76.1	93.0	https://huggingface.co/hustvl/Vim-tiny-midclstok
Vim-tiny+	7M	78.3	94.2	https://huggingface.co/hustvl/Vim-tiny-midclstok
Vim-small	26M	80.5	95.1	https://huggingface.co/hustvl/Vim-small-midclstok
Vim-small+	26M	81.6	95.4	https://huggingface.co/hustvl/Vim-small-midclstok

Notes:

• In all of our experiments, we have worked with Vim-small.

Vision-Mamba Explainability Notebook:

Follow the instructions in vim/vmamba_xai.ipynb notebook, in order to apply a single-image inference for the 3 introduced methods in the paper.

To-Do

• XAI - Single Image Inference Notebook
• XAI - Segmentation Experimnts
• XAI - Pertubation Experimnts
• NLP Experimnts

Citation

if you find our work useful, please consider citing us:

@misc{ali2024hidden,
      title={The Hidden Attention of Mamba Models}, 
      author={Ameen Ali and Itamar Zimerman and Lior Wolf},
      year={2024},
      eprint={2403.01590},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Acknowledgement

This repository is heavily based on Vim, Mamba and Transformer-Explainability. Thanks for their wonderful works.