Sparge Attention

This repository provides the official implementation of SpargeAttn.

SpargeAttn: Accurate Sparse Attention Accelerating Any Model Inference
Paper: View Local PDF (To be appear in Arxiv)
Jintao Zhang, Chendong Xiang, Haofeng Huang, Haocheng Xi, Jia Wei, Jun Zhu, Jianfei Chen

Installation

Base environment

• python>=3.9 , torch>=2.3.0

• CUDA:
  • >=12.8 for Blackwell
  • >=12.4 for fp8 support on Ada
  • >=12.3 for fp8 support on Hopper
  • >=12.0 for Ampere

Install Package

python setup.py install   # or pip install -e .

Avalible API

• spas_sage2_attn_meansim_cuda: SpargeAttn based on SageAttention2.

• spas_sage_attn_meansim_cuda: SpargeAttn based on SageAttention.

Usage Examples

Cogvideo

Tune:

python evaluate/cogvideo_example.py  --use_spas_sage_attn --model_out_path evaluate/models_dict/CogVideoX-2b_0.06_0.07.pt --tune

Inference:

python evaluate/cogvideo_example.py  --use_spas_sage_attn --model_out_path evaluate/models_dict/CogVideoX-2b_0.06_0.07.pt

Note: We provide a pre-tuned hyper-parameters CogVideoX-2b_0.06_0.07.pt that allows you to run the inference script directly. However, for better performance in both speed and quality, we recommend re-tuning because the provided hyper-parameters is tuned with SpargeAttn based on SageAttention, whereas the default API is based on SageAttention2 now.

LLama

The tuning and inference usage is similar to Cogvideo.

Performance

Citation

If you use this code or find our work valuable, please cite:

@misc{zhang2025spargeattn,
      title={SpargeAttn: Accurate Sparse Attention Accelerating Any Model Inference}, 
      author={Jintao Zhang and Chendong Xiang and Haofeng Huang and Haocheng Xi and Jia Wei and Jun Zhu and Jianfei Chen},
      year={2025}
}

@inproceedings{zhang2025sageattention,
      title={SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration}, 
      author={Zhang, Jintao and Wei, Jia and Zhang, Pengle and Zhu, Jun and Chen, Jianfei},
      booktitle={International Conference on Learning Representations (ICLR)},
      year={2025}
}

@misc{zhang2024sageattention2,
      title={SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization}, 
      author={Jintao Zhang and Haofeng Huang and Pengle Zhang and Jia Wei and Jun Zhu and Jianfei Chen},
      year={2024},
      eprint={2411.10958},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2411.10958}, 
}