VideoNSA: Native Sparse Attention for Video Understanding

[Paper] [Project Page] [Model]

VideoNSA is a learnable, hardware-aware sparse-attention framework for efficient video understanding, processing up to 128K vision-text tokens using only 3.6% of the full attention budget.

News

• [2025-10] We release our training data in Huggingface.
• [2025-10] Code and model released. Paper released on arXiv.
• [2025-10] Project website launched

Installation

For Training

Currently, we build on existing open-source implementations to support training:

• FLA: flash-linear-attention
• Tilde: nsa-impl

We also recommend exploring Scalable-Flash-Native-Sparse-Attention, which provides a highly optimized and scalable implementation of native sparse attention.

# Clone the repository
git clone https://github.com/Espere-1119-Song/VideoNSA.git
cd VideoNSA

# Install ms-swift
cd ms-swift
pip install -e .

# Install flash-linear-attention
pip uninstall fla-core flash-linear-attention -y
pip install -U git+https://github.com/fla-org/flash-linear-attention

cd ..

For Evaluation

# Install UV package manager
pip install uv

# Install lmms-eval
uv pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git

# Install additional dependencies
pip install flash-attn --no-build-isolation
pip install qwen_vl_utils
pip install accelerate

# Login to Hugging Face
huggingface-cli login

For Baseline Comparisons (Optional)

# Install MInference for testing other sparse attention baselines
pip install minference

Usage

Training

cd ms-swift

# Run training script
bash ../scrips/train.sh

Note: Before training, modify the following in scrips/train.sh:

• $YOUR_DATASET: Path to your training dataset
• --output_dir: Output directory for checkpoints
• --logging_dir: Directory for logs
• GPU settings (CUDA_VISIBLE_DEVICES, NPROC_PER_NODE, WORLD_SIZE)
• Hyperparameters (learning rate, batch size, etc.)

Evaluation

cd lmms-eval

# Run evaluation script
bash ../scrips/eval.sh

Note: Before evaluation, set the following environment variables in scrips/eval.sh:

• $MAX_PIXELS: Maximum pixels for video processing
• $FPS: Frames per second for video sampling
• $MAX_NUM_FRAMES: Maximum number of frames
• $TASK_NAME: Benchmark task name (e.g., mvbench, videomme, etc.)

You can also modify:

• --num_processes: Number of GPUs to use
• --batch_size: Batch size per device
• --output_path: Directory for evaluation results

Baseline Comparisons

To evaluate sparse attention baselines used in the paper:

cd lmms-eval

# Make sure minference is installed
pip install minference

# Run baseline evaluation
bash ../scrips/baselines.sh

Note: Modify the baseline script to select different sparse attention methods and configure their parameters.

TODO

• Training dataset release

Citation

@misc{song2025videonsanativesparseattention,
      title={VideoNSA: Native Sparse Attention Scales Video Understanding},
      author={Enxin Song and Wenhao Chai and Shusheng Yang and Ethan Armand and Xiaojun Shan and Haiyang Xu and Jianwen Xie and Zhuowen Tu},
      year={2025},
      eprint={2510.02295},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.02295},
}