hao-ai-lab-FastVideo/docs/source/video_sparse_attention/installation.md

(vsa-installation)=

🔧 Installation

You can install the Video Sparse Attention package using

pip install vsa

Building from Source

We support H100 (via ThunderKittens) and any other GPU (via Triton) for VSA.

First, install C++20 for ThunderKittens (if using H100):

sudo apt update
sudo apt install gcc-11 g++-11

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

sudo apt update
sudo apt install clang-11

Set up CUDA environment (if using CUDA 12.8):

export CUDA_HOME=/usr/local/cuda-12.8
export PATH=${CUDA_HOME}/bin:${PATH} 
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

Install VSA:

cd csrc/attn/video_sparse_attn/
git submodule update --init --recursive
python setup.py install

🧪 Test

python csrc/attn/tests/test_vsa.py

📋 Usage

from vsa import video_sparse_attn

# q, k, v: [batch_size, num_heads, seq_len, head_dim]
# variable_block_sizes: [num_blocks] - number of valid tokens in each block
# topk: int - number of top-k blocks to attend to
# block_size: int or tuple of 3 ints - size of each block (default: 64 tokens)
# compress_attn_weight: optional weight for compressed attention branch

output = video_sparse_attn(q, k, v, variable_block_sizes, topk, block_size, compress_attn_weight)

🚀Inference

bash scripts/inference/v1_inference_wan_VSA.sh