This will automatically detect your GPU architecture. If an NVIDIA Hopper (H100/sm_90a) GPU is detected, ThunderKittens kernels will be enabled. Otherwise, they will be skipped, and the package will use Triton fallbacks at runtime.

Before installation, set CUDA toolchain paths:

export CUDA_HOME=/usr/local/cuda
export CUDACXX=$CUDA_HOME/bin/nvcc

git submodule update --init --recursive
cd fastvideo-kernel
./build.sh

Rocm Build

If you are in a rocm environment without the compilation toolchaine of CUDA.

cd fastvideo-kernel
./build.sh --rocm

Usage

Sliding Tile Attention (STA) & Video Sparse Attention (VSA)

For detailed usage, please check the Attention Documentation.

from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_attn_varlen

# Example: Sliding Tile Attention
out = sliding_tile_attention(q, k, v, window_sizes, text_len)

# Example: Video Sparse Attention (with Triton fallback)
out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)

# Example: VMoBA
out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)

Benchmark

VSA (block-sparse) TFLOPs

After building/installing fastvideo-kernel, run:

cd fastvideo-kernel
python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64

TurboDiffusion Kernels

This package also includes kernels from TurboDiffusion, including INT8 GEMM, Quantization, RMSNorm and LayerNorm.

Requirements

Runtime:
- NVIDIA H100 (sm_90a) for C++ optimized kernels.
- Any CUDA GPU for Triton-based fallbacks.
Build:
- CUDA Toolkit 12.3+
- CUDA_HOME must be set (for example, /usr/local/cuda)
- CUDACXX must be set (for example, $CUDA_HOME/bin/nvcc)
- C++20 compatible compiler (GCC 10+, Clang 11+)

Acknowledgement

This package structure and build system are based on sgl-kernel from the SGLang project.

The implementation of turbodiffusion kernels is adapted from TurboDiffusion. If you use these kernels, please cite:

@article{zhang2025turbodiffusion,
  title={TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times},
  author={Zhang, Jintao and Zheng, Kaiwen and Jiang, Kai and Wang, Haoxu and Stoica, Ion and Gonzalez, Joseph E and Chen, Jianfei and Zhu, Jun},
  journal={arXiv preprint arXiv:2512.16093},
  year={2025}
}