hao-ai-lab-FastVideo/fastvideo-kernel

FastVideo Kernel

CUDA kernels for FastVideo video generation.

Installation

Standard Installation (Local Development)

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

Optional: FA4 CuTe block-sparse backend (VSA-256 fastpath)

The VSA-256 fastpath (tile volume 256, on NVIDIA Blackwell / sm_100) routes to the FlashAttention-4 CuTe-DSL block-sparse kernel exposed as flash_attn.cute. This is an optional dependency: it is imported lazily, and video_sparse_attn transparently falls back to the Triton backend when it is absent (so the package is fully usable without it).

The symbols the fastpath needs (flash_attn.cute.block_sparsity.BlockSparseTensorsTorch, flash_attn.cute.interface._flash_attn_fwd) are provided upstream by Dao-AILab/flash-attention. Pin to commit c19cd20e: the wrapper targets that revision's _flash_attn_fwd signature (m_block_size / n_block_size); later upstream revisions reshaped it into a tile_mn tuple and are not drop-in compatible.

pip install "nvidia-cutlass-dsl>=4.5.0" torchvision
pip install "git+https://github.com/Dao-AILab/flash-attention.git@c19cd20e#subdirectory=flash_attn/cute"

The CuTe kernel JIT-compiles on first use. Verified on Blackwell (sm_100) against tests/test_vsa256_forward*.py.

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}
}