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 940cd9680f3315f2f06b43ab5bea2c2cf2d96806, the revision FastVideo pins as the flash-attn-4 source in the repo-root pyproject.toml; other revisions may have an incompatible _flash_attn_fwd signature.

pip install "nvidia-cutlass-dsl>=4.5.0" torchvision
pip install "git+https://github.com/Dao-AILab/flash-attention.git@940cd9680f3315f2f06b43ab5bea2c2cf2d96806#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}
}

Project links

Data verified by PyPI on Jul 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for SolitaryThinker from gravatar.com SolitaryThinker

Credits

Author: Hao AI Lab

GitHub Statistics

Data verified by PyPI on Jul 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache License

Requires

Python >=3.10

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fastvideo_kernel-0.3.2.tar.gz (25.6 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_x86_64.manylinux_2_35_x86_64.whl (21.3 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.34+ x86-64manylinux: glibc 2.35+ x86-64

fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_aarch64.manylinux_2_35_aarch64.whl (5.0 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.34+ ARM64manylinux: glibc 2.35+ ARM64

File details

Details for the file fastvideo_kernel-0.3.2.tar.gz.

File metadata

  • Download URL: fastvideo_kernel-0.3.2.tar.gz
  • Upload date:
  • Size: 25.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for fastvideo_kernel-0.3.2.tar.gz
Algorithm Hash digest
SHA256 d1592272bf48d7cd4559d28d2a5048e65f732acd3228fccfee3c33a7ec150d53
MD5 e843c07899b902a500de1c6e91204ecb
BLAKE2b-256 5a776958e134d89df346174498217d8f4772123426ac45f17cc0532b80c2ab55

See more details on using hashes here.

Provenance

The following attestation bundles were made for fastvideo_kernel-0.3.2.tar.gz:

Publisher: publish-kernel.yml on hao-ai-lab/FastVideo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_x86_64.manylinux_2_35_x86_64.whl.

File metadata

File hashes

Hashes for fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_x86_64.manylinux_2_35_x86_64.whl
Algorithm Hash digest
SHA256 f5f1da94d9a1242596cf04f0c793b9693e3ff22aa0fee79f230e0875571f2485
MD5 7726cca45685f4aa2b9acfdd68f9c096
BLAKE2b-256 d7878abf4e6f8ebd6bae380e10bc751621c85831548cee8d8c46aba6c975354f

See more details on using hashes here.

Provenance

The following attestation bundles were made for fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_x86_64.manylinux_2_35_x86_64.whl:

Publisher: publish-kernel.yml on hao-ai-lab/FastVideo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_aarch64.manylinux_2_35_aarch64.whl.

File metadata

File hashes

Hashes for fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_aarch64.manylinux_2_35_aarch64.whl
Algorithm Hash digest
SHA256 6326861a03e268d7d88c27b34b00942caebe8e055c6b502f1db400502bff2350
MD5 e052f86dbe63e1e3d3b94b087b3c7210
BLAKE2b-256 963fcf0736252b9ebbde56a14f240042aca190509dab5665cd495fd629f39a0b

See more details on using hashes here.

Provenance

The following attestation bundles were made for fastvideo_kernel-0.3.2-cp312-cp312-manylinux_2_34_aarch64.manylinux_2_35_aarch64.whl:

Publisher: publish-kernel.yml on hao-ai-lab/FastVideo

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page