149 lines
6.5 KiB
Markdown
149 lines
6.5 KiB
Markdown
# FastVideo Kernel
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CUDA kernels for FastVideo video generation.
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## Kernel inventory
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Compiled CUDA extensions (CMake, see the build summary printed at the end of every configure):
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| Extension | Kernels | Sources | GPU arch | Build gate |
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| `fastvideo_kernel._C.fastvideo_kernel_ops` | TurboDiffusion INT8 GEMM, quant, RMSNorm, LayerNorm | `csrc/turbodiffusion/` | every arch in `TORCH_CUDA_ARCH_LIST` | always built |
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| same extension, optional part | ThunderKittens sliding-tile attention (`sta_fwd`) and VSA block-sparse (`block_sparse_fwd/bwd`) | `csrc/attention/*_h100.cu` | Hopper `sm_90a` only | `FASTVIDEO_KERNEL_BUILD_TK` (AUTO = ON iff `9.0a` is in the arch list; always OFF on aarch64 hosts — TK headers don't compile there) |
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| `fp4attn_cuda`, `fp4quant_cuda` | FP4 attention + quantization ("attn_qat_infer", modified SageAttention3) | `attn_qat_infer/` | consumer Blackwell `sm_120a` only, CUDA ≥ 12.8 | `FASTVIDEO_KERNEL_BUILD_ATTN_QAT_INFER` (AUTO = ON iff `12.0a` is in the arch list) |
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Runtime-JIT kernels (no build step, ship in every wheel/image):
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| Kernels | Where | Used when |
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|---|---|---|
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| Triton: STA, VSA block-sparse, SLA, fused compress+topk, FP4 QAT training, quant/norm utils | `python/fastvideo_kernel/triton_kernels/` | automatic fallback when the matching C++ op is absent (`ops.py`, `turbodiffusion_ops.py`) |
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| FA4 CuTe-DSL block-sparse (VSA-256 fastpath on `sm_100`) | `block_sparse_attn_cute_fwd.py` | optional `flash_attn.cute` dependency, see below |
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| VMoBA `moba_attn_varlen` | `vmoba.py` | wraps flash-attn varlen |
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## What gets built where, and when
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| Surface | Trigger | Leg | `TORCH_CUDA_ARCH_LIST` | TK | FP4 |
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| PyPI wheels (`.github/workflows/publish-kernel.yml`) | version bump in `fastvideo-kernel/pyproject.toml` on main, or manual dispatch | x86_64 cu126 | `9.0a` | ON | — (CUDA < 12.8) |
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| | | x86_64 cu130 | `9.0a;12.0a` | ON | ON |
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| | | aarch64 cu130 | `10.0a;12.0a` | — | ON |
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| Docker images `ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev` (`.github/workflows/infra-build-image.yml`) | `docker/Dockerfile` changes on main, or manual dispatch | amd64 cuda12.6.3 + cuda13.0.0 | `9.0a` | ON | — |
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| | | arm64 cuda12.6.3 (GH200) | `9.0a` | — (aarch64) | — |
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| | | arm64 cuda13.0.0 (GB10 / DGX Spark) | `12.1` | — | — |
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| Local `./build.sh` | manual | probes the visible GPU via torch | detected | ON iff sm_90 (non-aarch64 host) | ON iff sm_120 |
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Notes:
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- No Docker image ships the FP4 kernels; only the x86_64/aarch64 cu130 wheels do.
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- On arm64 images (GH200 included) STA/VSA run on the Triton fallbacks, since TK never builds on aarch64.
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- Kernel tests run on Buildkite GPU CI for PRs touching `fastvideo-kernel/**` (see `.buildkite/pipeline.yml`).
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## Installation
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### Standard Installation (Local Development)
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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.
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Before installation, set CUDA toolchain paths:
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```bash
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export CUDA_HOME=/usr/local/cuda
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export CUDACXX=$CUDA_HOME/bin/nvcc
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```
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```bash
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git submodule update --init --recursive
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cd fastvideo-kernel
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./build.sh
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```
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### Rocm Build
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If you are in a rocm environment without the compilation toolchaine of CUDA.
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```bash
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cd fastvideo-kernel
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./build.sh --rocm
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```
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### Optional: FA4 CuTe block-sparse backend (VSA-256 fastpath)
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The VSA-256 fastpath (tile volume 256, on NVIDIA Blackwell / sm_100) routes to the
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FlashAttention-4 CuTe-DSL block-sparse kernel exposed as `flash_attn.cute`. This is
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an **optional** dependency: it is imported lazily, and `video_sparse_attn`
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transparently falls back to the Triton backend when it is absent (so the package is
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fully usable without it).
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The symbols the fastpath needs (`flash_attn.cute.block_sparsity.BlockSparseTensorsTorch`,
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`flash_attn.cute.interface._flash_attn_fwd`) are provided upstream by
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[Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention). Pin to
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commit `940cd9680f3315f2f06b43ab5bea2c2cf2d96806`, the revision FastVideo pins as
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the `flash-attn-4` source in the repo-root `pyproject.toml`; other revisions may
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have an incompatible `_flash_attn_fwd` signature.
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```bash
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pip install "nvidia-cutlass-dsl>=4.5.0" torchvision
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pip install "git+https://github.com/Dao-AILab/flash-attention.git@940cd9680f3315f2f06b43ab5bea2c2cf2d96806#subdirectory=flash_attn/cute"
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```
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The CuTe kernel JIT-compiles on first use. Verified on Blackwell (sm_100) against
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`tests/test_vsa256_forward*.py`.
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## Usage
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### Sliding Tile Attention (STA) & Video Sparse Attention (VSA)
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For detailed usage, please check the [Attention Documentation](../docs/attention/index.md).
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```python
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from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_attn_varlen
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# Example: Sliding Tile Attention
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out = sliding_tile_attention(q, k, v, window_sizes, text_len)
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# Example: Video Sparse Attention (with Triton fallback)
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out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
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# Example: VMoBA
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out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
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```
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## Benchmark
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### VSA (block-sparse) TFLOPs
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After building/installing `fastvideo-kernel`, run:
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```bash
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cd fastvideo-kernel
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python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
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```
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### TurboDiffusion Kernels
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This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion), including INT8 GEMM, Quantization, RMSNorm and LayerNorm.
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## Requirements
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- **Runtime**:
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- NVIDIA H100 (sm_90a) for C++ optimized kernels.
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- Any CUDA GPU for Triton-based fallbacks.
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- **Build**:
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- CUDA Toolkit 12.3+
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- `CUDA_HOME` must be set (for example, `/usr/local/cuda`)
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- `CUDACXX` must be set (for example, `$CUDA_HOME/bin/nvcc`)
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- C++20 compatible compiler (GCC 10+, Clang 11+)
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## Acknowledgement
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This package structure and build system are based on [sgl-kernel](https://github.com/sgl-project/sglang/tree/main/sgl-kernel) from the SGLang project.
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The implementation of `turbodiffusion` kernels is adapted from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion). If you use these kernels, please cite:
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```bibtex
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@article{zhang2025turbodiffusion,
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title={TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times},
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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},
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journal={arXiv preprint arXiv:2512.16093},
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year={2025}
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}
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```
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