hao-ai-lab-FastVideo/fastvideo-kernel/README.md

# FastVideo Kernel

CUDA kernels for FastVideo video generation.

## Kernel inventory

Compiled CUDA extensions (CMake, see the build summary printed at the end of every configure):

| Extension | Kernels | Sources | GPU arch | Build gate |
|---|---|---|---|---|
| `fastvideo_kernel._C.fastvideo_kernel_ops` | TurboDiffusion INT8 GEMM, quant, RMSNorm, LayerNorm | `csrc/turbodiffusion/` | every arch in `TORCH_CUDA_ARCH_LIST` | always built |
| 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) |
| `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) |

Runtime-JIT kernels (no build step, ship in every wheel/image):

| Kernels | Where | Used when |
|---|---|---|
| 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`) |
| FA4 CuTe-DSL block-sparse forward/backward (VSA-128/256 fastpath on `sm_100`) | `block_sparse_attn_cute_fwd.py` | optional `flash_attn.cute` dependency, see below |
| VMoBA `moba_attn_varlen` | `vmoba.py` | wraps flash-attn varlen |

## What gets built where, and when

| Surface | Trigger | Leg | `TORCH_CUDA_ARCH_LIST` | TK | FP4 |
|---|---|---|---|---|---|
| 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) |
| | | x86_64 cu130 | `9.0a;12.0a` | ON | ON |
| | | aarch64 cu130 | `10.0a;12.0a` | — | ON |
| 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 | — |
| | | arm64 cuda12.6.3 (GH200) | `9.0a` | — (aarch64) | — |
| | | arm64 cuda13.0.0 (GB10 / DGX Spark) | `12.1` | — | — |
| Local `./build.sh` | manual | probes the visible GPU via torch | detected | ON iff sm_90 (non-aarch64 host) | ON iff sm_120 |

Notes:

- No Docker image ships the FP4 kernels; only the x86_64/aarch64 cu130 wheels do.
- On arm64 images (GH200 included) STA/VSA run on the Triton fallbacks, since TK never builds on aarch64.
- Kernel tests run on Buildkite GPU CI for PRs touching `fastvideo-kernel/**` (see `.buildkite/pipeline.yml`).

## 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:

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

```bash
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.

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

### Optional: FA4 CuTe block-sparse backend (VSA-128/256 fastpath)

The VSA-128/256 fastpaths (tile volume 128 or 256, on NVIDIA Blackwell / sm_100) route 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`
and the public/private forward-backward bridges in `flash_attn.cute.interface`) are provided upstream by
[Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention). Pin to
commit `14c377950125c70b7a9dabf9c561fca53715ac7d`, the revision FastVideo pins as
the `flash-attn-4` source in the repo-root `pyproject.toml`; other revisions may
have incompatible block-sparse forward/backward interfaces.

Install it under its distribution name so its own runtime stack resolves with it.
Do **not** pre-install `nvidia-cutlass-dsl` by hand: this revision pins
`nvidia-cutlass-dsl==4.6.0.dev0` exactly, and a hand-installed 4.5.x floor either
gets silently upgraded or, if something else holds it back, leaves the CuTe
kernels broken.

```bash
pip install torchvision
pip install "flash-attn-4 @ git+https://github.com/Dao-AILab/flash-attention.git@14c377950125c70b7a9dabf9c561fca53715ac7d#subdirectory=flash_attn/cute"
```

That resolves `nvidia-cutlass-dsl` to 4.6.0.dev0 and `quack-kernels` to 0.5.3, a
combination this revision works with. A mismatched CuTe DSL only surfaces when the
kernel JIT-compiles, so the error points at CuTe internals rather than at the
install:

| Error on first VSA-128/256 CuTe call | Cause |
|---|---|
| `TypeError: fmax() missing 1 required positional argument: 'b'` | `nvidia-cutlass-dsl` 4.5.x |
| `AttributeError: module 'cutlass.cute.core' has no attribute 'ThrMma'` | `quack-kernels` older than 0.5.1 |
| `ImportError: cannot import name 'alloc_reserved_mbarrier'` | `quack-kernels` 0.6.2 or newer |

An environment whose `flash_attn.cute` came from a prebuilt flash-attn wheel rather
than from this pin hits the first row; that is what the overlay step in
`docker/Dockerfile` works around.

The CuTe kernels JIT-compile on first use. Forward and backward are verified on
Blackwell (sm_100) against `tests/test_vsa128_*.py` and `tests/test_vsa256_*.py`.

## Usage

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

For detailed usage, please check the [Attention Documentation](../docs/attention/index.md).

```python
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

### Attn-QAT training

The default shape matches one sequence-parallel rank of the 4-GPU
Wan2.1-T2V-1.3B MixKit recipe (`B=1, H=3, L=31200, D=128`):

```bash
cd fastvideo-kernel
python benchmarks/benchmark_attn_qat_train.py
```

The benchmark reports both conventional attention FLOPs and the extra matrix
multiplications executed by the QAT straight-through path. Override
`--peak-tflops` when running on a GPU other than RTX 5090.

The QAT kernel is entirely Triton and routes by architecture at runtime. SM100
uses a large-tile forward and split 64x64 backward for the production
non-causal, head-dimension-128 configuration with a 16-aligned KV length. SM120
(including RTX 5090) keeps the previous tiling but joins the quantized and STE
P@V operations and uses a shallower backward pipeline for long sequences. Set
`FASTVIDEO_ATTN_QAT_SM120_JOIN_QAT_PV=0` to compare against the split P@V path.
Unsupported configurations retain the previous implementation. Set
`FASTVIDEO_ATTN_QAT_SM100_OPTIMIZED=0` to benchmark that previous path on SM100. Forward tuning is available through
`FASTVIDEO_ATTN_QAT_FWD_MODE=fast|balanced|reference`; exact reference-order
softmax statistics are controlled by `FASTVIDEO_ATTN_QAT_FWD_EXACT_M` and are
disabled by default for maximum throughput. Set it to `1` for reference-order
statistics and bitwise-compatible `dV`.

### VSA (block-sparse) TFLOPs

After building/installing `fastvideo-kernel`, run:

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

# VSA-256 FA4 CuTe forward/backward on Blackwell
python benchmarks/bench_vsa.py --block_size 256 --use_cute \
  --batch_size 1 --num_heads 12 --head_dim 128 --q_seq_lens 39936 --topk 20

# VSA-128 FA4 CuTe forward/backward on Blackwell
python benchmarks/bench_vsa.py --block_size 128 --use_cute \
  --batch_size 1 --num_heads 12 --head_dim 128 --q_seq_lens 39936 --topk 40
```

### TurboDiffusion Kernels

This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/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](https://github.com/sgl-project/sglang/tree/main/sgl-kernel) from the SGLang project.

The implementation of `turbodiffusion` kernels is adapted from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion). If you use these kernels, please cite:

```bibtex
@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}
}
```