73 lines
2.9 KiB
Markdown
73 lines
2.9 KiB
Markdown
# RainFusion Attention
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`RAINFUSION_ATTN` runs MindIE-SD RainFusion (`rf_v2`) block-sparse video
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attention on Ascend NPU. It pools 128-token key blocks, ranks them per query
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block, and attends to the highest-scoring blocks after arranging video tokens
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in `(t, h, w)` order.
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Only the video segment is sparse. Prefix rows such as text, visual conditions,
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and audio, plus first-frame blocks, remain dense. Unsupported calls—including
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warmup steps, skipped layers, missing video geometry, and video segments below
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32 blocks—delegate to `FLASH_ATTN`, so compatible models can select RainFusion
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globally.
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## Configuration
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| Key | Valid values | Meaning |
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| --- | --- | --- |
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| `sparsity` | finite, `[0, 1]` | Nominal dropped-key-block fraction; default `0.8`; `0` disables sparsity |
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| `start_step` | integer, `>= 0` | Number of early denoise steps kept dense |
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| `skip_layers` | selector such as `"0-3,38"` | DiT blocks kept dense |
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```bash
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vllm-omni serve MiniMaxAI/MiniMax-H3 \
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--diffusion-attention-config '{"default":{"backend":"RAINFUSION_ATTN",\
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"block_sparse":{"sparsity":0.8,"start_step":0}}}'
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```
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```python
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from vllm_omni.diffusion.data import AttentionConfig, AttentionSpec, BlockSparseSpec
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config = AttentionConfig(
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default=AttentionSpec(
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backend="RAINFUSION_ATTN",
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block_sparse=BlockSparseSpec(
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sparsity=0.8,
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start_step=0,
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skip_layers="0-1",
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),
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),
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)
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```
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Tune `start_step`, then `sparsity`, then `skip_layers`. Increasing the dense
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early-step window is usually the cheapest way to recover global structure;
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use layer exclusions only after a same-seed dense comparison identifies
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sensitive blocks.
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## Requirements and compatibility
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RainFusion requires Ascend NPU and `mindiesd`. Selecting it on another
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platform raises. It is incompatible with ring sequence parallelism because
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the kernel needs the complete key sequence for ranking; use Ulysses sequence
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parallelism with `ring_degree=1`.
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## Geometry handling
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RainFusion handles arbitrary video grids by rearranging video spatially and,
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when necessary, promoting a real-video suffix to the always-kept prefix. It
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does not pad because `rf_v2` does not consume a padding attention mask.
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For MiniMax-H3 at 1344x768, grid `(62, 24, 42)` produces 62,496 video rows.
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The implementation promotes 2,976 rows and leaves 59,520 sparse rows
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(`465 x 128`) so the mask and kernel tiling remain aligned.
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Spatial grids aligned to 8x8 are still preferable. A latent height or width
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not divisible by 8 creates an always-kept suffix and reduces realized
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sparsity. At the image level, that means width and height divisible by 256.
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Protected video tails require a compatible MindIE-SD release.
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For common configuration and selector behavior, see the
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[attention backend overview](../attention_backends.md) and the
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[backend selection design](../../../design/feature/attention_backend_selection.md).
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