chore: vendor sglang v0.5.10 snapshot
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third_party/sglang/docs/diffusion/performance/attention_backends.md
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# Attention Backends
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This document describes the attention backends available in sglang diffusion (`sglang.multimodal_gen`) and how to select them.
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## Overview
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Attention backends are defined by `AttentionBackendEnum` (`sglang.multimodal_gen.runtime.platforms.interface.AttentionBackendEnum`) and selected via the CLI flag `--attention-backend`.
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Backend selection is performed by the shared attention layers (e.g. `LocalAttention` / `USPAttention` / `UlyssesAttention` in `sglang.multimodal_gen.runtime.layers.attention.layer`) and therefore applies to any model component using these layers (e.g. diffusion transformer / DiT and encoders).
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When using the diffusers backend, `--attention-backend` is passed through to diffusers'
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`set_attention_backend` (e.g., `flash`, `_flash_3_hub`, `sage`, `xformers`, `native`).
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- **CUDA**: prefers FlashAttention (FA3/FA4) when supported; otherwise falls back to PyTorch SDPA.
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- **ROCm**: uses FlashAttention when available; otherwise falls back to PyTorch SDPA.
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- **MPS**: always uses PyTorch SDPA.
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- **NPU**: for ring attention uses FA otherwise uses PyTorch SDPA.
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## Backend options
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For SGLang-native pipelines, the CLI accepts the lowercase names of `AttentionBackendEnum`. The table below lists the backends implemented by the built-in platforms. `fa3`/`fa4` are accepted as aliases for `fa`.
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| CLI value | Enum value | Notes |
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|---|---|---|
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| `fa` / `fa3` / `fa4` | `FA` | FlashAttention. `fa3/fa4` are normalized to `fa` during argument parsing (`ServerArgs.__post_init__`). |
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| `torch_sdpa` | `TORCH_SDPA` | PyTorch `scaled_dot_product_attention`. |
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| `sliding_tile_attn` | `SLIDING_TILE_ATTN` | Sliding Tile Attention (STA). Requires `st_attn`. Configure via `--attention-backend-config`. |
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| `sage_attn` | `SAGE_ATTN` | Requires `sageattention`. Upstream SageAttention CUDA extensions target SM80/SM86/SM89/SM90/SM120 (compute capability 8.0/8.6/8.9/9.0/12.0); see upstream `setup.py`: https://github.com/thu-ml/SageAttention/blob/main/setup.py. |
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| `sage_attn_3` | `SAGE_ATTN_3` | Requires SageAttention3 installed per upstream instructions. |
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| `video_sparse_attn` | `VIDEO_SPARSE_ATTN` | Requires `vsa`. Configure `sparsity` via `--attention-backend-config`. |
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| `vmoba_attn` | `VMOBA_ATTN` | Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`. |
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| `aiter` | `AITER` | Requires `aiter`. |
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| `aiter_sage` | `AITER_SAGE` | Requires `aiter`. |
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| `sparse_video_gen_2_attn` | `SPARSE_VIDEO_GEN_2_ATTN` | Requires `svg`. See installation instructions at https://github.com/svg-project/Sparse-VideoGen. |
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## Selection priority
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The selection order in `runtime/layers/attention/selector.py` is:
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1. `global_force_attn_backend(...)` / `global_force_attn_backend_context_manager(...)`
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2. CLI `--attention-backend` (`ServerArgs.attention_backend`)
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3. Auto selection (platform capability, dtype, and installed packages)
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## Configuration
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Some backends require additional configuration. You can pass these parameters via `--attention-backend-config`. This argument accepts:
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- A path to a JSON or YAML configuration file.
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- A JSON string (e.g., `'{"sparsity": 0.5}'`).
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- Key-value pairs (e.g., `"sparsity=0.5,enable_x=true"`).
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### Supported Configuration Parameters
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**Sliding Tile Attention (`sliding_tile_attn`)**
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| Parameter | Type | Description | Default |
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| :--- | :--- | :--- | :--- |
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| `mask_strategy_file_path` | `str` | **Required.** Path to the mask strategy JSON file. | - |
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| `sta_mode` | `str` | Mode of STA. | `STA_inference` |
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| `skip_time_steps` | `int` | Number of steps to use full attention before switching to sparse attention. | `15` |
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**Video Sparse Attention (`video_sparse_attn`)**
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| Parameter | Type | Description | Default |
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| :--- | :--- | :--- | :--- |
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| `sparsity` | `float` | Validation sparsity (0.0 - 1.0). | `0.0` |
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**V-MoBA (`vmoba_attn`)**
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| Parameter | Type | Description | Default |
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| :--- | :--- | :--- | :--- |
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| `temporal_chunk_size` | `int` | Chunk size for temporal dimension. | - |
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| `temporal_topk` | `int` | Top-K tokens to select in temporal dimension. | - |
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| `spatial_chunk_size` | `list[int]` | Chunk size for spatial dimension (H, W). | - |
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| `spatial_topk` | `int` | Top-K tokens to select in spatial dimension. | - |
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| `st_chunk_size` | `list[int]` | Chunk size for spatiotemporal dimension (T, H, W). | - |
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| `st_topk` | `int` | Top-K tokens to select in spatiotemporal dimension. | - |
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| `moba_select_mode` | `str` | Selection mode (e.g., `threshold`). | `threshold` |
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| `moba_threshold` | `float` | Threshold value for selection. | `0.25` |
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| `moba_threshold_type` | `str` | Type of thresholding (e.g., `query_head`). | `query_head` |
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| `first_full_step` | `int` | Number of initial steps to use full attention. | `12` |
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| `first_full_layer` | `int` | Number of initial layers to use full attention. | `0` |
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| `temporal_layer` | `int` | Number of temporal layers. | `1` |
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| `spatial_layer` | `int` | Number of spatial layers. | `1` |
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| `st_layer` | `int` | Number of spatiotemporal layers. | `1` |
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## Platform support matrix
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| Backend | CUDA | ROCm | MPS | NPU | Notes |
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|---|---:|---:|---:|---:|---|
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| `fa` | ✅ | ✅ | ❌ | ✅ | CUDA requires SM80+ and fp16/bf16. FlashAttention is only used when the required runtime is installed; otherwise it falls back to `torch_sdpa`. No extra installations are required for NPU |
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| `torch_sdpa` | ✅ | ✅ | ✅ | ✅ | Most compatible option across platforms. |
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| `sliding_tile_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `st_attn`. Configure via `--attention-backend-config`. |
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| `sage_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only (optional dependency). |
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| `sage_attn_3` | ✅ | ❌ | ❌ | ❌ | CUDA-only (optional dependency). |
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| `video_sparse_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `vsa`. Configure `sparsity` via `--attention-backend-config`. |
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| `vmoba_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`. |
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| `aiter` | ❌ | ✅ | ❌ | ❌ | Requires `aiter`. |
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| `aiter_sage` | ❌ | ✅ | ❌ | ❌ | Requires `aiter`. |
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| `sparse_video_gen_2_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `svg`. |
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## Usage
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### Select a backend via CLI
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```bash
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sglang generate \
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--model-path <MODEL_PATH_OR_ID> \
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--prompt "..." \
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--attention-backend fa
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```
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```bash
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sglang generate \
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--model-path <MODEL_PATH_OR_ID> \
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--prompt "..." \
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--attention-backend torch_sdpa
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```
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### Using Sliding Tile Attention (STA)
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```bash
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# Pass the mask strategy file path via config
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sglang generate \
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--model-path <MODEL_PATH_OR_ID> \
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--prompt "..." \
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--attention-backend sliding_tile_attn \
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--attention-backend-config "mask_strategy_file_path=/abs/path/to/mask_strategy.json"
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```
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### Notes for ROCm / MPS
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- ROCm: use `--attention-backend torch_sdpa` or `fa` depending on what is available in your environment.
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- MPS: the platform implementation always uses `torch_sdpa`.
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