# Attention Backends

This document describes the attention backends available in sglang diffusion (`sglang.multimodal_gen`) and how to select them.

## Overview

Attention backends are defined by `AttentionBackendEnum` (`sglang.multimodal_gen.runtime.platforms.interface.AttentionBackendEnum`) and selected via the CLI flag `--attention-backend`.

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).

When using the diffusers backend, `--attention-backend` is passed through to diffusers'
`set_attention_backend` (e.g., `flash`, `_flash_3_hub`, `sage`, `xformers`, `native`).

- **CUDA**: prefers FlashAttention (FA3/FA4) when supported; otherwise falls back to PyTorch SDPA.
- **ROCm**: uses FlashAttention when available; otherwise falls back to PyTorch SDPA.
- **MPS**: always uses PyTorch SDPA.
- **NPU**: for ring attention uses FA otherwise uses PyTorch SDPA.

## Backend options

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`.

| CLI value | Enum value | Notes |
|---|---|---|
| `fa` / `fa3` / `fa4` | `FA` | FlashAttention. `fa3/fa4` are normalized to `fa` during argument parsing (`ServerArgs.__post_init__`). |
| `torch_sdpa` | `TORCH_SDPA` | PyTorch `scaled_dot_product_attention`. |
| `sliding_tile_attn` | `SLIDING_TILE_ATTN` | Sliding Tile Attention (STA). Requires `st_attn`. Configure via `--attention-backend-config`. |
| `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. |
| `sage_attn_3` | `SAGE_ATTN_3` | Requires SageAttention3 installed per upstream instructions. |
| `video_sparse_attn` | `VIDEO_SPARSE_ATTN` | Requires `vsa`. Configure `sparsity` via `--attention-backend-config`. |
| `vmoba_attn` | `VMOBA_ATTN` | Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`. |
| `aiter` | `AITER` | Requires `aiter`. |
| `aiter_sage` | `AITER_SAGE` | Requires `aiter`. |
| `sparse_video_gen_2_attn` | `SPARSE_VIDEO_GEN_2_ATTN` | Requires `svg`. See installation instructions at https://github.com/svg-project/Sparse-VideoGen. |

## Selection priority

The selection order in `runtime/layers/attention/selector.py` is:

1. `global_force_attn_backend(...)` / `global_force_attn_backend_context_manager(...)`
2. CLI `--attention-backend` (`ServerArgs.attention_backend`)
3. Auto selection (platform capability, dtype, and installed packages)

## Configuration

Some backends require additional configuration. You can pass these parameters via `--attention-backend-config`. This argument accepts:
- A path to a JSON or YAML configuration file.
- A JSON string (e.g., `'{"sparsity": 0.5}'`).
- Key-value pairs (e.g., `"sparsity=0.5,enable_x=true"`).

### Supported Configuration Parameters

**Sliding Tile Attention (`sliding_tile_attn`)**

| Parameter | Type | Description | Default |
| :--- | :--- | :--- | :--- |
| `mask_strategy_file_path` | `str` | **Required.** Path to the mask strategy JSON file. | - |
| `sta_mode` | `str` | Mode of STA. | `STA_inference` |
| `skip_time_steps` | `int` | Number of steps to use full attention before switching to sparse attention. | `15` |

**Video Sparse Attention (`video_sparse_attn`)**

| Parameter | Type | Description | Default |
| :--- | :--- | :--- | :--- |
| `sparsity` | `float` | Validation sparsity (0.0 - 1.0). | `0.0` |

**V-MoBA (`vmoba_attn`)**

| Parameter | Type | Description | Default |
| :--- | :--- | :--- | :--- |
| `temporal_chunk_size` | `int` | Chunk size for temporal dimension. | - |
| `temporal_topk` | `int` | Top-K tokens to select in temporal dimension. | - |
| `spatial_chunk_size` | `list[int]` | Chunk size for spatial dimension (H, W). | - |
| `spatial_topk` | `int` | Top-K tokens to select in spatial dimension. | - |
| `st_chunk_size` | `list[int]` | Chunk size for spatiotemporal dimension (T, H, W). | - |
| `st_topk` | `int` | Top-K tokens to select in spatiotemporal dimension. | - |
| `moba_select_mode` | `str` | Selection mode (e.g., `threshold`). | `threshold` |
| `moba_threshold` | `float` | Threshold value for selection. | `0.25` |
| `moba_threshold_type` | `str` | Type of thresholding (e.g., `query_head`). | `query_head` |
| `first_full_step` | `int` | Number of initial steps to use full attention. | `12` |
| `first_full_layer` | `int` | Number of initial layers to use full attention. | `0` |
| `temporal_layer` | `int` | Number of temporal layers. | `1` |
| `spatial_layer` | `int` | Number of spatial layers. | `1` |
| `st_layer` | `int` | Number of spatiotemporal layers. | `1` |

## Platform support matrix

| Backend | CUDA | ROCm | MPS | NPU | Notes |
|---|---:|---:|---:|---:|---|
| `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 |
| `torch_sdpa` | ✅ | ✅ | ✅ | ✅ | Most compatible option across platforms. |
| `sliding_tile_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `st_attn`. Configure via `--attention-backend-config`. |
| `sage_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only (optional dependency). |
| `sage_attn_3` | ✅ | ❌ | ❌ | ❌ | CUDA-only (optional dependency). |
| `video_sparse_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `vsa`. Configure `sparsity` via `--attention-backend-config`. |
| `vmoba_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `kernel.attn.vmoba_attn.vmoba`. Configure via `--attention-backend-config`. |
| `aiter` | ❌ | ✅ | ❌ | ❌ | Requires `aiter`. |
| `aiter_sage` | ❌ | ✅ | ❌ | ❌ | Requires `aiter`. |
| `sparse_video_gen_2_attn` | ✅ | ❌ | ❌ | ❌ | CUDA-only. Requires `svg`. |

## Usage

### Select a backend via CLI

```bash
sglang generate \
  --model-path <MODEL_PATH_OR_ID> \
  --prompt "..." \
  --attention-backend fa
```

```bash
sglang generate \
  --model-path <MODEL_PATH_OR_ID> \
  --prompt "..." \
  --attention-backend torch_sdpa
```

### Using Sliding Tile Attention (STA)

```bash
# Pass the mask strategy file path via config
sglang generate \
  --model-path <MODEL_PATH_OR_ID> \
  --prompt "..." \
  --attention-backend sliding_tile_attn \
  --attention-backend-config "mask_strategy_file_path=/abs/path/to/mask_strategy.json"
```

### Notes for ROCm / MPS

- ROCm: use `--attention-backend torch_sdpa` or `fa` depending on what is available in your environment.
- MPS: the platform implementation always uses `torch_sdpa`.
