[Hardware][Ascend] Integrate MindIE dense and sparse quantized attention for Wan2.2 T2V (#7561)

Signed-off-by: zqxu <xuzhenqiang3@huawei.com>
Co-authored-by: zqxu <xuzhenqiang3@huawei.com>

docs/user_guide/quantization/quantized_kvcache.md

@@ -2,41 +2,36 @@
 
 ## Overview
 
-In DiT-based image and video generation, Flash Attention can take a large share
-of denoising time, especially for high-resolution or long-frame workloads.
-vLLM-Omni supports online FP8 quantization for eligible diffusion Flash
-Attention (FA) to reduce FA latency while keeping model weights in their
-original dtype.
+In DiT-based image and video generation, attention can take a large share of
+denoising time, especially for high-resolution or long-frame workloads.
+vLLM-Omni supports online Q/K/V quantization for eligible diffusion attention
+paths while keeping model weights in their original dtype.
 
-This feature is configured through `diffusion_kv_cache_dtype` on
-`OmniDiffusionConfig` (CLI: `--diffusion-kv-cache-dtype`). It is intentionally
-**not** the same as vLLM's `--kv-cache-dtype`, which controls autoregressive
-language-model KV cache storage and defaults to `"auto"`. Diffusion FA
-quantization uses the dedicated diffusion flags so omni serve does not inherit
-that default.
+Runtime attention precision is configured through `diffusion_kv_cache_dtype`
+on `OmniDiffusionConfig` (CLI: `--diffusion-kv-cache-dtype`). This setting is
+separate from vLLM's `--kv-cache-dtype`, which controls autoregressive
+language-model KV cache storage.
 
 In vLLM-Omni diffusion pipelines, this is a runtime FA path: Q/K/V tensors are
 dynamically quantized before the attention operator. It does not quantize model
 weights and is separate from [FP8 W8A8](fp8.md), [Int8 W8A8](int8.md), or
 pre-quantized checkpoint formats.
 
-If `diffusion_kv_cache_dtype` is not set, behavior is unchanged and attention
-runs in the native dtype.
+If `diffusion_kv_cache_dtype` is not set, attention runs in the native dtype.
 
 ## Hardware Support
 
-| Device | FP8 FA |
-|--------|--------|
-| Ascend NPU | ✅ |
-| NVIDIA GPU | ❌ |
-| AMD ROCm | ❌ |
-| Intel XPU | ❌ |
+| Device | Dense FP8 | Dense MXFP8/MXFP4 | BSA FP8/MXFP4 |
+|--------|------------|-------------------|----------------|
+| Ascend NPU | ✅ | ✅ | ✅ |
+| NVIDIA GPU | ❌ | ❌ | ❌ |
+| AMD ROCm | ❌ | ❌ | ❌ |
+| Intel XPU | ❌ | ❌ | ❌ |
 
 Legend: `✅` supported, `❌` unsupported.
 
-FP8 FA is currently implemented only for the NPU Flash Attention backend. Other
-backends do not support `diffusion_kv_cache_dtype="fp8"` for diffusion attention
-and fall back to native dtype execution.
+These runtime methods are implemented only by the NPU Flash Attention and BSA
+backends. Other backends reject an incompatible explicit configuration.
 
 ## Model Type Support
 
@@ -44,7 +39,7 @@ and fall back to native dtype execution.
 
 | Model | Scope | Status | Notes |
 |-------|-------|--------|-------|
-| Wan2.2 | Eligible DiT full-attention FA on Ascend NPU | Tested | Compare quality and latency against a BF16 baseline before production use |
+| Wan2.2 | Self-attention on Ascend NPU | NPU interface and T2V smoke tested | Compare quality and latency against a BF16 baseline before production use |
 | Other diffusion models | Eligible DiT full-attention FA on Ascend NPU | Not tested | You can try `diffusion_kv_cache_dtype="fp8"`; tune `diffusion_kv_cache_skip_steps` and `diffusion_kv_cache_skip_layers` when higher precision is needed |
 
 ### Multi-Stage Omni/TTS Model (Qwen3-Omni, Qwen3-TTS)
@@ -105,12 +100,13 @@ The legacy keyword aliases `kv_cache_dtype`, `kv_cache_skip_steps`, and
 
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
-| `diffusion_kv_cache_dtype` | str \| None | `None` | Set to `"fp8"` to enable dynamic FP8 FA on supported attention backends |
+| `diffusion_kv_cache_dtype` | str \| None | `None` | Runtime attention method. Dense FA supports `fp8`, `mxfp8`, and `mxfp4`; BSA supports `fp8` and `mxfp4` |
 | `diffusion_kv_cache_skip_steps` | str \| None | `None` | Denoising step selector to keep in native dtype, for example `"0,1,4-6"` |
 | `diffusion_kv_cache_skip_layers` | str \| None | `None` | Transformer layer selector to keep in native dtype, for example `"0-2,10"` |
 
 Selectors use comma-separated integers and inclusive ranges. Listed steps or
-layers skip FP8 FA; all other eligible full-attention forwards use the FP8 path.
+layers run floating-point attention through the selected Dense or BSA backend;
+all other eligible forwards use `diffusion_kv_cache_dtype`.
 
 ## Validation and Notes
 
@@ -123,3 +119,60 @@ layers skip FP8 FA; all other eligible full-attention forwards use the FP8 path.
 4. Report both latency and quality results when enabling this option for a new
    model. For image or video models, include visual comparison and quantitative
    metrics when available, such as PSNR or SSIM.
+
+## Wan2.2 T2V quantized attention on Ascend
+
+Wan2.2 T2V A14B supports quantized self-attention through MindIE-SD. Model
+weights are unchanged, and model-level opt-out keeps cross-attention in its
+native dtype.
+
+| Attention path | self-attention `backend` | Supported `diffusion_kv_cache_dtype` |
+| --- | --- | --- |
+| Dense FA | `FLASH_ATTN` | `fp8`, `mxfp8`, `mxfp4` |
+| BSA | `RAINFUSION_ATTN` | `fp8`, `mxfp4` |
+
+Configure the method and fallback selectors once at the diffusion stage. The
+per-role attention config only selects the self-attention backend:
+
+```yaml
+stages:
+  - stage_id: 0
+    diffusion_kv_cache_dtype: "mxfp8"
+    diffusion_kv_cache_skip_layers: "0,39"
+    diffusion_kv_cache_skip_steps: "0,1,38,39"
+    diffusion_attention_config:
+      per_role:
+        self:
+          backend: FLASH_ATTN  # Use RAINFUSION_ATTN with fp8 or mxfp4 for BSA.
+```
+
+For 40 denoising steps, `diffusion_kv_cache_skip_steps: "0,1,38,39"` and
+`diffusion_kv_cache_skip_layers: "0,39"` are recommended starting points. Steps
+are zero-based across the complete request and do not reset when Wan switches
+transformers; layer indices are zero-based and local to each transformer.
+Selectors accept comma-separated indices and inclusive ranges such as
+`"0,3-5"`. A selected forward uses floating-point attention while preserving
+the Dense or BSA path.
+
+Unsupported precision/input combinations raise an error and must be corrected
+in the configuration; operator errors are not retried with another precision.
+
+For BSA, change the backend to `RAINFUSION_ATTN` and set
+`diffusion_kv_cache_dtype` to `fp8` or `mxfp4`.
+See [RainFusion attention](../diffusion/attention_backends/rainfusion.md) for
+sparse-path configuration and behavior.
+
+### Requirement and interface test
+
+Until these APIs are available in a MindIE-SD release, use revision
+[`8637b5333b0225381b215390fd09a8732e671cc4`](https://gitcode.com/zqxu/MindIE-SD/commit/8637b5333b0225381b215390fd09a8732e671cc4).
+Build the Python package, PyTorch plugin, and custom operators from that same
+checkout. With CANN sourced, run the NPU interface tests from the Omni checkout:
+
+```bash
+python -m pytest tests/platforms/npu/quant/test_kv_quant_npu.py \
+    -k real_npu -vv -s -o addopts=''
+```
+
+A skipped NPU test is not a pass. Validate generated video quality against a
+floating-point run with the same prompt, seed, resolution, and step count.