[Perf][Diffusion] support Sensenova online fp8 (#7955)

Signed-off-by: Dmaner <2663515256@qq.com>
Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>

tests/diffusion/quantization/test_quantization_quality.py

@@ -45,7 +45,6 @@ import pytest
 import torch
 from PIL import Image
 
-from tests.e2e.accuracy.helpers import resolve_device_threshold
 from tests.helpers.mark import hardware_marks
 
 _REPO_ROOT = Path(__file__).resolve().parents[3]
@@ -85,6 +84,7 @@ class QualityTestConfig:
     guidance_scale: float | None = None
     sigmas: list[float] | None = None
     enable_cpu_offload: bool = False
+    diffusion_attention_backend: str | None = None
 
     def baseline_ref(self) -> str:
         return self.baseline_model or self.model or ""
@@ -206,6 +206,21 @@ QUALITY_CONFIGS = [
         seed=142,
         num_inference_steps=20,
     ),
+    QualityTestConfig(
+        id="fp8_sensenova_u1",
+        model=os.environ.get("SENSENOVA_U1_MODEL_PATH", "SenseNova/SenseNova-U1.5-8B-MoT"),
+        quantization="fp8",
+        task="t2i",
+        prompt="Close portrait of an elderly woman by a farmhouse window, warm natural light.",
+        # Repository image-gate default, not a SenseNova-calibrated threshold.
+        max_lpips=0.15,
+        height=1024,
+        width=1024,
+        num_inference_steps=50,
+        seed=42,
+        negative_prompt=None,
+        diffusion_attention_backend="TORCH_SDPA",
+    ),
     QualityTestConfig(
         id="fp8_ltx2",
         model="Lightricks/LTX-2",
@@ -276,6 +291,15 @@ def _maybe_save_output(output_dir: Path | None, config: QualityTestConfig, label
 # ---------------------------------------------------------------------------
 
 
+def _build_omni_kwargs(config: QualityTestConfig, model: str) -> dict:
+    kwargs = {"model": model, "enforce_eager": True}
+    if config.enable_cpu_offload:
+        kwargs["enable_cpu_offload"] = True
+    if config.diffusion_attention_backend is not None:
+        kwargs["diffusion_attention_backend"] = config.diffusion_attention_backend
+    return kwargs
+
+
 def _generate_image(omni, config: QualityTestConfig):
     """Generate a single image, return (PIL.Image, peak_mem_gib)."""
     from vllm_omni.inputs.data import OmniDiffusionSamplingParams
@@ -294,6 +318,7 @@ def _generate_image(omni, config: QualityTestConfig):
         OmniDiffusionSamplingParams(
             height=config.height,
             width=config.width,
+            seed=config.seed,
             generator=generator,
             num_inference_steps=config.num_inference_steps,
             guidance_scale=config.guidance_scale,
@@ -534,6 +559,8 @@ _OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUA
 
 def _quality_param(c: QualityTestConfig):
     marks = list(_marks)
+    if c.id == "fp8_sensenova_u1":
+        marks = hardware_marks(res={"cuda": "H200"})
     if c.id == "fp8_qwen_image":
         marks.append(
             pytest.mark.skip(reason="Qwen-Image FP8 quality gate temporarily disabled (see CI / issue tracker).")
@@ -549,6 +576,7 @@ def _quality_param(c: QualityTestConfig):
 )
 def test_quantization_quality(config: QualityTestConfig):
     """Validate that quantized output stays within LPIPS threshold of BF16."""
+    from tests.e2e.accuracy.helpers import resolve_device_threshold
     from vllm_omni.entrypoints.omni import Omni
 
     generate_fn = _generate_video if config.task == "t2v" else _generate_image
@@ -560,9 +588,7 @@ def test_quantization_quality(config: QualityTestConfig):
     # to 0.1291 that way in build 2954). Mirrors
     # vllm_omni/quantization/tools/compare_diffusion_trajectory_similarity.py.
     # --- BF16 baseline ---
-    bl_kwargs: dict = {"model": config.baseline_ref(), "enforce_eager": True}
-    if config.enable_cpu_offload:
-        bl_kwargs["enable_cpu_offload"] = True
+    bl_kwargs = _build_omni_kwargs(config, config.baseline_ref())
     omni_bl = Omni(**bl_kwargs)
     baseline_out, bl_mem = generate_fn(omni_bl, config)
     omni_bl.shutdown()
@@ -572,9 +598,7 @@ def test_quantization_quality(config: QualityTestConfig):
 
     # --- Quantized ---
     quantization = config.quantization_ref()
-    qt_kwargs: dict = {"model": config.quantized_ref(), "enforce_eager": True}
-    if config.enable_cpu_offload:
-        qt_kwargs["enable_cpu_offload"] = True
+    qt_kwargs = _build_omni_kwargs(config, config.quantized_ref())
     if quantization is None:
         omni_qt = Omni(**qt_kwargs)
     else: