[CI/Build] Share nightly CUDA YAML between H100/L4 and B200 (#7028)

Signed-off-by: wangyu <410167048@qq.com>
Signed-off-by: [Your Name] [Your Email]
Signed-off-by: [Your Name] <your.email@example.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: amy-why-3459 <wuhaiyan17@huawei.com>

tests/diffusion/quantization/test_quantization_quality.py

@@ -45,6 +45,7 @@ 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]
@@ -70,7 +71,7 @@ class QualityTestConfig:
     id: str  # pytest ID, e.g. "fp8_z_image"
     task: str  # "t2i" or "t2v"
     prompt: str  # generation prompt
-    max_lpips: float  # fail threshold — higher = more lenient
+    max_lpips: float | dict[str, float]  # threshold, or {"H100": 0.15, "B200": 0.17}
     model: str | None = None  # HF model name
     quantization: str | dict[str, object] | None = None  # quantization method/config, e.g. "fp8"
     baseline_model: str | None = None  # explicit BF16/local baseline path
@@ -80,7 +81,6 @@ class QualityTestConfig:
     num_inference_steps: int = 20  # keep low for CI speed
     num_frames: int = 5  # only for t2v
     seed: int = 42
-    gpu: str = "H100"  # minimum GPU requirement
     negative_prompt: str | None = ""
     guidance_scale: float | None = None
     sigmas: list[float] | None = None
@@ -190,7 +190,7 @@ QUALITY_CONFIGS = [
         quantization={"text_encoder": "fp8", "transformer": None, "vae": None},
         task="t2i",
         prompt="a cup of coffee on a wooden table, morning light",
-        max_lpips=0.15,
+        max_lpips={"H100": 0.15, "B200": 0.17},
         num_inference_steps=10,
         enable_cpu_offload=True,
         height=1024,
@@ -528,7 +528,7 @@ def test_ltx_quality_gate_uses_official_eager_defaults(monkeypatch):
     assert captured.sampling.guidance_scale is None
 
 
-_marks = hardware_marks(res={"cuda": "H100"})
+_marks = hardware_marks(res={"cuda": ["H100", "B200"]})
 _OUTPUT_DIR = Path(os.environ["VLLM_OMNI_QUALITY_OUTPUT_DIR"]) if "VLLM_OMNI_QUALITY_OUTPUT_DIR" in os.environ else None
 
 
@@ -588,8 +588,9 @@ def test_quantization_quality(config: QualityTestConfig):
     # --- Similarity metrics ---
     lpips_score = _compute_lpips(baseline_out, quant_out, config.task)
     psnr_score, mae_score = _compute_psnr_and_mae(baseline_out, quant_out, config.task)
-    assert lpips_score <= config.max_lpips, (
-        f"LPIPS {lpips_score:.4f} exceeds threshold {config.max_lpips} "
+    gpu_key, max_lpips = resolve_device_threshold(config.max_lpips, label=f"{config.id} max_lpips")
+    assert lpips_score <= max_lpips, (
+        f"LPIPS {lpips_score:.4f} exceeds threshold {max_lpips} ({gpu_key}) "
         f"for {config.quantization_ref() or 'pre-quantized checkpoint'} on {config.quantized_ref()}"
     )
 
@@ -601,12 +602,12 @@ def test_quantization_quality(config: QualityTestConfig):
     print(f"  Baseline:      {config.baseline_ref()}")
     print(f"  Quantized:     {config.quantized_ref()}")
     print(f"  Method:        {config.quantization_ref() or 'pre-quantized checkpoint'}")
-    print(f"  LPIPS:         {lpips_score:.4f}  (threshold: {config.max_lpips})")
+    print(f"  LPIPS:         {lpips_score:.4f}  (threshold: {max_lpips}, gpu: {gpu_key})")
     print(f"  PSNR:          {psnr_score:.4f} dB  (higher is better)")
     print(f"  MAE:           {mae_score:.6f}  (lower is better)")
     print(f"  BF16 memory:   {bl_mem:.2f} GiB")
     print(f"  Quant memory:  {qt_mem:.2f} GiB  ({mem_reduction:.0f}% reduction)")
-    print(f"  Result:        {'PASS' if lpips_score <= config.max_lpips else 'FAIL'}")
+    print(f"  Result:        {'PASS' if lpips_score <= max_lpips else 'FAIL'}")
     print(f"{'=' * 60}\n")
 
     assert np.isfinite(psnr_score) or np.isinf(psnr_score), f"PSNR is invalid for {config.id}: {psnr_score}"