[Test] Unify L2/L3 test layout, Buildkite steps, and test helpers (#2556)

Signed-off-by: wangyu <410167048@qq.com>
Signed-off-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com>
Signed-off-by: wangyu <53896905+yenuo26@users.noreply.github.com>
Co-authored-by: Huang, Zeyu <11222265+fhfuih@users.noreply.github.com>

tests/diffusion/lora/test_diffusion_lora.py

@@ -0,0 +1,132 @@
+import json
+import sys
+from pathlib import Path
+
+import pytest
+import torch
+from safetensors.torch import save_file
+
+from vllm_omni.inputs.data import OmniDiffusionSamplingParams
+from vllm_omni.outputs import OmniRequestOutput
+from vllm_omni.platforms import current_omni_platform
+
+# ruff: noqa: E402
+REPO_ROOT = Path(__file__).resolve().parents[2]
+if str(REPO_ROOT) not in sys.path:
+    sys.path.insert(0, str(REPO_ROOT))
+
+# This test is specific to Z-Image LoRA behavior. Keep it focused on a single
+# model to reduce runtime and avoid extra downloads.
+models = ["Tongyi-MAI/Z-Image-Turbo"]
+omni_runner_params = [(model_name, None) for model_name in models]
+
+
+@pytest.mark.advanced_model
+@pytest.mark.parametrize("omni_runner", omni_runner_params, indirect=True)
+def test_diffusion_model(tmp_path: Path, omni_runner):
+    def _extract_images(outputs: list[OmniRequestOutput]):
+        if not outputs:
+            raise ValueError("Empty outputs from Omni.generate()")
+        first_output = outputs[0]
+        assert first_output.final_output_type == "image"
+        if not hasattr(first_output, "request_output") or not first_output.request_output:
+            raise ValueError("No request_output found in OmniRequestOutput")
+
+        req_out = first_output.request_output
+        if not isinstance(req_out, OmniRequestOutput) or not hasattr(req_out, "images"):
+            raise ValueError("Invalid request_output structure or missing 'images' key")
+        return req_out.images
+
+    def _write_zimage_lora(adapter_dir: Path) -> str:
+        adapter_dir.mkdir(parents=True, exist_ok=True)
+
+        # Z-Image transformer uses dim=3840 by default (see ZImageTransformer2DModel).
+        dim = 3840
+        module_name = "transformer.layers.0.attention.to_qkv"
+        rank = 1
+        lora_a = torch.zeros((rank, dim), dtype=torch.float32)
+        lora_a[0, 0] = 1.0
+
+        # QKVParallelLinear packs (Q, K, V). With tp=1 and n_kv_heads==n_heads in Z-Image,
+        # each slice is `dim`, so total out dim is `3 * dim`.
+        lora_b = torch.zeros((3 * dim, rank), dtype=torch.float32)
+        # Apply a visible delta to the Q slice only to keep the perturbation bounded.
+        lora_b[:dim, 0] = 0.1
+
+        save_file(
+            {
+                f"base_model.model.{module_name}.lora_A.weight": lora_a,
+                f"base_model.model.{module_name}.lora_B.weight": lora_b,
+            },
+            str(adapter_dir / "adapter_model.safetensors"),
+        )
+        (adapter_dir / "adapter_config.json").write_text(
+            json.dumps(
+                {
+                    "r": rank,
+                    "lora_alpha": rank,
+                    "target_modules": [module_name],
+                }
+            ),
+            encoding="utf-8",
+        )
+        return str(adapter_dir)
+
+    runner = omni_runner
+    m = runner.omni
+    # high resolution may cause OOM on L4
+    height = 256
+    width = 256
+    prompt = "a photo of a cat sitting on a laptop keyboard"
+
+    outputs = m.generate(
+        prompt,
+        OmniDiffusionSamplingParams(
+            height=height,
+            width=width,
+            num_inference_steps=2,
+            guidance_scale=0.0,
+            generator=torch.Generator(current_omni_platform.device_type).manual_seed(42),
+            num_outputs_per_prompt=1,
+        ),
+    )
+    images = _extract_images(outputs)
+
+    assert len(images) == 1
+    # check image size
+    assert images[0].width == width
+    assert images[0].height == height
+
+    # Real LoRA E2E: generate again with a real on-disk PEFT adapter and
+    # verify that output changes.
+    from vllm_omni.lora.request import LoRARequest
+    from vllm_omni.lora.utils import stable_lora_int_id
+
+    lora_dir = _write_zimage_lora(tmp_path / "zimage_lora")
+    lora_request = LoRARequest(
+        lora_name="test",
+        lora_int_id=stable_lora_int_id(lora_dir),
+        lora_path=lora_dir,
+    )
+    outputs_lora = m.generate(
+        prompt,
+        OmniDiffusionSamplingParams(
+            height=height,
+            width=width,
+            num_inference_steps=2,
+            guidance_scale=0.0,
+            generator=torch.Generator(current_omni_platform.device_type).manual_seed(42),
+            num_outputs_per_prompt=1,
+            lora_request=lora_request,
+            lora_scale=2.0,
+        ),
+    )
+    images_lora = _extract_images(outputs_lora)
+    assert len(images_lora) == 1
+    assert images_lora[0].width == width
+    assert images_lora[0].height == height
+
+    import numpy as np
+
+    diff = np.abs(np.array(images[0], dtype=np.int16) - np.array(images_lora[0], dtype=np.int16)).mean()
+    assert diff > 0.0