[CI/Build] Move L1/E2E coverage to weekly and split scheduled L4/L5 pipelines (#6311)

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

tests/examples/offline_inference/test_image_task_prompts.py

@@ -3,15 +3,27 @@
 
 from __future__ import annotations
 
+import functools
+import importlib.util
+
 import pytest
 from PIL import Image
 
-from examples.offline_inference.image_to_video.image_to_video import build_image_to_video_prompt
-from examples.offline_inference.text_to_image.text_to_image import build_text_to_image_prompt
+from tests.examples.helpers import EXAMPLES
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
+@functools.cache
+def _load_example_module(relative_path: str, module_name: str):
+    path = EXAMPLES / relative_path
+    spec = importlib.util.spec_from_file_location(module_name, path)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
 @pytest.mark.parametrize(
     ("negative_prompt", "expected_negative_prompt"),
     [(None, None), ("", ""), ("blurry", "blurry")],
@@ -20,7 +32,11 @@ def test_text_to_image_builds_canonical_prompt(
     negative_prompt: str | None,
     expected_negative_prompt: str | None,
 ) -> None:
-    result = build_text_to_image_prompt("a red fox", negative_prompt)
+    mod = _load_example_module(
+        "offline_inference/text_to_image/text_to_image.py",
+        "text_to_image_example",
+    )
+    result = mod.build_text_to_image_prompt("a red fox", negative_prompt)
 
     assert result["prompt"] == "a red fox"
     assert result["modalities"] == ["image"]
@@ -31,9 +47,13 @@ def test_text_to_image_builds_canonical_prompt(
 
 
 def test_image_to_video_builds_canonical_prompt() -> None:
+    mod = _load_example_module(
+        "offline_inference/image_to_video/image_to_video.py",
+        "image_to_video_example",
+    )
     image = Image.new("RGB", (32, 16), "red")
 
-    result = build_image_to_video_prompt(
+    result = mod.build_image_to_video_prompt(
         prompt="the fox turns toward the camera",
         negative_prompt="flicker",
         media_inputs={"image": image},

tests/examples/offline_inference/test_text_to_image.py

@@ -1,3 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
 """
 Offline inference tests: text-to-image.
 See examples/offline_inference/text_to_image/README.md
@@ -26,6 +29,8 @@ EXAMPLE_OUTPUT_SUBFOLDER = "example_offline_t2i"
 def _skip_readme_snippet(language: str, code: str, h2_title: str) -> tuple[bool, str]:
     if h2_title == "Web UI Demo":
         return True, f"README section '{h2_title}' is intentionally excluded for examples tests"
+    if "krea/Krea-2-Raw" in code:
+        return True, "krea/Krea-2-Raw is gated on Hugging Face Hub; CI has no access"
     return False, ""
 
 

tests/examples/offline_inference/test_text_to_video_prompts.py

@@ -3,19 +3,27 @@
 
 from __future__ import annotations
 
+import functools
+import importlib.util
+
 import pytest
 import torch
 
-from examples.offline_inference.text_to_video.text_to_video import (
-    _MODEL_PRESETS,
-    _detect_preset,
-    _normalize_float_tensor,
-    build_text_to_video_prompt,
-)
+from tests.examples.helpers import EXAMPLES
 
 pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
 
 
+@functools.cache
+def _load_text_to_video():
+    path = EXAMPLES / "offline_inference" / "text_to_video" / "text_to_video.py"
+    spec = importlib.util.spec_from_file_location("text_to_video_example", path)
+    assert spec is not None and spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
 @pytest.mark.parametrize(
     ("negative_prompt", "expected_negative_prompt"),
     [(None, None), ("", ""), ("blurry", "blurry")],
@@ -24,7 +32,8 @@ def test_text_to_video_builds_canonical_prompt(
     negative_prompt: str | None,
     expected_negative_prompt: str | None,
 ) -> None:
-    result = build_text_to_video_prompt("a robot waves", negative_prompt)
+    mod = _load_text_to_video()
+    result = mod.build_text_to_video_prompt("a robot waves", negative_prompt)
 
     assert result["prompt"] == "a robot waves"
     assert result["modalities"] == ["video"]
@@ -50,11 +59,13 @@ def test_detect_preset_is_scoped_to_model_family(
     model_class_name: str | None,
     preset_name: str,
 ) -> None:
-    assert _detect_preset(model, model_class_name) is _MODEL_PRESETS[preset_name]
+    mod = _load_text_to_video()
+    assert mod._detect_preset(model, model_class_name) is mod._MODEL_PRESETS[preset_name]
 
 
 def test_lingbot_preset_matches_lingbot_defaults() -> None:
-    assert _MODEL_PRESETS["lingbot"] == {
+    mod = _load_text_to_video()
+    assert mod._MODEL_PRESETS["lingbot"] == {
         "model_class_name": "LingBotVideoPipeline",
         "height": 192,
         "width": 320,
@@ -68,24 +79,27 @@ def test_lingbot_preset_matches_lingbot_defaults() -> None:
 
 
 def test_normalize_float_tensor_preserves_zero_to_one_frames() -> None:
+    mod = _load_text_to_video()
     frames = torch.tensor([0.0, 0.25, 1.0, 1.5])
 
-    assert torch.equal(_normalize_float_tensor(frames, "zero_to_one"), torch.tensor([0.0, 0.25, 1.0, 1.0]))
+    assert torch.equal(mod._normalize_float_tensor(frames, "zero_to_one"), torch.tensor([0.0, 0.25, 1.0, 1.0]))
 
 
 def test_normalize_float_tensor_maps_negative_one_to_one_frames() -> None:
+    mod = _load_text_to_video()
     frames = torch.tensor([-1.5, -1.0, 0.0, 1.0, 1.5])
 
     assert torch.equal(
-        _normalize_float_tensor(frames, "negative_one_to_one"),
+        mod._normalize_float_tensor(frames, "negative_one_to_one"),
         torch.tensor([0.0, 0.0, 0.5, 1.0, 1.0]),
     )
 
 
 def test_normalize_float_tensor_uses_one_contract_for_ambiguous_frames() -> None:
+    mod = _load_text_to_video()
     frames = [torch.tensor([0.0, 0.5, 1.0]), torch.tensor([-1.0, 0.0, 1.0])]
 
-    normalized = [_normalize_float_tensor(frame, "negative_one_to_one") for frame in frames]
+    normalized = [mod._normalize_float_tensor(frame, "negative_one_to_one") for frame in frames]
 
     assert torch.equal(normalized[0], torch.tensor([0.5, 0.75, 1.0]))
     assert torch.equal(normalized[1], torch.tensor([0.0, 0.5, 1.0]))