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

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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>
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wangyuandHuang, Zeyu authored and GitHub committed 2026-05-08 14:12:23 +08:00
1 parent b85833eeec
commit 6f2ad7b403
134 files changed
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tests/examples/offline_inference/test_internvla_a1.py
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Offline E2E test for InternVLA-A1 open-loop inference."""
from __future__ import annotations
import json
import os
import subprocess
import sys
import tempfile
from pathlib import Path
import pytest
from tests.helpers.mark import hardware_test
os.environ["VLLM_TEST_CLEAN_GPU_MEMORY"] = "1"
REPO_ROOT = Path(__file__).resolve().parents[3]
EXAMPLE_SCRIPT = REPO_ROOT / "examples" / "offline_inference" / "internvla_a1" / "end2end.py"
def _required_env(name: str) -> str:
value = os.getenv(name)
if not value:
pytest.skip(f"{name} is not set")
return value
@pytest.mark.core_model
@pytest.mark.advanced_model
@pytest.mark.diffusion
@hardware_test(res={"cuda": "H100", "rocm": "MI325"})
def test_internvla_a1_offline_open_loop(run_level: str) -> None:
if run_level != "advanced_model":
pytest.skip("InternVLA-A1 offline evaluation requires real local checkpoints and dataset.")
model_dir = _required_env("INTERNVLA_A1_MODEL_DIR")
dataset_dir = _required_env("INTERNVLA_A1_DATASET_DIR")
processor_dir = _required_env("INTERNVLA_A1_PROCESSOR_DIR")
cosmos_dir = _required_env("INTERNVLA_A1_COSMOS_DIR")
env = os.environ.copy()
env["INTERNVLA_A1_PROCESSOR_DIR"] = processor_dir
env["INTERNVLA_A1_COSMOS_DIR"] = cosmos_dir
with tempfile.TemporaryDirectory(prefix="internvla_a1_e2e_") as tmpdir:
output_dir = Path(tmpdir) / "outputs"
cmd = [
sys.executable,
str(EXAMPLE_SCRIPT),
"--model-dir",
model_dir,
"--dataset-dir",
dataset_dir,
"--output-dir",
str(output_dir),
"--num-samples",
"1",
"--num-episodes",
"1",
"--dtype",
"float32",
"--attn-implementation",
"eager",
"--skip-plots",
]
subprocess.run(cmd, check=True, env=env)
summary_path = output_dir / "summary.json"
assert summary_path.exists(), f"Missing summary file: {summary_path}"
with open(summary_path, encoding="utf-8") as f:
summary = json.load(f)
assert summary["mode"] == "registry"
assert summary["attn_implementation"] == "eager"
assert summary["dtype"] == "float32"
assert len(summary["results"]) == 1
assert "registry" in summary["eval_summaries"]
eval_summary = summary["eval_summaries"]["registry"]
assert eval_summary["num_episodes"] == 1
assert eval_summary["average_mse"] is not None
assert eval_summary["average_mae"] is not None
log_path = output_dir / "registry" / "log.json"
assert log_path.exists(), f"Missing evaluation log: {log_path}"
tests/examples/offline_inference/test_ltx2_cfg_parallel_parity.py
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import hashlib
import os
import subprocess
import sys
from pathlib import Path
import numpy as np
import pytest
from PIL import Image
from tests.helpers.mark import hardware_test
REPO_ROOT = Path(__file__).resolve().parents[3]
T2V_EXAMPLE = REPO_ROOT / "examples" / "offline_inference" / "text_to_video" / "text_to_video.py"
I2V_EXAMPLE = REPO_ROOT / "examples" / "offline_inference" / "image_to_video" / "image_to_video.py"
T2V_PROMPT = (
"At sunrise, a glowing paper lantern boat drifts through a narrow canal between mossy stone walls, "
"soft fog above the water, the camera slowly gliding forward as golden reflections shimmer across "
"the ripples, cinematic, realistic, highly detailed."
)
T2V_NEGATIVE_PROMPT = "worst quality, blurry, jittery motion, distorted, oversaturated, artifacts"
I2V_PROMPT = "A cinematic dolly shot of a boat drifting on calm water at sunset"
I2V_NEGATIVE_PROMPT = "worst quality, blurry, jittery motion"
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
def _get_ltx2_model() -> str:
return "Lightricks/LTX-2"
def _md5(path: Path) -> str:
digest = hashlib.md5(usedforsecurity=False)
with path.open("rb") as f:
for chunk in iter(lambda: f.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def _make_deterministic_test_image(path: Path) -> None:
"""Create a deterministic 256x256 test image for I2V tests."""
rng = np.random.RandomState(42)
img = Image.fromarray(rng.randint(0, 255, (256, 256, 3), dtype=np.uint8))
img.save(path)
def _run_and_check(cmd: list[str], env: dict, output_path: Path, expected_md5: str) -> None:
result = subprocess.run(cmd, cwd=REPO_ROOT, env=env, capture_output=True, text=True, check=False)
assert result.returncode == 0, (
f"Command failed (exit {result.returncode}).\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}"
)
generated_md5 = _md5(output_path)
assert generated_md5 == expected_md5, (
f"Unexpected output md5: {generated_md5} != {expected_md5}.\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}"
)
# ── T2V tests ──
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@pytest.mark.slow
@hardware_test(res={"cuda": "L4"}, num_cards=2)
def test_ltx2_t2v_cfg_parallel(tmp_path: Path):
"""T2V with CFG=4.0, cfg-parallel-size=2."""
output = tmp_path / "t2v_cfg4.mp4"
env = os.environ.copy()
env.setdefault("CUDA_VISIBLE_DEVICES", "0,1")
cmd = [
sys.executable,
str(T2V_EXAMPLE),
"--model",
_get_ltx2_model(),
"--prompt",
T2V_PROMPT,
"--negative-prompt",
T2V_NEGATIVE_PROMPT,
"--height",
"256",
"--width",
"256",
"--num-frames",
"145",
"--num-inference-steps",
"6",
"--guidance-scale",
"4.0",
"--frame-rate",
"24",
"--fps",
"24",
"--seed",
"42",
"--cfg-parallel-size",
"2",
"--enforce-eager",
"--output",
str(output),
]
_run_and_check(cmd, env, output, expected_md5="08e606b9c522fee4b6f30cee8b77db40")
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.slow
@hardware_test(res={"cuda": "L4"}, num_cards=1)
def test_ltx2_t2v_no_cfg(tmp_path: Path):
"""T2V with CFG=1.0 (no classifier-free guidance)."""
output = tmp_path / "t2v_nocfg.mp4"
env = os.environ.copy()
env.setdefault("CUDA_VISIBLE_DEVICES", "0")
cmd = [
sys.executable,
str(T2V_EXAMPLE),
"--model",
_get_ltx2_model(),
"--prompt",
T2V_PROMPT,
"--height",
"256",
"--width",
"256",
"--num-frames",
"145",
"--num-inference-steps",
"6",
"--guidance-scale",
"1.0",
"--frame-rate",
"24",
"--fps",
"24",
"--seed",
"42",
"--enforce-eager",
"--output",
str(output),
]
_run_and_check(cmd, env, output, expected_md5="a83994b94b6e67c54a524e0383c45ce8")
# ── I2V tests ──
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.parallel
@pytest.mark.slow
@hardware_test(res={"cuda": "L4"}, num_cards=2)
def test_ltx2_i2v_cfg_parallel(tmp_path: Path):
"""I2V with CFG=4.0, cfg-parallel-size=2."""
test_image = tmp_path / "test_input.png"
_make_deterministic_test_image(test_image)
output = tmp_path / "i2v_cfg4.mp4"
env = os.environ.copy()
env.setdefault("CUDA_VISIBLE_DEVICES", "0,1")
cmd = [
sys.executable,
str(I2V_EXAMPLE),
"--model",
_get_ltx2_model(),
"--model-class-name",
"LTX2ImageToVideoPipeline",
"--image",
str(test_image),
"--prompt",
I2V_PROMPT,
"--negative-prompt",
I2V_NEGATIVE_PROMPT,
"--height",
"256",
"--width",
"256",
"--num-frames",
"73",
"--num-inference-steps",
"6",
"--guidance-scale",
"4.0",
"--frame-rate",
"24",
"--fps",
"24",
"--seed",
"42",
"--cfg-parallel-size",
"2",
"--enforce-eager",
"--output",
str(output),
]
_run_and_check(cmd, env, output, expected_md5="aed7e56084b36373244d8f839b16d115")
@pytest.mark.full_model
@pytest.mark.diffusion
@pytest.mark.slow
@hardware_test(res={"cuda": "L4"}, num_cards=1)
def test_ltx2_i2v_no_cfg(tmp_path: Path):
"""I2V with CFG=1.0 (no classifier-free guidance)."""
test_image = tmp_path / "test_input.png"
_make_deterministic_test_image(test_image)
output = tmp_path / "i2v_nocfg.mp4"
env = os.environ.copy()
env.setdefault("CUDA_VISIBLE_DEVICES", "0")
cmd = [
sys.executable,
str(I2V_EXAMPLE),
"--model",
_get_ltx2_model(),
"--model-class-name",
"LTX2ImageToVideoPipeline",
"--image",
str(test_image),
"--prompt",
I2V_PROMPT,
"--height",
"256",
"--width",
"256",
"--num-frames",
"73",
"--num-inference-steps",
"6",
"--guidance-scale",
"1.0",
"--frame-rate",
"24",
"--fps",
"24",
"--seed",
"42",
"--enforce-eager",
"--output",
str(output),
]
_run_and_check(cmd, env, output, expected_md5="81b21ede12753e9e14a357a6c548b666")
tests/examples/online_serving/test_text_to_image.py
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@@ -133,5 +133,5 @@ def test_lora_001(omni_server: OmniServer, example_output_dir: Path, tmp_path: P
# --- ### Using curl with LoRA (Images API) ---
@pytest.mark.skip(reason="Covered by tests/e2e/online_serving/test_images_generations_lora.py")
@pytest.mark.skip(reason="Covered by tests/diffusion/lora/test_images_generations_lora.py")
def test_lora_002(): ...