[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>
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import gc
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import numpy as np
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import pytest
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import torch
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from vllm.distributed.parallel_state import cleanup_dist_env_and_memory
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from tests.helpers.env import DeviceMemoryMonitor
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from tests.helpers.mark import hardware_test
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from tests.helpers.runtime import OmniRunner
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams
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from vllm_omni.platforms import current_omni_platform
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AUDIO_MODEL = {
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"stabilityai/stable-audio-open-1.0": {"cuda": 100, "rocm": None},
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}
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IMAGE_VIDEO_MODELS = {
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"riverclouds/qwen_image_random": {"cuda": 2500, "rocm": 2100},
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}
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MODELS = {**AUDIO_MODEL, **IMAGE_VIDEO_MODELS}
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AUDIO_MODEL_PARAMS = {
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"runner_params": {},
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"sampler_params": {},
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}
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IMAGE_VIDEO_MODELS_PARAMS = {
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"runner_params": {},
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"sampler_params": {
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"height": 256,
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"width": 256,
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},
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}
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def inference(model_name: str, offload: bool = True):
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gc.collect()
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current_omni_platform.empty_cache()
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device_index = current_omni_platform.current_device()
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current_omni_platform.reset_peak_memory_stats()
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monitor = DeviceMemoryMonitor(device_index=device_index, interval=0.02)
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if model_name in AUDIO_MODEL:
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params = AUDIO_MODEL_PARAMS
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else:
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params = IMAGE_VIDEO_MODELS_PARAMS
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with OmniRunner(
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model_name,
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# TODO: we might want to add overlapped feature e2e tests
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# cache_backend="cache_dit",
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enable_cpu_offload=offload,
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**params["runner_params"],
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) as runner:
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current_omni_platform.reset_peak_memory_stats()
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monitor.start()
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output = runner.omni.generate(
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"a photo of a cat sitting on a laptop keyboard",
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OmniDiffusionSamplingParams(
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num_inference_steps=9,
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guidance_scale=0.0,
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generator=torch.Generator(device=current_omni_platform.device_type).manual_seed(42),
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**params["sampler_params"],
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),
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)
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peak = monitor.peak_used_mb
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monitor.stop()
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gc.collect()
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current_omni_platform.empty_cache()
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return peak, output
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def check_audio_determinism(audio1, audio2, atol=1e-2):
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device = current_omni_platform.device_type
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if isinstance(audio1, np.ndarray):
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audio1 = torch.from_numpy(audio1).to(device)
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if isinstance(audio2, np.ndarray):
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audio2 = torch.from_numpy(audio2).to(device)
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if not torch.allclose(audio1, audio2, atol=atol):
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diff = torch.abs(audio1 - audio2)
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print(f"Max difference: {diff.max().item()}")
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print(f"Mean difference: {diff.mean().item()}")
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raise AssertionError(f"Audio outputs differ beyond tolerance atol={atol}")
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return True
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@pytest.mark.core_model
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@pytest.mark.diffusion
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@hardware_test(res={"cuda": "L4", "rocm": "MI325"})
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@pytest.mark.parametrize("model_name", list(MODELS.keys()))
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def test_cpu_offload_diffusion_model(model_name: str):
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try:
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offload_peak_memory, output_offload = inference(model_name, offload=True)
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cleanup_dist_env_and_memory()
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no_offload_peak_memory, output_no_offload = inference(model_name, offload=False)
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except Exception:
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pytest.fail("Inference failed")
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print(f"Offload peak memory: {offload_peak_memory} MB")
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print(f"No offload peak memory: {no_offload_peak_memory} MB")
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if model_name == "stabilityai/stable-audio-open-1.0":
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audio_offload = output_offload[0].request_output.multimodal_output.get("audio")
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audio_no_offload = output_no_offload[0].request_output.multimodal_output.get("audio")
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check_audio_determinism(audio_offload, audio_no_offload, atol=1e-2)
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# Set platform-specific VRAM saving thresholds to account
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# for varying runtime memory overhead and fragmentation between CUDA and ROCm.
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is_rocm = torch.version.hip is not None
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platform = "rocm" if is_rocm else "cuda"
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threshold = MODELS[model_name][platform]
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if threshold is None:
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pytest.skip(f"Threshold not defined for {platform} on {model_name}")
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assert offload_peak_memory + threshold < no_offload_peak_memory, (
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f"Offload peak memory {offload_peak_memory} MB should be less than "
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f"no offload peak memory {no_offload_peak_memory} MB by {threshold} MB"
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)
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