64903b8e30
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>
126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
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"""
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Tests of common diffusion feature combinations in online serving mode
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for HunyuanVideo-1.5-T2V (480p).
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Coverage (H100, since model cannot fit L4):
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- CPU offloading (1 GPU) — ``core_model`` + ``advanced_model``
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- CacheDiT + Layerwise CPU offloading (1 GPU) — ``full_model``
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- CacheDiT + TP=2 + VAE patch parallel=2 (2 GPUs) — ``full_model``
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HunyuanVideo-1.5 is a high-priority model, so only the most basic single-card deployment
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row runs on every PR (L2) and on merge (L3). The heavyweight feature combinations stay
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nightly-only (L4), together with the video similarity suites in
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``tests/e2e/accuracy/hunyuanvideo15_{t2v,i2v}/``.
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From ``tests/``::
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pytest -s -v e2e/online_serving/test_hunyuan_video_15_expansion.py -m "core_model and diffusion" --run-level=core_model
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pytest -s -v e2e/online_serving/test_hunyuan_video_15_expansion.py -m "advanced_model and diffusion" --run-level=advanced_model
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pytest -s -v e2e/online_serving/test_hunyuan_video_15_expansion.py -m "full_model and diffusion" --run-level=full_model
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"""
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import pytest
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from tests.helpers.mark import hardware_marks
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from tests.helpers.runtime import OmniServer, OmniServerParams, OnlineOmniClient
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pytestmark = [pytest.mark.diffusion]
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PROMPT = "A cat walking across a sunlit garden, cinematic lighting, slow motion."
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NEGATIVE_PROMPT = "low quality, blurry, distorted"
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MODEL = "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
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def _get_diffusion_feature_cases(model: str):
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"""Return diffusion feature cases for HunyuanVideo-1.5.
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Designed for 2x H100 environment per issue #1832.
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Only the CPU-offload row is cheap enough for PR (L2) / merge (L3);
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CacheDiT / parallel combinations run nightly (L4).
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"""
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return [
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# (1 GPU) CPU offload
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pytest.param(
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OmniServerParams(
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model=model,
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server_args=[
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"--enable-cpu-offload",
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],
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),
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id="single_card_cpu_offload",
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marks=[
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*hardware_marks(res={"cuda": "H100"}),
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pytest.mark.core_model,
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pytest.mark.advanced_model,
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],
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),
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# (1 GPU) CacheDiT + Layerwise CPU offloading
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pytest.param(
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OmniServerParams(
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model=model,
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server_args=[
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"--cache-backend",
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"cache_dit",
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"--enable-layerwise-offload",
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],
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),
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id="single_card_cachedit_layerwise",
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marks=[
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*hardware_marks(res={"cuda": ["H100", "B200"]}),
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pytest.mark.full_model,
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],
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),
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# (2 GPUs) CacheDiT + TP=2 + VAE patch parallel=2
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pytest.param(
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OmniServerParams(
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model=model,
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server_args=[
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"--cache-backend",
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"cache_dit",
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"--tensor-parallel-size",
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"2",
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"--vae-patch-parallel-size",
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"2",
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"--vae-use-tiling",
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],
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),
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id="parallel_cachedit_tp2_vae2",
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marks=[
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*hardware_marks(res={"cuda": ["H100", "B200"]}, num_cards=2),
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pytest.mark.full_model,
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],
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),
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]
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@pytest.mark.parametrize(
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"omni_server",
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_get_diffusion_feature_cases(MODEL),
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indirect=True,
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)
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def test_hunyuan_video_15_t2v(
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omni_server: OmniServer,
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online_client: OnlineOmniClient,
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):
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"""Diffusion feature coverage for HunyuanVideo-1.5-T2V on H100."""
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form_data = {
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"prompt": PROMPT,
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"negative_prompt": NEGATIVE_PROMPT,
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"height": 480,
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"width": 640,
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"num_frames": 5,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"seed": 42,
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}
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request_config = {
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"model": omni_server.model,
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"form_data": form_data,
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}
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online_client.send_video_diffusion_request(request_config)
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