72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
"""
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Online serving E2E for NextStep-1.1 text-to-image (tensor parallel).
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"""
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import os
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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 (
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OmniServer,
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OmniServerParams,
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OpenAIClientHandler,
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dummy_messages_from_mix_data,
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)
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pytestmark = [pytest.mark.full_model, pytest.mark.diffusion]
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# L4: 4 GPUs + TP=4; XPU B60: 2 cards (use num_cards={"cuda": 4, "xpu": 4} if needed)
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FOUR_CARD_MARKS = hardware_marks(
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res={"cuda": "L4", "xpu": "B60"},
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num_cards={"cuda": 2, "xpu": 2},
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)
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POSITIVE_PROMPT = "A small red barn in a snowy field, simple illustration."
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NEGATIVE_PROMPT = "blurry, low quality"
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_DEFAULT_MODEL = "stepfun-ai/NextStep-1.1"
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def _get_diffusion_feature_cases(model: str):
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"""Single online config: TP=4, explicit pipeline class."""
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return [
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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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"--tensor-parallel-size",
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"2",
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"--model-class-name",
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"NextStep11Pipeline",
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],
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),
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id="nextstep_tp4_pipeline",
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marks=FOUR_CARD_MARKS,
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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=os.environ.get("VLLM_TEST_NEXTSTEP_MODEL", _DEFAULT_MODEL)),
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indirect=True,
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)
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def test_nextstep_11(omni_server: OmniServer, openai_client: OpenAIClientHandler):
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messages = dummy_messages_from_mix_data(content_text=POSITIVE_PROMPT)
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request_config = {
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"model": omni_server.model,
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"messages": messages,
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"extra_body": {
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"height": 512,
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"width": 512,
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"num_inference_steps": 2,
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"guidance_scale": 5.0,
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"guidance_scale_2": 1.0,
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"negative_prompt": NEGATIVE_PROMPT,
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"seed": 42,
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},
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}
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openai_client.send_diffusion_request(request_config)
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