Add 4-GPU runner tests and split existing tests (#6383)
This commit is contained in:
fzyzcjy
468
test/srt/test_vision_openai_server_common.py
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468
test/srt/test_vision_openai_server_common.py
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import base64
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import io
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import json
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import os
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import unittest
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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import openai
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import requests
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from PIL import Image
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from sglang.srt.utils import kill_process_tree
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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popen_launch_server,
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)
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# image
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IMAGE_MAN_IRONING_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/man_ironing_on_back_of_suv.png"
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IMAGE_SGL_LOGO_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/images/sgl_logo.png"
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# video
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VIDEO_JOBS_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/videos/jobs_presenting_ipod.mp4"
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# audio
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AUDIO_TRUMP_SPEECH_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/audios/Trump_WEF_2018_10s.mp3"
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AUDIO_BIRD_SONG_URL = "https://raw.githubusercontent.com/sgl-project/sgl-test-files/refs/heads/main/audios/bird_song.mp3"
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class TestOpenAIVisionServer(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.model = "lmms-lab/llava-onevision-qwen2-0.5b-ov"
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.api_key = "sk-123456"
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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api_key=cls.api_key,
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)
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cls.base_url += "/v1"
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def test_single_image_chat_completion(self):
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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response = client.chat.completions.create(
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model="default",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_MAN_IRONING_URL},
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},
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{
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"type": "text",
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"text": "Describe this image in a very short sentence.",
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},
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],
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},
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],
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temperature=0,
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)
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assert response.choices[0].message.role == "assistant"
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text = response.choices[0].message.content
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assert isinstance(text, str)
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# `driver` is for gemma-3-it
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assert (
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"man" in text or "person" or "driver" in text
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), f"text: {text}, should contain man, person or driver"
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assert (
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"cab" in text
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or "taxi" in text
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or "SUV" in text
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or "vehicle" in text
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or "car" in text
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), f"text: {text}, should contain cab, taxi, SUV, vehicle or car"
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# MiniCPMO fails to recognize `iron`, but `hanging`
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assert (
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"iron" in text or "hang" in text or "cloth" in text or "holding" in text
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), f"text: {text}, should contain iron, hang, cloth or holding"
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assert response.id
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assert response.created
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assert response.usage.prompt_tokens > 0
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assert response.usage.completion_tokens > 0
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assert response.usage.total_tokens > 0
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def test_multi_turn_chat_completion(self):
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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response = client.chat.completions.create(
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model="default",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_MAN_IRONING_URL},
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},
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{
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"type": "text",
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"text": "Describe this image in a very short sentence.",
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},
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],
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},
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{
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"role": "assistant",
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"content": [
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{
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"type": "text",
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"text": "There is a man at the back of a yellow cab ironing his clothes.",
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}
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],
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},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": "Repeat your previous answer."}
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],
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},
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],
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temperature=0,
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)
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assert response.choices[0].message.role == "assistant"
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text = response.choices[0].message.content
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assert isinstance(text, str)
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assert (
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"man" in text or "cab" in text
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), f"text: {text}, should contain man or cab"
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assert response.id
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assert response.created
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assert response.usage.prompt_tokens > 0
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assert response.usage.completion_tokens > 0
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assert response.usage.total_tokens > 0
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def test_multi_images_chat_completion(self):
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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response = client.chat.completions.create(
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model="default",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_MAN_IRONING_URL},
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"modalities": "multi-images",
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},
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_SGL_LOGO_URL},
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"modalities": "multi-images",
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},
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{
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"type": "text",
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"text": "I have two very different images. They are not related at all. "
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"Please describe the first image in one sentence, and then describe the second image in another sentence.",
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},
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],
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},
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],
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temperature=0,
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)
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assert response.choices[0].message.role == "assistant"
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text = response.choices[0].message.content
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assert isinstance(text, str)
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print("-" * 30)
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print(f"Multi images response:\n{text}")
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print("-" * 30)
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assert (
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"man" in text or "cab" in text or "SUV" in text or "taxi" in text
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), f"text: {text}, should contain man, cab, SUV or taxi"
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assert (
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"logo" in text or '"S"' in text or "SG" in text
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), f"text: {text}, should contain logo, S or SG"
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assert response.id
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assert response.created
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assert response.usage.prompt_tokens > 0
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assert response.usage.completion_tokens > 0
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assert response.usage.total_tokens > 0
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def prepare_video_messages(self, video_path):
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# the memory consumed by the Vision Attention varies a lot, e.g. blocked qkv vs full-sequence sdpa
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# the size of the video embeds differs from the `modality` argument when preprocessed
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# We import decord here to avoid a strange Segmentation fault (core dumped) issue.
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# The following import order will cause Segmentation fault.
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# import decord
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# from transformers import AutoTokenizer
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from decord import VideoReader, cpu
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max_frames_num = 20
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vr = VideoReader(video_path, ctx=cpu(0))
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total_frame_num = len(vr)
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uniform_sampled_frames = np.linspace(
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0, total_frame_num - 1, max_frames_num, dtype=int
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)
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frame_idx = uniform_sampled_frames.tolist()
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frames = vr.get_batch(frame_idx).asnumpy()
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base64_frames = []
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for frame in frames:
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pil_img = Image.fromarray(frame)
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buff = io.BytesIO()
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pil_img.save(buff, format="JPEG")
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base64_str = base64.b64encode(buff.getvalue()).decode("utf-8")
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base64_frames.append(base64_str)
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messages = [{"role": "user", "content": []}]
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frame_format = {
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"type": "image_url",
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"image_url": {"url": "data:image/jpeg;base64,{}"},
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"modalities": "video",
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}
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for base64_frame in base64_frames:
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frame_format["image_url"]["url"] = "data:image/jpeg;base64,{}".format(
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base64_frame
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)
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messages[0]["content"].append(frame_format.copy())
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prompt = {"type": "text", "text": "Please describe the video in detail."}
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messages[0]["content"].append(prompt)
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return messages
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def prepare_video_messages_video_direct(self, video_path):
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"video:{video_path}"},
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"modalities": "video",
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},
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{"type": "text", "text": "Please describe the video in detail."},
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],
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},
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]
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return messages
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def get_or_download_file(self, url: str) -> str:
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cache_dir = os.path.expanduser("~/.cache")
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if url is None:
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raise ValueError()
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file_name = url.split("/")[-1]
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file_path = os.path.join(cache_dir, file_name)
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os.makedirs(cache_dir, exist_ok=True)
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if not os.path.exists(file_path):
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response = requests.get(url)
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response.raise_for_status()
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with open(file_path, "wb") as f:
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f.write(response.content)
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return file_path
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def test_video_chat_completion(self):
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url = VIDEO_JOBS_URL
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file_path = self.get_or_download_file(url)
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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# messages = self.prepare_video_messages_video_direct(file_path)
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messages = self.prepare_video_messages(file_path)
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response = client.chat.completions.create(
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model="default",
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messages=messages,
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temperature=0,
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max_tokens=1024,
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stream=False,
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)
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video_response = response.choices[0].message.content
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print("-" * 30)
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print(f"Video response:\n{video_response}")
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print("-" * 30)
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# Add assertions to validate the video response
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assert "iPod" in video_response or "device" in video_response, video_response
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assert (
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"man" in video_response
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or "person" in video_response
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or "individual" in video_response
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or "speaker" in video_response
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), video_response
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assert (
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"present" in video_response
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or "examine" in video_response
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or "display" in video_response
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or "hold" in video_response
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)
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assert "black" in video_response or "dark" in video_response
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self.assertIsNotNone(video_response)
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self.assertGreater(len(video_response), 0)
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def test_regex(self):
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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regex = (
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r"""\{"""
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+ r""""color":"[\w]+","""
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+ r""""number_of_cars":[\d]+"""
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+ r"""\}"""
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)
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response = client.chat.completions.create(
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model="default",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_MAN_IRONING_URL},
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},
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{
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"type": "text",
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"text": "Describe this image in the JSON format.",
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},
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],
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},
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],
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temperature=0,
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extra_body={"regex": regex},
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)
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text = response.choices[0].message.content
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try:
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js_obj = json.loads(text)
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except (TypeError, json.decoder.JSONDecodeError):
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print("JSONDecodeError", text)
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raise
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assert isinstance(js_obj["color"], str)
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assert isinstance(js_obj["number_of_cars"], int)
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def run_decode_with_image(self, image_id):
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client = openai.Client(api_key=self.api_key, base_url=self.base_url)
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content = []
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if image_id == 0:
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content.append(
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_MAN_IRONING_URL},
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}
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)
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elif image_id == 1:
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content.append(
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{
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"type": "image_url",
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"image_url": {"url": IMAGE_SGL_LOGO_URL},
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}
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)
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else:
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pass
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content.append(
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{
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"type": "text",
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"text": "Describe this image in a very short sentence.",
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}
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)
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response = client.chat.completions.create(
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model="default",
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messages=[
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{"role": "user", "content": content},
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],
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temperature=0,
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)
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assert response.choices[0].message.role == "assistant"
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text = response.choices[0].message.content
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assert isinstance(text, str)
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def test_mixed_batch(self):
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image_ids = [0, 1, 2] * 4
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with ThreadPoolExecutor(4) as executor:
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list(executor.map(self.run_decode_with_image, image_ids))
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def prepare_audio_messages(self, prompt, audio_file_name):
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "audio_url",
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"audio_url": {"url": f"{audio_file_name}"},
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},
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{
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"type": "text",
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"text": prompt,
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},
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],
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}
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]
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return messages
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def get_audio_response(self, url: str, prompt, category):
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audio_file_path = self.get_or_download_file(url)
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client = openai.Client(api_key="sk-123456", base_url=self.base_url)
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messages = self.prepare_audio_messages(prompt, audio_file_path)
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response = client.chat.completions.create(
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model="default",
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messages=messages,
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temperature=0,
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max_tokens=128,
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stream=False,
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)
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audio_response = response.choices[0].message.content
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print("-" * 30)
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print(f"audio {category} response:\n{audio_response}")
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print("-" * 30)
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audio_response = audio_response.lower()
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self.assertIsNotNone(audio_response)
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self.assertGreater(len(audio_response), 0)
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return audio_response
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def _test_audio_speech_completion(self):
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# a fragment of Trump's speech
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audio_response = self.get_audio_response(
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AUDIO_TRUMP_SPEECH_URL,
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"I have an audio sample. Please repeat the person's words",
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category="speech",
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)
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assert "thank you" in audio_response
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assert "it's a privilege to be here" in audio_response
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assert "leader" in audio_response
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assert "science" in audio_response
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assert "art" in audio_response
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def _test_audio_ambient_completion(self):
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# bird song
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audio_response = self.get_audio_response(
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AUDIO_BIRD_SONG_URL,
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"Please listen to the audio snippet carefully and transcribe the content.",
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"ambient",
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)
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assert "bird" in audio_response
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def test_audio_chat_completion(self):
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pass
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