sglang/test/srt/test_vision_openai_server.py

"""
Usage:
python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_mixed_batch
python3 -m unittest test_vision_openai_server.TestOpenAIVisionServer.test_multi_images_chat_completion
"""

import base64
import io
import json
import os
import unittest
from concurrent.futures import ThreadPoolExecutor

import numpy as np
import openai
import requests
from PIL import Image

from sglang.srt.utils import kill_process_tree
from sglang.test.test_utils import (
    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
    DEFAULT_URL_FOR_TEST,
    popen_launch_server,
)


class TestOpenAIVisionServer(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = "lmms-lab/llava-onevision-qwen2-0.5b-ov"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
            other_args=[
                "--chat-template",
                "chatml-llava",
                # "--log-requests",
            ],
        )
        cls.base_url += "/v1"

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def test_single_image_chat_completion(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        response = client.chat.completions.create(
            model="default",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
                            },
                        },
                        {
                            "type": "text",
                            "text": "Describe this image in a very short sentence.",
                        },
                    ],
                },
            ],
            temperature=0,
        )

        assert response.choices[0].message.role == "assistant"
        text = response.choices[0].message.content
        assert isinstance(text, str)
        assert "man" in text or "person" in text, text
        assert "cab" in text or "taxi" in text or "SUV" in text, text
        assert "iron" in text, text
        assert response.id
        assert response.created
        assert response.usage.prompt_tokens > 0
        assert response.usage.completion_tokens > 0
        assert response.usage.total_tokens > 0

    def test_multi_turn_chat_completion(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        response = client.chat.completions.create(
            model="default",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
                            },
                        },
                        {
                            "type": "text",
                            "text": "Describe this image in a very short sentence.",
                        },
                    ],
                },
                {
                    "role": "assistant",
                    "content": [
                        {
                            "type": "text",
                            "text": "There is a man at the back of a yellow cab ironing his clothes.",
                        }
                    ],
                },
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Repeat your previous answer."}
                    ],
                },
            ],
            temperature=0,
        )

        assert response.choices[0].message.role == "assistant"
        text = response.choices[0].message.content
        assert isinstance(text, str)
        assert "man" in text or "cab" in text, text
        assert response.id
        assert response.created
        assert response.usage.prompt_tokens > 0
        assert response.usage.completion_tokens > 0
        assert response.usage.total_tokens > 0

    def test_multi_images_chat_completion(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        response = client.chat.completions.create(
            model="default",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://raw.githubusercontent.com/sgl-project/sglang/main/test/lang/example_image.png"
                            },
                            "modalities": "multi-images",
                        },
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
                            },
                            "modalities": "multi-images",
                        },
                        {
                            "type": "text",
                            "text": "I have two very different images. They are not related at all. "
                            "Please describe the first image in one sentence, and then describe the second image in another sentence.",
                        },
                    ],
                },
            ],
            temperature=0,
        )

        assert response.choices[0].message.role == "assistant"
        text = response.choices[0].message.content
        assert isinstance(text, str)
        print(f"LLM response: {text}")
        assert "man" in text or "cab" in text or "SUV" in text or "taxi" in text, text
        assert "logo" in text or '"S"' in text or "SG" in text, text
        assert response.id
        assert response.created
        assert response.usage.prompt_tokens > 0
        assert response.usage.completion_tokens > 0
        assert response.usage.total_tokens > 0

    def prepare_video_messages(self, video_path):
        # the memory consumed by the Vision Attention varies a lot, e.g. blocked qkv vs full-sequence sdpa
        # the size of the video embeds differs from the `modality` argument when preprocessed

        # We import decord here to avoid a strange Segmentation fault (core dumped) issue.
        # The following import order will cause Segmentation fault.
        # import decord
        # from transformers import AutoTokenizer
        from decord import VideoReader, cpu

        max_frames_num = 12
        vr = VideoReader(video_path, ctx=cpu(0))
        total_frame_num = len(vr)
        uniform_sampled_frames = np.linspace(
            0, total_frame_num - 1, max_frames_num, dtype=int
        )
        frame_idx = uniform_sampled_frames.tolist()
        frames = vr.get_batch(frame_idx).asnumpy()

        base64_frames = []
        for frame in frames:
            pil_img = Image.fromarray(frame)
            buff = io.BytesIO()
            pil_img.save(buff, format="JPEG")
            base64_str = base64.b64encode(buff.getvalue()).decode("utf-8")
            base64_frames.append(base64_str)

        messages = [{"role": "user", "content": []}]
        frame_format = {
            "type": "image_url",
            "image_url": {"url": "data:image/jpeg;base64,{}"},
            "modalities": "video",
        }

        for base64_frame in base64_frames:
            frame_format["image_url"]["url"] = "data:image/jpeg;base64,{}".format(
                base64_frame
            )
            messages[0]["content"].append(frame_format.copy())

        prompt = {"type": "text", "text": "Please describe the video in detail."}
        messages[0]["content"].append(prompt)

        return messages

    def test_video_chat_completion(self):
        url = "https://raw.githubusercontent.com/EvolvingLMMs-Lab/sglang/dev/onevision_local/assets/jobs.mp4"
        cache_dir = os.path.expanduser("~/.cache")
        file_path = os.path.join(cache_dir, "jobs.mp4")
        os.makedirs(cache_dir, exist_ok=True)

        if not os.path.exists(file_path):
            response = requests.get(url)
            response.raise_for_status()

            with open(file_path, "wb") as f:
                f.write(response.content)

        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        messages = self.prepare_video_messages(file_path)

        video_request = client.chat.completions.create(
            model="default",
            messages=messages,
            temperature=0,
            max_tokens=1024,
            stream=True,
        )

        print("-" * 30)
        video_response = ""
        for chunk in video_request:
            if chunk.choices[0].delta.content is not None:
                content = chunk.choices[0].delta.content
                video_response += content
                print(content, end="", flush=True)
        print("-" * 30)

        # Add assertions to validate the video response
        assert "iPod" in video_response or "device" in video_response, video_response
        assert (
            "man" in video_response
            or "person" in video_response
            or "individual" in video_response
        ), video_response
        assert (
            "present" in video_response
            or "examine" in video_response
            or "display" in video_response
        )
        assert "black" in video_response or "dark" in video_response
        self.assertIsNotNone(video_response)
        self.assertGreater(len(video_response), 0)

    def test_regex(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        regex = (
            r"""\{\n"""
            + r"""   "color": "[\w]+",\n"""
            + r"""   "number_of_cars": [\d]+\n"""
            + r"""\}"""
        )

        response = client.chat.completions.create(
            model="default",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
                            },
                        },
                        {
                            "type": "text",
                            "text": "Describe this image in the JSON format.",
                        },
                    ],
                },
            ],
            temperature=0,
            extra_body={"regex": regex},
        )
        text = response.choices[0].message.content

        try:
            js_obj = json.loads(text)
        except (TypeError, json.decoder.JSONDecodeError):
            print("JSONDecodeError", text)
            raise
        assert isinstance(js_obj["color"], str)
        assert isinstance(js_obj["number_of_cars"], int)

    def run_decode_with_image(self, image_id):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        content = []
        if image_id == 0:
            content.append(
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
                    },
                }
            )
        elif image_id == 1:
            content.append(
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"
                    },
                }
            )
        else:
            pass

        content.append(
            {
                "type": "text",
                "text": "Describe this image in a very short sentence.",
            }
        )

        response = client.chat.completions.create(
            model="default",
            messages=[
                {"role": "user", "content": content},
            ],
            temperature=0,
        )

        assert response.choices[0].message.role == "assistant"
        text = response.choices[0].message.content
        assert isinstance(text, str)

    def test_mixed_batch(self):
        image_ids = [0, 1, 2] * 4
        with ThreadPoolExecutor(4) as executor:
            list(executor.map(self.run_decode_with_image, image_ids))


class TestQWen2VLServer(TestOpenAIVisionServer):
    @classmethod
    def setUpClass(cls):
        cls.model = "Qwen/Qwen2-VL-7B-Instruct"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
            other_args=[
                "--chat-template",
                "qwen2-vl",
                "--chunked-prefill-size",
                "10000",
            ],
        )
        cls.base_url += "/v1"


class TestQWen2_5_VLServer(TestOpenAIVisionServer):
    @classmethod
    def setUpClass(cls):
        cls.model = "Qwen/Qwen2.5-VL-7B-Instruct"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
            other_args=[
                "--chat-template",
                "qwen2-vl",
                # FIXME: workaround to chunked prefill within image embeds
                "--chunked-prefill-size",
                "10000",
                "--mem-fraction-static",
                "0.4",
            ],
        )
        cls.base_url += "/v1"


class TestVLMContextLengthIssue(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.model = "Qwen/Qwen2-VL-7B-Instruct"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
            other_args=[
                "--chat-template",
                "qwen2-vl",
                "--context-length",
                "300",
                "--mem-fraction-static=0.80",
            ],
        )
        cls.base_url += "/v1"

    @classmethod
    def tearDownClass(cls):
        kill_process_tree(cls.process.pid)

    def test_single_image_chat_completion(self):
        client = openai.Client(api_key=self.api_key, base_url=self.base_url)

        with self.assertRaises(openai.BadRequestError) as cm:
            client.chat.completions.create(
                model="default",
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {
                                "type": "image_url",
                                "image_url": {
                                    "url": "https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true"
                                },
                            },
                            {
                                "type": "text",
                                "text": "Give a lengthy description of this picture",
                            },
                        ],
                    },
                ],
                temperature=0,
            )

        # context length is checked first, then max_req_input_len, which is calculated from the former
        assert (
            "Multimodal prompt is too long after expanding multimodal tokens."
            in str(cm.exception)
            or "is longer than the model's context length" in str(cm.exception)
        )


class TestMllamaServer(TestOpenAIVisionServer):
    @classmethod
    def setUpClass(cls):
        cls.model = "meta-llama/Llama-3.2-11B-Vision-Instruct"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            api_key=cls.api_key,
            other_args=[
                "--chat-template",
                "llama_3_vision",
            ],
        )
        cls.base_url += "/v1"

    def test_video_chat_completion(self):
        pass


class TestMinicpmvServer(TestOpenAIVisionServer):
    @classmethod
    def setUpClass(cls):
        cls.model = "openbmb/MiniCPM-V-2_6"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=[
                "--trust-remote-code",
                "--chat-template",
                "minicpmv",
            ],
        )
        cls.base_url += "/v1"


class TestJanusProServer(TestOpenAIVisionServer):
    @classmethod
    def setUpClass(cls):
        cls.model = "deepseek-ai/Janus-Pro-7B"
        cls.base_url = DEFAULT_URL_FOR_TEST
        cls.api_key = "sk-123456"
        cls.process = popen_launch_server(
            cls.model,
            cls.base_url,
            timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
            other_args=[
                "--trust-remote-code",
                "--chat-template",
                "janus-pro",
                "--mem-fraction-static",
                "0.4",
            ],
        )
        cls.base_url += "/v1"

    def test_video_chat_completion(self):
        pass

    def test_single_image_chat_completion(self):
        # Skip this test because it is flaky
        pass


if __name__ == "__main__":
    unittest.main()