FastDeploy/tests/e2e/test_Qwen2_5_VL_serving.py

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
import re
import signal
import socket
import subprocess
import sys
import time

import openai
import pytest
import requests

# Read ports from environment variables; use default values if not set
FD_API_PORT = int(os.getenv("FD_API_PORT", 8188))
FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8133))
FD_METRICS_PORT = int(os.getenv("FD_METRICS_PORT", 8233))

# List of ports to clean before and after tests
PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT]


def is_port_open(host: str, port: int, timeout=1.0):
    """
    Check if a TCP port is open on the given host.
    Returns True if connection succeeds, False otherwise.
    """
    try:
        with socket.create_connection((host, port), timeout):
            return True
    except Exception:
        return False


def kill_process_on_port(port: int):
    """
    Kill processes that are listening on the given port.
    Uses `lsof` to find process ids and sends SIGKILL.
    """
    try:
        output = subprocess.check_output(f"lsof -i:{port} -t", shell=True).decode().strip()
        for pid in output.splitlines():
            os.kill(int(pid), signal.SIGKILL)
            print(f"Killed process on port {port}, pid={pid}")
    except subprocess.CalledProcessError:
        pass


def clean_ports():
    """
    Kill all processes occupying the ports listed in PORTS_TO_CLEAN.
    """
    for port in PORTS_TO_CLEAN:
        kill_process_on_port(port)


@pytest.fixture(scope="session", autouse=True)
def setup_and_run_server():
    """
    Pytest fixture that runs once per test session:
    - Cleans ports before tests
    - Starts the API server as a subprocess
    - Waits for server port to open (up to 30 seconds)
    - Tears down server after all tests finish
    """
    print("Pre-test port cleanup...")
    clean_ports()

    model_path = "/ModelData/Qwen2.5-VL-7B-Instruct"

    log_path = "server.log"
    limit_mm_str = json.dumps({"image": 100, "video": 100})

    cmd = [
        sys.executable,
        "-m",
        "fastdeploy.entrypoints.openai.api_server",
        "--model",
        model_path,
        "--port",
        str(FD_API_PORT),
        # "--tensor-parallel-size",
        # "2",
        "--engine-worker-queue-port",
        str(FD_ENGINE_QUEUE_PORT),
        "--metrics-port",
        str(FD_METRICS_PORT),
        "--enable-mm",
        "--max-model-len",
        "32768",
        "--max-num-batched-tokens",
        "384",
        "--max-num-seqs",
        "128",
        "--limit-mm-per-prompt",
        limit_mm_str,
    ]

    print(cmd)
    # Start subprocess in new process group
    with open(log_path, "w") as logfile:
        process = subprocess.Popen(
            cmd,
            stdout=logfile,
            stderr=subprocess.STDOUT,
            start_new_session=True,  # Enables killing full group via os.killpg
        )

    print(f"Started API server with pid {process.pid}")
    # Wait up to 10 minutes for API server to be ready
    for _ in range(10 * 60):
        if is_port_open("127.0.0.1", FD_API_PORT):
            print(f"API server is up on port {FD_API_PORT}")
            break
        time.sleep(1)
    else:
        print("[TIMEOUT] API server failed to start in 10 minutes. Cleaning up...")
        try:
            os.killpg(process.pid, signal.SIGTERM)
        except Exception as e:
            print(f"Failed to kill process group: {e}")
        raise RuntimeError(f"API server did not start on port {FD_API_PORT}")

    yield  # Run tests

    print("\n===== Post-test server cleanup... =====")
    try:
        os.killpg(process.pid, signal.SIGTERM)
        print(f"API server (pid={process.pid}) terminated")
    except Exception as e:
        print(f"Failed to terminate API server: {e}")


@pytest.fixture(scope="session")
def api_url(request):
    """
    Returns the API endpoint URL for chat completions.
    """
    return f"http://0.0.0.0:{FD_API_PORT}/v1/chat/completions"


@pytest.fixture(scope="session")
def metrics_url(request):
    """
    Returns the metrics endpoint URL.
    """
    return f"http://0.0.0.0:{FD_METRICS_PORT}/metrics"


@pytest.fixture
def headers():
    """
    Returns common HTTP request headers.
    """
    return {"Content-Type": "application/json"}


@pytest.fixture
def consistent_payload():
    """
    Returns a fixed payload for consistency testing,
    including a fixed random seed and temperature.
    """
    return {
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            }
        ],
        "temperature": 0.8,
        "top_p": 0,  # fix top_p to reduce randomness
        "seed": 13,  # fixed random seed
    }


# ==========================
# Consistency test for repeated runs with fixed payload
# ==========================
def test_consistency_between_runs(api_url, headers, consistent_payload):
    """
    Test that result is same as the base result.
    """
    # request
    resp1 = requests.post(api_url, headers=headers, json=consistent_payload)
    assert resp1.status_code == 200
    result1 = resp1.json()
    content1 = result1["choices"][0]["message"]["content"]
    file_res_temp = "Qwen2.5-VL-7B-Instruct-temp"
    f_o = open(file_res_temp, "a")
    f_o.writelines(content1)
    f_o.close()

    # base result
    content2 = "这张图片展示了一群人在进行手工艺活动。前景中有两个孩子和一个成年人，他们似乎在制作或展示某种手工艺品。成年人手里拿着一个扇子，上面有彩色的图案，可能是通过某种方式绘制或涂鸦而成。孩子们看起来很专注，可能是在观察或参与这个过程。\n\n背景中还有其他几个人，其中一个人穿着粉色的衣服，背对着镜头。整个场景看起来像是在一个室内环境中，光线充足，氛围轻松愉快。"

    # Verify that result is same as the base result
    assert content1 == content2


# ==========================
# OpenAI Client Chat Completion Test
# ==========================


@pytest.fixture
def openai_client():
    ip = "0.0.0.0"
    service_http_port = str(FD_API_PORT)
    client = openai.Client(
        base_url=f"http://{ip}:{service_http_port}/v1",
        api_key="EMPTY_API_KEY",
    )
    return client


# Non-streaming test
def test_non_streaming_chat(openai_client):
    """Test non-streaming chat functionality with the local service"""
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful AI assistant.",
            },  # system不是必需，可选
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=53,
        stream=False,
    )

    assert hasattr(response, "choices")
    assert len(response.choices) > 0
    assert hasattr(response.choices[0], "message")
    assert hasattr(response.choices[0].message, "content")


# Streaming test
def test_streaming_chat(openai_client, capsys):
    """Test streaming chat functionality with the local service"""
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {
                "role": "system",
                "content": "You are a helpful AI assistant.",
            },  # system不是必需，可选
            {"role": "user", "content": "List 3 countries and their capitals."},
            {
                "role": "assistant",
                "content": "China(Beijing), France(Paris), Australia(Canberra).",
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://ku.baidu-int.com/vk-assets-ltd/space/2024/09/13/933d1e0a0760498e94ec0f2ccee865e0",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=512,
        stream=True,
    )

    output = []
    for chunk in response:
        if hasattr(chunk.choices[0], "delta") and hasattr(chunk.choices[0].delta, "content"):
            output.append(chunk.choices[0].delta.content)
    assert len(output) > 2


# ==========================
# OpenAI Client additional chat/completions test
# ==========================


def test_non_streaming_chat_with_return_token_ids(openai_client, capsys):
    """
    Test return_token_ids option in non-streaming chat functionality with the local service
    """
    # 设定 return_token_ids
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},  # system不是必需，可选
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=53,
        extra_body={"return_token_ids": True},
        stream=False,
    )
    assert hasattr(response, "choices")
    assert len(response.choices) > 0
    assert hasattr(response.choices[0], "message")
    assert hasattr(response.choices[0].message, "prompt_token_ids")
    assert isinstance(response.choices[0].message.prompt_token_ids, list)
    assert hasattr(response.choices[0].message, "completion_token_ids")
    assert isinstance(response.choices[0].message.completion_token_ids, list)

    # 不设定 return_token_ids
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},  # system不是必需，可选
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=53,
        extra_body={"return_token_ids": False},
        stream=False,
    )
    assert hasattr(response, "choices")
    assert len(response.choices) > 0
    assert hasattr(response.choices[0], "message")
    assert hasattr(response.choices[0].message, "prompt_token_ids")
    assert response.choices[0].message.prompt_token_ids is None
    assert hasattr(response.choices[0].message, "completion_token_ids")
    assert response.choices[0].message.completion_token_ids is None


def test_streaming_chat_with_return_token_ids(openai_client, capsys):
    """
    Test return_token_ids option in streaming chat functionality with the local service
    """
    # enable return_token_ids
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},  # system不是必需，可选
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=53,
        extra_body={"return_token_ids": True},
        stream=True,
    )
    is_first_chunk = True
    for chunk in response:
        assert hasattr(chunk, "choices")
        assert len(chunk.choices) > 0
        assert hasattr(chunk.choices[0], "delta")
        assert hasattr(chunk.choices[0].delta, "prompt_token_ids")
        assert hasattr(chunk.choices[0].delta, "completion_token_ids")
        if is_first_chunk:
            is_first_chunk = False
            assert isinstance(chunk.choices[0].delta.prompt_token_ids, list)
            assert chunk.choices[0].delta.completion_token_ids is None
        else:
            assert chunk.choices[0].delta.prompt_token_ids is None
            assert isinstance(chunk.choices[0].delta.completion_token_ids, list)

    # disable return_token_ids
    response = openai_client.chat.completions.create(
        model="default",
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},  # system不是必需，可选
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
                            "detail": "high",
                        },
                    },
                    {"type": "text", "text": "请描述图片内容"},
                ],
            },
        ],
        temperature=1,
        max_tokens=53,
        extra_body={"return_token_ids": False},
        stream=True,
    )
    for chunk in response:
        assert hasattr(chunk, "choices")
        assert len(chunk.choices) > 0
        assert hasattr(chunk.choices[0], "delta")
        assert hasattr(chunk.choices[0].delta, "prompt_token_ids")
        assert chunk.choices[0].delta.prompt_token_ids is None
        assert hasattr(chunk.choices[0].delta, "completion_token_ids")
        assert chunk.choices[0].delta.completion_token_ids is None


def test_profile_reset_block_num():
    """测试profile reset_block_num功能，与baseline diff不能超过15%"""
    log_file = "./log/config.log"
    baseline = 30000

    if not os.path.exists(log_file):
        pytest.fail(f"Log file not found: {log_file}")

    with open(log_file, "r") as f:
        log_lines = f.readlines()

    target_line = None
    for line in log_lines:
        if "Reset block num" in line:
            target_line = line.strip()
            break

    if target_line is None:
        pytest.fail("日志中没有Reset block num信息")

    match = re.search(r"total_block_num:(\d+)", target_line)
    if not match:
        pytest.fail(f"Failed to extract total_block_num from line: {target_line}")

    try:
        actual_value = int(match.group(1))
    except ValueError:
        pytest.fail(f"Invalid number format: {match.group(1)}")

    lower_bound = baseline * (1 - 0.15)
    upper_bound = baseline * (1 + 0.15)
    print(f"Reset total_block_num: {actual_value}. baseline: {baseline}")

    assert lower_bound <= actual_value <= upper_bound, (
        f"Reset total_block_num {actual_value} 与 baseline {baseline} diff需要在5%以内"
        f"Allowed range: [{lower_bound:.1f}, {upper_bound:.1f}]"
    )