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[CI] Adapt vl_model baseline changes due to Paddle update (#5576)

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YuBaoku
2025-12-16 11:42:31 +08:00
committed by GitHub
parent 5db08cc1d5
commit 63fff8df70
4 changed files with 5 additions and 481 deletions
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4
tests/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py
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@@ -205,9 +205,9 @@ def test_consistency_between_runs(api_url, headers, consistent_payload):
# base result # base result
base_path = os.getenv("MODEL_PATH") base_path = os.getenv("MODEL_PATH")
if base_path: if base_path:
base_file = os.path.join(base_path, "ernie-4_5-vl-base-tp2-dev") base_file = os.path.join(base_path, "ernie-4_5-vl-base-tp2-dev-1215")
else: else:
base_file = "ernie-4_5-vl-base-tp2-dev" base_file = "ernie-4_5-vl-base-tp2-dev-1215"
with open(base_file, "r") as f: with open(base_file, "r") as f:
content2 = f.read() content2 = f.read()

476
tests/ci_use/Qwen2_5_VL/test_Qwen2_5_VL_serving.py
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@@ -1,476 +0,0 @@
# 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 subprocess
import sys
import time
import openai
import pytest
import requests
tests_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", ".."))
sys.path.insert(0, tests_dir)
from e2e.utils.serving_utils import (
FD_API_PORT,
FD_CACHE_QUEUE_PORT,
FD_ENGINE_QUEUE_PORT,
FD_METRICS_PORT,
clean_ports,
is_port_open,
)
@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),
"--cache-queue-port",
str(FD_CACHE_QUEUE_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。
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}]"
)

4
tests/e2e/test_EB_VL_Lite_serving.py
View File

@@ -204,9 +204,9 @@ def test_consistency_between_runs(api_url, headers, consistent_payload):
# base result # base result
base_path = os.getenv("MODEL_PATH") base_path = os.getenv("MODEL_PATH")
if base_path: if base_path:
base_file = os.path.join(base_path, "ernie-4_5-vl-base-tp2-dev") base_file = os.path.join(base_path, "ernie-4_5-vl-base-tp2-dev-1215")
else: else:
base_file = "ernie-4_5-vl-base-tp2-dev" base_file = "ernie-4_5-vl-base-tp2-dev-1215"
with open(base_file, "r") as f: with open(base_file, "r") as f:
content2 = f.read() content2 = f.read()

2
tests/e2e/test_Qwen2_5_VL_serving.py
View File

@@ -179,7 +179,7 @@ def test_consistency_between_runs(api_url, headers, consistent_payload):
f_o.close() f_o.close()
# base result # 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。" 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一件艺术品。成年人手里拿着一个扇子，上面有各种颜色的颜料混合在一起，看起来像是通过某种方式创作的艺术品。孩子们也参与其中，一个孩子正在仔细观察，另一个孩子则在旁边观看。\n\n背景中还有其他人在进行类似的活动，环境看起来像是在一个室内空间，可能是教室或工作室。整体氛围显得非常温馨和愉快，大家似乎都在享受这个创作过程。"
# Verify that result is same as the base result # Verify that result is same as the base result
assert content1 == content2 assert content1 == content2