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# 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，
{
" 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，
{ " 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，
{
" 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，
{
" 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，
{
" 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，
{
" 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， % """
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 } ] "
)
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