mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-12-24 13:28:13 +08:00
28de91b50f
* 45TVL support sot+CUDAGraph * mv unitest from ce_deploy 2 e2e * add test_EB_VL_Lite_sot_serving * rm useless line * add openai_client * fix unitest && reduce computing resources
451 lines
16 KiB
Python
451 lines
16 KiB
Python
# 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 shutil
|
||
import signal
|
||
import socket
|
||
import subprocess
|
||
import sys
|
||
import time
|
||
|
||
import openai
|
||
import pytest
|
||
|
||
# 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))
|
||
FD_CACHE_QUEUE_PORT = int(os.getenv("FD_CACHE_QUEUE_PORT", 8333))
|
||
|
||
# List of ports to clean before and after tests
|
||
PORTS_TO_CLEAN = [FD_API_PORT, FD_ENGINE_QUEUE_PORT, FD_METRICS_PORT, FD_CACHE_QUEUE_PORT]
|
||
|
||
os.environ["FD_USE_MACHETE"] = "0"
|
||
|
||
|
||
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()
|
||
current_pid = os.getpid()
|
||
parent_pid = os.getppid()
|
||
for pid in output.splitlines():
|
||
pid = int(pid)
|
||
if pid in (current_pid, parent_pid):
|
||
print(f"Skip killing current process (pid={pid}) on port {port}")
|
||
continue
|
||
os.kill(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)
|
||
time.sleep(2)
|
||
|
||
|
||
@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()
|
||
print("log dir clean ")
|
||
if os.path.exists("log") and os.path.isdir("log"):
|
||
shutil.rmtree("log")
|
||
|
||
base_path = os.getenv("MODEL_PATH")
|
||
if base_path:
|
||
model_path = os.path.join(base_path, "ernie-4_5-vl-28b-a3b-bf16-paddle")
|
||
else:
|
||
model_path = "./ernie-4_5-vl-28b-a3b-bf16-paddle"
|
||
|
||
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",
|
||
"8192",
|
||
"--max-num-batched-tokens",
|
||
"172",
|
||
"--max-num-seqs",
|
||
"64",
|
||
"--limit-mm-per-prompt",
|
||
limit_mm_str,
|
||
"--enable-chunked-prefill",
|
||
"--kv-cache-ratio",
|
||
"0.71",
|
||
"--quantization",
|
||
"wint4",
|
||
"--reasoning-parser",
|
||
"ernie-45-vl",
|
||
"--graph-optimization-config",
|
||
'{"graph_opt_level": 1, "use_cudagraph": true, "full_cuda_graph": false}',
|
||
]
|
||
|
||
# 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
|
||
)
|
||
|
||
# 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 5 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")
|
||
clean_ports()
|
||
except Exception as e:
|
||
print(f"Failed to terminate API server: {e}")
|
||
|
||
|
||
# ==========================
|
||
# OpenAI Client additional chat/completions 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
|
||
|
||
|
||
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_chat_with_thinking(openai_client, capsys):
|
||
"""
|
||
Test enable_thinking & reasoning_max_tokens option in non-streaming chat functionality with the local service
|
||
"""
|
||
# enable thinking, non-streaming
|
||
response = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
stream=False,
|
||
max_tokens=10,
|
||
extra_body={"chat_template_kwargs": {"enable_thinking": True}},
|
||
)
|
||
assert response.choices[0].message.reasoning_content is not None
|
||
|
||
# disable thinking, non-streaming
|
||
response = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
stream=False,
|
||
max_tokens=10,
|
||
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
|
||
)
|
||
assert response.choices[0].message.reasoning_content is None
|
||
assert "</think>" not in response.choices[0].message.content
|
||
|
||
# test logic
|
||
reasoning_max_tokens = None
|
||
response = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
stream=False,
|
||
max_tokens=20,
|
||
extra_body={
|
||
"chat_template_kwargs": {"enable_thinking": True},
|
||
"reasoning_max_tokens": reasoning_max_tokens,
|
||
},
|
||
)
|
||
assert response.choices[0].message.reasoning_content is not None
|
||
|
||
# enable thinking, streaming
|
||
reasoning_max_tokens = 3
|
||
response = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
extra_body={
|
||
"chat_template_kwargs": {"enable_thinking": True},
|
||
"reasoning_max_tokens": reasoning_max_tokens,
|
||
"return_token_ids": True,
|
||
},
|
||
stream=True,
|
||
max_tokens=10,
|
||
)
|
||
completion_tokens = 1
|
||
reasoning_tokens = 0
|
||
total_tokens = 0
|
||
for chunk_id, chunk in enumerate(response):
|
||
if chunk_id == 0: # the first chunk is an extra chunk
|
||
continue
|
||
delta_message = chunk.choices[0].delta
|
||
if delta_message.content != "" and delta_message.reasoning_content == "":
|
||
completion_tokens += len(delta_message.completion_token_ids)
|
||
elif delta_message.reasoning_content != "" and delta_message.content == "":
|
||
reasoning_tokens += len(delta_message.completion_token_ids)
|
||
total_tokens += len(delta_message.completion_token_ids)
|
||
assert completion_tokens + reasoning_tokens == total_tokens
|
||
assert reasoning_tokens <= reasoning_max_tokens
|
||
|
||
|
||
def test_thinking_logic_flag(openai_client, capsys):
|
||
"""
|
||
Test the interaction between token calculation logic and conditional thinking.
|
||
This test covers:
|
||
1. Default max_tokens calculation when not provided.
|
||
2. Capping of max_tokens when it exceeds model limits.
|
||
3. Default reasoning_max_tokens calculation when not provided.
|
||
4. Activation of thinking based on the final state of reasoning_max_tokens.
|
||
"""
|
||
|
||
response_case_1 = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity briefly."}],
|
||
temperature=1,
|
||
stream=False,
|
||
extra_body={
|
||
"chat_template_kwargs": {"enable_thinking": True},
|
||
},
|
||
)
|
||
assert response_case_1.choices[0].message.reasoning_content is not None
|
||
|
||
response_case_2 = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
stream=False,
|
||
max_tokens=20,
|
||
extra_body={
|
||
"chat_template_kwargs": {"enable_thinking": True},
|
||
"reasoning_max_tokens": 5,
|
||
},
|
||
)
|
||
assert response_case_2.choices[0].message.reasoning_content is not None
|
||
|
||
response_case_3 = openai_client.chat.completions.create(
|
||
model="default",
|
||
messages=[{"role": "user", "content": "Explain gravity in a way that a five-year-old child can understand."}],
|
||
temperature=1,
|
||
stream=False,
|
||
max_tokens=20,
|
||
extra_body={
|
||
"chat_template_kwargs": {"enable_thinking": False},
|
||
},
|
||
)
|
||
assert response_case_3.choices[0].message.reasoning_content is None
|