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2.4 KiB
Python

import os
import sys
import time
import pytest
import subprocess
import torch
from modelscope.hub.snapshot_download import snapshot_download
sys.path.append(os.path.dirname(__file__) + "/..")
from utils import run_in_subprocess, simple_openai_api, TelnetPort
from ut_config import (
MODEL_TYPE,
DOCKER_VERSION_CU121,
DOCKER_MOUNT_DIR,
DOCKER_TEST_DIR,
)
@pytest.mark.parametrize(
"num_gpus,use_int4",
[
(1, False),
(1, True),
(2, False),
# ValueError: The input size is not aligned with the quantized weight shape. This can be caused by too large tensor parallel size.
# (2, True)
],
)
def test_inference_vllm_fschat(num_gpus, use_int4):
model_type = f"{MODEL_TYPE}-Chat-Int4" if use_int4 else f"{MODEL_TYPE}-Chat"
container_name = "test_inference_vllm_fschat"
cmd_docker = f'docker run --gpus all --ipc=host --network=host --rm --name="{container_name}" -p 8000:8000 -v {os.getcwd()}/../../../Qwen:{DOCKER_MOUNT_DIR} {DOCKER_VERSION_CU121} /bin/bash -c '
cmd = ""
cmd += f"""nohup python -m fastchat.serve.controller > /dev/null 2>&1 \
& python -m fastchat.serve.openai_api_server --host localhost --port 8000 > /dev/null 2>&1 \
& python -m fastchat.serve.vllm_worker --model-path {DOCKER_TEST_DIR}/{model_type} --tensor-parallel-size {num_gpus} --trust-remote-code"""
# for GPUS SM < 80 and use_int==True
is_ampere = torch.cuda.get_device_capability()[0] >= 8
if not is_ampere or use_int4:
cmd += " --dtype half"
snapshot_download(model_type, cache_dir=".", revision="master")
# start model server
run_in_subprocess(
f'docker rm -f {container_name} 2>/dev/null || echo "The container does not exist."'
)
print(cmd_docker + f'"{cmd}"')
run_in_subprocess("nohup " + cmd_docker + f'"{cmd}"' + " > tmp.log 2>&1 &")
while not TelnetPort("localhost", 21002):
print("Wait for the model service start.")
time.sleep(0.5)
if (
subprocess.run(
f"docker inspect {container_name}",
shell=True,
stdout=subprocess.DEVNULL,
).returncode
!= 0
):
break
try:
simple_openai_api(model_type.split("/")[-1])
except Exception as e:
time.sleep(1)
with open("tmp.log") as f:
raise Exception(f"{e} \n {f.read()}")
run_in_subprocess(f"docker rm -f {container_name}")