104 lines
2.8 KiB
Python
104 lines
2.8 KiB
Python
import os
|
|
import subprocess
|
|
import sys
|
|
import time
|
|
import warnings
|
|
from contextlib import contextmanager
|
|
|
|
import ray
|
|
import requests
|
|
|
|
from vllm.distributed import (ensure_model_parallel_initialized,
|
|
init_distributed_environment)
|
|
from vllm.utils import get_open_port
|
|
|
|
# Path to root of repository so that utilities can be imported by ray workers
|
|
VLLM_PATH = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir))
|
|
|
|
|
|
@ray.remote(num_gpus=1)
|
|
class ServerRunner:
|
|
MAX_SERVER_START_WAIT_S = 600 # wait for server to start for 60 seconds
|
|
|
|
def __init__(self, args):
|
|
env = os.environ.copy()
|
|
env["PYTHONUNBUFFERED"] = "1"
|
|
self.proc = subprocess.Popen(
|
|
["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
|
|
env=env,
|
|
stdout=sys.stdout,
|
|
stderr=sys.stderr,
|
|
)
|
|
self._wait_for_server()
|
|
|
|
def ready(self):
|
|
return True
|
|
|
|
def _wait_for_server(self):
|
|
# run health check
|
|
start = time.time()
|
|
while True:
|
|
try:
|
|
if requests.get(
|
|
"http://localhost:8000/health").status_code == 200:
|
|
break
|
|
except Exception as err:
|
|
if self.proc.poll() is not None:
|
|
raise RuntimeError("Server exited unexpectedly.") from err
|
|
|
|
time.sleep(0.5)
|
|
if time.time() - start > self.MAX_SERVER_START_WAIT_S:
|
|
raise RuntimeError(
|
|
"Server failed to start in time.") from err
|
|
|
|
def __del__(self):
|
|
if hasattr(self, "proc"):
|
|
self.proc.terminate()
|
|
|
|
|
|
def init_test_distributed_environment(
|
|
tp_size: int,
|
|
pp_size: int,
|
|
rank: int,
|
|
distributed_init_port: str,
|
|
local_rank: int = -1,
|
|
) -> None:
|
|
distributed_init_method = f"tcp://localhost:{distributed_init_port}"
|
|
init_distributed_environment(
|
|
world_size=pp_size * tp_size,
|
|
rank=rank,
|
|
distributed_init_method=distributed_init_method,
|
|
local_rank=local_rank)
|
|
ensure_model_parallel_initialized(tp_size, pp_size)
|
|
|
|
|
|
def multi_process_tensor_parallel(
|
|
tp_size: int,
|
|
pp_size: int,
|
|
test_target,
|
|
) -> None:
|
|
# Using ray helps debugging the error when it failed
|
|
# as compared to multiprocessing.
|
|
ray.init(runtime_env={"working_dir": VLLM_PATH})
|
|
|
|
distributed_init_port = get_open_port()
|
|
refs = []
|
|
for rank in range(tp_size * pp_size):
|
|
refs.append(
|
|
test_target.remote(tp_size, pp_size, rank, distributed_init_port))
|
|
ray.get(refs)
|
|
|
|
ray.shutdown()
|
|
|
|
|
|
@contextmanager
|
|
def error_on_warning():
|
|
"""
|
|
Within the scope of this context manager, tests will fail if any warning
|
|
is emitted.
|
|
"""
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error")
|
|
|
|
yield
|