157 lines
4.6 KiB
Python
157 lines
4.6 KiB
Python
import os
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import subprocess
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import sys
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import time
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import warnings
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from contextlib import contextmanager
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from typing import List
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import openai
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import ray
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import requests
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from vllm.distributed import (ensure_model_parallel_initialized,
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init_distributed_environment)
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from vllm.entrypoints.openai.cli_args import make_arg_parser
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from vllm.utils import get_open_port
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# Path to root of repository so that utilities can be imported by ray workers
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VLLM_PATH = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir))
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class RemoteOpenAIServer:
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DUMMY_API_KEY = "token-abc123" # vLLM's OpenAI server does not need API key
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MAX_SERVER_START_WAIT_S = 600 # wait for server to start for 60 seconds
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@ray.remote(num_gpus=1)
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class _RemoteRunner:
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def __init__(self, cli_args: List[str], *, wait_url: str,
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wait_timeout: float) -> None:
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env = os.environ.copy()
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env["PYTHONUNBUFFERED"] = "1"
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self.proc = subprocess.Popen(
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[
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sys.executable, "-m", "vllm.entrypoints.openai.api_server",
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*cli_args
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],
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env=env,
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stdout=sys.stdout,
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stderr=sys.stderr,
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)
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self._wait_for_server(url=wait_url, timeout=wait_timeout)
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def ready(self):
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return True
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def _wait_for_server(self, *, url: str, timeout: float):
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# run health check
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start = time.time()
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while True:
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try:
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if requests.get(url).status_code == 200:
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break
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except Exception as err:
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if self.proc.poll() is not None:
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raise RuntimeError(
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"Server exited unexpectedly.") from err
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time.sleep(0.5)
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if time.time() - start > timeout:
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raise RuntimeError(
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"Server failed to start in time.") from err
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def __del__(self):
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if hasattr(self, "proc"):
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self.proc.terminate()
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def __init__(self, cli_args: List[str], *, auto_port: bool = True) -> None:
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if auto_port:
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if "-p" in cli_args or "--port" in cli_args:
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raise ValueError("You have manually specified the port"
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"when `auto_port=True`.")
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cli_args = cli_args + ["--port", str(get_open_port())]
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parser = make_arg_parser()
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args = parser.parse_args(cli_args)
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self.host = str(args.host or 'localhost')
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self.port = int(args.port)
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self._runner = self._RemoteRunner.remote( # type: ignore
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cli_args,
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wait_url=self.url_for("health"),
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wait_timeout=self.MAX_SERVER_START_WAIT_S)
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self._wait_until_ready()
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@property
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def url_root(self) -> str:
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return f"http://{self.host}:{self.port}"
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def url_for(self, *parts: str) -> str:
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return self.url_root + "/" + "/".join(parts)
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def _wait_until_ready(self) -> None:
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ray.get(self._runner.ready.remote())
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def get_client(self):
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return openai.OpenAI(
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base_url=self.url_for("v1"),
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api_key=self.DUMMY_API_KEY,
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)
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def get_async_client(self):
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return openai.AsyncOpenAI(
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base_url=self.url_for("v1"),
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api_key=self.DUMMY_API_KEY,
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)
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def init_test_distributed_environment(
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tp_size: int,
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pp_size: int,
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rank: int,
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distributed_init_port: str,
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local_rank: int = -1,
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) -> None:
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distributed_init_method = f"tcp://localhost:{distributed_init_port}"
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init_distributed_environment(
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world_size=pp_size * tp_size,
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rank=rank,
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distributed_init_method=distributed_init_method,
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local_rank=local_rank)
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ensure_model_parallel_initialized(tp_size, pp_size)
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def multi_process_tensor_parallel(
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tp_size: int,
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pp_size: int,
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test_target,
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) -> None:
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# Using ray helps debugging the error when it failed
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# as compared to multiprocessing.
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ray.init(runtime_env={"working_dir": VLLM_PATH})
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distributed_init_port = get_open_port()
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refs = []
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for rank in range(tp_size * pp_size):
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refs.append(
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test_target.remote(tp_size, pp_size, rank, distributed_init_port))
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ray.get(refs)
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ray.shutdown()
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@contextmanager
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def error_on_warning():
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"""
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Within the scope of this context manager, tests will fail if any warning
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is emitted.
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"""
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with warnings.catch_warnings():
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warnings.simplefilter("error")
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yield
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