350f9e107f
Since #4335 was merged, I've noticed that the definition of ServerRunner in the tests is the same as in the test for OpenAI API. I have moved the class to the test utilities to avoid code duplication. (Although it only has been repeated twice so far, I will add another similar test suite in #4200 which would duplicate the code a third time) Also, I have moved the test utilities file (test_utils.py) to under the test directory (tests/utils.py), since none of its code is actually used in the main package. Note that I have added __init__.py to each test subpackage and updated the ray.init() call in the test utilities file in order to relative import tests/utils.py.
90 lines
2.5 KiB
Python
90 lines
2.5 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 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.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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@ray.remote(num_gpus=1)
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class ServerRunner:
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MAX_SERVER_START_WAIT_S = 600 # wait for server to start for 60 seconds
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def __init__(self, args):
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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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["python3", "-m", "vllm.entrypoints.openai.api_server"] + args,
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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()
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def ready(self):
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return True
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def _wait_for_server(self):
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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(
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"http://localhost:8000/health").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("Server exited unexpectedly.") from err
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time.sleep(0.5)
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if time.time() - start > self.MAX_SERVER_START_WAIT_S:
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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_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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