[1/N][CI] Add multi node test (#3359)
### What this PR does / why we need it? This pr purpose to add multi-node test, on the first step, add `deepseek-v3` dp+tp+ep test ### Does this PR introduce _any_ user-facing change? ### How was this patch tested? - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 --------- Signed-off-by: wangli <wangli858794774@gmail.com>
This commit is contained in:
Li Wang
@@ -19,11 +19,18 @@
|
||||
|
||||
import contextlib
|
||||
import gc
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from typing import Any, List, Optional, Tuple, TypeVar, Union
|
||||
|
||||
import httpx
|
||||
import numpy as np
|
||||
import openai
|
||||
import pytest
|
||||
import requests
|
||||
import torch
|
||||
from modelscope import snapshot_download # type: ignore[import-untyped]
|
||||
from PIL import Image
|
||||
@@ -33,9 +40,14 @@ from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
|
||||
from transformers.models.auto.auto_factory import _BaseAutoModelClass
|
||||
from vllm import LLM, SamplingParams
|
||||
from vllm.config.model import TaskOption, _get_and_verify_dtype
|
||||
from vllm.engine.arg_utils import AsyncEngineArgs
|
||||
from vllm.entrypoints.cli.serve import ServeSubcommand
|
||||
from vllm.inputs import TextPrompt
|
||||
from vllm.model_executor.model_loader import get_model_loader
|
||||
from vllm.outputs import RequestOutput
|
||||
from vllm.platforms import current_platform
|
||||
from vllm.transformers_utils.utils import maybe_model_redirect
|
||||
from vllm.utils import FlexibleArgumentParser, get_open_port
|
||||
|
||||
from tests.e2e.model_utils import (TokensTextLogprobs,
|
||||
TokensTextLogprobsPromptLogprobs)
|
||||
@@ -76,6 +88,181 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
|
||||
torch.npu.reset_peak_memory_stats()
|
||||
|
||||
|
||||
class RemoteOpenAIServer:
|
||||
DUMMY_API_KEY = "token-abc123" # vLLM's OpenAI server does not need API key
|
||||
|
||||
def _start_server(self, model: str, vllm_serve_args: list[str],
|
||||
env_dict: Optional[dict[str, str]]) -> None:
|
||||
"""Subclasses override this method to customize server process launch
|
||||
"""
|
||||
env = os.environ.copy()
|
||||
# the current process might initialize npu,
|
||||
# to be safe, we should use spawn method
|
||||
env['VLLM_WORKER_MULTIPROC_METHOD'] = 'spawn'
|
||||
if env_dict is not None:
|
||||
env.update(env_dict)
|
||||
self.proc: subprocess.Popen = subprocess.Popen(
|
||||
["vllm", "serve", model, *vllm_serve_args],
|
||||
env=env,
|
||||
stdout=sys.stdout,
|
||||
stderr=sys.stderr,
|
||||
)
|
||||
|
||||
def __init__(self,
|
||||
model: str,
|
||||
server_host: str,
|
||||
server_port: int,
|
||||
vllm_serve_args: list[str],
|
||||
*,
|
||||
env_dict: Optional[dict[str, str]] = None,
|
||||
seed: Optional[int] = 0,
|
||||
auto_port: bool = True,
|
||||
max_wait_seconds: Optional[float] = None,
|
||||
override_hf_configs: Optional[dict[str, Any]] = None) -> None:
|
||||
if auto_port:
|
||||
if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
|
||||
raise ValueError("You have manually specified the port "
|
||||
"when `auto_port=True`.")
|
||||
|
||||
# No need for a port if using unix sockets
|
||||
if "--uds" not in vllm_serve_args:
|
||||
# Don't mutate the input args
|
||||
vllm_serve_args = vllm_serve_args + [
|
||||
"--port", str(get_open_port())
|
||||
]
|
||||
if seed is not None:
|
||||
if "--seed" in vllm_serve_args:
|
||||
raise ValueError("You have manually specified the seed "
|
||||
f"when `seed={seed}`.")
|
||||
|
||||
vllm_serve_args = vllm_serve_args + ["--seed", str(seed)]
|
||||
|
||||
if override_hf_configs is not None:
|
||||
vllm_serve_args = vllm_serve_args + [
|
||||
"--hf-overrides",
|
||||
json.dumps(override_hf_configs)
|
||||
]
|
||||
|
||||
parser = FlexibleArgumentParser(
|
||||
description="vLLM's remote OpenAI server.")
|
||||
subparsers = parser.add_subparsers(required=False, dest="subparser")
|
||||
parser = ServeSubcommand().subparser_init(subparsers)
|
||||
args = parser.parse_args([*vllm_serve_args])
|
||||
self.uds = args.uds
|
||||
if args.uds:
|
||||
self.host = None
|
||||
self.port = None
|
||||
else:
|
||||
self.host = str(server_host)
|
||||
self.port = int(server_port)
|
||||
|
||||
self.show_hidden_metrics = \
|
||||
args.show_hidden_metrics_for_version is not None
|
||||
|
||||
# download the model before starting the server to avoid timeout
|
||||
is_local = os.path.isdir(model)
|
||||
if not is_local:
|
||||
engine_args = AsyncEngineArgs.from_cli_args(args)
|
||||
model_config = engine_args.create_model_config()
|
||||
load_config = engine_args.create_load_config()
|
||||
|
||||
model_loader = get_model_loader(load_config)
|
||||
model_loader.download_model(model_config)
|
||||
|
||||
self._start_server(model, vllm_serve_args, env_dict)
|
||||
max_wait_seconds = max_wait_seconds or 7200
|
||||
self._wait_for_server(url=self.url_for("health"),
|
||||
timeout=max_wait_seconds)
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
self.proc.terminate()
|
||||
try:
|
||||
self.proc.wait(8)
|
||||
except subprocess.TimeoutExpired:
|
||||
# force kill if needed
|
||||
self.proc.kill()
|
||||
|
||||
def _poll(self) -> Optional[int]:
|
||||
"""Subclasses override this method to customize process polling"""
|
||||
return self.proc.poll()
|
||||
|
||||
def hang_until_terminated(self) -> None:
|
||||
"""
|
||||
Wait until the server process terminates.
|
||||
This is for headless mode, where the api server
|
||||
process only exists in the leader node.
|
||||
"""
|
||||
if self.uds:
|
||||
client = httpx.Client(transport=httpx.HTTPTransport(uds=self.uds))
|
||||
else:
|
||||
client = requests
|
||||
|
||||
try:
|
||||
while True:
|
||||
try:
|
||||
resp = client.get(self.url_for("health"), timeout=5)
|
||||
if resp.status_code != 200:
|
||||
break
|
||||
time.sleep(5)
|
||||
except Exception:
|
||||
break
|
||||
finally:
|
||||
if isinstance(client, httpx.Client):
|
||||
client.close()
|
||||
|
||||
def _wait_for_server(self, *, url: str, timeout: float):
|
||||
# run health check
|
||||
start = time.time()
|
||||
client = (httpx.Client(transport=httpx.HTTPTransport(
|
||||
uds=self.uds)) if self.uds else requests)
|
||||
while True:
|
||||
try:
|
||||
if client.get(url).status_code == 200:
|
||||
break
|
||||
except Exception:
|
||||
# this exception can only be raised by requests.get,
|
||||
# which means the server is not ready yet.
|
||||
# the stack trace is not useful, so we suppress it
|
||||
# by using `raise from None`.
|
||||
result = self._poll()
|
||||
if result is not None and result != 0:
|
||||
raise RuntimeError("Server exited unexpectedly.") from None
|
||||
|
||||
time.sleep(1)
|
||||
if time.time() - start > timeout:
|
||||
raise RuntimeError(
|
||||
"Server failed to start in time.") from None
|
||||
|
||||
@property
|
||||
def url_root(self) -> str:
|
||||
return (f"http://{self.uds.split('/')[-1]}"
|
||||
if self.uds else f"http://{self.host}:{self.port}")
|
||||
|
||||
def url_for(self, *parts: str) -> str:
|
||||
return self.url_root + "/" + "/".join(parts)
|
||||
|
||||
def get_client(self, **kwargs):
|
||||
if "timeout" not in kwargs:
|
||||
kwargs["timeout"] = 600
|
||||
return openai.OpenAI(
|
||||
base_url=self.url_for("v1"),
|
||||
api_key=self.DUMMY_API_KEY,
|
||||
max_retries=0,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def get_async_client(self, **kwargs):
|
||||
if "timeout" not in kwargs:
|
||||
kwargs["timeout"] = 600
|
||||
return openai.AsyncOpenAI(base_url=self.url_for("v1"),
|
||||
api_key=self.DUMMY_API_KEY,
|
||||
max_retries=0,
|
||||
**kwargs)
|
||||
|
||||
|
||||
class VllmRunner:
|
||||
|
||||
def __init__(
|
||||
@@ -289,7 +476,6 @@ class VllmRunner:
|
||||
class HfRunner:
|
||||
|
||||
def get_default_device(self):
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
return ("cpu"
|
||||
if current_platform.is_cpu() else current_platform.device_type)
|
||||
|
||||
Reference in New Issue
Block a user