137 lines
4.6 KiB
Python
137 lines
4.6 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import argparse
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import signal
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import uvloop
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import vllm.envs as envs
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from vllm import AsyncEngineArgs
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from vllm.entrypoints.cli.types import CLISubcommand
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from vllm.entrypoints.openai.api_server import run_server
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from vllm.entrypoints.openai.cli_args import (make_arg_parser,
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validate_parsed_serve_args)
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from vllm.logger import init_logger
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import FlexibleArgumentParser, get_tcp_uri
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from vllm.v1.engine.core import EngineCoreProc
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from vllm.v1.engine.core_client import CoreEngineProcManager
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from vllm.v1.executor.abstract import Executor
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logger = init_logger(__name__)
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class ServeSubcommand(CLISubcommand):
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"""The `serve` subcommand for the vLLM CLI. """
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def __init__(self):
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self.name = "serve"
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super().__init__()
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@staticmethod
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def cmd(args: argparse.Namespace) -> None:
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# If model is specified in CLI (as positional arg), it takes precedence
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if hasattr(args, 'model_tag') and args.model_tag is not None:
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args.model = args.model_tag
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if args.headless:
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run_headless(args)
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else:
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uvloop.run(run_server(args))
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def validate(self, args: argparse.Namespace) -> None:
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validate_parsed_serve_args(args)
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def subparser_init(
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self,
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subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
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serve_parser = subparsers.add_parser(
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"serve",
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help="Start the vLLM OpenAI Compatible API server.",
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description="Start the vLLM OpenAI Compatible API server.",
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usage="vllm serve [model_tag] [options]")
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serve_parser.add_argument("model_tag",
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type=str,
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nargs='?',
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help="The model tag to serve "
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"(optional if specified in config)")
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serve_parser.add_argument(
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"--headless",
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action='store_true',
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default=False,
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help="Run in headless mode. See multi-node data parallel "
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"documentation for more details.")
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serve_parser.add_argument(
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'--data-parallel-start-rank',
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'-dpr',
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type=int,
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default=0,
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help='Starting data parallel rank for secondary nodes.')
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serve_parser.add_argument(
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"--config",
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type=str,
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default='',
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required=False,
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help="Read CLI options from a config file."
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"Must be a YAML with the following options:"
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"https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#cli-reference"
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)
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return make_arg_parser(serve_parser)
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def cmd_init() -> list[CLISubcommand]:
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return [ServeSubcommand()]
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def run_headless(args: argparse.Namespace):
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# Create the EngineConfig.
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engine_args = AsyncEngineArgs.from_cli_args(args)
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usage_context = UsageContext.OPENAI_API_SERVER
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vllm_config = engine_args.create_engine_config(usage_context=usage_context)
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if not envs.VLLM_USE_V1:
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raise RuntimeError("Headless mode is only supported for V1")
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parallel_config = vllm_config.parallel_config
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local_engine_count = parallel_config.data_parallel_size_local
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host = parallel_config.data_parallel_master_ip
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port = engine_args.data_parallel_rpc_port # add to config too
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input_address = get_tcp_uri(host, port)
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if local_engine_count <= 0:
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raise RuntimeError("data_parallel_size_local must be > 0 in "
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"headless mode")
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# Catch SIGTERM and SIGINT to allow graceful shutdown.
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def signal_handler(signum, frame):
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logger.debug("Received %d signal.", signum)
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raise SystemExit
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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logger.info(
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"Launching %d data parallel engine(s) in headless mode, "
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"with head node address %s.", local_engine_count, input_address)
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# Create the engines.
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engine_manager = CoreEngineProcManager(
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target_fn=EngineCoreProc.run_engine_core,
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local_engine_count=local_engine_count,
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start_index=args.data_parallel_start_rank,
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local_start_index=0,
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vllm_config=vllm_config,
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on_head_node=False,
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input_address=input_address,
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executor_class=Executor.get_class(vllm_config),
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log_stats=not engine_args.disable_log_stats,
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)
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try:
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engine_manager.join_first()
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finally:
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logger.info("Shutting down.")
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engine_manager.close()
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