325 lines
12 KiB
Python
325 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import os
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import signal
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import sys
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import uvloop
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import zmq
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import vllm
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import vllm.envs as envs
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from vllm.entrypoints.cli.types import CLISubcommand
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from vllm.entrypoints.openai.api_server import (run_server, run_server_worker,
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setup_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.entrypoints.utils import (VLLM_SUBCMD_PARSER_EPILOG,
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show_filtered_argument_or_group_from_help)
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from vllm.executor.multiproc_worker_utils import _add_prefix
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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, zmq_socket_ctx
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from vllm.v1.engine.coordinator import DPCoordinator
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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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from vllm.v1.metrics.prometheus import setup_multiprocess_prometheus
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from vllm.v1.utils import (APIServerProcessManager, CoreEngine,
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CoreEngineActorManager, EngineZmqAddresses,
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get_engine_client_zmq_addr,
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wait_for_completion_or_failure,
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wait_for_engine_startup)
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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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name = "serve"
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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 or args.api_server_count < 1:
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run_headless(args)
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elif args.api_server_count > 1:
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run_multi_api_server(args)
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else:
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# Single API server (this process).
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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('--api-server-count',
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'-asc',
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type=int,
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default=1,
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help='How many API server processes to run.')
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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/configuration/serve_args.html")
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serve_parser = make_arg_parser(serve_parser)
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show_filtered_argument_or_group_from_help(serve_parser, "serve")
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serve_parser.epilog = VLLM_SUBCMD_PARSER_EPILOG
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return 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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if args.api_server_count > 1:
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raise ValueError("api_server_count can't be set in headless mode")
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# Create the EngineConfig.
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engine_args = vllm.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 ValueError("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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handshake_address = get_tcp_uri(host, port)
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if local_engine_count <= 0:
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raise ValueError("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, handshake_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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handshake_address=handshake_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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def run_multi_api_server(args: argparse.Namespace):
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assert not args.headless
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num_api_servers = args.api_server_count
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assert num_api_servers > 0
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if num_api_servers > 1:
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setup_multiprocess_prometheus()
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listen_address, sock = setup_server(args)
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engine_args = vllm.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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model_config = vllm_config.model_config
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if num_api_servers > 1:
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if not envs.VLLM_USE_V1:
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raise ValueError("api_server_count > 1 is only supported for V1")
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if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
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raise ValueError("VLLM_ALLOW_RUNTIME_LORA_UPDATING cannot be used "
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"with api_server_count > 1")
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if model_config.is_multimodal_model and not (
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model_config.disable_mm_preprocessor_cache):
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logger.warning(
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"Multi-model preprocessor cache will be disabled for"
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" api_server_count > 1")
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model_config.disable_mm_preprocessor_cache = True
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parallel_config = vllm_config.parallel_config
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assert parallel_config.data_parallel_rank == 0
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dp_size = parallel_config.data_parallel_size
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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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local_only = local_engine_count == dp_size
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# Set up input and output addresses.
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input_addresses = [
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get_engine_client_zmq_addr(local_only, host)
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for _ in range(num_api_servers)
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]
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output_addresses = [
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get_engine_client_zmq_addr(local_only, host)
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for _ in range(num_api_servers)
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]
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addresses = EngineZmqAddresses(
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inputs=input_addresses,
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outputs=output_addresses,
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)
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# Set up coordinator for dp > 1.
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coordinator = None
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stats_update_address = None
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if dp_size > 1:
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coordinator = DPCoordinator(parallel_config)
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addresses.coordinator_input, addresses.coordinator_output = (
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coordinator.get_engine_socket_addresses())
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stats_update_address = coordinator.get_stats_publish_address()
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logger.info("Started DP Coordinator process (PID: %d)",
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coordinator.proc.pid)
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if parallel_config.data_parallel_backend == "ray":
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logger.info("Starting ray-based data parallel backend")
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engine_actor_manager = CoreEngineActorManager(
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vllm_config=vllm_config,
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addresses=addresses,
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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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# Start API servers using the manager
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api_server_manager = APIServerProcessManager(
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target_server_fn=run_api_server_worker_proc,
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listen_address=listen_address,
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sock=sock,
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args=args,
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num_servers=num_api_servers,
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input_addresses=input_addresses,
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output_addresses=output_addresses,
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stats_update_address=stats_update_address)
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wait_for_completion_or_failure(api_server_manager=api_server_manager,
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engine_manager=engine_actor_manager,
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coordinator=coordinator)
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return
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handshake_address = get_engine_client_zmq_addr(
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local_only, host, parallel_config.data_parallel_rpc_port)
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with zmq_socket_ctx(handshake_address, zmq.ROUTER,
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bind=True) as handshake_socket:
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# Start local engines.
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if not local_engine_count:
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local_engine_manager = None
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else:
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local_engine_manager = CoreEngineProcManager(
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EngineCoreProc.run_engine_core,
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vllm_config=vllm_config,
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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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handshake_address=handshake_address,
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on_head_node=True,
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local_engine_count=local_engine_count,
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start_index=0,
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local_start_index=0)
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# Start API servers using the manager
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api_server_manager = APIServerProcessManager(
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target_server_fn=run_api_server_worker_proc,
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listen_address=listen_address,
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sock=sock,
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args=args,
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num_servers=num_api_servers,
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input_addresses=input_addresses,
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output_addresses=output_addresses,
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stats_update_address=stats_update_address)
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# Wait for engine handshakes to complete.
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core_engines = [
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CoreEngine(index=i, local=(i < local_engine_count))
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for i in range(dp_size)
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]
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wait_for_engine_startup(
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handshake_socket,
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addresses,
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core_engines,
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parallel_config,
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vllm_config.cache_config,
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local_engine_manager,
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coordinator.proc if coordinator else None,
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)
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# Wait for API servers
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wait_for_completion_or_failure(api_server_manager=api_server_manager,
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engine_manager=local_engine_manager,
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coordinator=coordinator)
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def run_api_server_worker_proc(listen_address,
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sock,
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args,
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client_config=None,
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**uvicorn_kwargs) -> None:
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"""Entrypoint for individual API server worker processes."""
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# Add process-specific prefix to stdout and stderr.
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from multiprocessing import current_process
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process_name = current_process().name
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pid = os.getpid()
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_add_prefix(sys.stdout, process_name, pid)
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_add_prefix(sys.stderr, process_name, pid)
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uvloop.run(
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run_server_worker(listen_address, sock, args, client_config,
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**uvicorn_kwargs))
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