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262 lines
11 KiB
Python
262 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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This file contains the command line arguments for the vLLM's
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OpenAI-compatible server. It is kept in a separate file for documentation
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purposes.
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"""
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import argparse
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import json
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import ssl
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from collections.abc import Sequence
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from dataclasses import field
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from typing import Literal, Optional, Union
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from pydantic.dataclasses import dataclass
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import vllm.envs as envs
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from vllm.config import config
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from vllm.engine.arg_utils import AsyncEngineArgs, optional_type
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from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
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validate_chat_template)
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from vllm.entrypoints.openai.serving_models import LoRAModulePath
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from vllm.entrypoints.openai.tool_parsers import ToolParserManager
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from vllm.logger import init_logger
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from vllm.utils import FlexibleArgumentParser
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logger = init_logger(__name__)
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class LoRAParserAction(argparse.Action):
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def __call__(
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self,
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parser: argparse.ArgumentParser,
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namespace: argparse.Namespace,
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values: Optional[Union[str, Sequence[str]]],
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option_string: Optional[str] = None,
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):
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if values is None:
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values = []
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if isinstance(values, str):
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raise TypeError("Expected values to be a list")
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lora_list: list[LoRAModulePath] = []
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for item in values:
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if item in [None, '']: # Skip if item is None or empty string
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continue
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if '=' in item and ',' not in item: # Old format: name=path
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name, path = item.split('=')
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lora_list.append(LoRAModulePath(name, path))
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else: # Assume JSON format
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try:
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lora_dict = json.loads(item)
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lora = LoRAModulePath(**lora_dict)
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lora_list.append(lora)
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except json.JSONDecodeError:
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parser.error(
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f"Invalid JSON format for --lora-modules: {item}")
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except TypeError as e:
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parser.error(
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f"Invalid fields for --lora-modules: {item} - {str(e)}"
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)
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setattr(namespace, self.dest, lora_list)
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@config
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@dataclass
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class FrontendArgs:
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"""Arguments for the OpenAI-compatible frontend server."""
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host: Optional[str] = None
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"""Host name."""
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port: int = 8000
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"""Port number."""
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uvicorn_log_level: Literal["debug", "info", "warning", "error", "critical",
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"trace"] = "info"
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"""Log level for uvicorn."""
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disable_uvicorn_access_log: bool = False
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"""Disable uvicorn access log."""
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allow_credentials: bool = False
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"""Allow credentials."""
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allowed_origins: list[str] = field(default_factory=lambda: ["*"])
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"""Allowed origins."""
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allowed_methods: list[str] = field(default_factory=lambda: ["*"])
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"""Allowed methods."""
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allowed_headers: list[str] = field(default_factory=lambda: ["*"])
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"""Allowed headers."""
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api_key: Optional[str] = None
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"""If provided, the server will require this key to be presented in the
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header."""
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lora_modules: Optional[list[LoRAModulePath]] = None
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"""LoRA modules configurations in either 'name=path' format or JSON format
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or JSON list format. Example (old format): `'name=path'` Example (new
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format): `{\"name\": \"name\", \"path\": \"lora_path\",
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\"base_model_name\": \"id\"}`"""
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chat_template: Optional[str] = None
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"""The file path to the chat template, or the template in single-line form
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for the specified model."""
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chat_template_content_format: ChatTemplateContentFormatOption = "auto"
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"""The format to render message content within a chat template.
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* "string" will render the content as a string. Example: `"Hello World"`
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* "openai" will render the content as a list of dictionaries, similar to OpenAI
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schema. Example: `[{"type": "text", "text": "Hello world!"}]`"""
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response_role: str = "assistant"
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"""The role name to return if `request.add_generation_prompt=true`."""
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ssl_keyfile: Optional[str] = None
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"""The file path to the SSL key file."""
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ssl_certfile: Optional[str] = None
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"""The file path to the SSL cert file."""
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ssl_ca_certs: Optional[str] = None
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"""The CA certificates file."""
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enable_ssl_refresh: bool = False
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"""Refresh SSL Context when SSL certificate files change"""
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ssl_cert_reqs: int = int(ssl.CERT_NONE)
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"""Whether client certificate is required (see stdlib ssl module's)."""
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root_path: Optional[str] = None
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"""FastAPI root_path when app is behind a path based routing proxy."""
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middleware: list[str] = field(default_factory=lambda: [])
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"""Additional ASGI middleware to apply to the app. We accept multiple
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--middleware arguments. The value should be an import path. If a function
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is provided, vLLM will add it to the server using
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`@app.middleware('http')`. If a class is provided, vLLM will
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add it to the server using `app.add_middleware()`."""
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return_tokens_as_token_ids: bool = False
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"""When `--max-logprobs` is specified, represents single tokens as
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strings of the form 'token_id:{token_id}' so that tokens that are not
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JSON-encodable can be identified."""
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disable_frontend_multiprocessing: bool = False
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"""If specified, will run the OpenAI frontend server in the same process as
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the model serving engine."""
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enable_request_id_headers: bool = False
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"""If specified, API server will add X-Request-Id header to responses.
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Caution: this hurts performance at high QPS."""
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enable_auto_tool_choice: bool = False
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"""If specified, exclude tool definitions in prompts when
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tool_choice='none'."""
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exclude_tools_when_tool_choice_none: bool = False
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"""Enable auto tool choice for supported models. Use `--tool-call-parser`
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to specify which parser to use."""
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tool_call_parser: Optional[str] = None
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"""Select the tool call parser depending on the model that you're using.
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This is used to parse the model-generated tool call into OpenAI API format.
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Required for `--enable-auto-tool-choice`. You can choose any option from
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the built-in parsers or register a plugin via `--tool-parser-plugin`."""
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tool_parser_plugin: str = ""
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"""Special the tool parser plugin write to parse the model-generated tool
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into OpenAI API format, the name register in this plugin can be used in
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`--tool-call-parser`."""
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log_config_file: Optional[str] = envs.VLLM_LOGGING_CONFIG_PATH
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"""Path to logging config JSON file for both vllm and uvicorn"""
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max_log_len: Optional[int] = None
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"""Max number of prompt characters or prompt ID numbers being printed in
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log. The default of None means unlimited."""
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disable_fastapi_docs: bool = False
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"""Disable FastAPI's OpenAPI schema, Swagger UI, and ReDoc endpoint."""
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enable_prompt_tokens_details: bool = False
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"""If set to True, enable prompt_tokens_details in usage."""
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enable_server_load_tracking: bool = False
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"""If set to True, enable tracking server_load_metrics in the app state."""
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enable_force_include_usage: bool = False
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"""If set to True, including usage on every request."""
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enable_tokenizer_info_endpoint: bool = False
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"""Enable the /get_tokenizer_info endpoint. May expose chat
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templates and other tokenizer configuration."""
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@staticmethod
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def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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from vllm.engine.arg_utils import get_kwargs
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frontend_kwargs = get_kwargs(FrontendArgs)
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# Special case: allowed_origins, allowed_methods, allowed_headers all
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# need json.loads type
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# Should also remove nargs
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frontend_kwargs["allowed_origins"]["type"] = json.loads
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frontend_kwargs["allowed_methods"]["type"] = json.loads
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frontend_kwargs["allowed_headers"]["type"] = json.loads
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del frontend_kwargs["allowed_origins"]["nargs"]
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del frontend_kwargs["allowed_methods"]["nargs"]
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del frontend_kwargs["allowed_headers"]["nargs"]
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# Special case: LoRA modules need custom parser action and
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# optional_type(str)
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frontend_kwargs["lora_modules"]["type"] = optional_type(str)
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frontend_kwargs["lora_modules"]["action"] = LoRAParserAction
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# Special case: Middleware needs append action
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frontend_kwargs["middleware"]["action"] = "append"
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frontend_kwargs["middleware"]["type"] = str
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if "nargs" in frontend_kwargs["middleware"]:
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del frontend_kwargs["middleware"]["nargs"]
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frontend_kwargs["middleware"]["default"] = []
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# Special case: Tool call parser shows built-in options.
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valid_tool_parsers = list(ToolParserManager.tool_parsers.keys())
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frontend_kwargs["tool_call_parser"]["choices"] = valid_tool_parsers
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frontend_group = parser.add_argument_group(
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title="Frontend",
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description=FrontendArgs.__doc__,
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)
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for key, value in frontend_kwargs.items():
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frontend_group.add_argument(f"--{key.replace('_', '-')}", **value)
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return parser
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def make_arg_parser(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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"""Create the CLI argument parser used by the OpenAI API server.
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We rely on the helper methods of `FrontendArgs` and `AsyncEngineArgs` to
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register all arguments instead of manually enumerating them here. This
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avoids code duplication and keeps the argument definitions in one place.
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"""
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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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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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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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parser.add_argument(
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"--config",
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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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parser = FrontendArgs.add_cli_args(parser)
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parser = AsyncEngineArgs.add_cli_args(parser)
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return parser
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def validate_parsed_serve_args(args: argparse.Namespace):
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"""Quick checks for model serve args that raise prior to loading."""
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if hasattr(args, "subparser") and args.subparser != "serve":
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return
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# Ensure that the chat template is valid; raises if it likely isn't
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validate_chat_template(args.chat_template)
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# Enable auto tool needs a tool call parser to be valid
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if args.enable_auto_tool_choice and not args.tool_call_parser:
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raise TypeError("Error: --enable-auto-tool-choice requires "
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"--tool-call-parser")
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def create_parser_for_docs() -> FlexibleArgumentParser:
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parser_for_docs = FlexibleArgumentParser(
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prog="-m vllm.entrypoints.openai.api_server")
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return make_arg_parser(parser_for_docs)
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