[Frontend] Move CLI code into vllm.cmd package (#12971)

docs/source/design/arch_overview.md

@@ -66,7 +66,7 @@ This server can be started using the `vllm serve` command.
 vllm serve <model>
 ```
 
-The code for the `vllm` CLI can be found in <gh-file:vllm/scripts.py>.
+The code for the `vllm` CLI can be found in <gh-file:vllm/entrypoints/cli/main.py>.
 
 Sometimes you may see the API server entrypoint used directly instead of via the
 `vllm` CLI command. For example:

setup.py

@@ -689,7 +689,7 @@ setup(
     package_data=package_data,
     entry_points={
         "console_scripts": [
-            "vllm=vllm.scripts:main",
+            "vllm=vllm.entrypoints.cli.main:main",
         ],
     },
 )

vllm/entrypoints/cli/main.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# The CLI entrypoint to vLLM.
+import os
+import signal
+import sys
+
+import vllm.entrypoints.cli.openai
+import vllm.entrypoints.cli.serve
+import vllm.version
+from vllm.logger import init_logger
+from vllm.utils import FlexibleArgumentParser
+
+logger = init_logger(__name__)
+
+CMD_MODULES = [
+    vllm.entrypoints.cli.openai,
+    vllm.entrypoints.cli.serve,
+]
+
+
+def register_signal_handlers():
+
+    def signal_handler(sig, frame):
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTSTP, signal_handler)
+
+
+def env_setup():
+    # The safest multiprocessing method is `spawn`, as the default `fork` method
+    # is not compatible with some accelerators. The default method will be
+    # changing in future versions of Python, so we should use it explicitly when
+    # possible.
+    #
+    # We only set it here in the CLI entrypoint, because changing to `spawn`
+    # could break some existing code using vLLM as a library. `spawn` will cause
+    # unexpected behavior if the code is not protected by
+    # `if __name__ == "__main__":`.
+    #
+    # References:
+    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
+    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
+    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
+    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
+    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
+        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
+        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
+
+def main():
+    env_setup()
+
+    parser = FlexibleArgumentParser(description="vLLM CLI")
+    parser.add_argument('-v',
+                        '--version',
+                        action='version',
+                        version=vllm.version.__version__)
+    subparsers = parser.add_subparsers(required=False, dest="subparser")
+    cmds = {}
+    for cmd_module in CMD_MODULES:
+        new_cmds = cmd_module.cmd_init()
+        for cmd in new_cmds:
+            cmd.subparser_init(subparsers).set_defaults(
+                dispatch_function=cmd.cmd)
+            cmds[cmd.name] = cmd
+    args = parser.parse_args()
+    if args.subparser in cmds:
+        cmds[args.subparser].validate(args)
+
+    if hasattr(args, "dispatch_function"):
+        args.dispatch_function(args)
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

vllm/entrypoints/cli/openai.py

@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: Apache-2.0
+# Commands that act as an interactive OpenAI API client
+
+import argparse
+import os
+import signal
+import sys
+from typing import List, Optional, Tuple
+
+from openai import OpenAI
+from openai.types.chat import ChatCompletionMessageParam
+
+from vllm.entrypoints.cli.types import CLISubcommand
+from vllm.utils import FlexibleArgumentParser
+
+
+def _register_signal_handlers():
+
+    def signal_handler(sig, frame):
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTSTP, signal_handler)
+
+
+def _interactive_cli(args: argparse.Namespace) -> Tuple[str, OpenAI]:
+    _register_signal_handlers()
+
+    base_url = args.url
+    api_key = args.api_key or os.environ.get("OPENAI_API_KEY", "EMPTY")
+    openai_client = OpenAI(api_key=api_key, base_url=base_url)
+
+    if args.model_name:
+        model_name = args.model_name
+    else:
+        available_models = openai_client.models.list()
+        model_name = available_models.data[0].id
+
+    print(f"Using model: {model_name}")
+
+    return model_name, openai_client
+
+
+def chat(system_prompt: Optional[str], model_name: str,
+         client: OpenAI) -> None:
+    conversation: List[ChatCompletionMessageParam] = []
+    if system_prompt is not None:
+        conversation.append({"role": "system", "content": system_prompt})
+
+    print("Please enter a message for the chat model:")
+    while True:
+        try:
+            input_message = input("> ")
+        except EOFError:
+            return
+        conversation.append({"role": "user", "content": input_message})
+
+        chat_completion = client.chat.completions.create(model=model_name,
+                                                         messages=conversation)
+
+        response_message = chat_completion.choices[0].message
+        output = response_message.content
+
+        conversation.append(response_message)  # type: ignore
+        print(output)
+
+
+def _add_query_options(
+        parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
+    parser.add_argument(
+        "--url",
+        type=str,
+        default="http://localhost:8000/v1",
+        help="url of the running OpenAI-Compatible RESTful API server")
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default=None,
+        help=("The model name used in prompt completion, default to "
+              "the first model in list models API call."))
+    parser.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help=(
+            "API key for OpenAI services. If provided, this api key "
+            "will overwrite the api key obtained through environment variables."
+        ))
+    return parser
+
+
+class ChatCommand(CLISubcommand):
+    """The `chat` subcommand for the vLLM CLI. """
+
+    def __init__(self):
+        self.name = "chat"
+        super().__init__()
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        model_name, client = _interactive_cli(args)
+        system_prompt = args.system_prompt
+        conversation: List[ChatCompletionMessageParam] = []
+        if system_prompt is not None:
+            conversation.append({"role": "system", "content": system_prompt})
+
+        print("Please enter a message for the chat model:")
+        while True:
+            try:
+                input_message = input("> ")
+            except EOFError:
+                return
+            conversation.append({"role": "user", "content": input_message})
+
+            chat_completion = client.chat.completions.create(
+                model=model_name, messages=conversation)
+
+            response_message = chat_completion.choices[0].message
+            output = response_message.content
+
+            conversation.append(response_message)  # type: ignore
+            print(output)
+
+    def subparser_init(
+            self,
+            subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
+        chat_parser = subparsers.add_parser(
+            "chat",
+            help="Generate chat completions via the running API server",
+            usage="vllm chat [options]")
+        _add_query_options(chat_parser)
+        chat_parser.add_argument(
+            "--system-prompt",
+            type=str,
+            default=None,
+            help=("The system prompt to be added to the chat template, "
+                  "used for models that support system prompts."))
+        return chat_parser
+
+
+class CompleteCommand(CLISubcommand):
+    """The `complete` subcommand for the vLLM CLI. """
+
+    def __init__(self):
+        self.name = "complete"
+        super().__init__()
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        model_name, client = _interactive_cli(args)
+        print("Please enter prompt to complete:")
+        while True:
+            input_prompt = input("> ")
+            completion = client.completions.create(model=model_name,
+                                                   prompt=input_prompt)
+            output = completion.choices[0].text
+            print(output)
+
+    def subparser_init(
+            self,
+            subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
+        complete_parser = subparsers.add_parser(
+            "complete",
+            help=("Generate text completions based on the given prompt "
+                  "via the running API server"),
+            usage="vllm complete [options]")
+        _add_query_options(complete_parser)
+        return complete_parser
+
+
+def cmd_init() -> List[CLISubcommand]:
+    return [ChatCommand(), CompleteCommand()]

vllm/entrypoints/cli/serve.py

@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+from typing import List
+
+import uvloop
+
+from vllm.engine.arg_utils import EngineArgs
+from vllm.entrypoints.cli.types import CLISubcommand
+from vllm.entrypoints.openai.api_server import run_server
+from vllm.entrypoints.openai.cli_args import (make_arg_parser,
+                                              validate_parsed_serve_args)
+from vllm.utils import FlexibleArgumentParser
+
+
+class ServeSubcommand(CLISubcommand):
+    """The `serve` subcommand for the vLLM CLI. """
+
+    def __init__(self):
+        self.name = "serve"
+        super().__init__()
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        # The default value of `--model`
+        if args.model != EngineArgs.model:
+            raise ValueError(
+                "With `vllm serve`, you should provide the model as a "
+                "positional argument instead of via the `--model` option.")
+
+        # EngineArgs expects the model name to be passed as --model.
+        args.model = args.model_tag
+
+        uvloop.run(run_server(args))
+
+    def validate(self, args: argparse.Namespace) -> None:
+        validate_parsed_serve_args(args)
+
+    def subparser_init(
+            self,
+            subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
+        serve_parser = subparsers.add_parser(
+            "serve",
+            help="Start the vLLM OpenAI Compatible API server",
+            usage="vllm serve <model_tag> [options]")
+        serve_parser.add_argument("model_tag",
+                                  type=str,
+                                  help="The model tag to serve")
+        serve_parser.add_argument(
+            "--config",
+            type=str,
+            default='',
+            required=False,
+            help="Read CLI options from a config file."
+            "Must be a YAML with the following options:"
+            "https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#cli-reference"
+        )
+
+        return make_arg_parser(serve_parser)
+
+
+def cmd_init() -> List[CLISubcommand]:
+    return [ServeSubcommand()]

vllm/entrypoints/cli/types.py

@@ -0,0 +1,24 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+
+from vllm.utils import FlexibleArgumentParser
+
+
+class CLISubcommand:
+    """Base class for CLI argument handlers."""
+
+    name: str
+
+    @staticmethod
+    def cmd(args: argparse.Namespace) -> None:
+        raise NotImplementedError("Subclasses should implement this method")
+
+    def validate(self, args: argparse.Namespace) -> None:
+        # No validation by default
+        pass
+
+    def subparser_init(
+            self,
+            subparsers: argparse._SubParsersAction) -> FlexibleArgumentParser:
+        raise NotImplementedError("Subclasses should implement this method")

vllm/entrypoints/openai/api_server.py

@@ -901,7 +901,8 @@ async def run_server(args, **uvicorn_kwargs) -> None:
 
 if __name__ == "__main__":
     # NOTE(simon):
-    # This section should be in sync with vllm/scripts.py for CLI entrypoints.
+    # This section should be in sync with vllm/entrypoints/cli/main.py for CLI
+    # entrypoints.
     parser = FlexibleArgumentParser(
         description="vLLM OpenAI-Compatible RESTful API server.")
     parser = make_arg_parser(parser)

vllm/scripts.py

@@ -1,210 +1,14 @@
 # SPDX-License-Identifier: Apache-2.0
 
-# The CLI entrypoint to vLLM.
-import argparse
-import os
-import signal
-import sys
-from typing import List, Optional
-
-import uvloop
-from openai import OpenAI
-from openai.types.chat import ChatCompletionMessageParam
-
-import vllm.version
-from vllm.engine.arg_utils import EngineArgs
-from vllm.entrypoints.openai.api_server import run_server
-from vllm.entrypoints.openai.cli_args import (make_arg_parser,
-                                              validate_parsed_serve_args)
+from vllm.entrypoints.cli.main import main as vllm_main
 from vllm.logger import init_logger
-from vllm.utils import FlexibleArgumentParser
 
 logger = init_logger(__name__)
 
 
-def register_signal_handlers():
-
-    def signal_handler(sig, frame):
-        sys.exit(0)
-
-    signal.signal(signal.SIGINT, signal_handler)
-    signal.signal(signal.SIGTSTP, signal_handler)
-
-
-def serve(args: argparse.Namespace) -> None:
-    # The default value of `--model`
-    if args.model != EngineArgs.model:
-        raise ValueError(
-            "With `vllm serve`, you should provide the model as a "
-            "positional argument instead of via the `--model` option.")
-
-    # EngineArgs expects the model name to be passed as --model.
-    args.model = args.model_tag
-
-    uvloop.run(run_server(args))
-
-
-def interactive_cli(args: argparse.Namespace) -> None:
-    register_signal_handlers()
-
-    base_url = args.url
-    api_key = args.api_key or os.environ.get("OPENAI_API_KEY", "EMPTY")
-    openai_client = OpenAI(api_key=api_key, base_url=base_url)
-
-    if args.model_name:
-        model_name = args.model_name
-    else:
-        available_models = openai_client.models.list()
-        model_name = available_models.data[0].id
-
-    print(f"Using model: {model_name}")
-
-    if args.command == "complete":
-        complete(model_name, openai_client)
-    elif args.command == "chat":
-        chat(args.system_prompt, model_name, openai_client)
-
-
-def complete(model_name: str, client: OpenAI) -> None:
-    print("Please enter prompt to complete:")
-    while True:
-        input_prompt = input("> ")
-
-        completion = client.completions.create(model=model_name,
-                                               prompt=input_prompt)
-        output = completion.choices[0].text
-        print(output)
-
-
-def chat(system_prompt: Optional[str], model_name: str,
-         client: OpenAI) -> None:
-    conversation: List[ChatCompletionMessageParam] = []
-    if system_prompt is not None:
-        conversation.append({"role": "system", "content": system_prompt})
-
-    print("Please enter a message for the chat model:")
-    while True:
-        input_message = input("> ")
-        conversation.append({"role": "user", "content": input_message})
-
-        chat_completion = client.chat.completions.create(model=model_name,
-                                                         messages=conversation)
-
-        response_message = chat_completion.choices[0].message
-        output = response_message.content
-
-        conversation.append(response_message)  # type: ignore
-        print(output)
-
-
-def _add_query_options(
-        parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
-    parser.add_argument(
-        "--url",
-        type=str,
-        default="http://localhost:8000/v1",
-        help="url of the running OpenAI-Compatible RESTful API server")
-    parser.add_argument(
-        "--model-name",
-        type=str,
-        default=None,
-        help=("The model name used in prompt completion, default to "
-              "the first model in list models API call."))
-    parser.add_argument(
-        "--api-key",
-        type=str,
-        default=None,
-        help=(
-            "API key for OpenAI services. If provided, this api key "
-            "will overwrite the api key obtained through environment variables."
-        ))
-    return parser
-
-
-def env_setup():
-    # The safest multiprocessing method is `spawn`, as the default `fork` method
-    # is not compatible with some accelerators. The default method will be
-    # changing in future versions of Python, so we should use it explicitly when
-    # possible.
-    #
-    # We only set it here in the CLI entrypoint, because changing to `spawn`
-    # could break some existing code using vLLM as a library. `spawn` will cause
-    # unexpected behavior if the code is not protected by
-    # `if __name__ == "__main__":`.
-    #
-    # References:
-    # - https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
-    # - https://pytorch.org/docs/stable/notes/multiprocessing.html#cuda-in-multiprocessing
-    # - https://pytorch.org/docs/stable/multiprocessing.html#sharing-cuda-tensors
-    # - https://docs.habana.ai/en/latest/PyTorch/Getting_Started_with_PyTorch_and_Gaudi/Getting_Started_with_PyTorch.html?highlight=multiprocessing#torch-multiprocessing-for-dataloaders
-    if "VLLM_WORKER_MULTIPROC_METHOD" not in os.environ:
-        logger.debug("Setting VLLM_WORKER_MULTIPROC_METHOD to 'spawn'")
-        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
-
+# Backwards compatibility for the move from vllm.scripts to
+# vllm.entrypoints.cli.main
 def main():
-    env_setup()
-
-    parser = FlexibleArgumentParser(description="vLLM CLI")
-    parser.add_argument('-v',
-                        '--version',
-                        action='version',
-                        version=vllm.version.__version__)
-
-    subparsers = parser.add_subparsers(required=True, dest="subparser")
-
-    serve_parser = subparsers.add_parser(
-        "serve",
-        help="Start the vLLM OpenAI Compatible API server",
-        usage="vllm serve <model_tag> [options]")
-    serve_parser.add_argument("model_tag",
-                              type=str,
-                              help="The model tag to serve")
-    serve_parser.add_argument(
-        "--config",
-        type=str,
-        default='',
-        required=False,
-        help="Read CLI options from a config file."
-        "Must be a YAML with the following options:"
-        "https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#cli-reference"
-    )
-
-    serve_parser = make_arg_parser(serve_parser)
-    serve_parser.set_defaults(dispatch_function=serve)
-
-    complete_parser = subparsers.add_parser(
-        "complete",
-        help=("Generate text completions based on the given prompt "
-              "via the running API server"),
-        usage="vllm complete [options]")
-    _add_query_options(complete_parser)
-    complete_parser.set_defaults(dispatch_function=interactive_cli,
-                                 command="complete")
-
-    chat_parser = subparsers.add_parser(
-        "chat",
-        help="Generate chat completions via the running API server",
-        usage="vllm chat [options]")
-    _add_query_options(chat_parser)
-    chat_parser.add_argument(
-        "--system-prompt",
-        type=str,
-        default=None,
-        help=("The system prompt to be added to the chat template, "
-              "used for models that support system prompts."))
-    chat_parser.set_defaults(dispatch_function=interactive_cli, command="chat")
-
-    args = parser.parse_args()
-    if args.subparser == "serve":
-        validate_parsed_serve_args(args)
-
-    # One of the sub commands should be executed.
-    if hasattr(args, "dispatch_function"):
-        args.dispatch_function(args)
-    else:
-        parser.print_help()
-
-
-if __name__ == "__main__":
-    main()
+    logger.warning("vllm.scripts.main() is deprecated. Please re-install "
+                   "vllm or use vllm.entrypoints.cli.main.main() instead.")
+    vllm_main()