vllm/vllm/entrypoints/utils.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
import dataclasses
import functools
import os
from argparse import Namespace
from pathlib import Path
from typing import Any

from fastapi import Request
from fastapi.responses import JSONResponse, StreamingResponse
from starlette.background import BackgroundTask, BackgroundTasks

from vllm.config import ModelConfig
from vllm.engine.arg_utils import EngineArgs
from vllm.engine.protocol import EngineClient
from vllm.entrypoints.chat_utils import (
    load_chat_template,
    resolve_hf_chat_template,
    resolve_mistral_chat_template,
)
from vllm.entrypoints.openai.cli_args import make_arg_parser
from vllm.entrypoints.openai.protocol import (
    ChatCompletionRequest,
    CompletionRequest,
    StreamOptions,
)
from vllm.entrypoints.openai.serving_models import LoRAModulePath
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.transformers_utils.tokenizers import MistralTokenizer
from vllm.utils.argparse_utils import FlexibleArgumentParser

logger = init_logger(__name__)

VLLM_SUBCMD_PARSER_EPILOG = (
    "For full list:            vllm {subcmd} --help=all\n"
    "For a section:            vllm {subcmd} --help=ModelConfig    (case-insensitive)\n"  # noqa: E501
    "For a flag:               vllm {subcmd} --help=max-model-len  (_ or - accepted)\n"  # noqa: E501
    "Documentation:            https://docs.vllm.ai\n"
)


async def listen_for_disconnect(request: Request) -> None:
    """Returns if a disconnect message is received"""
    while True:
        message = await request.receive()
        if message["type"] == "http.disconnect":
            # If load tracking is enabled *and* the counter exists, decrement
            # it. Combines the previous nested checks into a single condition
            # to satisfy the linter rule.
            if getattr(
                request.app.state, "enable_server_load_tracking", False
            ) and hasattr(request.app.state, "server_load_metrics"):
                request.app.state.server_load_metrics -= 1
            break


def with_cancellation(handler_func):
    """Decorator that allows a route handler to be cancelled by client
    disconnections.

    This does _not_ use request.is_disconnected, which does not work with
    middleware. Instead this follows the pattern from
    starlette.StreamingResponse, which simultaneously awaits on two tasks- one
    to wait for an http disconnect message, and the other to do the work that we
    want done. When the first task finishes, the other is cancelled.

    A core assumption of this method is that the body of the request has already
    been read. This is a safe assumption to make for fastapi handlers that have
    already parsed the body of the request into a pydantic model for us.
    This decorator is unsafe to use elsewhere, as it will consume and throw away
    all incoming messages for the request while it looks for a disconnect
    message.

    In the case where a `StreamingResponse` is returned by the handler, this
    wrapper will stop listening for disconnects and instead the response object
    will start listening for disconnects.
    """

    # Functools.wraps is required for this wrapper to appear to fastapi as a
    # normal route handler, with the correct request type hinting.
    @functools.wraps(handler_func)
    async def wrapper(*args, **kwargs):
        # The request is either the second positional arg or `raw_request`
        request = args[1] if len(args) > 1 else kwargs["raw_request"]

        handler_task = asyncio.create_task(handler_func(*args, **kwargs))
        cancellation_task = asyncio.create_task(listen_for_disconnect(request))

        done, pending = await asyncio.wait(
            [handler_task, cancellation_task], return_when=asyncio.FIRST_COMPLETED
        )
        for task in pending:
            task.cancel()

        if handler_task in done:
            return handler_task.result()
        return None

    return wrapper


def decrement_server_load(request: Request):
    request.app.state.server_load_metrics -= 1


def load_aware_call(func):
    @functools.wraps(func)
    async def wrapper(*args, **kwargs):
        raw_request = kwargs.get("raw_request", args[1] if len(args) > 1 else None)

        if raw_request is None:
            raise ValueError(
                "raw_request required when server load tracking is enabled"
            )

        if not getattr(raw_request.app.state, "enable_server_load_tracking", False):
            return await func(*args, **kwargs)

        # ensure the counter exists
        if not hasattr(raw_request.app.state, "server_load_metrics"):
            raw_request.app.state.server_load_metrics = 0

        raw_request.app.state.server_load_metrics += 1
        try:
            response = await func(*args, **kwargs)
        except Exception:
            raw_request.app.state.server_load_metrics -= 1
            raise

        if isinstance(response, (JSONResponse, StreamingResponse)):
            if response.background is None:
                response.background = BackgroundTask(decrement_server_load, raw_request)
            elif isinstance(response.background, BackgroundTasks):
                response.background.add_task(decrement_server_load, raw_request)
            elif isinstance(response.background, BackgroundTask):
                # Convert the single BackgroundTask to BackgroundTasks
                # and chain the decrement_server_load task to it
                tasks = BackgroundTasks()
                tasks.add_task(
                    response.background.func,
                    *response.background.args,
                    **response.background.kwargs,
                )
                tasks.add_task(decrement_server_load, raw_request)
                response.background = tasks
        else:
            raw_request.app.state.server_load_metrics -= 1

        return response

    return wrapper


def cli_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 _validate_truncation_size(
    max_model_len: int,
    truncate_prompt_tokens: int | None,
    tokenization_kwargs: dict[str, Any] | None = None,
) -> int | None:
    if truncate_prompt_tokens is not None:
        if truncate_prompt_tokens <= -1:
            truncate_prompt_tokens = max_model_len

        if truncate_prompt_tokens > max_model_len:
            raise ValueError(
                f"truncate_prompt_tokens value ({truncate_prompt_tokens}) "
                f"is greater than max_model_len ({max_model_len})."
                f" Please, select a smaller truncation size."
            )

        if tokenization_kwargs is not None:
            tokenization_kwargs["truncation"] = True
            tokenization_kwargs["max_length"] = truncate_prompt_tokens

    else:
        if tokenization_kwargs is not None:
            tokenization_kwargs["truncation"] = False

    return truncate_prompt_tokens


def get_max_tokens(
    max_model_len: int,
    request: ChatCompletionRequest | CompletionRequest,
    input_length: int,
    default_sampling_params: dict,
) -> int:
    max_tokens = getattr(request, "max_completion_tokens", None) or request.max_tokens
    default_max_tokens = max_model_len - input_length
    max_output_tokens = current_platform.get_max_output_tokens(input_length)

    return min(
        val
        for val in (
            default_max_tokens,
            max_tokens,
            max_output_tokens,
            default_sampling_params.get("max_tokens"),
        )
        if val is not None
    )


def log_non_default_args(args: Namespace | EngineArgs):
    non_default_args = {}

    # Handle Namespace
    if isinstance(args, Namespace):
        parser = make_arg_parser(FlexibleArgumentParser())
        for arg, default in vars(parser.parse_args([])).items():
            if default != getattr(args, arg):
                non_default_args[arg] = getattr(args, arg)

    # Handle EngineArgs instance
    elif isinstance(args, EngineArgs):
        default_args = EngineArgs(model=args.model)  # Create default instance
        for field in dataclasses.fields(args):
            current_val = getattr(args, field.name)
            default_val = getattr(default_args, field.name)
            if current_val != default_val:
                non_default_args[field.name] = current_val
        if default_args.model != EngineArgs.model:
            non_default_args["model"] = default_args.model
    else:
        raise TypeError(
            "Unsupported argument type. Must be Namespace or EngineArgs instance."
        )

    logger.info("non-default args: %s", non_default_args)


def should_include_usage(
    stream_options: StreamOptions | None, enable_force_include_usage: bool
) -> tuple[bool, bool]:
    if stream_options:
        include_usage = stream_options.include_usage or enable_force_include_usage
        include_continuous_usage = include_usage and bool(
            stream_options.continuous_usage_stats
        )
    else:
        include_usage, include_continuous_usage = enable_force_include_usage, False
    return include_usage, include_continuous_usage


def process_lora_modules(
    args_lora_modules: list[LoRAModulePath], default_mm_loras: dict[str, str] | None
) -> list[LoRAModulePath]:
    lora_modules = args_lora_modules
    if default_mm_loras:
        default_mm_lora_paths = [
            LoRAModulePath(
                name=modality,
                path=lora_path,
            )
            for modality, lora_path in default_mm_loras.items()
        ]
        if args_lora_modules is None:
            lora_modules = default_mm_lora_paths
        else:
            lora_modules += default_mm_lora_paths
    return lora_modules


async def process_chat_template(
    args_chat_template: Path | str | None,
    engine_client: EngineClient,
    model_config: ModelConfig,
) -> str | None:
    resolved_chat_template = load_chat_template(args_chat_template)
    if resolved_chat_template is not None:
        # Get the tokenizer to check official template
        tokenizer = await engine_client.get_tokenizer()

        if isinstance(tokenizer, MistralTokenizer):
            # The warning is logged in resolve_mistral_chat_template.
            resolved_chat_template = resolve_mistral_chat_template(
                chat_template=resolved_chat_template
            )
        else:
            hf_chat_template = resolve_hf_chat_template(
                tokenizer=tokenizer,
                chat_template=None,
                tools=None,
                model_config=model_config,
            )

            if hf_chat_template != resolved_chat_template:
                logger.warning(
                    "Using supplied chat template: %s\n"
                    "It is different from official chat template '%s'. "
                    "This discrepancy may lead to performance degradation.",
                    resolved_chat_template,
                    model_config.model,
                )
    return resolved_chat_template