__init__.py

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

import contextlib
import datetime
import enum
import getpass
import importlib.util
import inspect
import json
import multiprocessing
import os
import signal
import sys
import tempfile
import textwrap
import threading
import traceback
import uuid
import warnings
import weakref
from argparse import (
    Action,
    ArgumentDefaultsHelpFormatter,
    ArgumentParser,
    ArgumentTypeError,
    RawDescriptionHelpFormatter,
    _ArgumentGroup,
)
from collections import defaultdict
from collections.abc import (
    Callable,
    Sequence,
)
from concurrent.futures.process import ProcessPoolExecutor
from functools import cache, partial, wraps
from typing import TYPE_CHECKING, Any, TypeVar

import cloudpickle
import psutil
import regex as re
import torch
import yaml

import vllm.envs as envs
from vllm.logger import enable_trace_function_call, init_logger
from vllm.ray.lazy_utils import is_in_ray_actor

_DEPRECATED_MAPPINGS = {
    "cprofile": "profiling",
    "cprofile_context": "profiling",
    "get_open_port": "network_utils",
}


def __getattr__(name: str) -> Any:  # noqa: D401 - short deprecation docstring
    """Module-level getattr to handle deprecated utilities."""
    if name in _DEPRECATED_MAPPINGS:
        submodule_name = _DEPRECATED_MAPPINGS[name]
        warnings.warn(
            f"vllm.utils.{name} is deprecated and will be removed in a future version. "
            f"Use vllm.utils.{submodule_name}.{name} instead.",
            DeprecationWarning,
            stacklevel=2,
        )
        module = __import__(f"vllm.utils.{submodule_name}", fromlist=[submodule_name])
        return getattr(module, name)
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


def __dir__() -> list[str]:
    # expose deprecated names in dir() for better UX/tab-completion
    return sorted(list(globals().keys()) + list(_DEPRECATED_MAPPINGS.keys()))


if TYPE_CHECKING:
    from argparse import Namespace

    from vllm.config import ModelConfig, VllmConfig
else:
    Namespace = object

    ModelConfig = object
    VllmConfig = object

logger = init_logger(__name__)

# This value is chosen to have a balance between ITL and TTFT. Note it is
# not optimized for throughput.
DEFAULT_MAX_NUM_BATCHED_TOKENS = 2048
POOLING_MODEL_MAX_NUM_BATCHED_TOKENS = 32768
MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS = 5120

# Constants related to forcing the attention backend selection

# String name of register which may be set in order to
# force auto-selection of attention backend by Attention
# wrapper
STR_BACKEND_ENV_VAR: str = "VLLM_ATTENTION_BACKEND"

# Possible string values of STR_BACKEND_ENV_VAR
# register, corresponding to possible backends
STR_FLASHINFER_ATTN_VAL: str = "FLASHINFER"
STR_TORCH_SDPA_ATTN_VAL: str = "TORCH_SDPA"
STR_XFORMERS_ATTN_VAL: str = "XFORMERS"
STR_FLASH_ATTN_VAL: str = "FLASH_ATTN"
STR_INVALID_VAL: str = "INVALID"


# ANSI color codes
CYAN = "\033[1;36m"
RESET = "\033[0;0m"


T = TypeVar("T")
U = TypeVar("U")


class Device(enum.Enum):
    GPU = enum.auto()
    CPU = enum.auto()


class LayerBlockType(enum.Enum):
    attention = "attention"
    mamba = "mamba"


class Counter:
    def __init__(self, start: int = 0) -> None:
        self.counter = start

    def __next__(self) -> int:
        i = self.counter
        self.counter += 1
        return i

    def reset(self) -> None:
        self.counter = 0


def random_uuid() -> str:
    return str(uuid.uuid4().hex)


def cdiv(a: int, b: int) -> int:
    """Ceiling division."""
    return -(a // -b)


def next_power_of_2(n) -> int:
    """The next power of 2 (inclusive)"""
    if n < 1:
        return 1
    return 1 << (n - 1).bit_length()


def prev_power_of_2(n: int) -> int:
    """The previous power of 2 (inclusive)"""
    if n <= 0:
        return 0
    return 1 << (n.bit_length() - 1)


def round_up(x: int, y: int) -> int:
    return ((x + y - 1) // y) * y


def round_down(x: int, y: int) -> int:
    return (x // y) * y


@cache
def is_pin_memory_available() -> bool:
    from vllm.platforms import current_platform

    return current_platform.is_pin_memory_available()


@cache
def is_uva_available() -> bool:
    """Check if Unified Virtual Addressing (UVA) is available."""
    # UVA requires pinned memory.
    # TODO: Add more requirements for UVA if needed.
    return is_pin_memory_available()


# TODO: This function can be removed if transformer_modules classes are
# serialized by value when communicating between processes
def init_cached_hf_modules() -> None:
    """
    Lazy initialization of the Hugging Face modules.
    """
    from transformers.dynamic_module_utils import init_hf_modules

    init_hf_modules()


def enable_trace_function_call_for_thread(vllm_config: VllmConfig) -> None:
    """Set up function tracing for the current thread,
    if enabled via the VLLM_TRACE_FUNCTION environment variable
    """

    if envs.VLLM_TRACE_FUNCTION:
        tmp_dir = tempfile.gettempdir()
        # add username to tmp_dir to avoid permission issues
        tmp_dir = os.path.join(tmp_dir, getpass.getuser())
        filename = (
            f"VLLM_TRACE_FUNCTION_for_process_{os.getpid()}"
            f"_thread_{threading.get_ident()}_"
            f"at_{datetime.datetime.now()}.log"
        ).replace(" ", "_")
        log_path = os.path.join(
            tmp_dir, "vllm", f"vllm-instance-{vllm_config.instance_id}", filename
        )
        os.makedirs(os.path.dirname(log_path), exist_ok=True)
        enable_trace_function_call(log_path)


def cuda_is_initialized() -> bool:
    """Check if CUDA is initialized."""
    if not torch.cuda._is_compiled():
        return False
    return torch.cuda.is_initialized()


def xpu_is_initialized() -> bool:
    """Check if XPU is initialized."""
    if not torch.xpu._is_compiled():
        return False
    return torch.xpu.is_initialized()


def cuda_get_device_properties(
    device, names: Sequence[str], init_cuda=False
) -> tuple[Any, ...]:
    """Get specified CUDA device property values without initializing CUDA in
    the current process."""
    if init_cuda or cuda_is_initialized():
        props = torch.cuda.get_device_properties(device)
        return tuple(getattr(props, name) for name in names)

    # Run in subprocess to avoid initializing CUDA as a side effect.
    mp_ctx = multiprocessing.get_context("fork")
    with ProcessPoolExecutor(max_workers=1, mp_context=mp_ctx) as executor:
        return executor.submit(cuda_get_device_properties, device, names, True).result()


def weak_bind(
    bound_method: Callable[..., Any],
) -> Callable[..., None]:
    """Make an instance method that weakly references
    its associated instance and no-ops once that
    instance is collected."""
    ref = weakref.ref(bound_method.__self__)  # type: ignore[attr-defined]
    unbound = bound_method.__func__  # type: ignore[attr-defined]

    def weak_bound(*args, **kwargs) -> None:
        if inst := ref():
            unbound(inst, *args, **kwargs)

    return weak_bound


class StoreBoolean(Action):
    def __call__(self, parser, namespace, values, option_string=None):
        if values.lower() == "true":
            setattr(namespace, self.dest, True)
        elif values.lower() == "false":
            setattr(namespace, self.dest, False)
        else:
            raise ValueError(
                f"Invalid boolean value: {values}. Expected 'true' or 'false'."
            )


class SortedHelpFormatter(ArgumentDefaultsHelpFormatter, RawDescriptionHelpFormatter):
    """SortedHelpFormatter that sorts arguments by their option strings."""

    def _split_lines(self, text, width):
        """
        1. Sentences split across lines have their single newlines removed.
        2. Paragraphs and explicit newlines are split into separate lines.
        3. Each line is wrapped to the specified width (width of terminal).
        """
        # The patterns also include whitespace after the newline
        single_newline = re.compile(r"(?<!\n)\n(?!\n)\s*")
        multiple_newlines = re.compile(r"\n{2,}\s*")
        text = single_newline.sub(" ", text)
        lines = re.split(multiple_newlines, text)
        return sum([textwrap.wrap(line, width) for line in lines], [])

    def add_arguments(self, actions):
        actions = sorted(actions, key=lambda x: x.option_strings)
        super().add_arguments(actions)


class FlexibleArgumentParser(ArgumentParser):
    """ArgumentParser that allows both underscore and dash in names."""

    _deprecated: set[Action] = set()
    _json_tip: str = (
        "When passing JSON CLI arguments, the following sets of arguments "
        "are equivalent:\n"
        '   --json-arg \'{"key1": "value1", "key2": {"key3": "value2"}}\'\n'
        "   --json-arg.key1 value1 --json-arg.key2.key3 value2\n\n"
        "Additionally, list elements can be passed individually using +:\n"
        '   --json-arg \'{"key4": ["value3", "value4", "value5"]}\'\n'
        "   --json-arg.key4+ value3 --json-arg.key4+='value4,value5'\n\n"
    )
    _search_keyword: str | None = None

    def __init__(self, *args, **kwargs):
        # Set the default "formatter_class" to SortedHelpFormatter
        if "formatter_class" not in kwargs:
            kwargs["formatter_class"] = SortedHelpFormatter
        # Pop kwarg "add_json_tip" to control whether to add the JSON tip
        self.add_json_tip = kwargs.pop("add_json_tip", True)
        super().__init__(*args, **kwargs)

    if sys.version_info < (3, 13):
        # Enable the deprecated kwarg for Python 3.12 and below

        def parse_known_args(self, args=None, namespace=None):
            if args is not None and "--disable-log-requests" in args:
                # Special case warning because the warning below won't trigger
                # if –-disable-log-requests because its value is default.
                logger.warning_once(
                    "argument '--disable-log-requests' is deprecated and "
                    "replaced with '--enable-log-requests'. This will be "
                    "removed in v0.12.0."
                )
            namespace, args = super().parse_known_args(args, namespace)
            for action in FlexibleArgumentParser._deprecated:
                if (
                    hasattr(namespace, dest := action.dest)
                    and getattr(namespace, dest) != action.default
                ):
                    logger.warning_once("argument '%s' is deprecated", dest)
            return namespace, args

        def add_argument(self, *args, **kwargs):
            deprecated = kwargs.pop("deprecated", False)
            action = super().add_argument(*args, **kwargs)
            if deprecated:
                FlexibleArgumentParser._deprecated.add(action)
            return action

        class _FlexibleArgumentGroup(_ArgumentGroup):
            def add_argument(self, *args, **kwargs):
                deprecated = kwargs.pop("deprecated", False)
                action = super().add_argument(*args, **kwargs)
                if deprecated:
                    FlexibleArgumentParser._deprecated.add(action)
                return action

        def add_argument_group(self, *args, **kwargs):
            group = self._FlexibleArgumentGroup(self, *args, **kwargs)
            self._action_groups.append(group)
            return group

    def format_help(self):
        # Only use custom help formatting for bottom level parsers
        if self._subparsers is not None:
            return super().format_help()

        formatter = self._get_formatter()

        # Handle keyword search of the args
        if (search_keyword := self._search_keyword) is not None:
            # Normalise the search keyword
            search_keyword = search_keyword.lower().replace("_", "-")
            # Return full help if searching for 'all'
            if search_keyword == "all":
                self.epilog = self._json_tip
                return super().format_help()

            # Return group help if searching for a group title
            for group in self._action_groups:
                if group.title and group.title.lower() == search_keyword:
                    formatter.start_section(group.title)
                    formatter.add_text(group.description)
                    formatter.add_arguments(group._group_actions)
                    formatter.end_section()
                    formatter.add_text(self._json_tip)
                    return formatter.format_help()

            # Return matched args if searching for an arg name
            matched_actions = []
            for group in self._action_groups:
                for action in group._group_actions:
                    # search option name
                    if any(
                        search_keyword in opt.lower() for opt in action.option_strings
                    ):
                        matched_actions.append(action)
            if matched_actions:
                formatter.start_section(f"Arguments matching '{search_keyword}'")
                formatter.add_arguments(matched_actions)
                formatter.end_section()
                formatter.add_text(self._json_tip)
                return formatter.format_help()

            # No match found
            formatter.add_text(
                f"No group or arguments matching '{search_keyword}'.\n"
                "Use '--help' to see available groups or "
                "'--help=all' to see all available parameters."
            )
            return formatter.format_help()

        # usage
        formatter.add_usage(self.usage, self._actions, self._mutually_exclusive_groups)

        # description
        formatter.add_text(self.description)

        # positionals, optionals and user-defined groups
        formatter.start_section("Config Groups")
        config_groups = ""
        for group in self._action_groups:
            if not group._group_actions:
                continue
            title = group.title
            description = group.description or ""
            config_groups += f"{title: <24}{description}\n"
        formatter.add_text(config_groups)
        formatter.end_section()

        # epilog
        formatter.add_text(self.epilog)

        # determine help from format above
        return formatter.format_help()

    def parse_args(  # type: ignore[override]
        self,
        args: list[str] | None = None,
        namespace: Namespace | None = None,
    ):
        if args is None:
            args = sys.argv[1:]

        # Check for --model in command line arguments first
        if args and args[0] == "serve":
            try:
                model_idx = next(
                    i
                    for i, arg in enumerate(args)
                    if arg == "--model" or arg.startswith("--model=")
                )
                logger.warning(
                    "With `vllm serve`, you should provide the model as a "
                    "positional argument or in a config file instead of via "
                    "the `--model` option. "
                    "The `--model` option will be removed in v0.13."
                )

                if args[model_idx] == "--model":
                    model_tag = args[model_idx + 1]
                    rest_start_idx = model_idx + 2
                else:
                    model_tag = args[model_idx].removeprefix("--model=")
                    rest_start_idx = model_idx + 1

                # Move <model> to the front, e,g:
                # [Before]
                # vllm serve -tp 2 --model <model> --enforce-eager --port 8001
                # [After]
                # vllm serve <model> -tp 2 --enforce-eager --port 8001
                args = [
                    "serve",
                    model_tag,
                    *args[1:model_idx],
                    *args[rest_start_idx:],
                ]
                print("args", args)
            except StopIteration:
                pass

        if "--config" in args:
            args = self._pull_args_from_config(args)

        def repl(match: re.Match) -> str:
            """Replaces underscores with dashes in the matched string."""
            return match.group(0).replace("_", "-")

        # Everything between the first -- and the first .
        pattern = re.compile(r"(?<=--)[^\.]*")

        # Convert underscores to dashes and vice versa in argument names
        processed_args = list[str]()
        for i, arg in enumerate(args):
            if arg.startswith("--help="):
                FlexibleArgumentParser._search_keyword = arg.split("=", 1)[-1].lower()
                processed_args.append("--help")
            elif arg.startswith("--"):
                if "=" in arg:
                    key, value = arg.split("=", 1)
                    key = pattern.sub(repl, key, count=1)
                    processed_args.append(f"{key}={value}")
                else:
                    key = pattern.sub(repl, arg, count=1)
                    processed_args.append(key)
            elif arg.startswith("-O") and arg != "-O" and arg[2] != ".":
                # allow -O flag to be used without space, e.g. -O3 or -Odecode
                # -O.<...> handled later
                # also handle -O=<mode> here
                mode = arg[3:] if arg[2] == "=" else arg[2:]
                processed_args.append(f"-O.mode={mode}")
            elif (
                arg == "-O"
                and i + 1 < len(args)
                and args[i + 1] in {"0", "1", "2", "3"}
            ):
                # Convert -O <n> to -O.mode <n>
                processed_args.append("-O.mode")
            else:
                processed_args.append(arg)

        def create_nested_dict(keys: list[str], value: str) -> dict[str, Any]:
            """Creates a nested dictionary from a list of keys and a value.

            For example, `keys = ["a", "b", "c"]` and `value = 1` will create:
            `{"a": {"b": {"c": 1}}}`
            """
            nested_dict: Any = value
            for key in reversed(keys):
                nested_dict = {key: nested_dict}
            return nested_dict

        def recursive_dict_update(
            original: dict[str, Any],
            update: dict[str, Any],
        ) -> set[str]:
            """Recursively updates a dictionary with another dictionary.
            Returns a set of duplicate keys that were overwritten.
            """
            duplicates = set[str]()
            for k, v in update.items():
                if isinstance(v, dict) and isinstance(original.get(k), dict):
                    nested_duplicates = recursive_dict_update(original[k], v)
                    duplicates |= {f"{k}.{d}" for d in nested_duplicates}
                elif isinstance(v, list) and isinstance(original.get(k), list):
                    original[k] += v
                else:
                    if k in original:
                        duplicates.add(k)
                    original[k] = v
            return duplicates

        delete = set[int]()
        dict_args = defaultdict[str, dict[str, Any]](dict)
        duplicates = set[str]()
        for i, processed_arg in enumerate(processed_args):
            if i in delete:  # skip if value from previous arg
                continue

            if processed_arg.startswith("-") and "." in processed_arg:
                if "=" in processed_arg:
                    processed_arg, value_str = processed_arg.split("=", 1)
                    if "." not in processed_arg:
                        # False positive, '.' was only in the value
                        continue
                else:
                    value_str = processed_args[i + 1]
                    delete.add(i + 1)

                if processed_arg.endswith("+"):
                    processed_arg = processed_arg[:-1]
                    value_str = json.dumps(list(value_str.split(",")))

                key, *keys = processed_arg.split(".")
                try:
                    value = json.loads(value_str)
                except json.decoder.JSONDecodeError:
                    value = value_str

                # Merge all values with the same key into a single dict
                arg_dict = create_nested_dict(keys, value)
                arg_duplicates = recursive_dict_update(dict_args[key], arg_dict)
                duplicates |= {f"{key}.{d}" for d in arg_duplicates}
                delete.add(i)
        # Filter out the dict args we set to None
        processed_args = [a for i, a in enumerate(processed_args) if i not in delete]
        if duplicates:
            logger.warning("Found duplicate keys %s", ", ".join(duplicates))

        # Add the dict args back as if they were originally passed as JSON
        for dict_arg, dict_value in dict_args.items():
            processed_args.append(dict_arg)
            processed_args.append(json.dumps(dict_value))

        return super().parse_args(processed_args, namespace)

    def check_port(self, value):
        try:
            value = int(value)
        except ValueError:
            msg = "Port must be an integer"
            raise ArgumentTypeError(msg) from None

        if not (1024 <= value <= 65535):
            raise ArgumentTypeError("Port must be between 1024 and 65535")

        return value

    def _pull_args_from_config(self, args: list[str]) -> list[str]:
        """Method to pull arguments specified in the config file
        into the command-line args variable.

        The arguments in config file will be inserted between
        the argument list.

        example:
        ```yaml
            port: 12323
            tensor-parallel-size: 4
        ```
        ```python
        $: vllm {serve,chat,complete} "facebook/opt-12B" \
            --config config.yaml -tp 2
        $: args = [
            "serve,chat,complete",
            "facebook/opt-12B",
            '--config', 'config.yaml',
            '-tp', '2'
        ]
        $: args = [
            "serve,chat,complete",
            "facebook/opt-12B",
            '--port', '12323',
            '--tensor-parallel-size', '4',
            '-tp', '2'
            ]
        ```

        Please note how the config args are inserted after the sub command.
        this way the order of priorities is maintained when these are args
        parsed by super().
        """
        assert args.count("--config") <= 1, "More than one config file specified!"

        index = args.index("--config")
        if index == len(args) - 1:
            raise ValueError(
                "No config file specified! \
                             Please check your command-line arguments."
            )

        file_path = args[index + 1]

        config_args = self.load_config_file(file_path)

        # 0th index might be the sub command {serve,chat,complete,...}
        # optionally followed by model_tag (only for serve)
        # followed by config args
        # followed by rest of cli args.
        # maintaining this order will enforce the precedence
        # of cli > config > defaults
        if args[0].startswith("-"):
            # No sub command (e.g., api_server entry point)
            args = config_args + args[0:index] + args[index + 2 :]
        elif args[0] == "serve":
            model_in_cli = len(args) > 1 and not args[1].startswith("-")
            model_in_config = any(arg == "--model" for arg in config_args)

            if not model_in_cli and not model_in_config:
                raise ValueError(
                    "No model specified! Please specify model either "
                    "as a positional argument or in a config file."
                )

            if model_in_cli:
                # Model specified as positional arg, keep CLI version
                args = (
                    [args[0]]
                    + [args[1]]
                    + config_args
                    + args[2:index]
                    + args[index + 2 :]
                )
            else:
                # No model in CLI, use config if available
                args = [args[0]] + config_args + args[1:index] + args[index + 2 :]
        else:
            args = [args[0]] + config_args + args[1:index] + args[index + 2 :]

        return args

    def load_config_file(self, file_path: str) -> list[str]:
        """Loads a yaml file and returns the key value pairs as a
        flattened list with argparse like pattern
        ```yaml
            port: 12323
            tensor-parallel-size: 4
        ```
        returns:
            processed_args: list[str] = [
                '--port': '12323',
                '--tensor-parallel-size': '4'
            ]
        """
        extension: str = file_path.split(".")[-1]
        if extension not in ("yaml", "yml"):
            raise ValueError(
                "Config file must be of a yaml/yml type.\
                              %s supplied",
                extension,
            )

        # only expecting a flat dictionary of atomic types
        processed_args: list[str] = []

        config: dict[str, int | str] = {}
        try:
            with open(file_path) as config_file:
                config = yaml.safe_load(config_file)
        except Exception as ex:
            logger.error(
                "Unable to read the config file at %s. \
                Make sure path is correct",
                file_path,
            )
            raise ex

        store_boolean_arguments = [
            action.dest for action in self._actions if isinstance(action, StoreBoolean)
        ]

        for key, value in config.items():
            if isinstance(value, bool) and key not in store_boolean_arguments:
                if value:
                    processed_args.append("--" + key)
            elif isinstance(value, list):
                if value:
                    processed_args.append("--" + key)
                    for item in value:
                        processed_args.append(str(item))
            else:
                processed_args.append("--" + key)
                processed_args.append(str(value))

        return processed_args


class AtomicCounter:
    """An atomic, thread-safe counter"""

    def __init__(self, initial=0):
        """Initialize a new atomic counter to given initial value"""
        self._value = initial
        self._lock = threading.Lock()

    def inc(self, num=1):
        """Atomically increment the counter by num and return the new value"""
        with self._lock:
            self._value += num
            return self._value

    def dec(self, num=1):
        """Atomically decrement the counter by num and return the new value"""
        with self._lock:
            self._value -= num
            return self._value

    @property
    def value(self):
        return self._value


def kill_process_tree(pid: int):
    """
    Kills all descendant processes of the given pid by sending SIGKILL.

    Args:
        pid (int): Process ID of the parent process
    """
    try:
        parent = psutil.Process(pid)
    except psutil.NoSuchProcess:
        return

    # Get all children recursively
    children = parent.children(recursive=True)

    # Send SIGKILL to all children first
    for child in children:
        with contextlib.suppress(ProcessLookupError):
            os.kill(child.pid, signal.SIGKILL)

    # Finally kill the parent
    with contextlib.suppress(ProcessLookupError):
        os.kill(pid, signal.SIGKILL)


# Adapted from: https://github.com/sgl-project/sglang/blob/v0.4.1/python/sglang/srt/utils.py#L630 # noqa: E501
def set_ulimit(target_soft_limit=65535):
    if sys.platform.startswith("win"):
        logger.info("Windows detected, skipping ulimit adjustment.")
        return

    import resource

    resource_type = resource.RLIMIT_NOFILE
    current_soft, current_hard = resource.getrlimit(resource_type)

    if current_soft < target_soft_limit:
        try:
            resource.setrlimit(resource_type, (target_soft_limit, current_hard))
        except ValueError as e:
            logger.warning(
                "Found ulimit of %s and failed to automatically increase "
                "with error %s. This can cause fd limit errors like "
                "`OSError: [Errno 24] Too many open files`. Consider "
                "increasing with ulimit -n",
                current_soft,
                e,
            )


# Adapted from: https://github.com/sgl-project/sglang/blob/v0.4.1/python/sglang/utils.py#L28 # noqa: E501
def get_exception_traceback():
    etype, value, tb = sys.exc_info()
    err_str = "".join(traceback.format_exception(etype, value, tb))
    return err_str


def _maybe_force_spawn():
    """Check if we need to force the use of the `spawn` multiprocessing start
    method.
    """
    if os.environ.get("VLLM_WORKER_MULTIPROC_METHOD") == "spawn":
        return

    reasons = []
    if is_in_ray_actor():
        # even if we choose to spawn, we need to pass the ray address
        # to the subprocess so that it knows how to connect to the ray cluster.
        # env vars are inherited by subprocesses, even if we use spawn.
        import ray

        os.environ["RAY_ADDRESS"] = ray.get_runtime_context().gcs_address
        reasons.append("In a Ray actor and can only be spawned")

    if cuda_is_initialized():
        reasons.append("CUDA is initialized")
    elif xpu_is_initialized():
        reasons.append("XPU is initialized")

    if reasons:
        logger.warning(
            "We must use the `spawn` multiprocessing start method. "
            "Overriding VLLM_WORKER_MULTIPROC_METHOD to 'spawn'. "
            "See https://docs.vllm.ai/en/latest/usage/"
            "troubleshooting.html#python-multiprocessing "
            "for more information. Reasons: %s",
            "; ".join(reasons),
        )
        os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"


def get_mp_context():
    """Get a multiprocessing context with a particular method (spawn or fork).
    By default we follow the value of the VLLM_WORKER_MULTIPROC_METHOD to
    determine the multiprocessing method (default is fork). However, under
    certain conditions, we may enforce spawn and override the value of
    VLLM_WORKER_MULTIPROC_METHOD.
    """
    _maybe_force_spawn()
    mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
    return multiprocessing.get_context(mp_method)


def bind_kv_cache(
    ctx: dict[str, Any],
    kv_cache: list[list[torch.Tensor]],  # [virtual_engine][layer_index]
    shared_kv_cache_layers: dict[str, str] | None = None,
) -> None:
    # Bind the kv_cache tensor to Attention modules, similar to
    # ctx[layer_name].kv_cache[ve]=kv_cache[ve][extract_layer_index(layer_name)]
    # Special things handled here:
    # 1. Some models have non-attention layers, e.g., Jamba
    # 2. Pipeline parallelism, each rank only has a subset of layers
    # 3. Encoder attention has no kv cache
    # 4. Encoder-decoder models, encoder-decoder attention and decoder-only
    #    attention of the same layer (e.g., bart's decoder.layers.1.self_attn
    #    and decoder.layers.1.encoder_attn) is mapped to the same kv cache
    #    tensor
    # 5. Some models have attention layers that share kv cache with previous
    #    layers, this is specified through shared_kv_cache_layers
    if shared_kv_cache_layers is None:
        shared_kv_cache_layers = {}
    from vllm.attention import AttentionType
    from vllm.model_executor.models.utils import extract_layer_index

    layer_need_kv_cache = [
        layer_name
        for layer_name in ctx
        if (
            hasattr(ctx[layer_name], "attn_type")
            and ctx[layer_name].attn_type
            in (AttentionType.DECODER, AttentionType.ENCODER_DECODER)
        )
        and ctx[layer_name].kv_sharing_target_layer_name is None
    ]
    layer_index_sorted = sorted(
        set(extract_layer_index(layer_name) for layer_name in layer_need_kv_cache)
    )
    for layer_name in layer_need_kv_cache:
        kv_cache_idx = layer_index_sorted.index(extract_layer_index(layer_name))
        forward_ctx = ctx[layer_name]
        assert len(forward_ctx.kv_cache) == len(kv_cache)
        for ve, ve_kv_cache in enumerate(kv_cache):
            forward_ctx.kv_cache[ve] = ve_kv_cache[kv_cache_idx]
    if shared_kv_cache_layers is not None:
        for layer_name, target_layer_name in shared_kv_cache_layers.items():
            assert extract_layer_index(target_layer_name) < extract_layer_index(
                layer_name
            ), "v0 doesn't support interleaving kv sharing"
            ctx[layer_name].kv_cache = ctx[target_layer_name].kv_cache


def run_method(
    obj: Any,
    method: str | bytes | Callable,
    args: tuple[Any],
    kwargs: dict[str, Any],
) -> Any:
    """
    Run a method of an object with the given arguments and keyword arguments.
    If the method is string, it will be converted to a method using getattr.
    If the method is serialized bytes and will be deserialized using
    cloudpickle.
    If the method is a callable, it will be called directly.
    """
    if isinstance(method, bytes):
        func = partial(cloudpickle.loads(method), obj)
    elif isinstance(method, str):
        try:
            func = getattr(obj, method)
        except AttributeError:
            raise NotImplementedError(
                f"Method {method!r} is not implemented."
            ) from None
    else:
        func = partial(method, obj)  # type: ignore
    return func(*args, **kwargs)


def import_pynvml():
    """
    Historical comments:

    libnvml.so is the library behind nvidia-smi, and
    pynvml is a Python wrapper around it. We use it to get GPU
    status without initializing CUDA context in the current process.
    Historically, there are two packages that provide pynvml:
    - `nvidia-ml-py` (https://pypi.org/project/nvidia-ml-py/): The official
        wrapper. It is a dependency of vLLM, and is installed when users
        install vLLM. It provides a Python module named `pynvml`.
    - `pynvml` (https://pypi.org/project/pynvml/): An unofficial wrapper.
        Prior to version 12.0, it also provides a Python module `pynvml`,
        and therefore conflicts with the official one. What's worse,
        the module is a Python package, and has higher priority than
        the official one which is a standalone Python file.
        This causes errors when both of them are installed.
        Starting from version 12.0, it migrates to a new module
        named `pynvml_utils` to avoid the conflict.
    It is so confusing that many packages in the community use the
    unofficial one by mistake, and we have to handle this case.
    For example, `nvcr.io/nvidia/pytorch:24.12-py3` uses the unofficial
    one, and it will cause errors, see the issue
    https://github.com/vllm-project/vllm/issues/12847 for example.
    After all the troubles, we decide to copy the official `pynvml`
    module to our codebase, and use it directly.
    """
    import vllm.third_party.pynvml as pynvml

    return pynvml


def warn_for_unimplemented_methods(cls: type[T]) -> type[T]:
    """
    A replacement for `abc.ABC`.
    When we use `abc.ABC`, subclasses will fail to instantiate
    if they do not implement all abstract methods.
    Here, we only require `raise NotImplementedError` in the
    base class, and log a warning if the method is not implemented
    in the subclass.
    """

    original_init = cls.__init__

    def find_unimplemented_methods(self: object):
        unimplemented_methods = []
        for attr_name in dir(self):
            # bypass inner method
            if attr_name.startswith("_"):
                continue

            try:
                attr = getattr(self, attr_name)
                # get the func of callable method
                if callable(attr):
                    attr_func = attr.__func__
            except AttributeError:
                continue
            src = inspect.getsource(attr_func)
            if "NotImplementedError" in src:
                unimplemented_methods.append(attr_name)
        if unimplemented_methods:
            method_names = ",".join(unimplemented_methods)
            msg = f"Methods {method_names} not implemented in {self}"
            logger.debug(msg)

    @wraps(original_init)
    def wrapped_init(self, *args, **kwargs) -> None:
        original_init(self, *args, **kwargs)
        find_unimplemented_methods(self)

    type.__setattr__(cls, "__init__", wrapped_init)
    return cls


# Only relevant for models using ALiBi (e.g, MPT)
def check_use_alibi(model_config: ModelConfig) -> bool:
    cfg = model_config.hf_text_config
    return (
        getattr(cfg, "alibi", False)  # Falcon
        or (
            "BloomForCausalLM" in getattr(model_config.hf_config, "architectures", [])
        )  # Bloom
        or getattr(cfg, "position_encoding_type", "") == "alibi"  # codellm_1b_alibi
        or (
            hasattr(cfg, "attn_config")  # MPT
            and (
                (
                    isinstance(cfg.attn_config, dict)
                    and cfg.attn_config.get("alibi", False)
                )
                or (
                    not isinstance(cfg.attn_config, dict)
                    and getattr(cfg.attn_config, "alibi", False)
                )
            )
        )
    )


@cache
def _has_module(module_name: str) -> bool:
    """Return True if *module_name* can be found in the current environment.

    The result is cached so that subsequent queries for the same module incur
    no additional overhead.
    """
    return importlib.util.find_spec(module_name) is not None


def has_pplx() -> bool:
    """Whether the optional `pplx_kernels` package is available."""

    return _has_module("pplx_kernels")


def has_deep_ep() -> bool:
    """Whether the optional `deep_ep` package is available."""

    return _has_module("deep_ep")


def has_deep_gemm() -> bool:
    """Whether the optional `deep_gemm` package is available."""

    return _has_module("deep_gemm")


def has_triton_kernels() -> bool:
    """Whether the optional `triton_kernels` package is available."""

    return _has_module("triton_kernels")


def has_tilelang() -> bool:
    """Whether the optional `tilelang` package is available."""

    return _has_module("tilelang")


def length_from_prompt_token_ids_or_embeds(
    prompt_token_ids: list[int] | None,
    prompt_embeds: torch.Tensor | None,
) -> int:
    """Calculate the request length (in number of tokens) give either
    prompt_token_ids or prompt_embeds.
    """
    prompt_token_len = None if prompt_token_ids is None else len(prompt_token_ids)
    prompt_embeds_len = None if prompt_embeds is None else len(prompt_embeds)

    if prompt_token_len is None:
        if prompt_embeds_len is None:
            raise ValueError("Neither prompt_token_ids nor prompt_embeds were defined.")
        return prompt_embeds_len
    else:
        if prompt_embeds_len is not None and prompt_embeds_len != prompt_token_len:
            raise ValueError(
                "Prompt token ids and prompt embeds had different lengths"
                f" prompt_token_ids={prompt_token_len}"
                f" prompt_embeds={prompt_embeds_len}"
            )
        return prompt_token_len