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arg_utils.py 79.9 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import argparse
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import copy
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import dataclasses
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import functools
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import json
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import sys
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from dataclasses import MISSING, dataclass, fields, is_dataclass
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from itertools import permutations
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from typing import (
    TYPE_CHECKING,
    Annotated,
    Any,
    Callable,
    Literal,
    Optional,
    TypeVar,
    Union,
    cast,
    get_args,
    get_origin,
)
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import huggingface_hub
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import regex as re
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import torch
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from pydantic import TypeAdapter, ValidationError
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from pydantic.fields import FieldInfo
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from typing_extensions import TypeIs, deprecated
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import vllm.envs as envs
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from vllm.config import (
    CacheConfig,
    CompilationConfig,
    ConfigType,
    DeviceConfig,
    EPLBConfig,
    KVEventsConfig,
    KVTransferConfig,
    LoadConfig,
    LoRAConfig,
    ModelConfig,
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    MultiModalConfig,
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    ObservabilityConfig,
    ParallelConfig,
    PoolerConfig,
    SchedulerConfig,
    SpeculativeConfig,
    StructuredOutputsConfig,
    VllmConfig,
    get_attr_docs,
)
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from vllm.config.cache import BlockSize, CacheDType, MambaDType, PrefixCachingHashAlgo
from vllm.config.device import Device
from vllm.config.model import (
    ConvertOption,
    HfOverrides,
    LogprobsMode,
    ModelDType,
    RunnerOption,
    TaskOption,
    TokenizerMode,
)
from vllm.config.multimodal import MMCacheType, MMEncoderTPMode
from vllm.config.observability import DetailedTraceModules
from vllm.config.parallel import DistributedExecutorBackend, ExpertPlacementStrategy
from vllm.config.scheduler import SchedulerPolicy
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from vllm.config.utils import get_field
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from vllm.logger import init_logger
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from vllm.platforms import CpuArchEnum, current_platform
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from vllm.plugins import load_general_plugins
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from vllm.ray.lazy_utils import is_ray_initialized
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from vllm.reasoning import ReasoningParserManager
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from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
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from vllm.transformers_utils.config import (
    get_model_path,
    is_interleaved,
    maybe_override_with_speculators,
)
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.utils import FlexibleArgumentParser, GiB_bytes, get_ip, is_in_ray_actor
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from vllm.v1.sample.logits_processor import LogitsProcessor
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if TYPE_CHECKING:
    from vllm.executor.executor_base import ExecutorBase
    from vllm.model_executor.layers.quantization import QuantizationMethods
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    from vllm.model_executor.model_loader import LoadFormats
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    from vllm.usage.usage_lib import UsageContext
else:
    ExecutorBase = Any
    QuantizationMethods = Any
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    LoadFormats = Any
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    UsageContext = Any

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logger = init_logger(__name__)

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# object is used to allow for special typing forms
T = TypeVar("T")
TypeHint = Union[type[Any], object]
TypeHintT = Union[type[T], object]

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def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
    def _parse_type(val: str) -> T:
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        try:
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
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                f"Value {val} cannot be converted to {return_type}."
            ) from e
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    return _parse_type


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def optional_type(return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:
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    def _optional_type(val: str) -> Optional[T]:
        if val == "" or val == "None":
            return None
        return parse_type(return_type)(val)

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    return _optional_type
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def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
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    if not re.match(r"(?s)^\s*{.*}\s*$", val):
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        return str(val)
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    return optional_type(json.loads)(val)
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def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]:
    """Check if the type hint is a specific type."""
    return type_hint is type or get_origin(type_hint) is type


def contains_type(type_hints: set[TypeHint], type: TypeHintT) -> bool:
    """Check if the type hints contain a specific type."""
    return any(is_type(type_hint, type) for type_hint in type_hints)


def get_type(type_hints: set[TypeHint], type: TypeHintT) -> TypeHintT:
    """Get the specific type from the type hints."""
    return next((th for th in type_hints if is_type(th, type)), None)


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def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
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    """Get the `type` and `choices` from a `Literal` type hint in `type_hints`.

    If `type_hints` also contains `str`, we use `metavar` instead of `choices`.
    """
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    type_hint = get_type(type_hints, Literal)
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    options = get_args(type_hint)
    option_type = type(options[0])
    if not all(isinstance(option, option_type) for option in options):
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        raise ValueError(
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            "All options must be of the same type. "
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            f"Got {options} with types {[type(c) for c in options]}"
        )
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    kwarg = "metavar" if contains_type(type_hints, str) else "choices"
    return {"type": option_type, kwarg: sorted(options)}
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def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


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def get_type_hints(type_hint: TypeHint) -> set[TypeHint]:
    """Extract type hints from Annotated or Union type hints."""
    type_hints: set[TypeHint] = set()
    origin = get_origin(type_hint)
    args = get_args(type_hint)

    if origin is Annotated:
        type_hints.update(get_type_hints(args[0]))
    elif origin is Union:
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints


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def is_online_quantization(quantization: Any) -> bool:
    return quantization in ["inc"]


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NEEDS_HELP = (
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    any("--help" in arg for arg in sys.argv)  # vllm SUBCOMMAND --help
    or (argv0 := sys.argv[0]).endswith("mkdocs")  # mkdocs SUBCOMMAND
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    or argv0.endswith("mkdocs/__main__.py")  # python -m mkdocs SUBCOMMAND
)


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@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
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    # Save time only getting attr docs if we're generating help text
    cls_docs = get_attr_docs(cls) if NEEDS_HELP else {}
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    kwargs = {}
    for field in fields(cls):
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        # Get the set of possible types for the field
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        type_hints: set[TypeHint] = get_type_hints(field.type)
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        # If the field is a dataclass, we can use the model_validate_json
        generator = (th for th in type_hints if is_dataclass(th))
        dataclass_cls = next(generator, None)

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        # Get the default value of the field
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        if field.default is not MISSING:
            default = field.default
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            # Handle pydantic.Field defaults
            if isinstance(default, FieldInfo):
                default = (
                    default.default
                    if default.default_factory is None
                    else default.default_factory()
                )
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        elif field.default_factory is not MISSING:
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            default = field.default_factory()
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        # Get the help text for the field
        name = field.name
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        help = cls_docs.get(name, "").strip()
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        # Escape % for argparse
        help = help.replace("%", "%%")

        # Initialise the kwargs dictionary for the field
        kwargs[name] = {"default": default, "help": help}

        # Set other kwargs based on the type hints
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        json_tip = (
            "Should either be a valid JSON string or JSON keys passed individually."
        )
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        if dataclass_cls is not None:
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            def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
                try:
                    return TypeAdapter(cls).validate_json(val)
                except ValidationError as e:
                    raise argparse.ArgumentTypeError(repr(e)) from e

            kwargs[name]["type"] = parse_dataclass
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            kwargs[name]["help"] += f"\n\n{json_tip}"
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        elif contains_type(type_hints, bool):
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            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
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            kwargs[name].update(literal_to_kwargs(type_hints))
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        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
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                f"type. Got {types}."
            )
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            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
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            list_type = types[0]
            if get_origin(list_type) is Union:
                msg = "List type must contain str if it is a Union."
                assert str in get_args(list_type), msg
                list_type = str
            kwargs[name]["type"] = list_type
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            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
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            # Special case for large integers
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            human_readable_ints = {
                "max_model_len",
                "max_num_batched_tokens",
                "kv_cache_memory_bytes",
            }
            if name in human_readable_ints:
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                kwargs[name]["type"] = human_readable_int
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                kwargs[name]["help"] += f"\n\n{human_readable_int.__doc__}"
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        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
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        elif contains_type(type_hints, dict) and (
            contains_type(type_hints, str)
            or any(is_not_builtin(th) for th in type_hints)
        ):
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            kwargs[name]["type"] = union_dict_and_str
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        elif contains_type(type_hints, dict):
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            kwargs[name]["type"] = parse_type(json.loads)
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            kwargs[name]["help"] += f"\n\n{json_tip}"
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        elif contains_type(type_hints, str) or any(
            is_not_builtin(th) for th in type_hints
        ):
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            kwargs[name]["type"] = str
        else:
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            raise ValueError(f"Unsupported type {type_hints} for argument {name}.")
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        # If the type hint was a sequence of literals, use the helper function
        # to update the type and choices
        if get_origin(kwargs[name].get("type")) is Literal:
            kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]}))

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        # If None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs
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def get_kwargs(cls: ConfigType) -> dict[str, Any]:
    """Return argparse kwargs for the given Config dataclass.

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    If `--help` or `mkdocs` are not present in the command line command, the
    attribute documentation will not be included in the help output.

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    The heavy computation is cached via functools.lru_cache, and a deep copy
    is returned so callers can mutate the dictionary without affecting the
    cached version.
    """
    return copy.deepcopy(_compute_kwargs(cls))


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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = ModelConfig.model
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    served_model_name: Optional[Union[str, list[str]]] = ModelConfig.served_model_name
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    tokenizer: Optional[str] = ModelConfig.tokenizer
    hf_config_path: Optional[str] = ModelConfig.hf_config_path
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    runner: RunnerOption = ModelConfig.runner
    convert: ConvertOption = ModelConfig.convert
    task: Optional[TaskOption] = ModelConfig.task
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    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
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    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
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    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
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    allowed_media_domains: Optional[list[str]] = ModelConfig.allowed_media_domains
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    download_dir: Optional[str] = LoadConfig.download_dir
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    safetensors_load_strategy: str = LoadConfig.safetensors_load_strategy
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    load_format: Union[str, LoadFormats] = LoadConfig.load_format
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    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
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    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
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    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
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    cuda_graph_sizes: list[int] = get_field(SchedulerConfig, "cuda_graph_sizes")
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    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
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    distributed_executor_backend: Optional[
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        Union[str, DistributedExecutorBackend, type[ExecutorBase]]
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    ] = ParallelConfig.distributed_executor_backend
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    # number of P/D disaggregation (or other disaggregation) workers
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    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
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    decode_context_parallel_size: int = ParallelConfig.decode_context_parallel_size
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    data_parallel_size: int = ParallelConfig.data_parallel_size
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    data_parallel_rank: Optional[int] = None
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    data_parallel_start_rank: Optional[int] = None
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    data_parallel_size_local: Optional[int] = None
    data_parallel_address: Optional[str] = None
    data_parallel_rpc_port: Optional[int] = None
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    data_parallel_hybrid_lb: bool = False
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    data_parallel_backend: str = ParallelConfig.data_parallel_backend
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    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
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    enable_dbo: bool = ParallelConfig.enable_dbo
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    dbo_decode_token_threshold: int = ParallelConfig.dbo_decode_token_threshold
    dbo_prefill_token_threshold: int = ParallelConfig.dbo_prefill_token_threshold
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    disable_nccl_for_dp_synchronization: bool = (
        ParallelConfig.disable_nccl_for_dp_synchronization
    )
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    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
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    enable_eplb: bool = ParallelConfig.enable_eplb
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    expert_placement_strategy: ExpertPlacementStrategy = (
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        ParallelConfig.expert_placement_strategy
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    )
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    _api_process_count: int = ParallelConfig._api_process_count
    _api_process_rank: int = ParallelConfig._api_process_rank
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    num_redundant_experts: int = EPLBConfig.num_redundant_experts
    eplb_window_size: int = EPLBConfig.window_size
    eplb_step_interval: int = EPLBConfig.step_interval
    eplb_log_balancedness: bool = EPLBConfig.log_balancedness
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    max_parallel_loading_workers: Optional[int] = (
        ParallelConfig.max_parallel_loading_workers
    )
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    block_size: Optional[BlockSize] = CacheConfig.block_size
    enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
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    prefix_caching_hash_algo: PrefixCachingHashAlgo = (
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        CacheConfig.prefix_caching_hash_algo
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    )
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    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
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    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
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    kv_cache_memory_bytes: Optional[int] = CacheConfig.kv_cache_memory_bytes
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    max_num_batched_tokens: Optional[int] = SchedulerConfig.max_num_batched_tokens
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    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
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    long_prefill_token_threshold: int = SchedulerConfig.long_prefill_token_threshold
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    max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
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    max_logprobs: int = ModelConfig.max_logprobs
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    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
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    disable_log_stats: bool = False
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    revision: Optional[str] = ModelConfig.revision
    code_revision: Optional[str] = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: Optional[float] = ModelConfig.rope_theta
    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
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    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
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    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
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    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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    limit_mm_per_prompt: dict[str, Union[int, dict[str, int]]] = get_field(
        MultiModalConfig, "limit_per_prompt"
    )
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    interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings
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    media_io_kwargs: dict[str, dict[str, Any]] = get_field(
        MultiModalConfig, "media_io_kwargs"
    )
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    mm_processor_kwargs: Optional[dict[str, Any]] = MultiModalConfig.mm_processor_kwargs
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    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
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    mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
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    mm_processor_cache_type: Optional[MMCacheType] = (
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        MultiModalConfig.mm_processor_cache_type
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    )
    mm_shm_cache_max_object_size_mb: int = (
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        MultiModalConfig.mm_shm_cache_max_object_size_mb
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    )
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    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
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    io_processor_plugin: Optional[str] = None
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    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
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    video_pruning_rate: float = MultiModalConfig.video_pruning_rate
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    # LoRA fields
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    enable_lora: bool = False
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    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
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    default_mm_loras: Optional[dict[str, str]] = LoRAConfig.default_mm_loras
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    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size

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    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
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    num_gpu_blocks_override: Optional[int] = CacheConfig.num_gpu_blocks_override
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    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
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    model_loader_extra_config: dict = get_field(LoadConfig, "model_loader_extra_config")
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    ignore_patterns: Optional[Union[str, list[str]]] = LoadConfig.ignore_patterns
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    enable_chunked_prefill: Optional[bool] = SchedulerConfig.enable_chunked_prefill
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    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
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    disable_hybrid_kv_cache_manager: bool = (
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        SchedulerConfig.disable_hybrid_kv_cache_manager
    )
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    structured_outputs_config: StructuredOutputsConfig = get_field(
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        VllmConfig, "structured_outputs_config"
    )
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    reasoning_parser: str = StructuredOutputsConfig.reasoning_parser
    # Deprecated guided decoding fields
    guided_decoding_backend: Optional[str] = None
    guided_decoding_disable_fallback: Optional[bool] = None
    guided_decoding_disable_any_whitespace: Optional[bool] = None
    guided_decoding_disable_additional_properties: Optional[bool] = None

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    logits_processor_pattern: Optional[str] = ModelConfig.logits_processor_pattern
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    speculative_config: Optional[dict[str, Any]] = None
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    show_hidden_metrics_for_version: Optional[str] = (
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        ObservabilityConfig.show_hidden_metrics_for_version
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    )
    otlp_traces_endpoint: Optional[str] = ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = (
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        ObservabilityConfig.collect_detailed_traces
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    )
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    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
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    scheduler_cls: Union[str, type[object]] = SchedulerConfig.scheduler_cls
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    pooler_config: Optional[PoolerConfig] = ModelConfig.pooler_config
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    override_pooler_config: Optional[Union[dict, PoolerConfig]] = (
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        ModelConfig.override_pooler_config
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    )
    compilation_config: CompilationConfig = get_field(VllmConfig, "compilation_config")
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    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
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    kv_transfer_config: Optional[KVTransferConfig] = None
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    kv_events_config: Optional[KVEventsConfig] = None
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    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
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    override_generation_config: dict[str, Any] = get_field(
        ModelConfig, "override_generation_config"
    )
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    model_impl: str = ModelConfig.model_impl
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    override_attention_dtype: str = ModelConfig.override_attention_dtype
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    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
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    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
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    additional_config: dict[str, Any] = get_field(VllmConfig, "additional_config")
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    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
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    pt_load_map_location: str = LoadConfig.pt_load_map_location
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    # DEPRECATED
    enable_multimodal_encoder_data_parallel: bool = False
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    logits_processors: Optional[list[Union[str, type[LogitsProcessor]]]] = (
        ModelConfig.logits_processors
    )
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    """Custom logitproc types"""

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    async_scheduling: bool = SchedulerConfig.async_scheduling

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    kv_sharing_fast_prefill: bool = CacheConfig.kv_sharing_fast_prefill
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    def __post_init__(self):
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        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
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        if isinstance(self.compilation_config, dict):
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            self.compilation_config = CompilationConfig(**self.compilation_config)
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        if isinstance(self.eplb_config, dict):
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            self.eplb_config = EPLBConfig(**self.eplb_config)
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
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        load_general_plugins()
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        # when use hf offline,replace model id to local model path
        if huggingface_hub.constants.HF_HUB_OFFLINE:
            model_id = self.model
            self.model = get_model_path(self.model, self.revision)
            logger.info(
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                "HF_HUB_OFFLINE is True, replace model_id [%s] to model_path [%s]",
                model_id,
                self.model,
            )
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
Woosuk Kwon's avatar
Woosuk Kwon committed
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        """Shared CLI arguments for vLLM engine."""
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        # Model arguments
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        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
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        if not ("serve" in sys.argv[1:] and "--help" in sys.argv[1:]):
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            model_group.add_argument("--model", **model_kwargs["model"])
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        model_group.add_argument("--runner", **model_kwargs["runner"])
        model_group.add_argument("--convert", **model_kwargs["convert"])
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        model_group.add_argument("--task", **model_kwargs["task"], deprecated=True)
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        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
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        model_group.add_argument("--tokenizer-mode", **model_kwargs["tokenizer_mode"])
        model_group.add_argument(
            "--trust-remote-code", **model_kwargs["trust_remote_code"]
        )
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        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
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        model_group.add_argument("--hf-config-path", **model_kwargs["hf_config_path"])
        model_group.add_argument(
            "--allowed-local-media-path", **model_kwargs["allowed_local_media_path"]
        )
        model_group.add_argument(
            "--allowed-media-domains", **model_kwargs["allowed_media_domains"]
        )
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        model_group.add_argument("--revision", **model_kwargs["revision"])
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        model_group.add_argument("--code-revision", **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling", **model_kwargs["rope_scaling"])
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        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
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        model_group.add_argument(
            "--tokenizer-revision", **model_kwargs["tokenizer_revision"]
        )
        model_group.add_argument("--max-model-len", **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q", **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager", **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-logprobs", **model_kwargs["max_logprobs"])
        model_group.add_argument("--logprobs-mode", **model_kwargs["logprobs_mode"])
        model_group.add_argument(
            "--disable-sliding-window", **model_kwargs["disable_sliding_window"]
        )
        model_group.add_argument(
            "--disable-cascade-attn", **model_kwargs["disable_cascade_attn"]
        )
        model_group.add_argument(
            "--skip-tokenizer-init", **model_kwargs["skip_tokenizer_init"]
        )
        model_group.add_argument(
            "--enable-prompt-embeds", **model_kwargs["enable_prompt_embeds"]
        )
        model_group.add_argument(
            "--served-model-name", **model_kwargs["served_model_name"]
        )
        model_group.add_argument("--config-format", **model_kwargs["config_format"])
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        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
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        model_group.add_argument(
            "--hf-token",
            type=str,
            nargs="?",
            const=True,
            default=model_kwargs["hf_token"]["default"],
            help=model_kwargs["hf_token"]["help"],
        )
        model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"])
        model_group.add_argument("--pooler-config", **model_kwargs["pooler_config"])
        model_group.add_argument(
            "--override-pooler-config",
            **model_kwargs["override_pooler_config"],
            deprecated=True,
        )
        model_group.add_argument(
            "--logits-processor-pattern", **model_kwargs["logits_processor_pattern"]
        )
        model_group.add_argument(
            "--generation-config", **model_kwargs["generation_config"]
        )
        model_group.add_argument(
            "--override-generation-config", **model_kwargs["override_generation_config"]
        )
        model_group.add_argument(
            "--enable-sleep-mode", **model_kwargs["enable_sleep_mode"]
        )
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        model_group.add_argument("--model-impl", **model_kwargs["model_impl"])
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        model_group.add_argument(
            "--override-attention-dtype", **model_kwargs["override_attention_dtype"]
        )
        model_group.add_argument(
            "--logits-processors", **model_kwargs["logits_processors"]
        )
        model_group.add_argument(
            "--io-processor-plugin", **model_kwargs["io_processor_plugin"]
        )
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        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
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        load_group.add_argument("--load-format", **load_kwargs["load_format"])
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        load_group.add_argument("--download-dir", **load_kwargs["download_dir"])
        load_group.add_argument(
            "--safetensors-load-strategy", **load_kwargs["safetensors_load_strategy"]
        )
        load_group.add_argument(
            "--model-loader-extra-config", **load_kwargs["model_loader_extra_config"]
        )
        load_group.add_argument("--ignore-patterns", **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load", **load_kwargs["use_tqdm_on_load"])
        load_group.add_argument(
            "--pt-load-map-location", **load_kwargs["pt_load_map_location"]
        )
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        # Structured outputs arguments
        structured_outputs_kwargs = get_kwargs(StructuredOutputsConfig)
        structured_outputs_group = parser.add_argument_group(
            title="StructuredOutputsConfig",
            description=StructuredOutputsConfig.__doc__,
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        )
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        structured_outputs_group.add_argument(
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            "--reasoning-parser",
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            # This choice is a special case because it's not static
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            choices=list(ReasoningParserManager.reasoning_parsers),
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            **structured_outputs_kwargs["reasoning_parser"],
        )
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        # Deprecated guided decoding arguments
        for arg, type in [
            ("--guided-decoding-backend", str),
            ("--guided-decoding-disable-fallback", bool),
            ("--guided-decoding-disable-any-whitespace", bool),
            ("--guided-decoding-disable-additional-properties", bool),
        ]:
            structured_outputs_group.add_argument(
                arg,
                type=type,
                help=(f"[DEPRECATED] {arg} will be removed in v0.12.0."),
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                deprecated=True,
            )
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        # Parallel arguments
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        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
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            "--distributed-executor-backend",
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            **parallel_kwargs["distributed_executor_backend"],
        )
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        parallel_group.add_argument(
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            "--pipeline-parallel-size",
            "-pp",
            **parallel_kwargs["pipeline_parallel_size"],
        )
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        parallel_group.add_argument(
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            "--tensor-parallel-size", "-tp", **parallel_kwargs["tensor_parallel_size"]
        )
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        parallel_group.add_argument(
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            "--decode-context-parallel-size",
            "-dcp",
            **parallel_kwargs["decode_context_parallel_size"],
        )
        parallel_group.add_argument(
            "--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]
        )
        parallel_group.add_argument(
            "--data-parallel-rank",
            "-dpn",
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            type=int,
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            help="Data parallel rank of this instance. "
            "When set, enables external load balancer mode.",
        )
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        parallel_group.add_argument(
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            "--data-parallel-start-rank",
            "-dpr",
            type=int,
            help="Starting data parallel rank for secondary nodes.",
        )
        parallel_group.add_argument(
            "--data-parallel-size-local",
            "-dpl",
            type=int,
            help="Number of data parallel replicas to run on this node.",
        )
        parallel_group.add_argument(
            "--data-parallel-address",
            "-dpa",
            type=str,
            help="Address of data parallel cluster head-node.",
        )
        parallel_group.add_argument(
            "--data-parallel-rpc-port",
            "-dpp",
            type=int,
            help="Port for data parallel RPC communication.",
        )
        parallel_group.add_argument(
            "--data-parallel-backend",
            "-dpb",
            type=str,
            default="mp",
            help='Backend for data parallel, either "mp" or "ray".',
        )
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        parallel_group.add_argument(
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            "--data-parallel-hybrid-lb", **parallel_kwargs["data_parallel_hybrid_lb"]
        )
        parallel_group.add_argument(
            "--enable-expert-parallel", **parallel_kwargs["enable_expert_parallel"]
        )
        parallel_group.add_argument("--enable-dbo", **parallel_kwargs["enable_dbo"])
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        parallel_group.add_argument(
            "--dbo-decode-token-threshold",
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            **parallel_kwargs["dbo_decode_token_threshold"],
        )
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        parallel_group.add_argument(
            "--dbo-prefill-token-threshold",
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            **parallel_kwargs["dbo_prefill_token_threshold"],
        )
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        parallel_group.add_argument(
            "--disable-nccl-for-dp-synchronization",
            **parallel_kwargs["disable_nccl_for_dp_synchronization"],
        )
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        parallel_group.add_argument("--enable-eplb", **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--eplb-config", **parallel_kwargs["eplb_config"])
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        parallel_group.add_argument(
            "--expert-placement-strategy",
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            **parallel_kwargs["expert_placement_strategy"],
        )
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        parallel_group.add_argument(
            "--num-redundant-experts",
            type=int,
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            help="[DEPRECATED] --num-redundant-experts will be removed in v0.12.0.",
            deprecated=True,
        )
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        parallel_group.add_argument(
            "--eplb-window-size",
            type=int,
            help="[DEPRECATED] --eplb-window-size will be removed in v0.12.0.",
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            deprecated=True,
        )
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        parallel_group.add_argument(
            "--eplb-step-interval",
            type=int,
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            help="[DEPRECATED] --eplb-step-interval will be removed in v0.12.0.",
            deprecated=True,
        )
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        parallel_group.add_argument(
            "--eplb-log-balancedness",
            action=argparse.BooleanOptionalAction,
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            help="[DEPRECATED] --eplb-log-balancedness will be removed in v0.12.0.",
            deprecated=True,
        )
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        parallel_group.add_argument(
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            "--max-parallel-loading-workers",
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            **parallel_kwargs["max_parallel_loading_workers"],
        )
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        parallel_group.add_argument(
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            "--ray-workers-use-nsight", **parallel_kwargs["ray_workers_use_nsight"]
        )
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        parallel_group.add_argument(
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            "--disable-custom-all-reduce",
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            **parallel_kwargs["disable_custom_all_reduce"],
        )
        parallel_group.add_argument("--worker-cls", **parallel_kwargs["worker_cls"])
        parallel_group.add_argument(
            "--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]
        )
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        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
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            action="store_true",
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            deprecated=True,
        )
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        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
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        )
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        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
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        cache_group.add_argument(
            "--gpu-memory-utilization", **cache_kwargs["gpu_memory_utilization"]
        )
        cache_group.add_argument(
            "--kv-cache-memory-bytes", **cache_kwargs["kv_cache_memory_bytes"]
        )
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        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
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        cache_group.add_argument("--kv-cache-dtype", **cache_kwargs["cache_dtype"])
        cache_group.add_argument(
            "--num-gpu-blocks-override", **cache_kwargs["num_gpu_blocks_override"]
        )
        cache_group.add_argument(
            "--enable-prefix-caching", **cache_kwargs["enable_prefix_caching"]
        )
        cache_group.add_argument(
            "--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]
        )
        cache_group.add_argument("--cpu-offload-gb", **cache_kwargs["cpu_offload_gb"])
        cache_group.add_argument(
            "--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]
        )
        cache_group.add_argument(
            "--kv-sharing-fast-prefill", **cache_kwargs["kv_sharing_fast_prefill"]
        )
        cache_group.add_argument(
            "--mamba-cache-dtype", **cache_kwargs["mamba_cache_dtype"]
        )
        cache_group.add_argument(
            "--mamba-ssm-cache-dtype", **cache_kwargs["mamba_ssm_cache_dtype"]
        )
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        # Multimodal related configs
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        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
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        multimodal_group.add_argument(
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            "--limit-mm-per-prompt", **multimodal_kwargs["limit_per_prompt"]
        )
        multimodal_group.add_argument(
            "--media-io-kwargs", **multimodal_kwargs["media_io_kwargs"]
        )
        multimodal_group.add_argument(
            "--mm-processor-kwargs", **multimodal_kwargs["mm_processor_kwargs"]
        )
        multimodal_group.add_argument(
            "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]
        )
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        multimodal_group.add_argument(
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            "--disable-mm-preprocessor-cache", action="store_true", deprecated=True
        )
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        multimodal_group.add_argument(
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            "--mm-processor-cache-type", **multimodal_kwargs["mm_processor_cache_type"]
        )
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        multimodal_group.add_argument(
            "--mm-shm-cache-max-object-size-mb",
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            **multimodal_kwargs["mm_shm_cache_max_object_size_mb"],
        )
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        multimodal_group.add_argument(
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            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"]
        )
        multimodal_group.add_argument(
            "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]
        )
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        multimodal_group.add_argument(
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            "--skip-mm-profiling", **multimodal_kwargs["skip_mm_profiling"]
        )
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        multimodal_group.add_argument(
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            "--video-pruning-rate", **multimodal_kwargs["video_pruning_rate"]
        )
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        # LoRA related configs
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        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
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            "--enable-lora",
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            action=argparse.BooleanOptionalAction,
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            help="If True, enable handling of LoRA adapters.",
        )
        lora_group.add_argument("--enable-lora-bias", **lora_kwargs["bias_enabled"])
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        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
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        lora_group.add_argument("--max-lora-rank", **lora_kwargs["max_lora_rank"])
        lora_group.add_argument(
            "--lora-extra-vocab-size", **lora_kwargs["lora_extra_vocab_size"]
        )
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        lora_group.add_argument(
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            "--lora-dtype",
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            **lora_kwargs["lora_dtype"],
        )
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        lora_group.add_argument("--max-cpu-loras", **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument(
            "--fully-sharded-loras", **lora_kwargs["fully_sharded_loras"]
        )
        lora_group.add_argument("--default-mm-loras", **lora_kwargs["default_mm_loras"])
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        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
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            **observability_kwargs["show_hidden_metrics_for_version"],
        )
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        observability_group.add_argument(
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            "--otlp-traces-endpoint", **observability_kwargs["otlp_traces_endpoint"]
        )
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        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
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            ",".join(p) for p in permutations(get_args(DetailedTraceModules), r=2)
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        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
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            **observability_kwargs["collect_detailed_traces"],
        )
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        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
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            "--max-num-batched-tokens", **scheduler_kwargs["max_num_batched_tokens"]
        )
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        scheduler_group.add_argument(
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            "--max-num-seqs", **scheduler_kwargs["max_num_seqs"]
        )
        scheduler_group.add_argument(
            "--max-num-partial-prefills", **scheduler_kwargs["max_num_partial_prefills"]
        )
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        scheduler_group.add_argument(
            "--max-long-partial-prefills",
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            **scheduler_kwargs["max_long_partial_prefills"],
        )
        scheduler_group.add_argument(
            "--cuda-graph-sizes", **scheduler_kwargs["cuda_graph_sizes"]
        )
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        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
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            **scheduler_kwargs["long_prefill_token_threshold"],
        )
        scheduler_group.add_argument(
            "--num-lookahead-slots", **scheduler_kwargs["num_lookahead_slots"]
        )
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        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
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        scheduler_group.add_argument(
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            "--scheduling-policy", **scheduler_kwargs["policy"]
        )
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        scheduler_group.add_argument(
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            "--enable-chunked-prefill", **scheduler_kwargs["enable_chunked_prefill"]
        )
        scheduler_group.add_argument(
            "--disable-chunked-mm-input", **scheduler_kwargs["disable_chunked_mm_input"]
        )
        scheduler_group.add_argument(
            "--scheduler-cls", **scheduler_kwargs["scheduler_cls"]
        )
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        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
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            **scheduler_kwargs["disable_hybrid_kv_cache_manager"],
        )
        scheduler_group.add_argument(
            "--async-scheduling", **scheduler_kwargs["async_scheduling"]
        )
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        # vLLM arguments
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        vllm_kwargs = get_kwargs(VllmConfig)
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        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
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        # We construct SpeculativeConfig using fields from other configs in
        # create_engine_config. So we set the type to a JSON string here to
        # delay the Pydantic validation that comes with SpeculativeConfig.
        vllm_kwargs["speculative_config"]["type"] = optional_type(json.loads)
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        vllm_group.add_argument(
            "--speculative-config", **vllm_kwargs["speculative_config"]
        )
        vllm_group.add_argument(
            "--kv-transfer-config", **vllm_kwargs["kv_transfer_config"]
        )
        vllm_group.add_argument("--kv-events-config", **vllm_kwargs["kv_events_config"])
        vllm_group.add_argument(
            "--compilation-config", "-O", **vllm_kwargs["compilation_config"]
        )
        vllm_group.add_argument(
            "--additional-config", **vllm_kwargs["additional_config"]
        )
        vllm_group.add_argument(
            "--structured-outputs-config", **vllm_kwargs["structured_outputs_config"]
        )
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        # Other arguments
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        parser.add_argument(
            "--disable-log-stats",
            action="store_true",
            help="Disable logging statistics.",
        )
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        return parser
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    @classmethod
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    def from_cli_args(cls, args: argparse.Namespace):
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        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
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        engine_args = cls(
            **{attr: getattr(args, attr) for attr in attrs if hasattr(args, attr)}
        )
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        return engine_args
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    def create_model_config(self) -> ModelConfig:
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        # gguf file needs a specific model loader and doesn't use hf_repo
        if check_gguf_file(self.model):
            self.quantization = self.load_format = "gguf"

        # NOTE: This is to allow model loading from S3 in CI
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        if (
            not isinstance(self, AsyncEngineArgs)
            and envs.VLLM_CI_USE_S3
            and self.model in MODELS_ON_S3
            and self.load_format == "auto"
        ):
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            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"

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        if self.disable_mm_preprocessor_cache:
            logger.warning(
                "`--disable-mm-preprocessor-cache` is deprecated "
                "and will be removed in v0.13. "
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                "Please use `--mm-processor-cache-gb 0` instead.",
            )
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            self.mm_processor_cache_gb = 0
        elif envs.VLLM_MM_INPUT_CACHE_GIB != 4:
            logger.warning(
                "VLLM_MM_INPUT_CACHE_GIB` is deprecated "
                "and will be removed in v0.13. "
                "Please use `--mm-processor-cache-gb %d` instead.",
                envs.VLLM_MM_INPUT_CACHE_GIB,
            )

            self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB

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        if self.enable_multimodal_encoder_data_parallel:
            logger.warning(
                "--enable-multimodal-encoder-data-parallel` is deprecated "
                "and will be removed in v0.13. "
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                "Please use `--mm-encoder-tp-mode data` instead."
            )
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            self.mm_encoder_tp_mode = "data"

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        return ModelConfig(
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            model=self.model,
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            hf_config_path=self.hf_config_path,
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            runner=self.runner,
            convert=self.convert,
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            task=self.task,
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            tokenizer=self.tokenizer,
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            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
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            allowed_local_media_path=self.allowed_local_media_path,
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            allowed_media_domains=self.allowed_media_domains,
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            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
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            rope_theta=self.rope_theta,
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            hf_token=self.hf_token,
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            hf_overrides=self.hf_overrides,
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            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_logprobs=self.max_logprobs,
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            logprobs_mode=self.logprobs_mode,
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            disable_sliding_window=self.disable_sliding_window,
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            disable_cascade_attn=self.disable_cascade_attn,
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            skip_tokenizer_init=self.skip_tokenizer_init,
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            enable_prompt_embeds=self.enable_prompt_embeds,
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            served_model_name=self.served_model_name,
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            limit_mm_per_prompt=self.limit_mm_per_prompt,
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            interleave_mm_strings=self.interleave_mm_strings,
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            media_io_kwargs=self.media_io_kwargs,
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            skip_mm_profiling=self.skip_mm_profiling,
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            config_format=self.config_format,
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            mm_processor_kwargs=self.mm_processor_kwargs,
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            mm_processor_cache_gb=self.mm_processor_cache_gb,
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            mm_processor_cache_type=self.mm_processor_cache_type,
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            mm_shm_cache_max_object_size_mb=self.mm_shm_cache_max_object_size_mb,
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            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
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            pooler_config=self.pooler_config,
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            override_pooler_config=self.override_pooler_config,
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            logits_processor_pattern=self.logits_processor_pattern,
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            generation_config=self.generation_config,
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            override_generation_config=self.override_generation_config,
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            enable_sleep_mode=self.enable_sleep_mode,
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            model_impl=self.model_impl,
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            override_attention_dtype=self.override_attention_dtype,
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            logits_processors=self.logits_processors,
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            video_pruning_rate=self.video_pruning_rate,
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            io_processor_plugin=self.io_processor_plugin,
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        )
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    def validate_tensorizer_args(self):
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        from vllm.model_executor.model_loader.tensorizer import TensorizerConfig

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        for key in self.model_loader_extra_config:
            if key in TensorizerConfig._fields:
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                self.model_loader_extra_config["tensorizer_config"][key] = (
                    self.model_loader_extra_config[key]
                )
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    def create_load_config(self) -> LoadConfig:
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        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
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        if self.load_format == "tensorizer":
            if hasattr(self.model_loader_extra_config, "to_serializable"):
                self.model_loader_extra_config = (
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                    self.model_loader_extra_config.to_serializable()
                )
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            self.model_loader_extra_config["tensorizer_config"] = {}
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            self.model_loader_extra_config["tensorizer_config"]["tensorizer_dir"] = (
                self.model
            )
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            self.validate_tensorizer_args()
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        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
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            safetensors_load_strategy=self.safetensors_load_strategy,
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            device="cpu" if is_online_quantization(self.quantization) else None,
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            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
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            use_tqdm_on_load=self.use_tqdm_on_load,
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            pt_load_map_location=self.pt_load_map_location,
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        )
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    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
    ) -> Optional["SpeculativeConfig"]:
        """Initializes and returns a SpeculativeConfig object based on
        `speculative_config`.

        This function utilizes `speculative_config` to create a
        SpeculativeConfig object. The `speculative_config` can either be
        provided as a JSON string input via CLI arguments or directly as a
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        dictionary from the engine.
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        """
        if self.speculative_config is None:
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            return None
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        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
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        self.speculative_config.update(
            {
                "target_model_config": target_model_config,
                "target_parallel_config": target_parallel_config,
                "enable_chunked_prefill": enable_chunked_prefill,
                "disable_log_stats": disable_log_stats,
            }
        )
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        return SpeculativeConfig(**self.speculative_config)
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    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
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        headless: bool = False,
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    ) -> VllmConfig:
        """
        Create the VllmConfig.

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.
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        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
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        current_platform.pre_register_and_update()
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        device_config = DeviceConfig(device=cast(Device, current_platform.device_type))
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        model_config = self.create_model_config()
        self.model = model_config.model
        self.tokenizer = model_config.tokenizer

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        (self.model, self.tokenizer, self.speculative_config) = (
            maybe_override_with_speculators(
                model=self.model,
                tokenizer=self.tokenizer,
                revision=self.revision,
                trust_remote_code=self.trust_remote_code,
                vllm_speculative_config=self.speculative_config,
            )
        )
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        # * If VLLM_USE_V1 is unset, we enable V1 for "supported features"
        #   and fall back to V0 for experimental or unsupported features.
        # * If VLLM_USE_V1=1, we enable V1 for supported + experimental
        #   features and raise error for unsupported features.
        # * If VLLM_USE_V1=0, we disable V1.
        use_v1 = False
        try_v1 = envs.VLLM_USE_V1 or not envs.is_set("VLLM_USE_V1")
        if try_v1 and self._is_v1_supported_oracle(model_config):
            use_v1 = True

        # If user explicitly set VLLM_USE_V1, sanity check we respect it.
        if envs.is_set("VLLM_USE_V1"):
            assert use_v1 == envs.VLLM_USE_V1
        # Otherwise, set the VLLM_USE_V1 variable globally.
        else:
            envs.set_vllm_use_v1(use_v1)

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        # Set default arguments for V1 Engine.
        self._set_default_args(usage_context, model_config)
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        # Disable chunked prefill for POWER (ppc64le)/ARM/s390x/RISCV CPUs in V1
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        if current_platform.is_cpu() and current_platform.get_cpu_architecture() in (
            CpuArchEnum.POWERPC,
            CpuArchEnum.S390X,
            CpuArchEnum.ARM,
            CpuArchEnum.RISCV,
        ):
            logger.info(
                "Chunked prefill is not supported for ARM and POWER, "
                "S390X and RISC-V CPUs; "
                "disabling it for V1 backend."
            )
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            self.enable_chunked_prefill = False
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        assert self.enable_chunked_prefill is not None

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        sliding_window: Optional[int] = None
        if not is_interleaved(model_config.hf_text_config):
            # Only set CacheConfig.sliding_window if the model is all sliding
            # window. Otherwise CacheConfig.sliding_window will override the
            # global layers in interleaved sliding window models.
            sliding_window = model_config.get_sliding_window()

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        # Note(hc): In the current implementation of decode context
        # parallel(DCP), tp_size needs to be divisible by dcp_size,
        # because the world size does not change by dcp, it simply
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        # reuses the GPUs of TP group, and split one TP group into
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        # tp_size//dcp_size DCP groups.
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        assert self.tensor_parallel_size % self.decode_context_parallel_size == 0, (
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            f"tp_size={self.tensor_parallel_size} must be divisible by"
            f"dcp_size={self.decode_context_parallel_size}."
        )

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        cache_config = CacheConfig(
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            block_size=self.block_size,
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            gpu_memory_utilization=self.gpu_memory_utilization,
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            kv_cache_memory_bytes=self.kv_cache_memory_bytes,
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            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
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            is_attention_free=model_config.is_attention_free,
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            num_gpu_blocks_override=self.num_gpu_blocks_override,
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            sliding_window=sliding_window,
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            enable_prefix_caching=self.enable_prefix_caching,
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            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
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            cpu_offload_gb=self.cpu_offload_gb,
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            calculate_kv_scales=self.calculate_kv_scales,
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            kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
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            mamba_cache_dtype=self.mamba_cache_dtype,
            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
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        )
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        ray_runtime_env = None
        if is_ray_initialized():
            # Ray Serve LLM calls `create_engine_config` in the context
            # of a Ray task, therefore we check is_ray_initialized()
            # as opposed to is_in_ray_actor().
            import ray
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            ray_runtime_env = ray.get_runtime_context().runtime_env
            logger.info("Using ray runtime env: %s", ray_runtime_env)

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        # Get the current placement group if Ray is initialized and
        # we are in a Ray actor. If so, then the placement group will be
        # passed to spawned processes.
        placement_group = None
        if is_in_ray_actor():
            import ray

            # This call initializes Ray automatically if it is not initialized,
            # but we should not do this here.
            placement_group = ray.util.get_current_placement_group()

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        assert not headless or not self.data_parallel_hybrid_lb, (
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            "data_parallel_hybrid_lb is not applicable in headless mode"
        )
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        data_parallel_external_lb = self.data_parallel_rank is not None
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        # Local DP rank = 1, use pure-external LB.
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        if data_parallel_external_lb:
            assert self.data_parallel_size_local in (1, None), (
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                "data_parallel_size_local must be 1 when data_parallel_rank is set"
            )
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            data_parallel_size_local = 1
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            # Use full external lb if we have local_size of 1.
            self.data_parallel_hybrid_lb = False
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        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local
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            if self.data_parallel_start_rank and not headless:
                # Infer hybrid LB mode.
                self.data_parallel_hybrid_lb = True

            if self.data_parallel_hybrid_lb and data_parallel_size_local == 1:
                # Use full external lb if we have local_size of 1.
                data_parallel_external_lb = True
                self.data_parallel_hybrid_lb = False

            if data_parallel_size_local == self.data_parallel_size:
                # Disable hybrid LB mode if set for a single node
                self.data_parallel_hybrid_lb = False

            self.data_parallel_rank = self.data_parallel_start_rank or 0
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        else:
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            assert not self.data_parallel_hybrid_lb, (
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                "data_parallel_size_local must be set to use data_parallel_hybrid_lb."
            )
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            # Local DP size defaults to global DP size if not set.
            data_parallel_size_local = self.data_parallel_size
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        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
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        if self.data_parallel_address is None:
            if self.data_parallel_backend == "ray":
                host_ip = get_ip()
                logger.info(
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                    "Using host IP %s as ray-based data parallel address", host_ip
                )
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                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
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                    self.data_parallel_backend,
                )
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                data_parallel_address = ParallelConfig.data_parallel_master_ip
        else:
            data_parallel_address = self.data_parallel_address
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        # This port is only used when there are remote data parallel engines,
        # otherwise the local IPC transport is used.
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        data_parallel_rpc_port = (
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            self.data_parallel_rpc_port
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            if (self.data_parallel_rpc_port is not None)
            else ParallelConfig.data_parallel_rpc_port
        )
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        if self.async_scheduling:
            # Async scheduling does not work with the uniprocess backend.
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "mp"
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                logger.info(
                    "Defaulting to mp-based distributed executor "
                    "backend for async scheduling."
                )
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            if self.pipeline_parallel_size > 1:
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                raise ValueError(
                    "Async scheduling is not supported with pipeline-parallel-size > 1."
                )
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            # Currently, async scheduling does not support speculative decoding.
            # TODO(woosuk): Support it.
            if self.speculative_config is not None:
                raise ValueError(
                    "Currently, speculative decoding is not supported with "
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                    "async scheduling."
                )
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        # Forward the deprecated CLI args to the EPLB config.
        if self.num_redundant_experts is not None:
            self.eplb_config.num_redundant_experts = self.num_redundant_experts
        if self.eplb_window_size is not None:
            self.eplb_config.window_size = self.eplb_window_size
        if self.eplb_step_interval is not None:
            self.eplb_config.step_interval = self.eplb_step_interval
        if self.eplb_log_balancedness is not None:
            self.eplb_config.log_balancedness = self.eplb_log_balancedness

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        parallel_config = ParallelConfig(
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            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
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            data_parallel_size=self.data_parallel_size,
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            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
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            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
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            data_parallel_backend=self.data_parallel_backend,
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            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
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            enable_expert_parallel=self.enable_expert_parallel,
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            enable_dbo=self.enable_dbo,
            dbo_decode_token_threshold=self.dbo_decode_token_threshold,
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            dbo_prefill_token_threshold=self.dbo_prefill_token_threshold,
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            disable_nccl_for_dp_synchronization=self.disable_nccl_for_dp_synchronization,
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            enable_eplb=self.enable_eplb,
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            eplb_config=self.eplb_config,
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            expert_placement_strategy=self.expert_placement_strategy,
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            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            ray_workers_use_nsight=self.ray_workers_use_nsight,
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            ray_runtime_env=ray_runtime_env,
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            placement_group=placement_group,
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            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
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            worker_extension_cls=self.worker_extension_cls,
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            decode_context_parallel_size=self.decode_context_parallel_size,
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            _api_process_count=self._api_process_count,
            _api_process_rank=self._api_process_rank,
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        )
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        speculative_config = self.create_speculative_config(
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            target_model_config=model_config,
            target_parallel_config=parallel_config,
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            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_log_stats=self.disable_log_stats,
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        )

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        # make sure num_lookahead_slots is set appropriately depending on
        # whether speculative decoding is enabled
        num_lookahead_slots = self.num_lookahead_slots
        if speculative_config is not None:
            num_lookahead_slots = speculative_config.num_lookahead_slots
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        scheduler_config = SchedulerConfig(
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            runner_type=model_config.runner_type,
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            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
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            cuda_graph_sizes=self.cuda_graph_sizes,
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            num_lookahead_slots=num_lookahead_slots,
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            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_chunked_mm_input=self.disable_chunked_mm_input,
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            is_multimodal_model=model_config.is_multimodal_model,
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            is_encoder_decoder=model_config.is_encoder_decoder,
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            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER and parallel_config.use_ray),
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            policy=self.scheduling_policy,
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            scheduler_cls=self.scheduler_cls,
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            max_num_partial_prefills=self.max_num_partial_prefills,
            max_long_partial_prefills=self.max_long_partial_prefills,
            long_prefill_token_threshold=self.long_prefill_token_threshold,
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            disable_hybrid_kv_cache_manager=self.disable_hybrid_kv_cache_manager,
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            async_scheduling=self.async_scheduling,
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        )
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        if not model_config.is_multimodal_model and self.default_mm_loras:
            raise ValueError(
                "Default modality-specific LoRA(s) were provided for a "
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                "non multimodal model"
            )

        lora_config = (
            LoRAConfig(
                bias_enabled=self.enable_lora_bias,
                max_lora_rank=self.max_lora_rank,
                max_loras=self.max_loras,
                default_mm_loras=self.default_mm_loras,
                fully_sharded_loras=self.fully_sharded_loras,
                lora_extra_vocab_size=self.lora_extra_vocab_size,
                lora_dtype=self.lora_dtype,
                max_cpu_loras=self.max_cpu_loras
                if self.max_cpu_loras and self.max_cpu_loras > 0
                else None,
            )
            if self.enable_lora
            else None
        )
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        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

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        load_config = self.create_load_config()
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        # Pass reasoning_parser into StructuredOutputsConfig
        if self.reasoning_parser:
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            self.structured_outputs_config.reasoning_parser = self.reasoning_parser
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        # Forward the deprecated CLI args to the StructuredOutputsConfig
        so_config = self.structured_outputs_config
        if self.guided_decoding_backend is not None:
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            so_config.guided_decoding_backend = self.guided_decoding_backend
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        if self.guided_decoding_disable_fallback is not None:
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            so_config.guided_decoding_disable_fallback = (
                self.guided_decoding_disable_fallback
            )
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        if self.guided_decoding_disable_any_whitespace is not None:
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            so_config.guided_decoding_disable_any_whitespace = (
                self.guided_decoding_disable_any_whitespace
            )
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        if self.guided_decoding_disable_additional_properties is not None:
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            so_config.guided_decoding_disable_additional_properties = (
                self.guided_decoding_disable_additional_properties
            )
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        observability_config = ObservabilityConfig(
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            show_hidden_metrics_for_version=(self.show_hidden_metrics_for_version),
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            otlp_traces_endpoint=self.otlp_traces_endpoint,
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            collect_detailed_traces=self.collect_detailed_traces,
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        )
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        config = VllmConfig(
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            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
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            structured_outputs_config=self.structured_outputs_config,
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            observability_config=observability_config,
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            compilation_config=self.compilation_config,
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            kv_transfer_config=self.kv_transfer_config,
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            kv_events_config=self.kv_events_config,
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            additional_config=self.additional_config,
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        )
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        return config

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    def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
        """Oracle for whether to use V0 or V1 Engine by default."""

        #############################################################
        # Unsupported Feature Flags on V1.

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        if self.logits_processor_pattern != EngineArgs.logits_processor_pattern:
            _raise_or_fallback(
                feature_name="--logits-processor-pattern", recommend_to_remove=False
            )
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            return False

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        # No Mamba or Encoder-Decoder so far.
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        if not model_config.is_v1_compatible:
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            _raise_or_fallback(
                feature_name=model_config.architectures, recommend_to_remove=False
            )
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            return False

        # No Concurrent Partial Prefills so far.
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        if (
            self.max_num_partial_prefills != SchedulerConfig.max_num_partial_prefills
            or self.max_long_partial_prefills
            != SchedulerConfig.max_long_partial_prefills
        ):
            _raise_or_fallback(
                feature_name="Concurrent Partial Prefill", recommend_to_remove=False
            )
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            return False

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        # V1 supports N-gram, Medusa, and Eagle speculative decoding.
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        if self.speculative_config is not None:
            # speculative_config could still be a dict at this point
            if isinstance(self.speculative_config, dict):
                method = self.speculative_config.get("method", None)
            else:
                method = self.speculative_config.method

            if method == "draft_model":
                raise NotImplementedError(
                    "Draft model speculative decoding is not supported yet. "
                    "Please consider using other speculative decoding methods "
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                    "such as ngram, medusa, eagle, or mtp."
                )
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        V1_BACKENDS = [
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            "FLASH_ATTN",
            "PALLAS",
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            "TRITON_ATTN",
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            "TRITON_MLA",
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            "CUTLASS_MLA",
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            "FLASHMLA",
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            "FLASH_ATTN_MLA",
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            "FLASHINFER",
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            "FLASHINFER_MLA",
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            "ROCM_AITER_MLA",
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            "TORCH_SDPA",
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            "FLEX_ATTENTION",
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            "TREE_ATTN",
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            "XFORMERS",
            "ROCM_ATTN",
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            "ROCM_AITER_UNIFIED_ATTN",
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        ]
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        if (
            envs.is_set("VLLM_ATTENTION_BACKEND")
            and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS
        ):
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            name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
            _raise_or_fallback(feature_name=name, recommend_to_remove=True)
            return False

        #############################################################
        # Experimental Features - allow users to opt in.

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        if self.pipeline_parallel_size > 1:
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            supports_pp = getattr(
                self.distributed_executor_backend, "supports_pp", False
            )
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            if not supports_pp and self.distributed_executor_backend not in (
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                ParallelConfig.distributed_executor_backend,
                "ray",
                "mp",
                "external_launcher",
            ):
                name = (
                    "Pipeline Parallelism without Ray distributed "
                    "executor or multiprocessing executor or external "
                    "launcher"
                )
                _raise_or_fallback(feature_name=name, recommend_to_remove=False)
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                return False
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        if current_platform.is_cpu() and model_config.get_sliding_window() is not None:
            _raise_or_fallback(
                feature_name="sliding window (CPU backend)", recommend_to_remove=False
            )
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            return False

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        #############################################################

        return True

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    def _set_default_args(
        self, usage_context: UsageContext, model_config: ModelConfig
    ) -> None:
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        """Set Default Arguments for V1 Engine."""
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        # V1 always uses chunked prefills and prefix caching
        # for non-pooling tasks.
        # For pooling tasks the default is False
        if model_config.runner_type != "pooling":
            self.enable_chunked_prefill = True
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            # TODO: When prefix caching supports prompt embeds inputs, this
            # check can be removed.
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            if self.enable_prompt_embeds and self.enable_prefix_caching is not False:
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                logger.warning(
                    "--enable-prompt-embeds and --enable-prefix-caching "
                    "are not supported together in V1. Prefix caching has "
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                    "been disabled."
                )
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                self.enable_prefix_caching = False

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            if self.enable_prefix_caching is None:
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                # Disable prefix caching default for hybrid models
                # since the feature is still experimental.
                if model_config.is_hybrid:
                    self.enable_prefix_caching = False
                else:
                    self.enable_prefix_caching = True
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        else:
            pooling_type = model_config.pooler_config.pooling_type
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            is_causal = getattr(model_config.hf_config, "is_causal", True)
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            incremental_prefill_supported = (
                pooling_type is not None
                and pooling_type.lower() == "last"
                and is_causal
            )
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            action = "Enabling" if incremental_prefill_supported else "Disabling"
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            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = incremental_prefill_supported
                logger.info("(%s) chunked prefill by default", action)
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = incremental_prefill_supported
                logger.info("(%s) prefix caching by default", action)

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        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
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            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
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        # When no user override, set the default values based on the usage
        # context.
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        # Use different default values for different hardware.
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        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
        try:
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            device_memory = current_platform.get_device_total_memory()
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            device_name = current_platform.get_device_name().lower()
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        except Exception:
            # This is only used to set default_max_num_batched_tokens
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            device_memory = 0
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        # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
        # throughput, see PR #17885 for more details.
        # So here we do an extra device name check to prevent such regression.
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        from vllm.usage.usage_lib import UsageContext
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        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
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            # For GPUs like H100 and MI300x, use larger default values.
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            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
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            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
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        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
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            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }
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        # tpu specific default values.
        if current_platform.is_tpu():
            default_max_num_batched_tokens_tpu = {
                UsageContext.LLM_CLASS: {
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                    "V6E": 2048,
                    "V5E": 1024,
                    "V5P": 512,
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                },
                UsageContext.OPENAI_API_SERVER: {
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                    "V6E": 1024,
                    "V5E": 512,
                    "V5P": 256,
                },
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            }

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        # cpu specific default values.
        if current_platform.is_cpu():
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            world_size = self.pipeline_parallel_size * self.tensor_parallel_size
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            default_max_num_batched_tokens = {
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                UsageContext.LLM_CLASS: 4096 * world_size,
                UsageContext.OPENAI_API_SERVER: 2048 * world_size,
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            }
            default_max_num_seqs = {
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                UsageContext.LLM_CLASS: 256 * world_size,
                UsageContext.OPENAI_API_SERVER: 128 * world_size,
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            }

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        use_context_value = usage_context.value if usage_context else None
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        if (
            self.max_num_batched_tokens is None
            and usage_context in default_max_num_batched_tokens
        ):
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            if current_platform.is_tpu():
                chip_name = current_platform.get_device_name()
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                if chip_name in default_max_num_batched_tokens_tpu[usage_context]:
                    self.max_num_batched_tokens = default_max_num_batched_tokens_tpu[
                        usage_context
                    ][chip_name]
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                else:
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                    self.max_num_batched_tokens = default_max_num_batched_tokens[
                        usage_context
                    ]
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            else:
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                if not self.enable_chunked_prefill:
                    self.max_num_batched_tokens = model_config.max_model_len
                else:
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                    self.max_num_batched_tokens = default_max_num_batched_tokens[
                        usage_context
                    ]
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            logger.debug(
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                "Setting max_num_batched_tokens to %d for %s usage context.",
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                self.max_num_batched_tokens,
                use_context_value,
            )
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        if self.max_num_seqs is None and usage_context in default_max_num_seqs:
            self.max_num_seqs = min(
                default_max_num_seqs[usage_context],
                self.max_num_batched_tokens or sys.maxsize,
            )
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            logger.debug(
                "Setting max_num_seqs to %d for %s usage context.",
                self.max_num_seqs,
                use_context_value,
            )
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@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
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class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
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    """Arguments for asynchronous vLLM engine."""
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    enable_log_requests: bool = False

    @property
    @deprecated(
        "`disable_log_requests` is deprecated and has been replaced with "
        "`enable_log_requests`. This will be removed in v0.12.0. Please use "
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        "`enable_log_requests` instead."
    )
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    def disable_log_requests(self) -> bool:
        return not self.enable_log_requests

    @disable_log_requests.setter
    @deprecated(
        "`disable_log_requests` is deprecated and has been replaced with "
        "`enable_log_requests`. This will be removed in v0.12.0. Please use "
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        "`enable_log_requests` instead."
    )
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    def disable_log_requests(self, value: bool):
        self.enable_log_requests = not value
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    @staticmethod
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    def add_cli_args(
        parser: FlexibleArgumentParser, async_args_only: bool = False
    ) -> FlexibleArgumentParser:
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        # Initialize plugin to update the parser, for example, The plugin may
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        # add a new kind of quantization method to --quantization argument or
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        # a new device to --device argument.
        load_general_plugins()
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        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
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        parser.add_argument(
            "--enable-log-requests",
            action=argparse.BooleanOptionalAction,
            default=AsyncEngineArgs.enable_log_requests,
            help="Enable logging requests.",
        )
        parser.add_argument(
            "--disable-log-requests",
            action=argparse.BooleanOptionalAction,
            default=not AsyncEngineArgs.enable_log_requests,
            help="[DEPRECATED] Disable logging requests.",
            deprecated=True,
        )
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        current_platform.pre_register_and_update(parser)
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        return parser
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def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        raise NotImplementedError(
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            f"VLLM_USE_V1=1 is not supported with {feature_name}."
        )
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    msg = f"{feature_name} is not supported by the V1 Engine. "
    msg += "Falling back to V0. "
    if recommend_to_remove:
        msg += f"We recommend to remove {feature_name} from your config "
        msg += "in favor of the V1 Engine."
    logger.warning(msg)


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def human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
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    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
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    match = re.fullmatch(r"(\d+(?:\.\d+)?)([kKmMgGtT])", value)
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    if match:
        decimal_multiplier = {
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            "k": 10**3,
            "m": 10**6,
            "g": 10**9,
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        }
        binary_multiplier = {
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            "K": 2**10,
            "M": 2**20,
            "G": 2**30,
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        }

        number, suffix = match.groups()
        if suffix in decimal_multiplier:
            mult = decimal_multiplier[suffix]
            return int(float(number) * mult)
        elif suffix in binary_multiplier:
            mult = binary_multiplier[suffix]
            # Do not allow decimals with binary multipliers
            try:
                return int(number) * mult
            except ValueError as e:
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                raise argparse.ArgumentTypeError(
                    "Decimals are not allowed "
                    f"with binary suffixes like {suffix}. Did you mean to use "
                    f"{number}{suffix.lower()} instead?"
                ) from e
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    # Regular plain number.
    return int(value)