arg_utils.py 83.3 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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# yapf: disable
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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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import threading
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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, Dict, List,
                    Literal, Optional, Type, TypeVar, Union, cast, get_args,
                    get_origin)
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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 typing_extensions import TypeIs, deprecated
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import vllm.envs as envs
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from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
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                         ConfigFormat, ConfigType, ConvertOption,
                         DecodingConfig, DetailedTraceModules, Device,
                         DeviceConfig, DistributedExecutorBackend,
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                         GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LogprobsMode,
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                         LoRAConfig, MambaDType, ModelConfig, ModelDType,
                         ModelImpl, MultiModalConfig, ObservabilityConfig,
                         ParallelConfig, PoolerConfig, PrefixCachingHashAlgo,
                         RunnerOption, SchedulerConfig, SchedulerPolicy,
                         SpeculativeConfig, TaskOption, TokenizerMode,
                         VllmConfig, get_attr_docs, 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 is_interleaved
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
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                        GiB_bytes, get_ip, is_in_ray_actor)
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from vllm.v1.sample.logits_processor import LogitsProcessor
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# yapf: enable
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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]:
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    def _parse_type(val: str) -> T:
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        try:
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
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    return _parse_type


def optional_type(
        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:

    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. "
            f"Got {options} with types {[type(c) for c in options]}")
    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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@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
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    cls_docs = get_attr_docs(cls)
    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
        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[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 "
                f"type. Got {types}.")
            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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            if name in {"max_model_len", "max_num_batched_tokens"}:
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                kwargs[name]["type"] = human_readable_int
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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)):
            kwargs[name]["type"] = str
        else:
            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.

    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
    served_model_name: Optional[Union[
        str, List[str]]] = ModelConfig.served_model_name
    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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    download_dir: Optional[str] = LoadConfig.download_dir
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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[Union[
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        DistributedExecutorBackend,
        Type[ExecutorBase]]] = 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
    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_eplb: bool = ParallelConfig.enable_eplb
    num_redundant_experts: int = ParallelConfig.num_redundant_experts
    eplb_window_size: int = ParallelConfig.eplb_window_size
    eplb_step_interval: int = ParallelConfig.eplb_step_interval
    eplb_log_balancedness: bool = ParallelConfig.eplb_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
    prefix_caching_hash_algo: PrefixCachingHashAlgo = \
        CacheConfig.prefix_caching_hash_algo
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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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    max_num_batched_tokens: Optional[
        int] = SchedulerConfig.max_num_batched_tokens
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
    long_prefill_token_threshold: int = \
        SchedulerConfig.long_prefill_token_threshold
    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
    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
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    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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    limit_mm_per_prompt: dict[str, int] = \
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        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
    mm_processor_cache_gb: int = MultiModalConfig.mm_processor_cache_gb
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    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
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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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    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
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    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
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    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager)

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    guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
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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] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
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    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
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    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
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    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        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
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    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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    reasoning_parser: str = DecodingConfig.reasoning_backend

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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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    enable_multimodal_encoder_data_parallel: bool = \
        ParallelConfig.enable_multimodal_encoder_data_parallel

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    logits_processors: Optional[list[Union[
        str, type[LogitsProcessor]]]] = ModelConfig.logits_processors
    """Custom logitproc types"""

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    async_scheduling: bool = SchedulerConfig.async_scheduling
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    # DEPRECATED
    enable_prompt_adapter: bool = False
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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):
            self.compilation_config = CompilationConfig(
                **self.compilation_config)
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
        load_general_plugins()
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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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"])
        model_group.add_argument("--task",
                                 **model_kwargs["task"],
                                 deprecated=True)
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        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        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("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        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-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
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        model_group.add_argument("--logprobs-mode",
                                 **model_kwargs["logprobs_mode"])
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        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"])
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        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
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        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        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("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        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"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])
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        model_group.add_argument("--override-attention-dtype",
                                 **model_kwargs["override_attention_dtype"])
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        model_group.add_argument("--logits-processors",
                                 **model_kwargs["logits_processors"])
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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",
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                                **load_kwargs["download_dir"])
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        load_group.add_argument("--model-loader-extra-config",
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                                **load_kwargs["model_loader_extra_config"])
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        load_group.add_argument("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
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                                **load_kwargs["use_tqdm_on_load"])
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        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])
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        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
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        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
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        guided_decoding_group.add_argument(
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            "--guided-decoding-disable-fallback",
            **guided_decoding_kwargs["disable_fallback"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-any-whitespace",
            **guided_decoding_kwargs["disable_any_whitespace"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-additional-properties",
            **guided_decoding_kwargs["disable_additional_properties"])
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        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

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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"])
        parallel_group.add_argument(
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            "--pipeline-parallel-size", "-pp",
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            **parallel_kwargs["pipeline_parallel_size"])
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        parallel_group.add_argument("--tensor-parallel-size", "-tp",
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                                    **parallel_kwargs["tensor_parallel_size"])
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        parallel_group.add_argument("--data-parallel-size", "-dp",
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                                    **parallel_kwargs["data_parallel_size"])
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        parallel_group.add_argument(
            '--data-parallel-rank',
            '-dpn',
            type=int,
            help='Data parallel rank of this instance. '
            'When set, enables external load balancer mode.')
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        parallel_group.add_argument('--data-parallel-start-rank',
                                    '-dpr',
                                    type=int,
                                    help='Starting data parallel rank '
                                    'for secondary nodes.')
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        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.')
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        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(
            "--data-parallel-hybrid-lb",
            **parallel_kwargs["data_parallel_hybrid_lb"])
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        parallel_group.add_argument(
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            "--enable-expert-parallel",
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            **parallel_kwargs["enable_expert_parallel"])
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        parallel_group.add_argument("--enable-eplb",
                                    **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--num-redundant-experts",
                                    **parallel_kwargs["num_redundant_experts"])
        parallel_group.add_argument("--eplb-window-size",
                                    **parallel_kwargs["eplb_window_size"])
        parallel_group.add_argument("--eplb-step-interval",
                                    **parallel_kwargs["eplb_step_interval"])
        parallel_group.add_argument("--eplb-log-balancedness",
                                    **parallel_kwargs["eplb_log_balancedness"])
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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"])
        parallel_group.add_argument(
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            "--ray-workers-use-nsight",
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            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
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            "--disable-custom-all-reduce",
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            **parallel_kwargs["disable_custom_all_reduce"])
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        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",
            **parallel_kwargs["enable_multimodal_encoder_data_parallel"])
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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"])
        cache_group.add_argument("--gpu-memory-utilization",
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                                 **cache_kwargs["gpu_memory_utilization"])
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        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
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                                 **cache_kwargs["cache_dtype"])
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        cache_group.add_argument("--num-gpu-blocks-override",
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                                 **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"])
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        cache_group.add_argument("--cpu-offload-gb",
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                                 **cache_kwargs["cpu_offload_gb"])
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        cache_group.add_argument("--calculate-kv-scales",
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                                 **cache_kwargs["calculate_kv_scales"])
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        cache_group.add_argument("--kv-sharing-fast-prefill",
                                 **cache_kwargs["kv_sharing_fast_prefill"])
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        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("--limit-mm-per-prompt",
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                                      **multimodal_kwargs["limit_per_prompt"])
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        multimodal_group.add_argument("--media-io-kwargs",
                                      **multimodal_kwargs["media_io_kwargs"])
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        multimodal_group.add_argument(
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            "--mm-processor-kwargs",
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            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
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            "--mm-processor-cache-gb",
            **multimodal_kwargs["mm_processor_cache_gb"])
        multimodal_group.add_argument("--disable-mm-preprocessor-cache",
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                                      action="store_true",
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                                      deprecated=True)
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        multimodal_group.add_argument(
            "--interleave-mm-strings",
            **multimodal_kwargs["interleave_mm_strings"])
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        multimodal_group.add_argument("--skip-mm-profiling",
                                      **multimodal_kwargs["skip_mm_profiling"])
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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",
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                                **lora_kwargs["bias_enabled"])
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        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
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                                **lora_kwargs["max_lora_rank"])
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        lora_group.add_argument("--lora-extra-vocab-size",
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                                **lora_kwargs["lora_extra_vocab_size"])
        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",
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                                **lora_kwargs["max_cpu_loras"])
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        lora_group.add_argument("--fully-sharded-loras",
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                                **lora_kwargs["fully_sharded_loras"])
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        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",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # 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"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **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",
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            **scheduler_kwargs["max_num_batched_tokens"])
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        scheduler_group.add_argument("--max-num-seqs",
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                                     **scheduler_kwargs["max_num_seqs"])
        scheduler_group.add_argument(
            "--max-num-partial-prefills",
            **scheduler_kwargs["max_num_partial_prefills"])
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
            **scheduler_kwargs["max_long_partial_prefills"])
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        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
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        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
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        scheduler_group.add_argument("--num-lookahead-slots",
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                                     **scheduler_kwargs["num_lookahead_slots"])
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        scheduler_group.add_argument("--scheduler-delay-factor",
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                                     **scheduler_kwargs["delay_factor"])
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        scheduler_group.add_argument("--preemption-mode",
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                                     **scheduler_kwargs["preemption_mode"])
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        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
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        scheduler_group.add_argument("--scheduling-policy",
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                                     **scheduler_kwargs["policy"])
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        scheduler_group.add_argument(
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            "--enable-chunked-prefill",
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            **scheduler_kwargs["enable_chunked_prefill"])
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        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
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        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])
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        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"])
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        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"])
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        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"])
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        # Other arguments
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
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        parser.add_argument('--enable-prompt-adapter',
                            action='store_true',
                            deprecated=True,
                            help='[DEPRECATED] Prompt adapter has been '
                            'removed. Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
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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})
        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
        if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3
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                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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            self.load_format = "runai_streamer"
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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. "
                "Please use `--mm-processor-cache-gb 0` instead.", )

            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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        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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            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_seq_len_to_capture=self.max_seq_len_to_capture,
            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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            use_async_output_proc=not self.disable_async_output_proc,
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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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            override_neuron_config=self.override_neuron_config,
            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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        )
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    def validate_tensorizer_args(self):
        from vllm.model_executor.model_loader.tensorizer import (
            TensorizerConfig)
        for key in self.model_loader_extra_config:
            if key in TensorizerConfig._fields:
                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 = (
                    self.model_loader_extra_config.to_serializable())
            self.model_loader_extra_config["tensorizer_config"] = {}
            self.model_loader_extra_config["tensorizer_config"][
                "tensorizer_dir"] = self.model
            self.validate_tensorizer_args()
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        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
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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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        """
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        from vllm.transformers_utils.config import get_config
        from vllm.transformers_utils.configs.speculators.base import (
            SpeculatorsConfig)

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        if self.speculative_config is None:
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            hf_config = get_config(self.hf_config_path or self.model,
                                   self.trust_remote_code, self.revision,
                                   self.code_revision, self.config_format)

            # if loading a SpeculatorsConfig, load the specualtive_config
            # details from the config directly
            # no user input required / expected
            if isinstance(hf_config, SpeculatorsConfig):
                # We create one since we dont create one
                self.speculative_config = {}
                self.speculative_config[
                    "num_speculative_tokens"] = hf_config.num_lookahead_tokens
                self.speculative_config["model"] = self.model
                self.speculative_config["method"] = hf_config.method
            else:
                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.
        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()

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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)

        # Set default arguments for V0 or V1 Engine.
        if use_v1:
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            self._set_default_args_v1(usage_context, model_config)
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            # Disable chunked prefill for POWER (ppc64le)/ARM/s390x CPUs in V1
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            if current_platform.is_cpu(
            ) and current_platform.get_cpu_architecture() in (
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                    CpuArchEnum.POWERPC, CpuArchEnum.S390X, CpuArchEnum.ARM):
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                logger.info(
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                    "Chunked prefill is not supported for ARM and POWER "
                    "and S390X CPUs; "
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                    "disabling it for V1 backend.")
                self.enable_chunked_prefill = False
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        else:
            self._set_default_args_v0(model_config)
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        assert self.enable_chunked_prefill is not None

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        if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
            assert self.enforce_eager, (
                "Cuda graph is not supported with DualChunkFlashAttention. "
                "To run the model in eager mode, set 'enforce_eager=True' "
                "or use '--enforce-eager' in the CLI.")
            assert current_platform.is_cuda(), (
                "DualChunkFlashAttention is only supported on CUDA platform.")
            assert not use_v1, (
                "DualChunkFlashAttention is not supported on V1 engine. "
                "To run the model in V0 engine, try set 'VLLM_USE_V1=0'")

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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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        cache_config = CacheConfig(
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            block_size=self.block_size,
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            gpu_memory_utilization=self.gpu_memory_utilization,
            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
            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, (
            "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), (
                "data_parallel_size_local must be 1 when data_parallel_rank "
                "is set")
            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, (
                "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(
                    "Using host IP %s as ray-based data parallel address",
                    host_ip)
                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
                    self.data_parallel_backend)
                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.
        data_parallel_rpc_port = self.data_parallel_rpc_port 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"
                logger.info("Using mp-based distributed executor backend "
                            "for async scheduling.")
            if self.distributed_executor_backend == "uni":
                raise ValueError("Async scheduling is not supported with "
                                 "uni-process backend.")
            if self.pipeline_parallel_size > 1:
                raise ValueError("Async scheduling is not supported with "
                                 "pipeline-parallel-size > 1.")

            # 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 "
                    "async scheduling.")

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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_eplb=self.enable_eplb,
            num_redundant_experts=self.num_redundant_experts,
            eplb_window_size=self.eplb_window_size,
            eplb_step_interval=self.eplb_step_interval,
            eplb_log_balancedness=self.eplb_log_balancedness,
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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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            enable_multimodal_encoder_data_parallel=self.
            enable_multimodal_encoder_data_parallel,
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        )
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        if model_config.is_multimodal_model:
            dp_supports_mm_processor_cache = (self.data_parallel_size == 1
                                              or data_parallel_external_lb)
            if (not dp_supports_mm_processor_cache
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                    and model_config.mm_processor_cache_gb > 0):
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                logger.warning(
                    "Multi-modal processor cache is disabled because "
                    "it is not compatible with data parallelism when "
                    "there does not exist a one-to-one correspondance "
                    "between API and engine core processes.")
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                model_config.set_mm_processor_cache_gb(0)
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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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            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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            delay_factor=self.scheduler_delay_factor,
            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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            preemption_mode=self.preemption_mode,
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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 "
                "non multimodal model")

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        lora_config = LoRAConfig(
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            bias_enabled=self.enable_lora_bias,
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            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
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            default_mm_loras=self.default_mm_loras,
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            fully_sharded_loras=self.fully_sharded_loras,
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            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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        decoding_config = DecodingConfig(
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            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
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            reasoning_backend=self.reasoning_parser
        )
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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,
            decoding_config=decoding_config,
            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.load_format == "sharded_state":
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            _raise_or_fallback(
                feature_name=f"--load_format {self.load_format}",
                recommend_to_remove=False)
            return False

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

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        if self.preemption_mode != SchedulerConfig.preemption_mode:
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            _raise_or_fallback(feature_name="--preemption-mode",
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            return False

        if (self.disable_async_output_proc
                != EngineArgs.disable_async_output_proc):
            _raise_or_fallback(feature_name="--disable-async-output-proc",
                               recommend_to_remove=True)
            return False

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            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

        # Need at least Ampere for now (FA support required).
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        # Skip this check if we are running on a non-GPU platform,
        # or if the device capability is not available
        # (e.g. in a Ray actor without GPUs).
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        if (current_platform.is_cuda()
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                and current_platform.get_device_capability()
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                and current_platform.get_device_capability().major < 8):
            _raise_or_fallback(feature_name="Compute Capability < 8.0",
                               recommend_to_remove=False)
            return False

        # No Fp8 KV cache so far.
        if self.kv_cache_dtype != "auto":
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            supported = current_platform.is_kv_cache_dtype_supported(
                self.kv_cache_dtype)
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            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
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        # No text embedding inputs so far.
        if self.enable_prompt_embeds:
            _raise_or_fallback(feature_name="--enable-prompt-embeds",
                               recommend_to_remove=False)
            return False

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

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        # V1 mamba models are unoptimized.
        if model_config.has_inner_state and _warn_or_fallback(
                feature_name="Mamba"):
            return False

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

        # No OTLP observability so far.
        if (self.otlp_traces_endpoint or self.collect_detailed_traces):
            _raise_or_fallback(feature_name="--otlp-traces-endpoint",
                               recommend_to_remove=False)
            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
                and self.speculative_config.get("method") == "draft_model"):
            raise NotImplementedError(
                "Speculative decoding with draft model is not supported yet. "
                "Please consider using other speculative decoding methods "
                "such as ngram, medusa, eagle, or deepseek_mtp.")
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        V1_BACKENDS = [
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            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
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            "CUTLASS_MLA",
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            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
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            "ROCM_AITER_MLA",
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            "TORCH_SDPA_VLLM_V1",
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            "FLEX_ATTENTION",
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            "TREE_ATTN",
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            "XFORMERS_VLLM_V1",
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        ]
        if (envs.is_set("VLLM_ATTENTION_BACKEND")
                and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):
            name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
            _raise_or_fallback(feature_name=name, recommend_to_remove=True)
            return False

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        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
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            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
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        #############################################################
        # Experimental Features - allow users to opt in.

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        # Signal Handlers requires running in main thread.
        if (threading.current_thread() != threading.main_thread()
                and _warn_or_fallback("Engine in background thread")):
            return False

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        if self.pipeline_parallel_size > 1:
            supports_pp = getattr(self.distributed_executor_backend,
                                  'supports_pp', False)
            if not supports_pp and self.distributed_executor_backend not in (
                    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)
                return False
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        # The platform may be supported on V1, but off by default for now.
        if not current_platform.default_v1(  # noqa: SIM103
                model_config=model_config) and _warn_or_fallback(
                    current_platform.device_name):
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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)
            return False

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

        return True

    def _set_default_args_v0(self, model_config: ModelConfig) -> None:
        """Set Default Arguments for V0 Engine."""

        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # Chunked prefill not supported for Multimodal or MLA in V0.
            if model_config.is_multimodal_model or model_config.use_mla:
                self.enable_chunked_prefill = False

            # Enable chunked prefill by default for long context (> 32K)
            # models to avoid OOM errors in initial memory profiling phase.
            elif use_long_context:
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
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                use_spec_decode = self.speculative_config is not None
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                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = False

        if not self.enable_chunked_prefill and use_long_context:
            logger.warning(
                "The model has a long context length (%s). This may cause"
                "OOM during the initial memory profiling phase, or result "
                "in low performance due to small KV cache size. Consider "
                "setting --max-model-len to a smaller value.", max_model_len)
        elif (self.enable_chunked_prefill
              and model_config.runner_type == "pooling"):
            msg = "Chunked prefill is not supported for pooling models"
            raise ValueError(msg)

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        # if using prefix caching, we must set a hash algo
        if self.enable_prefix_caching:
            # Disable prefix caching for multimodal models for VLLM_V0.
            if model_config.is_multimodal_model:
                logger.warning(
                    "--enable-prefix-caching is not supported for multimodal "
                    "models in V0 and has been disabled.")
                self.enable_prefix_caching = False

            # VLLM_V0 only supports builtin hash algo for prefix caching.
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            if self.prefix_caching_hash_algo == "sha256":
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                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")
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        # Set max_num_seqs to 256 for VLLM_V0.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256

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    def _set_default_args_v1(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
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = True
        else:

            pooling_type = model_config.pooler_config.pooling_type
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            is_causal = getattr(model_config.hf_config, "is_causal", True)
            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"

            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: {
                    'V6E': 2048,
                    'V5E': 1024,
                    'V5P': 512,
                },
                UsageContext.OPENAI_API_SERVER: {
                    'V6E': 1024,
                    'V5E': 512,
                    'V5P': 256,
                }
            }

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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()
                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]
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
            else:
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                if not self.enable_chunked_prefill:
                    self.max_num_batched_tokens = model_config.max_model_len
                else:
                    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):
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            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
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class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
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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 "
        "`enable_log_requests` instead.")
    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 "
        "`enable_log_requests` instead.")
    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
        # adding a new kind of quantization method to --quantization argument or
        # 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.')
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        parser.add_argument('--disable-log-requests',
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                            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(
            f"VLLM_USE_V1=1 is not supported with {feature_name}.")
    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)


def _warn_or_fallback(feature_name: str) -> bool:
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        logger.warning(
            "Detected VLLM_USE_V1=1 with %s. Usage should "
            "be considered experimental. Please report any "
            "issues on Github.", feature_name)
        should_exit = False
    else:
        logger.info(
            "%s is experimental on VLLM_USE_V1=1. "
            "Falling back to V0 Engine.", feature_name)
        should_exit = True
    return should_exit


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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()
    match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value)
    if match:
        decimal_multiplier = {
            'k': 10**3,
            'm': 10**6,
            'g': 10**9,
        }
        binary_multiplier = {
            'K': 2**10,
            'M': 2**20,
            'G': 2**30,
        }

        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:
                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

    # Regular plain number.
    return int(value)