arg_utils.py

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

import argparse
import copy
import dataclasses
import functools
import json
import sys
from collections.abc import Callable
from dataclasses import MISSING, dataclass, fields, is_dataclass
from itertools import permutations
from types import UnionType
from typing import (
    TYPE_CHECKING,
    Annotated,
    Any,
    Literal,
    TypeAlias,
    TypeVar,
    Union,
    cast,
    get_args,
    get_origin,
)

import huggingface_hub
import regex as re
import torch
from pydantic import TypeAdapter, ValidationError
from pydantic.fields import FieldInfo
from typing_extensions import TypeIs, deprecated

import vllm.envs as envs
from vllm.config import (
    CacheConfig,
    CompilationConfig,
    ConfigType,
    DeviceConfig,
    EPLBConfig,
    KVEventsConfig,
    KVTransferConfig,
    LoadConfig,
    LoRAConfig,
    ModelConfig,
    MultiModalConfig,
    ObservabilityConfig,
    ParallelConfig,
    PoolerConfig,
    SchedulerConfig,
    SpeculativeConfig,
    StructuredOutputsConfig,
    VllmConfig,
    get_attr_docs,
)
from vllm.config.cache import BlockSize, CacheDType, MambaDType, PrefixCachingHashAlgo
from vllm.config.device import Device
from vllm.config.model import (
    ConvertOption,
    HfOverrides,
    LogprobsMode,
    ModelDType,
    RunnerOption,
    TaskOption,
    TokenizerMode,
)
from vllm.config.multimodal import MMCacheType, MMEncoderTPMode
from vllm.config.observability import DetailedTraceModules
from vllm.config.parallel import DistributedExecutorBackend, ExpertPlacementStrategy
from vllm.config.scheduler import SchedulerPolicy
from vllm.config.utils import get_field
from vllm.logger import init_logger
from vllm.platforms import CpuArchEnum, current_platform
from vllm.plugins import load_general_plugins
from vllm.ray.lazy_utils import is_ray_initialized
from vllm.reasoning import ReasoningParserManager
from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
from vllm.transformers_utils.config import (
    get_model_path,
    is_interleaved,
    maybe_override_with_speculators,
)
from vllm.transformers_utils.utils import check_gguf_file
from vllm.utils import FlexibleArgumentParser, get_ip, is_in_ray_actor
from vllm.utils.mem_constants import GiB_bytes
from vllm.v1.sample.logits_processor import LogitsProcessor

if TYPE_CHECKING:
    from vllm.executor.executor_base import ExecutorBase
    from vllm.model_executor.layers.quantization import QuantizationMethods
    from vllm.model_executor.model_loader import LoadFormats
    from vllm.usage.usage_lib import UsageContext
else:
    ExecutorBase = Any
    QuantizationMethods = Any
    LoadFormats = Any
    UsageContext = Any

logger = init_logger(__name__)

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


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

    return _parse_type


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

    return _optional_type


def union_dict_and_str(val: str) -> str | dict[str, str] | None:
    if not re.match(r"(?s)^\s*{.*}\s*$", val):
        return str(val)
    return optional_type(json.loads)(val)


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)


def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
    """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`.
    """
    type_hint = get_type(type_hints, Literal)
    options = get_args(type_hint)
    option_type = type(options[0])
    if not all(isinstance(option, option_type) for option in options):
        raise ValueError(
            "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)}


def collection_to_kwargs(type_hints: set[TypeHint], type: TypeHint) -> dict[str, Any]:
    type_hint = get_type(type_hints, type)
    types = get_args(type_hint)
    elem_type = types[0]

    # Handle Ellipsis
    assert all(t is elem_type for t in types if t is not Ellipsis), (
        f"All non-Ellipsis elements must be of the same type. Got {types}."
    )

    # Handle Union types
    if get_origin(elem_type) in {Union, UnionType}:
        # Union for Union[X, Y] and UnionType for X | Y
        assert str in get_args(elem_type), (
            "If element can have multiple types, one must be 'str' "
            f"(i.e. 'list[int | str]'). Got {elem_type}."
        )
        elem_type = str

    return {
        "type": elem_type,
        "nargs": "+" if type is not tuple or Ellipsis in types else len(types),
    }


def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


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 in {Union, UnionType}:
        # Union for Union[X, Y] and UnionType for X | Y
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints


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


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


@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
    # Save time only getting attr docs if we're generating help text
    cls_docs = get_attr_docs(cls) if NEEDS_HELP else {}
    kwargs = {}
    for field in fields(cls):
        # Get the set of possible types for the field
        type_hints: set[TypeHint] = get_type_hints(field.type)

        # 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)

        # Get the default value of the field
        if field.default is not MISSING:
            default = field.default
            # Handle pydantic.Field defaults
            if isinstance(default, FieldInfo):
                default = (
                    default.default
                    if default.default_factory is None
                    else default.default_factory()
                )
        elif field.default_factory is not MISSING:
            default = field.default_factory()

        # Get the help text for the field
        name = field.name
        help = cls_docs.get(name, "").strip()
        # 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
        json_tip = (
            "Should either be a valid JSON string or JSON keys passed individually."
        )
        if dataclass_cls is not None:

            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
            kwargs[name]["help"] += f"\n\n{json_tip}"
        elif contains_type(type_hints, bool):
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
            kwargs[name].update(literal_to_kwargs(type_hints))
        elif contains_type(type_hints, tuple):
            kwargs[name].update(collection_to_kwargs(type_hints, tuple))
        elif contains_type(type_hints, list):
            kwargs[name].update(collection_to_kwargs(type_hints, list))
        elif contains_type(type_hints, set):
            kwargs[name].update(collection_to_kwargs(type_hints, set))
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
            # Special case for large integers
            human_readable_ints = {
                "max_model_len",
                "max_num_batched_tokens",
                "kv_cache_memory_bytes",
            }
            if name in human_readable_ints:
                kwargs[name]["type"] = human_readable_int
                kwargs[name]["help"] += f"\n\n{human_readable_int.__doc__}"
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
        elif contains_type(type_hints, dict) and (
            contains_type(type_hints, str)
            or any(is_not_builtin(th) for th in type_hints)
        ):
            kwargs[name]["type"] = union_dict_and_str
        elif contains_type(type_hints, dict):
            kwargs[name]["type"] = parse_type(json.loads)
            kwargs[name]["help"] += f"\n\n{json_tip}"
        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}.")

        # 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"]}))

        # 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


def get_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
    """Return argparse kwargs for the given Config dataclass.

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

    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))


@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""

    model: str = ModelConfig.model
    served_model_name: str | list[str] | None = ModelConfig.served_model_name
    tokenizer: str | None = ModelConfig.tokenizer
    hf_config_path: str | None = ModelConfig.hf_config_path
    runner: RunnerOption = ModelConfig.runner
    convert: ConvertOption = ModelConfig.convert
    task: TaskOption | None = ModelConfig.task
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
    allowed_media_domains: list[str] | None = ModelConfig.allowed_media_domains
    download_dir: str | None = LoadConfig.download_dir
    safetensors_load_strategy: str = LoadConfig.safetensors_load_strategy
    load_format: str | LoadFormats = LoadConfig.load_format
    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
    seed: int | None = ModelConfig.seed
    max_model_len: int | None = ModelConfig.max_model_len
    cuda_graph_sizes: list[int] = get_field(SchedulerConfig, "cuda_graph_sizes")
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    distributed_executor_backend: (
        str | DistributedExecutorBackend | type[ExecutorBase] | None
    ) = ParallelConfig.distributed_executor_backend
    # number of P/D disaggregation (or other disaggregation) workers
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    decode_context_parallel_size: int = ParallelConfig.decode_context_parallel_size
    data_parallel_size: int = ParallelConfig.data_parallel_size
    data_parallel_rank: int | None = None
    data_parallel_start_rank: int | None = None
    data_parallel_size_local: int | None = None
    data_parallel_address: str | None = None
    data_parallel_rpc_port: int | None = None
    data_parallel_hybrid_lb: bool = False
    data_parallel_backend: str = ParallelConfig.data_parallel_backend
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
    all2all_backend: str | None = ParallelConfig.all2all_backend
    enable_dbo: bool = ParallelConfig.enable_dbo
    dbo_decode_token_threshold: int = ParallelConfig.dbo_decode_token_threshold
    dbo_prefill_token_threshold: int = ParallelConfig.dbo_prefill_token_threshold
    disable_nccl_for_dp_synchronization: bool = (
        ParallelConfig.disable_nccl_for_dp_synchronization
    )
    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
    enable_eplb: bool = ParallelConfig.enable_eplb
    expert_placement_strategy: ExpertPlacementStrategy = (
        ParallelConfig.expert_placement_strategy
    )
    _api_process_count: int = ParallelConfig._api_process_count
    _api_process_rank: int = ParallelConfig._api_process_rank
    num_redundant_experts: int = EPLBConfig.num_redundant_experts
    eplb_window_size: int = EPLBConfig.window_size
    eplb_step_interval: int = EPLBConfig.step_interval
    eplb_log_balancedness: bool = EPLBConfig.log_balancedness
    max_parallel_loading_workers: int | None = (
        ParallelConfig.max_parallel_loading_workers
    )
    block_size: BlockSize | None = CacheConfig.block_size
    enable_prefix_caching: bool | None = CacheConfig.enable_prefix_caching
    prefix_caching_hash_algo: PrefixCachingHashAlgo = (
        CacheConfig.prefix_caching_hash_algo
    )
    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
    kv_cache_memory_bytes: int | None = CacheConfig.kv_cache_memory_bytes
    max_num_batched_tokens: int | None = 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: int | None = SchedulerConfig.max_num_seqs
    max_logprobs: int = ModelConfig.max_logprobs
    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
    disable_log_stats: bool = False
    aggregate_engine_logging: bool = False
    revision: str | None = ModelConfig.revision
    code_revision: str | None = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: float | None = ModelConfig.rope_theta
    hf_token: bool | str | None = ModelConfig.hf_token
    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
    tokenizer_revision: str | None = ModelConfig.tokenizer_revision
    quantization: QuantizationMethods | None = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
    limit_mm_per_prompt: dict[str, int | dict[str, int]] = get_field(
        MultiModalConfig, "limit_per_prompt"
    )
    interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings
    media_io_kwargs: dict[str, dict[str, Any]] = get_field(
        MultiModalConfig, "media_io_kwargs"
    )
    mm_processor_kwargs: dict[str, Any] | None = MultiModalConfig.mm_processor_kwargs
    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
    mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
    mm_processor_cache_type: MMCacheType | None = (
        MultiModalConfig.mm_processor_cache_type
    )
    mm_shm_cache_max_object_size_mb: int = (
        MultiModalConfig.mm_shm_cache_max_object_size_mb
    )
    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
    io_processor_plugin: str | None = None
    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
    video_pruning_rate: float = MultiModalConfig.video_pruning_rate
    # LoRA fields
    enable_lora: bool = False
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
    default_mm_loras: dict[str, str] | None = LoRAConfig.default_mm_loras
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: int | None = LoRAConfig.max_cpu_loras
    lora_dtype: str | torch.dtype | None = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size

    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
    num_gpu_blocks_override: int | None = CacheConfig.num_gpu_blocks_override
    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
    model_loader_extra_config: dict = get_field(LoadConfig, "model_loader_extra_config")
    ignore_patterns: str | list[str] = get_field(LoadConfig, "ignore_patterns")

    enable_chunked_prefill: bool | None = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input

    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager
    )

    structured_outputs_config: StructuredOutputsConfig = get_field(
        VllmConfig, "structured_outputs_config"
    )
    reasoning_parser: str = StructuredOutputsConfig.reasoning_parser

    # Deprecated guided decoding fields
    guided_decoding_backend: str | None = None
    guided_decoding_disable_fallback: bool | None = None
    guided_decoding_disable_any_whitespace: bool | None = None
    guided_decoding_disable_additional_properties: bool | None = None

    logits_processor_pattern: str | None = ModelConfig.logits_processor_pattern

    speculative_config: dict[str, Any] | None = None

    show_hidden_metrics_for_version: str | None = (
        ObservabilityConfig.show_hidden_metrics_for_version
    )
    otlp_traces_endpoint: str | None = ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: list[DetailedTraceModules] | None = (
        ObservabilityConfig.collect_detailed_traces
    )
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: str | type[object] = SchedulerConfig.scheduler_cls

    pooler_config: PoolerConfig | None = ModelConfig.pooler_config
    override_pooler_config: dict | PoolerConfig | None = (
        ModelConfig.override_pooler_config
    )
    compilation_config: CompilationConfig = get_field(VllmConfig, "compilation_config")
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls

    kv_transfer_config: KVTransferConfig | None = None
    kv_events_config: KVEventsConfig | None = None

    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
    override_attention_dtype: str = ModelConfig.override_attention_dtype

    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype

    additional_config: dict[str, Any] = get_field(VllmConfig, "additional_config")

    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
    pt_load_map_location: str = LoadConfig.pt_load_map_location

    # DEPRECATED
    enable_multimodal_encoder_data_parallel: bool = False

    logits_processors: list[str | type[LogitsProcessor]] | None = (
        ModelConfig.logits_processors
    )
    """Custom logitproc types"""

    async_scheduling: bool = SchedulerConfig.async_scheduling

    kv_sharing_fast_prefill: bool = CacheConfig.kv_sharing_fast_prefill

    def __post_init__(self):
        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
        if isinstance(self.compilation_config, dict):
            self.compilation_config = CompilationConfig(**self.compilation_config)
        if isinstance(self.eplb_config, dict):
            self.eplb_config = EPLBConfig(**self.eplb_config)
        # Setup plugins
        from vllm.plugins import load_general_plugins

        load_general_plugins()
        # when use hf offline,replace model id to local model path
        if huggingface_hub.constants.HF_HUB_OFFLINE:
            model_id = self.model
            self.model = get_model_path(self.model, self.revision)
            logger.info(
                "HF_HUB_OFFLINE is True, replace model_id [%s] to model_path [%s]",
                model_id,
                self.model,
            )

    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
        """Shared CLI arguments for vLLM engine."""

        # Model arguments
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
        if not ("serve" in sys.argv[1:] and "--help" in sys.argv[1:]):
            model_group.add_argument("--model", **model_kwargs["model"])
        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)
        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(
            "--allowed-media-domains", **model_kwargs["allowed_media_domains"]
        )
        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-logprobs", **model_kwargs["max_logprobs"])
        model_group.add_argument("--logprobs-mode", **model_kwargs["logprobs_mode"])
        model_group.add_argument(
            "--disable-sliding-window", **model_kwargs["disable_sliding_window"]
        )
        model_group.add_argument(
            "--disable-cascade-attn", **model_kwargs["disable_cascade_attn"]
        )
        model_group.add_argument(
            "--skip-tokenizer-init", **model_kwargs["skip_tokenizer_init"]
        )
        model_group.add_argument(
            "--enable-prompt-embeds", **model_kwargs["enable_prompt_embeds"]
        )
        model_group.add_argument(
            "--served-model-name", **model_kwargs["served_model_name"]
        )
        model_group.add_argument("--config-format", **model_kwargs["config_format"])
        # 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("--pooler-config", **model_kwargs["pooler_config"])
        model_group.add_argument(
            "--override-pooler-config",
            **model_kwargs["override_pooler_config"],
            deprecated=True,
        )
        model_group.add_argument(
            "--logits-processor-pattern", **model_kwargs["logits_processor_pattern"]
        )
        model_group.add_argument(
            "--generation-config", **model_kwargs["generation_config"]
        )
        model_group.add_argument(
            "--override-generation-config", **model_kwargs["override_generation_config"]
        )
        model_group.add_argument(
            "--enable-sleep-mode", **model_kwargs["enable_sleep_mode"]
        )
        model_group.add_argument("--model-impl", **model_kwargs["model_impl"])
        model_group.add_argument(
            "--override-attention-dtype", **model_kwargs["override_attention_dtype"]
        )
        model_group.add_argument(
            "--logits-processors", **model_kwargs["logits_processors"]
        )
        model_group.add_argument(
            "--io-processor-plugin", **model_kwargs["io_processor_plugin"]
        )

        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
        load_group.add_argument("--load-format", **load_kwargs["load_format"])
        load_group.add_argument("--download-dir", **load_kwargs["download_dir"])
        load_group.add_argument(
            "--safetensors-load-strategy", **load_kwargs["safetensors_load_strategy"]
        )
        load_group.add_argument(
            "--model-loader-extra-config", **load_kwargs["model_loader_extra_config"]
        )
        load_group.add_argument("--ignore-patterns", **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load", **load_kwargs["use_tqdm_on_load"])
        load_group.add_argument(
            "--pt-load-map-location", **load_kwargs["pt_load_map_location"]
        )

        # Structured outputs arguments
        structured_outputs_kwargs = get_kwargs(StructuredOutputsConfig)
        structured_outputs_group = parser.add_argument_group(
            title="StructuredOutputsConfig",
            description=StructuredOutputsConfig.__doc__,
        )
        structured_outputs_group.add_argument(
            "--reasoning-parser",
            # This choice is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **structured_outputs_kwargs["reasoning_parser"],
        )
        # Deprecated guided decoding arguments
        for arg, type in [
            ("--guided-decoding-backend", str),
            ("--guided-decoding-disable-fallback", bool),
            ("--guided-decoding-disable-any-whitespace", bool),
            ("--guided-decoding-disable-additional-properties", bool),
        ]:
            structured_outputs_group.add_argument(
                arg,
                type=type,
                help=(f"[DEPRECATED] {arg} will be removed in v0.12.0."),
                deprecated=True,
            )

        # Parallel arguments
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
            "--distributed-executor-backend",
            **parallel_kwargs["distributed_executor_backend"],
        )
        parallel_group.add_argument(
            "--pipeline-parallel-size",
            "-pp",
            **parallel_kwargs["pipeline_parallel_size"],
        )
        parallel_group.add_argument(
            "--tensor-parallel-size", "-tp", **parallel_kwargs["tensor_parallel_size"]
        )
        parallel_group.add_argument(
            "--decode-context-parallel-size",
            "-dcp",
            **parallel_kwargs["decode_context_parallel_size"],
        )
        parallel_group.add_argument(
            "--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]
        )
        parallel_group.add_argument(
            "--data-parallel-rank",
            "-dpn",
            type=int,
            help="Data parallel rank of this instance. "
            "When set, enables external load balancer mode.",
        )
        parallel_group.add_argument(
            "--data-parallel-start-rank",
            "-dpr",
            type=int,
            help="Starting data parallel rank for secondary nodes.",
        )
        parallel_group.add_argument(
            "--data-parallel-size-local",
            "-dpl",
            type=int,
            help="Number of data parallel replicas to run on this node.",
        )
        parallel_group.add_argument(
            "--data-parallel-address",
            "-dpa",
            type=str,
            help="Address of data parallel cluster head-node.",
        )
        parallel_group.add_argument(
            "--data-parallel-rpc-port",
            "-dpp",
            type=int,
            help="Port for data parallel RPC communication.",
        )
        parallel_group.add_argument(
            "--data-parallel-backend",
            "-dpb",
            type=str,
            default="mp",
            help='Backend for data parallel, either "mp" or "ray".',
        )
        parallel_group.add_argument(
            "--data-parallel-hybrid-lb", **parallel_kwargs["data_parallel_hybrid_lb"]
        )
        parallel_group.add_argument(
            "--enable-expert-parallel", **parallel_kwargs["enable_expert_parallel"]
        )
        parallel_group.add_argument(
            "--all2all-backend", **parallel_kwargs["all2all_backend"]
        )
        parallel_group.add_argument("--enable-dbo", **parallel_kwargs["enable_dbo"])
        parallel_group.add_argument(
            "--dbo-decode-token-threshold",
            **parallel_kwargs["dbo_decode_token_threshold"],
        )
        parallel_group.add_argument(
            "--dbo-prefill-token-threshold",
            **parallel_kwargs["dbo_prefill_token_threshold"],
        )
        parallel_group.add_argument(
            "--disable-nccl-for-dp-synchronization",
            **parallel_kwargs["disable_nccl_for_dp_synchronization"],
        )
        parallel_group.add_argument("--enable-eplb", **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--eplb-config", **parallel_kwargs["eplb_config"])
        parallel_group.add_argument(
            "--expert-placement-strategy",
            **parallel_kwargs["expert_placement_strategy"],
        )
        parallel_group.add_argument(
            "--num-redundant-experts",
            type=int,
            help="[DEPRECATED] --num-redundant-experts will be removed in v0.12.0.",
            deprecated=True,
        )
        parallel_group.add_argument(
            "--eplb-window-size",
            type=int,
            help="[DEPRECATED] --eplb-window-size will be removed in v0.12.0.",
            deprecated=True,
        )
        parallel_group.add_argument(
            "--eplb-step-interval",
            type=int,
            help="[DEPRECATED] --eplb-step-interval will be removed in v0.12.0.",
            deprecated=True,
        )
        parallel_group.add_argument(
            "--eplb-log-balancedness",
            action=argparse.BooleanOptionalAction,
            help="[DEPRECATED] --eplb-log-balancedness will be removed in v0.12.0.",
            deprecated=True,
        )

        parallel_group.add_argument(
            "--max-parallel-loading-workers",
            **parallel_kwargs["max_parallel_loading_workers"],
        )
        parallel_group.add_argument(
            "--ray-workers-use-nsight", **parallel_kwargs["ray_workers_use_nsight"]
        )
        parallel_group.add_argument(
            "--disable-custom-all-reduce",
            **parallel_kwargs["disable_custom_all_reduce"],
        )
        parallel_group.add_argument("--worker-cls", **parallel_kwargs["worker_cls"])
        parallel_group.add_argument(
            "--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]
        )
        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
            action="store_true",
            deprecated=True,
        )

        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
        )
        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
        cache_group.add_argument(
            "--gpu-memory-utilization", **cache_kwargs["gpu_memory_utilization"]
        )
        cache_group.add_argument(
            "--kv-cache-memory-bytes", **cache_kwargs["kv_cache_memory_bytes"]
        )
        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype", **cache_kwargs["cache_dtype"])
        cache_group.add_argument(
            "--num-gpu-blocks-override", **cache_kwargs["num_gpu_blocks_override"]
        )
        cache_group.add_argument(
            "--enable-prefix-caching", **cache_kwargs["enable_prefix_caching"]
        )
        cache_group.add_argument(
            "--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]
        )
        cache_group.add_argument("--cpu-offload-gb", **cache_kwargs["cpu_offload_gb"])
        cache_group.add_argument(
            "--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]
        )
        cache_group.add_argument(
            "--kv-sharing-fast-prefill", **cache_kwargs["kv_sharing_fast_prefill"]
        )
        cache_group.add_argument(
            "--mamba-cache-dtype", **cache_kwargs["mamba_cache_dtype"]
        )
        cache_group.add_argument(
            "--mamba-ssm-cache-dtype", **cache_kwargs["mamba_ssm_cache_dtype"]
        )

        # Multimodal related configs
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
        multimodal_group.add_argument(
            "--limit-mm-per-prompt", **multimodal_kwargs["limit_per_prompt"]
        )
        multimodal_group.add_argument(
            "--media-io-kwargs", **multimodal_kwargs["media_io_kwargs"]
        )
        multimodal_group.add_argument(
            "--mm-processor-kwargs", **multimodal_kwargs["mm_processor_kwargs"]
        )
        multimodal_group.add_argument(
            "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]
        )
        multimodal_group.add_argument(
            "--disable-mm-preprocessor-cache", action="store_true", deprecated=True
        )
        multimodal_group.add_argument(
            "--mm-processor-cache-type", **multimodal_kwargs["mm_processor_cache_type"]
        )
        multimodal_group.add_argument(
            "--mm-shm-cache-max-object-size-mb",
            **multimodal_kwargs["mm_shm_cache_max_object_size_mb"],
        )
        multimodal_group.add_argument(
            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"]
        )
        multimodal_group.add_argument(
            "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]
        )
        multimodal_group.add_argument(
            "--skip-mm-profiling", **multimodal_kwargs["skip_mm_profiling"]
        )

        multimodal_group.add_argument(
            "--video-pruning-rate", **multimodal_kwargs["video_pruning_rate"]
        )

        # LoRA related configs
        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
            "--enable-lora",
            action=argparse.BooleanOptionalAction,
            help="If True, enable handling of LoRA adapters.",
        )
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank", **lora_kwargs["max_lora_rank"])
        lora_group.add_argument(
            "--lora-extra-vocab-size", **lora_kwargs["lora_extra_vocab_size"]
        )
        lora_group.add_argument(
            "--lora-dtype",
            **lora_kwargs["lora_dtype"],
        )
        lora_group.add_argument("--max-cpu-loras", **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument(
            "--fully-sharded-loras", **lora_kwargs["fully_sharded_loras"]
        )
        lora_group.add_argument("--default-mm-loras", **lora_kwargs["default_mm_loras"])

        # 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"],
        )

        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
            "--max-num-batched-tokens", **scheduler_kwargs["max_num_batched_tokens"]
        )
        scheduler_group.add_argument(
            "--max-num-seqs", **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"],
        )
        scheduler_group.add_argument(
            "--cuda-graph-sizes", **scheduler_kwargs["cuda_graph_sizes"]
        )
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"],
        )
        scheduler_group.add_argument(
            "--num-lookahead-slots", **scheduler_kwargs["num_lookahead_slots"]
        )
        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
        scheduler_group.add_argument(
            "--scheduling-policy", **scheduler_kwargs["policy"]
        )
        scheduler_group.add_argument(
            "--enable-chunked-prefill", **scheduler_kwargs["enable_chunked_prefill"]
        )
        scheduler_group.add_argument(
            "--disable-chunked-mm-input", **scheduler_kwargs["disable_chunked_mm_input"]
        )
        scheduler_group.add_argument(
            "--scheduler-cls", **scheduler_kwargs["scheduler_cls"]
        )
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"],
        )
        scheduler_group.add_argument(
            "--async-scheduling", **scheduler_kwargs["async_scheduling"]
        )

        # vLLM arguments
        vllm_kwargs = get_kwargs(VllmConfig)
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
        # 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)
        vllm_group.add_argument(
            "--speculative-config", **vllm_kwargs["speculative_config"]
        )
        vllm_group.add_argument(
            "--kv-transfer-config", **vllm_kwargs["kv_transfer_config"]
        )
        vllm_group.add_argument("--kv-events-config", **vllm_kwargs["kv_events_config"])
        vllm_group.add_argument(
            "--compilation-config", "-O", **vllm_kwargs["compilation_config"]
        )
        vllm_group.add_argument(
            "--additional-config", **vllm_kwargs["additional_config"]
        )
        vllm_group.add_argument(
            "--structured-outputs-config", **vllm_kwargs["structured_outputs_config"]
        )

        # Other arguments
        parser.add_argument(
            "--disable-log-stats",
            action="store_true",
            help="Disable logging statistics.",
        )

        parser.add_argument(
            "--aggregate-engine-logging",
            action="store_true",
            help="Log aggregate rather than per-engine statistics "
            "when using data parallelism.",
        )
        return parser

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace):
        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
        engine_args = cls(
            **{attr: getattr(args, attr) for attr in attrs if hasattr(args, attr)}
        )
        return engine_args

    def create_model_config(self) -> ModelConfig:
        # 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
            and self.model in MODELS_ON_S3
            and self.load_format == "auto"
        ):
            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"

        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

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

            self.mm_encoder_tp_mode = "data"

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

    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]
                )

    def create_load_config(self) -> LoadConfig:
        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"

        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()

        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
            safetensors_load_strategy=self.safetensors_load_strategy,
            device="cpu" if is_online_quantization(self.quantization) else None,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
            use_tqdm_on_load=self.use_tqdm_on_load,
            pt_load_map_location=self.pt_load_map_location,
        )

    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
    ) -> SpeculativeConfig | None:
        """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
        dictionary from the engine.
        """
        if self.speculative_config is None:
            return None

        # 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,
            }
        )
        return SpeculativeConfig(**self.speculative_config)

    def create_engine_config(
        self,
        usage_context: UsageContext | None = None,
        headless: bool = False,
    ) -> 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.

        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.
        """
        current_platform.pre_register_and_update()

        device_config = DeviceConfig(device=cast(Device, current_platform.device_type))

        model_config = self.create_model_config()
        self.model = model_config.model
        self.tokenizer = model_config.tokenizer

        (self.model, self.tokenizer, self.speculative_config) = (
            maybe_override_with_speculators(
                model=self.model,
                tokenizer=self.tokenizer,
                revision=self.revision,
                trust_remote_code=self.trust_remote_code,
                vllm_speculative_config=self.speculative_config,
            )
        )

        # * 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 V1 Engine.
        self._set_default_args(usage_context, model_config)
        # Disable chunked prefill and prefix caching for:
        # POWER (ppc64le)/ARM/s390x/RISCV CPUs in V1
        if current_platform.is_cpu() and current_platform.get_cpu_architecture() in (
            CpuArchEnum.POWERPC,
            CpuArchEnum.S390X,
            CpuArchEnum.ARM,
            CpuArchEnum.RISCV,
        ):
            logger.info(
                "Chunked prefill is not supported for ARM and POWER, "
                "S390X and RISC-V CPUs; "
                "disabling it for V1 backend."
            )
            self.enable_chunked_prefill = False
            logger.info(
                "Prefix caching is not supported for ARM and POWER, "
                "S390X and RISC-V CPUs; "
                "disabling it for V1 backend."
            )
            self.enable_prefix_caching = False

        assert self.enable_chunked_prefill is not None

        sliding_window: int | None = 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()

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

        cache_config = CacheConfig(
            block_size=self.block_size,
            gpu_memory_utilization=self.gpu_memory_utilization,
            kv_cache_memory_bytes=self.kv_cache_memory_bytes,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
            is_attention_free=model_config.is_attention_free,
            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=sliding_window,
            enable_prefix_caching=self.enable_prefix_caching,
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
            cpu_offload_gb=self.cpu_offload_gb,
            calculate_kv_scales=self.calculate_kv_scales,
            kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
            mamba_cache_dtype=self.mamba_cache_dtype,
            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
        )

        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
            # Avoid logging sensitive environment variables
            sanitized_env = ray_runtime_env.to_dict() if ray_runtime_env else {}
            if "env_vars" in sanitized_env:
                sanitized_env["env_vars"] = {
                    k: "***" for k in sanitized_env["env_vars"]
                }
            logger.info("Using ray runtime env (env vars redacted): %s", sanitized_env)

        # 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()

        assert not headless or not self.data_parallel_hybrid_lb, (
            "data_parallel_hybrid_lb is not applicable in headless mode"
        )

        data_parallel_external_lb = self.data_parallel_rank is not None
        # Local DP rank = 1, use pure-external LB.
        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
            # Use full external lb if we have local_size of 1.
            self.data_parallel_hybrid_lb = False
        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local

            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
        else:
            assert not self.data_parallel_hybrid_lb, (
                "data_parallel_size_local must be set to use data_parallel_hybrid_lb."
            )

            if self.data_parallel_backend == "ray" and (
                envs.VLLM_RAY_DP_PACK_STRATEGY == "span"
            ):
                # Data parallel size defaults to 1 if DP ranks are spanning
                # multiple nodes
                data_parallel_size_local = 1
            else:
                # Otherwise local DP size defaults to global DP size if not set
                data_parallel_size_local = self.data_parallel_size

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
        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

        # 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
        )

        if self.async_scheduling:
            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."
                )

        # Forward the deprecated CLI args to the EPLB config.
        if self.num_redundant_experts is not None:
            self.eplb_config.num_redundant_experts = self.num_redundant_experts
        if self.eplb_window_size is not None:
            self.eplb_config.window_size = self.eplb_window_size
        if self.eplb_step_interval is not None:
            self.eplb_config.step_interval = self.eplb_step_interval
        if self.eplb_log_balancedness is not None:
            self.eplb_config.log_balancedness = self.eplb_log_balancedness

        parallel_config = ParallelConfig(
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            data_parallel_size=self.data_parallel_size,
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
            data_parallel_backend=self.data_parallel_backend,
            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
            enable_expert_parallel=self.enable_expert_parallel,
            all2all_backend=self.all2all_backend,
            enable_dbo=self.enable_dbo,
            dbo_decode_token_threshold=self.dbo_decode_token_threshold,
            dbo_prefill_token_threshold=self.dbo_prefill_token_threshold,
            disable_nccl_for_dp_synchronization=self.disable_nccl_for_dp_synchronization,
            enable_eplb=self.enable_eplb,
            eplb_config=self.eplb_config,
            expert_placement_strategy=self.expert_placement_strategy,
            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,
            ray_runtime_env=ray_runtime_env,
            placement_group=placement_group,
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
            worker_extension_cls=self.worker_extension_cls,
            decode_context_parallel_size=self.decode_context_parallel_size,
            _api_process_count=self._api_process_count,
            _api_process_rank=self._api_process_rank,
        )

        if self.async_scheduling and (
            parallel_config.distributed_executor_backend not in ("mp", "uni")
        ):
            raise ValueError(
                "Currently, async scheduling only supports `mp` or `uni` "
                "distributed executor backend, but you choose "
                f"`{parallel_config.distributed_executor_backend}`."
            )

        speculative_config = self.create_speculative_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_log_stats=self.disable_log_stats,
        )

        # 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

        scheduler_config = SchedulerConfig(
            runner_type=model_config.runner_type,
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
            cuda_graph_sizes=self.cuda_graph_sizes,
            num_lookahead_slots=num_lookahead_slots,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_chunked_mm_input=self.disable_chunked_mm_input,
            is_multimodal_model=model_config.is_multimodal_model,
            is_encoder_decoder=model_config.is_encoder_decoder,
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER and parallel_config.use_ray),
            policy=self.scheduling_policy,
            scheduler_cls=self.scheduler_cls,
            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,
            disable_hybrid_kv_cache_manager=self.disable_hybrid_kv_cache_manager,
            async_scheduling=self.async_scheduling,
        )

        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"
            )

        lora_config = (
            LoRAConfig(
                max_lora_rank=self.max_lora_rank,
                max_loras=self.max_loras,
                default_mm_loras=self.default_mm_loras,
                fully_sharded_loras=self.fully_sharded_loras,
                lora_extra_vocab_size=self.lora_extra_vocab_size,
                lora_dtype=self.lora_dtype,
                max_cpu_loras=self.max_cpu_loras
                if self.max_cpu_loras and self.max_cpu_loras > 0
                else None,
            )
            if self.enable_lora
            else None
        )

        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

        load_config = self.create_load_config()

        # Pass reasoning_parser into StructuredOutputsConfig
        if self.reasoning_parser:
            self.structured_outputs_config.reasoning_parser = self.reasoning_parser

        # Forward the deprecated CLI args to the StructuredOutputsConfig
        so_config = self.structured_outputs_config
        if self.guided_decoding_backend is not None:
            so_config.guided_decoding_backend = self.guided_decoding_backend
        if self.guided_decoding_disable_fallback is not None:
            so_config.guided_decoding_disable_fallback = (
                self.guided_decoding_disable_fallback
            )
        if self.guided_decoding_disable_any_whitespace is not None:
            so_config.guided_decoding_disable_any_whitespace = (
                self.guided_decoding_disable_any_whitespace
            )
        if self.guided_decoding_disable_additional_properties is not None:
            so_config.guided_decoding_disable_additional_properties = (
                self.guided_decoding_disable_additional_properties
            )

        observability_config = ObservabilityConfig(
            show_hidden_metrics_for_version=(self.show_hidden_metrics_for_version),
            otlp_traces_endpoint=self.otlp_traces_endpoint,
            collect_detailed_traces=self.collect_detailed_traces,
        )

        config = VllmConfig(
            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,
            structured_outputs_config=self.structured_outputs_config,
            observability_config=observability_config,
            compilation_config=self.compilation_config,
            kv_transfer_config=self.kv_transfer_config,
            kv_events_config=self.kv_events_config,
            additional_config=self.additional_config,
        )

        return config

    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.

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

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

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

            if method == "draft_model":
                raise NotImplementedError(
                    "Draft model speculative decoding is not supported yet. "
                    "Please consider using other speculative decoding methods "
                    "such as ngram, medusa, eagle, or mtp."
                )

        V1_BACKENDS = [
            "FLASH_ATTN",
            "PALLAS",
            "TRITON_ATTN",
            "TRITON_MLA",
            "CUTLASS_MLA",
            "FLASHMLA",
            "FLASH_ATTN_MLA",
            "FLASHINFER",
            "FLASHINFER_MLA",
            "ROCM_AITER_MLA",
            "TORCH_SDPA",
            "FLEX_ATTENTION",
            "TREE_ATTN",
            "XFORMERS",
            "ROCM_ATTN",
            "ROCM_AITER_UNIFIED_ATTN",
        ]
        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

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

        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

        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

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

        return True

    def _set_default_args(
        self, usage_context: UsageContext, model_config: ModelConfig
    ) -> None:
        """Set Default Arguments for V1 Engine."""

        # V1 uses chunked prefills and prefix caching by default
        # for non-pooling tasks.
        # For pooling tasks the default is False
        if model_config.runner_type != "pooling":
            self.enable_chunked_prefill = True

            # TODO: When prefix caching supports prompt embeds inputs, this
            # check can be removed.
            if self.enable_prompt_embeds and self.enable_prefix_caching is not False:
                logger.warning(
                    "--enable-prompt-embeds and --enable-prefix-caching "
                    "are not supported together in V1. Prefix caching has "
                    "been disabled."
                )
                self.enable_prefix_caching = False

            if self.enable_prefix_caching is None:
                # Disable prefix caching default for hybrid models
                # since the feature is still experimental.
                if model_config.is_hybrid:
                    self.enable_prefix_caching = False
                else:
                    self.enable_prefix_caching = True
        else:
            pooling_type = model_config.pooler_config.pooling_type
            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
            )

            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)

        # When no user override, set the default values based on the usage
        # context.
        # Use different default values for different hardware.

        # 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:
            device_memory = current_platform.get_device_total_memory()
            device_name = current_platform.get_device_name().lower()
        except Exception:
            # This is only used to set default_max_num_batched_tokens
            device_memory = 0

        # 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.
        from vllm.usage.usage_lib import UsageContext

        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
            # For GPUs like H100 and MI300x, use larger default values.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }

        # 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,
                },
            }

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

        use_context_value = usage_context.value if usage_context else None
        if (
            self.max_num_batched_tokens is None
            and usage_context in default_max_num_batched_tokens
        ):
            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:
                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
                    ]
            logger.debug(
                "Setting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens,
                use_context_value,
            )

        if self.max_num_seqs is None and usage_context in default_max_num_seqs:
            self.max_num_seqs = min(
                default_max_num_seqs[usage_context],
                self.max_num_batched_tokens or sys.maxsize,
            )

            logger.debug(
                "Setting max_num_seqs to %d for %s usage context.",
                self.max_num_seqs,
                use_context_value,
            )


@dataclass
class AsyncEngineArgs(EngineArgs):
    """Arguments for asynchronous vLLM engine."""

    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

    @staticmethod
    def add_cli_args(
        parser: FlexibleArgumentParser, async_args_only: bool = False
    ) -> FlexibleArgumentParser:
        # Initialize plugin to update the parser, for example, The plugin may
        # add a new kind of quantization method to --quantization argument or
        # a new device to --device argument.
        load_general_plugins()
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
        parser.add_argument(
            "--enable-log-requests",
            action=argparse.BooleanOptionalAction,
            default=AsyncEngineArgs.enable_log_requests,
            help="Enable logging requests.",
        )
        parser.add_argument(
            "--disable-log-requests",
            action=argparse.BooleanOptionalAction,
            default=not AsyncEngineArgs.enable_log_requests,
            help="[DEPRECATED] Disable logging requests.",
            deprecated=True,
        )
        current_platform.pre_register_and_update(parser)
        return parser


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 human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.

    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)