# 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 os
import sys
from collections.abc import Callable
from dataclasses import MISSING, asdict, 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

import vllm.envs as envs
from vllm.config import (
    AttentionConfig,
    CacheConfig,
    CompilationConfig,
    ConfigType,
    DeviceConfig,
    DiffusionConfig,
    ECTransferConfig,
    EPLBConfig,
    KernelConfig,
    KVEventsConfig,
    KVTransferConfig,
    LoadConfig,
    LoRAConfig,
    MambaConfig,
    ModelConfig,
    MultiModalConfig,
    ObservabilityConfig,
    OffloadConfig,
    ParallelConfig,
    PoolerConfig,
    PrefetchOffloadConfig,
    ProfilerConfig,
    ReasoningConfig,
    SchedulerConfig,
    SpeculativeConfig,
    StructuredOutputsConfig,
    UVAOffloadConfig,
    VllmConfig,
    WeightTransferConfig,
    get_attr_docs,
)
from vllm.config.cache import (
    CacheDType,
    KVOffloadingBackend,
    MambaCacheMode,
    MambaDType,
    PrefixCachingHashAlgo,
)
from vllm.config.device import Device
from vllm.config.kernel import IrOpPriorityConfig, LinearBackend, MoEBackend
from vllm.config.load import SafetensorsLoadStrategy
from vllm.config.lora import MaxLoRARanks
from vllm.config.mamba import MambaBackendEnum
from vllm.config.model import (
    ConvertOption,
    HfOverrides,
    LogprobsMode,
    ModelDType,
    RunnerOption,
    TokenizerMode,
)
from vllm.config.multimodal import MMCacheType, MMEncoderTPMode, MMTensorIPC
from vllm.config.observability import DetailedTraceModules
from vllm.config.parallel import (
    All2AllBackend,
    DataParallelBackend,
    DCPCommBackend,
    DistributedExecutorBackend,
    ExpertPlacementStrategy,
)
from vllm.config.scheduler import SchedulerPolicy
from vllm.config.utils import get_field
from vllm.config.vllm import OptimizationLevel, PerformanceMode
from vllm.logger import init_logger, suppress_logging
from vllm.platforms import CpuArchEnum, current_platform
from vllm.plugins import load_general_plugins
from vllm.ray.lazy_utils import is_in_ray_actor, is_ray_initialized
from vllm.transformers_utils.config import (
    is_interleaved,
    maybe_override_with_speculators,
)
from vllm.transformers_utils.repo_utils import get_model_path
from vllm.transformers_utils.utils import is_cloud_storage
from vllm.utils.argparse_utils import (
    FlexibleArgumentParser,
    human_readable_int,
    human_readable_int_or_auto,
)
from vllm.utils.mem_constants import GiB_bytes
from vllm.utils.network_utils import get_ip
from vllm.utils.torch_utils import resolve_kv_cache_dtype_string
from vllm.v1.attention.backends.registry import AttentionBackendEnum
from vllm.v1.sample.logits_processor import LogitsProcessor
from vllm.version import __version__ as VLLM_VERSION

if TYPE_CHECKING:
    from vllm.config.quantization import QuantizationConfigArgs
    from vllm.model_executor.layers.quantization import QuantizationMethods
    from vllm.model_executor.model_loader import LoadFormats
    from vllm.usage.usage_lib import UsageContext
    from vllm.v1.executor import Executor
else:
    Executor = Any
    QuantizationMethods = str
    LoadFormats = str
    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


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
)


def _maybe_add_docs_url(cls: Any) -> str:
    """Generate API docs URL for a vllm config class."""
    if not cls.__module__.startswith("vllm.config"):
        return ""
    version = f"v{VLLM_VERSION}" if "dev" not in VLLM_VERSION else "latest"
    return f"\n\nAPI docs: https://docs.vllm.ai/en/{version}/api/vllm/config/#vllm.config.{cls.__name__}"


def _expand_json_human_readable_numbers(val: str) -> str:
    """Expand human-readable number suffixes in a JSON string.

    Based on :func:`human_readable_int` so that the ``k/m/g/t`` (decimal) and
    ``K/M/G/T`` (binary) conventions work out the box.
    Also works inside JSON config arguments such
    as ``--kv-transfer-config '{"cpu_bytes_to_use": 80m}'``.

    Only bare (unquoted) tokens are replaced so that JSON string values
    like ``"model_name"`` are never modified.
    """
    # Split on quoted strings so we only touch non-string regions.
    parts = re.split(r'("(?:[^"\\]|\\.)*")', val)
    for i in range(0, len(parts), 2):  # even indices = outside strings
        parts[i] = re.sub(
            r"\b\d+(?:\.\d+)?[kKmMgGtT]\b",
            lambda m: str(human_readable_int(m.group())),
            parts[i],
        )
    return "".join(parts)


@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):
                if default.default_factory is None:
                    default = default.default
                else:
                    # VllmConfig's Fields have default_factory set to config classes.
                    # These could emit logs on init, which would be confusing.
                    with suppress_logging():
                        default = default.default_factory()  # type: ignore[call-arg]
        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:
                    val = _expand_json_human_readable_numbers(val)
                    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"] += _maybe_add_docs_url(dataclass_cls)
            kwargs[name]["help"] += f"\n\n{json_tip}"
        elif type_hints == {bool, str, type(None)}:
            # Optional-valued flag: bare flag -> True, value -> str.
            kwargs[name]["type"] = str
            kwargs[name]["nargs"] = "?"
            kwargs[name]["const"] = True
        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):
            if name == "max_model_len":
                kwargs[name]["type"] = human_readable_int_or_auto
                kwargs[name]["help"] += f"\n\n{human_readable_int_or_auto.__doc__}"
            elif name in (
                "max_num_batched_tokens",
                "kv_cache_memory_bytes",
                "safetensors_prefetch_block_size",
            ):
                kwargs[name]["type"] = human_readable_int
                kwargs[name]["help"] += f"\n\n{human_readable_int.__doc__}"
            else:
                kwargs[name]["type"] = int
        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
    enable_return_routed_experts: bool = ModelConfig.enable_return_routed_experts
    model_weights: str = ModelConfig.model_weights
    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
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
    tokenizer_mode: TokenizerMode | str = 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: SafetensorsLoadStrategy | None = (
        LoadConfig.safetensors_load_strategy
    )
    safetensors_prefetch_num_threads: int = LoadConfig.safetensors_prefetch_num_threads
    safetensors_prefetch_block_size: int = LoadConfig.safetensors_prefetch_block_size
    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 = ModelConfig.seed
    max_model_len: int = ModelConfig.max_model_len
    cudagraph_capture_sizes: list[int] | None = (
        CompilationConfig.cudagraph_capture_sizes
    )
    max_cudagraph_capture_size: int | None = get_field(
        CompilationConfig, "max_cudagraph_capture_size"
    )
    ir_op_priority: IrOpPriorityConfig = get_field(KernelConfig, "ir_op_priority")
    # 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[Executor] | None
    ) = ParallelConfig.distributed_executor_backend
    # number of P/D disaggregation (or other disaggregation) workers
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    master_addr: str = ParallelConfig.master_addr
    master_port: int = ParallelConfig.master_port
    nnodes: int = ParallelConfig.nnodes
    node_rank: int = ParallelConfig.node_rank
    distributed_timeout_seconds: int | None = ParallelConfig.distributed_timeout_seconds
    cpu_distributed_timeout_seconds: int | None = (
        ParallelConfig.cpu_distributed_timeout_seconds
    )
    numa_bind: bool = ParallelConfig.numa_bind
    numa_bind_nodes: list[int] | None = ParallelConfig.numa_bind_nodes
    numa_bind_cpus: list[str] | None = ParallelConfig.numa_bind_cpus
    device_ids: list[int | str] | None = None
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    prefill_context_parallel_size: int = ParallelConfig.prefill_context_parallel_size
    decode_context_parallel_size: int = ParallelConfig.decode_context_parallel_size
    dcp_comm_backend: DCPCommBackend = ParallelConfig.dcp_comm_backend
    dcp_kv_cache_interleave_size: int = ParallelConfig.dcp_kv_cache_interleave_size
    cp_kv_cache_interleave_size: int = ParallelConfig.cp_kv_cache_interleave_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_external_lb: bool = False
    data_parallel_multi_port_external_lb: bool = False
    data_parallel_backend: DataParallelBackend = ParallelConfig.data_parallel_backend
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
    enable_ep_weight_filter: bool = ParallelConfig.enable_ep_weight_filter
    moe_backend: MoEBackend = KernelConfig.moe_backend
    linear_backend: LinearBackend = KernelConfig.linear_backend
    all2all_backend: All2AllBackend = ParallelConfig.all2all_backend
    enable_elastic_ep: bool = ParallelConfig.enable_elastic_ep
    enable_dbo: bool = ParallelConfig.enable_dbo
    ubatch_size: int = ParallelConfig.ubatch_size
    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 | None = (
        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
    max_parallel_loading_workers: int | None = (
        ParallelConfig.max_parallel_loading_workers
    )
    block_size: int | None = None
    enable_prefix_caching: bool | None = None
    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
    offload_backend: str = OffloadConfig.offload_backend
    cpu_offload_gb: float = UVAOffloadConfig.cpu_offload_gb
    cpu_offload_params: set[str] = get_field(UVAOffloadConfig, "cpu_offload_params")
    offload_group_size: int = PrefetchOffloadConfig.offload_group_size
    offload_num_in_group: int = PrefetchOffloadConfig.offload_num_in_group
    offload_prefetch_step: int = PrefetchOffloadConfig.offload_prefetch_step
    offload_params: set[str] = get_field(PrefetchOffloadConfig, "offload_params")
    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 = None
    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 = None
    max_logprobs: int = ModelConfig.max_logprobs
    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
    use_fp64_gumbel: bool = ModelConfig.use_fp64_gumbel
    disable_log_stats: bool = False
    aggregate_engine_logging: bool = False
    revision: str | None = ModelConfig.revision
    code_revision: str | None = ModelConfig.code_revision
    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 | str | None = ModelConfig.quantization
    quantization_config: "dict[str, Any] | QuantizationConfigArgs | None" = None
    """User-facing quantization configuration. Carries per-layer-kind
    QuantSpecs (linear, moe) and ignore patterns; see
    :class:`QuantizationConfigArgs`. Auto-populated from the matching online
    shorthand when `quantization` is one of the values in
    `ONLINE_QUANT_SHORTHAND_NAMES`."""
    allow_deprecated_quantization: bool = ModelConfig.allow_deprecated_quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
    language_model_only: bool = MultiModalConfig.language_model_only
    limit_mm_per_prompt: dict[str, int | dict[str, int]] = get_field(
        MultiModalConfig, "limit_per_prompt"
    )
    enable_mm_embeds: bool = MultiModalConfig.enable_mm_embeds
    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
    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_only: bool = MultiModalConfig.mm_encoder_only
    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
    mm_encoder_attn_backend: AttentionBackendEnum | str | None = (
        MultiModalConfig.mm_encoder_attn_backend
    )
    mm_encoder_attn_dtype: str | None = MultiModalConfig.mm_encoder_attn_dtype
    mm_encoder_fp8_scale_path: str | None = MultiModalConfig.mm_encoder_fp8_scale_path
    mm_encoder_fp8_scale_save_path: str | None = (
        MultiModalConfig.mm_encoder_fp8_scale_save_path
    )
    mm_encoder_fp8_scale_save_margin: float = (
        MultiModalConfig.mm_encoder_fp8_scale_save_margin
    )
    io_processor_plugin: str | None = None
    renderer_num_workers: int = 1
    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
    video_pruning_rate: float | None = MultiModalConfig.video_pruning_rate
    mm_tensor_ipc: MMTensorIPC = MultiModalConfig.mm_tensor_ipc
    mm_ipc_gpu_memory_gb: float = MultiModalConfig.mm_ipc_gpu_memory_gb
    # LoRA fields
    enable_lora: bool = False
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: MaxLoRARanks = 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_target_modules: list[str] | None = LoRAConfig.target_modules
    enable_tower_connector_lora: bool = LoRAConfig.enable_tower_connector_lora
    specialize_active_lora: bool = LoRAConfig.specialize_active_lora
    enable_mixed_moe_lora_format: bool = LoRAConfig.enable_mixed_moe_lora_format

    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
    num_gpu_blocks_override: int | None = CacheConfig.num_gpu_blocks_override
    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 = None
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input

    scheduler_reserve_full_isl: bool = SchedulerConfig.scheduler_reserve_full_isl
    prefill_schedule_interval: int = SchedulerConfig.prefill_schedule_interval

    watermark: float = SchedulerConfig.watermark

    disable_hybrid_kv_cache_manager: bool | None = (
        SchedulerConfig.disable_hybrid_kv_cache_manager
    )

    structured_outputs_config: StructuredOutputsConfig = get_field(
        VllmConfig, "structured_outputs_config"
    )
    reasoning_parser: str = StructuredOutputsConfig.reasoning_parser
    reasoning_parser_plugin: str | None = None

    speculative_config: dict[str, Any] | None = None
    spec_method: str | None = None
    spec_model: str | None = None
    spec_tokens: int | None = None
    diffusion_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
    )
    kv_cache_metrics: bool = ObservabilityConfig.kv_cache_metrics
    kv_cache_metrics_sample: float = get_field(
        ObservabilityConfig, "kv_cache_metrics_sample"
    )
    cudagraph_metrics: bool = ObservabilityConfig.cudagraph_metrics
    enable_layerwise_nvtx_tracing: bool = (
        ObservabilityConfig.enable_layerwise_nvtx_tracing
    )
    enable_mfu_metrics: bool = ObservabilityConfig.enable_mfu_metrics
    enable_logging_iteration_details: bool = (
        ObservabilityConfig.enable_logging_iteration_details
    )
    jit_monitor_mode: Literal["warn", "error"] = ObservabilityConfig.jit_monitor_mode
    jit_monitor_verbose: bool = ObservabilityConfig.jit_monitor_verbose
    enable_mm_processor_stats: bool = ObservabilityConfig.enable_mm_processor_stats
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: str | type[object] | None = SchedulerConfig.scheduler_cls

    pooler_config: PoolerConfig | None = ModelConfig.pooler_config
    compilation_config: CompilationConfig = get_field(VllmConfig, "compilation_config")
    attention_config: AttentionConfig = get_field(VllmConfig, "attention_config")
    mamba_config: MambaConfig = get_field(VllmConfig, "mamba_config")
    kernel_config: KernelConfig = get_field(VllmConfig, "kernel_config")
    enable_flashinfer_autotune: bool = get_field(
        KernelConfig, "enable_flashinfer_autotune"
    )
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls

    profiler_config: ProfilerConfig = get_field(VllmConfig, "profiler_config")

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

    ec_transfer_config: ECTransferConfig | None = None
    reasoning_config: ReasoningConfig = get_field(VllmConfig, "reasoning_config")

    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    enable_cumem_allocator: bool = ModelConfig.enable_cumem_allocator
    override_generation_config: dict[str, Any] = get_field(
        ModelConfig, "override_generation_config"
    )
    model_impl: str = ModelConfig.model_impl
    override_attention_dtype: str | None = ModelConfig.override_attention_dtype
    attention_backend: AttentionBackendEnum | None = AttentionConfig.backend

    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
    kv_cache_dtype_skip_layers: list[str] = get_field(
        CacheConfig, "kv_cache_dtype_skip_layers"
    )
    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
    mamba_block_size: int | None = get_field(CacheConfig, "mamba_block_size")
    mamba_cache_mode: MambaCacheMode = CacheConfig.mamba_cache_mode

    mamba_backend: MambaBackendEnum = MambaBackendEnum.TRITON
    enable_mamba_cache_stochastic_rounding: bool = (
        MambaConfig.enable_stochastic_rounding
    )
    mamba_cache_philox_rounds: int = MambaConfig.stochastic_rounding_philox_rounds

    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 | dict[str, str] = LoadConfig.pt_load_map_location

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

    async_scheduling: bool | None = SchedulerConfig.async_scheduling

    stream_interval: int = SchedulerConfig.stream_interval

    kv_sharing_fast_prefill: bool = CacheConfig.kv_sharing_fast_prefill
    optimization_level: OptimizationLevel = VllmConfig.optimization_level
    performance_mode: PerformanceMode = VllmConfig.performance_mode

    kv_offloading_size: float | None = CacheConfig.kv_offloading_size
    kv_offloading_backend: KVOffloadingBackend = CacheConfig.kv_offloading_backend
    tokens_only: bool = False

    shutdown_timeout: int = 0

    weight_transfer_config: WeightTransferConfig | None = get_field(
        VllmConfig,
        "weight_transfer_config",
    )

    fail_on_environ_validation: bool = False
    gdn_prefill_backend: Literal["flashinfer", "triton", "cutedsl"] | None = None

    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.attention_config, dict):
            self.attention_config = AttentionConfig(**self.attention_config)
        if isinstance(self.mamba_config, dict):
            self.mamba_config = MambaConfig(**self.mamba_config)
        if isinstance(self.kernel_config, dict):
            self.kernel_config = KernelConfig(**self.kernel_config)
        if isinstance(self.eplb_config, dict):
            self.eplb_config = EPLBConfig(**self.eplb_config)
        if isinstance(self.weight_transfer_config, dict):
            self.weight_transfer_config = WeightTransferConfig(
                **self.weight_transfer_config
            )
        if isinstance(self.ir_op_priority, dict):
            self.ir_op_priority = IrOpPriorityConfig(**self.ir_op_priority)

        from vllm.config.quantization import resolve_quantization_config

        self.quantization_config = resolve_quantization_config(
            self.quantization, self.quantization_config
        )

        # Setup plugins
        from vllm.plugins import load_general_plugins

        load_general_plugins()
        # when use hf offline,replace model and tokenizer id to local model path
        if huggingface_hub.constants.HF_HUB_OFFLINE:
            # Skip cloud storage URIs (s3://, gs://, az://) — they are not
            # HF repo IDs and will be resolved later by
            # ModelConfig.maybe_pull_model_tokenizer_for_runai().
            if not is_cloud_storage(self.model):
                model_id = self.model
                self.model = get_model_path(self.model, self.revision)
                if model_id is not self.model:
                    logger.info(
                        "HF_HUB_OFFLINE is True, replace model_id "
                        "[%s] to model_path [%s]",
                        model_id,
                        self.model,
                    )
            if self.tokenizer is not None and not is_cloud_storage(self.tokenizer):
                tokenizer_id = self.tokenizer
                self.tokenizer = get_model_path(self.tokenizer, self.tokenizer_revision)
                if tokenizer_id is not self.tokenizer:
                    logger.info(
                        "HF_HUB_OFFLINE is True, replace tokenizer_id [%s] "
                        "to tokenizer_path [%s]",
                        tokenizer_id,
                        self.tokenizer,
                    )

    @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("--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(
            "--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(
            "--quantization-config", **model_kwargs["quantization_config"]
        )
        model_group.add_argument(
            "--allow-deprecated-quantization",
            **model_kwargs["allow_deprecated_quantization"],
        )
        model_group.add_argument("--enforce-eager", **model_kwargs["enforce_eager"])
        model_group.add_argument(
            "--enable-return-routed-experts",
            **model_kwargs["enable_return_routed_experts"],
        )
        model_group.add_argument("--max-logprobs", **model_kwargs["max_logprobs"])
        model_group.add_argument("--logprobs-mode", **model_kwargs["logprobs_mode"])
        model_group.add_argument("--use-fp64-gumbel", **model_kwargs["use_fp64_gumbel"])
        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"])
        model_group.add_argument("--hf-token", **model_kwargs["hf_token"])
        model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"])
        model_group.add_argument("--pooler-config", **model_kwargs["pooler_config"])
        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(
            "--enable-cumem-allocator", **model_kwargs["enable_cumem_allocator"]
        )
        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_group.add_argument(
            "--renderer-num-workers",
            **model_kwargs["renderer_num_workers"],
        )

        # 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(
            "--safetensors-prefetch-num-threads",
            **load_kwargs["safetensors_prefetch_num_threads"],
        )
        load_group.add_argument(
            "--safetensors-prefetch-block-size",
            **load_kwargs["safetensors_prefetch_block_size"],
        )
        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"]
        )

        # Attention arguments
        attention_kwargs = get_kwargs(AttentionConfig)
        attention_group = parser.add_argument_group(
            title="AttentionConfig",
            description=AttentionConfig.__doc__,
        )
        attention_group.add_argument(
            "--attention-backend", **attention_kwargs["backend"]
        )

        # Mamba arguments
        mamba_kwargs = get_kwargs(MambaConfig)
        mamba_group = parser.add_argument_group(
            title="MambaConfig",
            description=MambaConfig.__doc__,
        )
        mamba_group.add_argument("--mamba-backend", **mamba_kwargs["backend"])
        mamba_group.add_argument(
            "--enable-mamba-cache-stochastic-rounding",
            **mamba_kwargs["enable_stochastic_rounding"],
        )
        mamba_group.add_argument(
            "--mamba-cache-philox-rounds",
            **mamba_kwargs["stochastic_rounding_philox_rounds"],
        )

        # 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",
            # Choices need to be validated after parsing to include plugins
            **structured_outputs_kwargs["reasoning_parser"],
        )
        structured_outputs_group.add_argument(
            "--reasoning-parser-plugin",
            **structured_outputs_kwargs["reasoning_parser_plugin"],
        )

        # 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("--master-addr", **parallel_kwargs["master_addr"])
        parallel_group.add_argument("--master-port", **parallel_kwargs["master_port"])
        parallel_group.add_argument("--nnodes", "-n", **parallel_kwargs["nnodes"])
        parallel_group.add_argument("--node-rank", "-r", **parallel_kwargs["node_rank"])
        parallel_group.add_argument(
            "--distributed-timeout-seconds",
            **parallel_kwargs["distributed_timeout_seconds"],
        )
        parallel_group.add_argument(
            "--cpu-distributed-timeout-seconds",
            **parallel_kwargs["cpu_distributed_timeout_seconds"],
        )
        parallel_group.add_argument("--numa-bind", **parallel_kwargs["numa_bind"])
        parallel_group.add_argument(
            "--numa-bind-nodes", **parallel_kwargs["numa_bind_nodes"]
        )
        parallel_group.add_argument(
            "--numa-bind-cpus", **parallel_kwargs["numa_bind_cpus"]
        )
        parallel_group.add_argument(
            "--device-ids",
            type=lambda s: [
                int(device_id) if device_id.isdigit() else device_id
                for device_id in (part.strip() for part in s.split(","))
            ],
            default=None,
            help="Comma-separated physical GPU device IDs or UUIDs to use "
            '(e.g. --device-ids "2,3,5,7"). Avoids setting '
            "CUDA_VISIBLE_DEVICES, preserving full GPU topology "
            "visibility for GPU-NIC affinity and DeepGEMM. "
            "Note: has no effect with Ray executors; use Ray "
            "placement groups for GPU selection instead.",
        )
        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(
            "--dcp-comm-backend",
            **parallel_kwargs["dcp_comm_backend"],
        )
        parallel_group.add_argument(
            "--dcp-kv-cache-interleave-size",
            **parallel_kwargs["dcp_kv_cache_interleave_size"],
        )
        parallel_group.add_argument(
            "--cp-kv-cache-interleave-size",
            **parallel_kwargs["cp_kv_cache_interleave_size"],
        )
        parallel_group.add_argument(
            "--prefill-context-parallel-size",
            "-pcp",
            **parallel_kwargs["prefill_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 for MoE "
            "data-parallel deployments. Unsupported for non-MoE models; "
            "launch independent vLLM instances instead.",
        )
        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",
            "-dph",
            **parallel_kwargs["data_parallel_hybrid_lb"],
        )
        parallel_group.add_argument(
            "--data-parallel-external-lb",
            "-dpe",
            **parallel_kwargs["data_parallel_external_lb"],
        )
        parallel_group.add_argument(
            "--data-parallel-multi-port-external-lb",
            "-dpm",
            action="store_true",
            default=False,
            help="Run a node-local supervisor that launches one external-LB API "
            "server per local data parallel rank and exposes aggregated health on "
            "a supervisor port.",
        )
        parallel_group.add_argument(
            "--enable-expert-parallel",
            "-ep",
            **parallel_kwargs["enable_expert_parallel"],
        )
        parallel_group.add_argument(
            "--enable-ep-weight-filter",
            **parallel_kwargs["enable_ep_weight_filter"],
        )
        parallel_group.add_argument(
            "--all2all-backend", **parallel_kwargs["all2all_backend"]
        )
        parallel_group.add_argument("--enable-dbo", **parallel_kwargs["enable_dbo"])
        parallel_group.add_argument(
            "--ubatch-size",
            **parallel_kwargs["ubatch_size"],
        )
        parallel_group.add_argument(
            "--enable-elastic-ep", **parallel_kwargs["enable_elastic_ep"]
        )
        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(
            "--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"]
        )

        # 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("--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"],
                "default": None,
            },
        )
        cache_group.add_argument(
            "--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]
        )
        cache_group.add_argument(
            "--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]
        )
        cache_group.add_argument(
            "--kv-cache-dtype-skip-layers", **cache_kwargs["kv_cache_dtype_skip_layers"]
        )
        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"]
        )
        cache_group.add_argument(
            "--mamba-block-size", **cache_kwargs["mamba_block_size"]
        )
        cache_group.add_argument(
            "--mamba-cache-mode", **cache_kwargs["mamba_cache_mode"]
        )
        cache_group.add_argument(
            "--kv-offloading-size", **cache_kwargs["kv_offloading_size"]
        )
        cache_group.add_argument(
            "--kv-offloading-backend", **cache_kwargs["kv_offloading_backend"]
        )

        # Model weight offload related configs
        offload_kwargs = get_kwargs(OffloadConfig)
        uva_kwargs = get_kwargs(UVAOffloadConfig)
        prefetch_kwargs = get_kwargs(PrefetchOffloadConfig)
        offload_group = parser.add_argument_group(
            title="OffloadConfig",
            description=OffloadConfig.__doc__,
        )
        offload_group.add_argument(
            "--offload-backend", **offload_kwargs["offload_backend"]
        )
        offload_group.add_argument("--cpu-offload-gb", **uva_kwargs["cpu_offload_gb"])
        offload_group.add_argument(
            "--cpu-offload-params", **uva_kwargs["cpu_offload_params"]
        )
        offload_group.add_argument(
            "--offload-group-size",
            **prefetch_kwargs["offload_group_size"],
        )
        offload_group.add_argument(
            "--offload-num-in-group",
            **prefetch_kwargs["offload_num_in_group"],
        )
        offload_group.add_argument(
            "--offload-prefetch-step",
            **prefetch_kwargs["offload_prefetch_step"],
        )
        offload_group.add_argument(
            "--offload-params", **prefetch_kwargs["offload_params"]
        )

        # Multimodal related configs
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
        multimodal_group.add_argument(
            "--language-model-only", **multimodal_kwargs["language_model_only"]
        )
        multimodal_group.add_argument(
            "--limit-mm-per-prompt", **multimodal_kwargs["limit_per_prompt"]
        )
        multimodal_group.add_argument(
            "--enable-mm-embeds", **multimodal_kwargs["enable_mm_embeds"]
        )
        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(
            "--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-only", **multimodal_kwargs["mm_encoder_only"]
        )
        multimodal_group.add_argument(
            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"]
        )
        multimodal_group.add_argument(
            "--mm-encoder-attn-backend",
            **multimodal_kwargs["mm_encoder_attn_backend"],
        )
        multimodal_group.add_argument(
            "--mm-encoder-attn-dtype",
            **multimodal_kwargs["mm_encoder_attn_dtype"],
        )
        multimodal_group.add_argument(
            "--mm-encoder-fp8-scale-path",
            **multimodal_kwargs["mm_encoder_fp8_scale_path"],
        )
        multimodal_group.add_argument(
            "--mm-encoder-fp8-scale-save-path",
            **multimodal_kwargs["mm_encoder_fp8_scale_save_path"],
        )
        multimodal_group.add_argument(
            "--mm-encoder-fp8-scale-save-margin",
            **multimodal_kwargs["mm_encoder_fp8_scale_save_margin"],
        )
        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"]
        )
        multimodal_group.add_argument(
            "--mm-tensor-ipc", **multimodal_kwargs["mm_tensor_ipc"]
        )
        multimodal_group.add_argument(
            "--mm-ipc-gpu-memory-gb",
            **multimodal_kwargs["mm_ipc_gpu_memory_gb"],
        )

        # 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-dtype",
            **lora_kwargs["lora_dtype"],
        )
        lora_group.add_argument(
            "--enable-tower-connector-lora",
            **lora_kwargs["enable_tower_connector_lora"],
        )
        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(
            "--lora-target-modules", **lora_kwargs["target_modules"]
        )
        lora_group.add_argument("--default-mm-loras", **lora_kwargs["default_mm_loras"])
        lora_group.add_argument(
            "--specialize-active-lora", **lora_kwargs["specialize_active_lora"]
        )
        lora_group.add_argument(
            "--enable-mixed-moe-lora-format",
            **lora_kwargs["enable_mixed_moe_lora_format"],
        )

        # 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"],
        )
        observability_group.add_argument(
            "--kv-cache-metrics", **observability_kwargs["kv_cache_metrics"]
        )
        observability_group.add_argument(
            "--kv-cache-metrics-sample",
            **observability_kwargs["kv_cache_metrics_sample"],
        )
        observability_group.add_argument(
            "--cudagraph-metrics",
            **observability_kwargs["cudagraph_metrics"],
        )
        observability_group.add_argument(
            "--enable-layerwise-nvtx-tracing",
            **observability_kwargs["enable_layerwise_nvtx_tracing"],
        )
        observability_group.add_argument(
            "--enable-mfu-metrics",
            **observability_kwargs["enable_mfu_metrics"],
        )
        observability_group.add_argument(
            "--enable-logging-iteration-details",
            **observability_kwargs["enable_logging_iteration_details"],
        )
        observability_group.add_argument(
            "--jit-monitor-mode",
            **observability_kwargs["jit_monitor_mode"],
        )
        observability_group.add_argument(
            "--jit-monitor-verbose",
            **observability_kwargs["jit_monitor_verbose"],
        )

        # 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"],
                "default": None,
            },
        )
        scheduler_group.add_argument(
            "--max-num-seqs",
            **{
                **scheduler_kwargs["max_num_seqs"],
                "default": None,
            },
        )
        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(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"],
        )
        # 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"],
                "default": None,
            },
        )
        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(
            "--scheduler-reserve-full-isl",
            **scheduler_kwargs["scheduler_reserve_full_isl"],
        )
        scheduler_group.add_argument("--watermark", **scheduler_kwargs["watermark"])
        scheduler_group.add_argument(
            "--prefill-schedule-interval",
            **scheduler_kwargs["prefill_schedule_interval"],
        )
        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"]
        )
        scheduler_group.add_argument(
            "--stream-interval", **scheduler_kwargs["stream_interval"]
        )

        # Compilation arguments
        compilation_kwargs = get_kwargs(CompilationConfig)
        compilation_group = parser.add_argument_group(
            title="CompilationConfig",
            description=CompilationConfig.__doc__,
        )
        compilation_group.add_argument(
            "--cudagraph-capture-sizes", **compilation_kwargs["cudagraph_capture_sizes"]
        )
        compilation_group.add_argument(
            "--max-cudagraph-capture-size",
            **compilation_kwargs["max_cudagraph_capture_size"],
        )

        # Kernel arguments
        kernel_kwargs = get_kwargs(KernelConfig)
        kernel_group = parser.add_argument_group(
            title="KernelConfig",
            description=KernelConfig.__doc__,
        )
        kernel_group.add_argument("--ir-op-priority", **kernel_kwargs["ir_op_priority"])
        kernel_group.add_argument(
            "--enable-flashinfer-autotune",
            **kernel_kwargs["enable_flashinfer_autotune"],
        )
        moe_backend_kwargs = kernel_kwargs["moe_backend"]
        moe_backend_kwargs["type"] = lambda s: s.lower().replace("-", "_")
        kernel_group.add_argument("--moe-backend", **moe_backend_kwargs)
        linear_backend_kwargs = kernel_kwargs["linear_backend"]
        linear_backend_kwargs["type"] = lambda s: s.lower().replace("-", "_")
        kernel_group.add_argument("--linear-backend", **linear_backend_kwargs)

        # 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", "-sc", **vllm_kwargs["speculative_config"]
        )
        speculative_kwargs = get_kwargs(SpeculativeConfig)
        vllm_group.add_argument("--spec-method", **speculative_kwargs["method"])
        vllm_group.add_argument("--spec-model", **speculative_kwargs["model"])
        vllm_group.add_argument(
            "--spec-tokens", **speculative_kwargs["num_speculative_tokens"]
        )
        vllm_kwargs["diffusion_config"]["type"] = optional_type(json.loads)
        vllm_group.add_argument(
            "--diffusion-config", "-dc", **vllm_kwargs["diffusion_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(
            "--ec-transfer-config", **vllm_kwargs["ec_transfer_config"]
        )
        vllm_group.add_argument(
            "--compilation-config", "-cc", **vllm_kwargs["compilation_config"]
        )
        vllm_group.add_argument(
            "--attention-config", "-ac", **vllm_kwargs["attention_config"]
        )
        vllm_group.add_argument("--reasoning-config", **vllm_kwargs["reasoning_config"])
        vllm_group.add_argument("--kernel-config", **vllm_kwargs["kernel_config"])
        vllm_group.add_argument(
            "--additional-config", **vllm_kwargs["additional_config"]
        )
        vllm_group.add_argument(
            "--structured-outputs-config", **vllm_kwargs["structured_outputs_config"]
        )
        vllm_group.add_argument("--profiler-config", **vllm_kwargs["profiler_config"])
        vllm_group.add_argument(
            "--optimization-level", **vllm_kwargs["optimization_level"]
        )
        vllm_group.add_argument("--performance-mode", **vllm_kwargs["performance_mode"])
        vllm_group.add_argument(
            "--weight-transfer-config", **vllm_kwargs["weight_transfer_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.",
        )

        parser.add_argument(
            "--fail-on-environ-validation",
            help="If set, the engine will raise an error if "
            "environment validation fails.",
            default=False,
            action=argparse.BooleanOptionalAction,
        )

        parser.add_argument(
            "--shutdown-timeout",
            type=int,
            default=0,
            help="Shutdown timeout in seconds. 0 = abort, >0 = wait.",
        )

        parser.add_argument(
            "--gdn-prefill-backend",
            dest="gdn_prefill_backend",
            choices=["flashinfer", "triton", "cutedsl"],
            default=None,
            help="Select GDN prefill backend.",
        )
        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:
        if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
            logger.warning(
                "The global random seed is set to %d. Since "
                "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
                "affect the random state of the Python process that "
                "launched vLLM.",
                self.seed,
            )

        return ModelConfig(
            model=self.model,
            model_weights=self.model_weights,
            hf_config_path=self.hf_config_path,
            runner=self.runner,
            convert=self.convert,
            tokenizer=self.tokenizer,  # type: ignore[arg-type]
            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,
            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,
            quantization_config=self.quantization_config,
            allow_deprecated_quantization=self.allow_deprecated_quantization,
            enforce_eager=self.enforce_eager,
            enable_return_routed_experts=self.enable_return_routed_experts,
            max_logprobs=self.max_logprobs,
            logprobs_mode=self.logprobs_mode,
            use_fp64_gumbel=self.use_fp64_gumbel,
            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,
            language_model_only=self.language_model_only,
            limit_mm_per_prompt=self.limit_mm_per_prompt,
            enable_mm_embeds=self.enable_mm_embeds,
            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_only=self.mm_encoder_only,
            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
            mm_encoder_attn_backend=self.mm_encoder_attn_backend,
            mm_encoder_attn_dtype=self.mm_encoder_attn_dtype,
            mm_encoder_fp8_scale_path=self.mm_encoder_fp8_scale_path,
            mm_encoder_fp8_scale_save_path=self.mm_encoder_fp8_scale_save_path,
            mm_encoder_fp8_scale_save_margin=self.mm_encoder_fp8_scale_save_margin,
            pooler_config=self.pooler_config,
            generation_config=self.generation_config,
            override_generation_config=self.override_generation_config,
            enable_sleep_mode=self.enable_sleep_mode,
            enable_cumem_allocator=self.enable_cumem_allocator,
            model_impl=self.model_impl,
            override_attention_dtype=self.override_attention_dtype,
            logits_processors=self.logits_processors,
            video_pruning_rate=self.video_pruning_rate,
            mm_tensor_ipc=self.mm_tensor_ipc,
            mm_ipc_gpu_memory_gb=self.mm_ipc_gpu_memory_gb,
            io_processor_plugin=self.io_processor_plugin,
            renderer_num_workers=self.renderer_num_workers,
        )

    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,
            safetensors_prefetch_num_threads=self.safetensors_prefetch_num_threads,
            safetensors_prefetch_block_size=self.safetensors_prefetch_block_size,
            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,
    ) -> SpeculativeConfig | None:
        """Initializes and returns a SpeculativeConfig object based on
        `speculative_config`.
        """
        for flag, key, value in (
            ("--spec-method", "method", self.spec_method),
            ("--spec-model", "model", self.spec_model),
            ("--spec-tokens", "num_speculative_tokens", self.spec_tokens),
        ):
            if value is None:
                continue
            if self.speculative_config is None:
                self.speculative_config = {}
            if key in self.speculative_config:
                raise ValueError(
                    f"{flag} and --speculative-config['{key}'] are mutually exclusive"
                )
            self.speculative_config[key] = value

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

    def _resolve_device_ids(self) -> list[int] | None:
        if not self.device_ids:
            return None
        if self.distributed_executor_backend == "ray":
            logger.warning(
                "--device-ids has no effect when using the Ray executor. "
                "Use Ray placement groups for GPU selection instead."
            )
        ids = self.device_ids
        if len(set(ids)) != len(ids):
            raise ValueError(f"--device-ids must not contain duplicates: {ids}")
        if all(isinstance(i, str) for i in ids):
            return [
                current_platform.device_control_id_to_physical_device_id(i)
                for i in cast(list[str], ids)
            ]
        if any(isinstance(i, str) for i in ids):
            raise ValueError("--device-ids must not mix integer IDs and UUIDs")
        int_ids = cast(list[int], ids)
        # Compose with CUDA_VISIBLE_DEVICES: if CVD is set, treat
        # --device-ids values as indices into the CVD-visible set.
        cvd = getattr(
            envs,
            current_platform.device_control_env_var,
            os.environ.get(current_platform.device_control_env_var),
        )
        if cvd:
            cvd_ids = [
                current_platform.device_control_id_to_physical_device_id(x)
                for x in cvd.split(",")
            ]
            for i in int_ids:
                if i >= len(cvd_ids):
                    raise ValueError(
                        f"--device-ids index {i} is out of range for "
                        f"{current_platform.device_control_env_var}"
                        f"={cvd} ({len(cvd_ids)} devices visible)"
                    )
            return [cvd_ids[i] for i in int_ids]
        return int_ids

    def create_diffusion_config(self) -> DiffusionConfig | None:
        if self.diffusion_config is None:
            return None
        cfg = self.diffusion_config
        if isinstance(cfg, str):
            cfg = json.loads(cfg)
        return DiffusionConfig(**cfg)

    def create_observability_config(self) -> ObservabilityConfig:
        return 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,
            kv_cache_metrics=self.kv_cache_metrics,
            kv_cache_metrics_sample=self.kv_cache_metrics_sample,
            cudagraph_metrics=self.cudagraph_metrics,
            enable_layerwise_nvtx_tracing=self.enable_layerwise_nvtx_tracing,
            enable_mfu_metrics=self.enable_mfu_metrics,
            enable_mm_processor_stats=self.enable_mm_processor_stats,
            enable_logging_iteration_details=self.enable_logging_iteration_details,
            jit_monitor_mode=self.jit_monitor_mode,
            jit_monitor_verbose=self.jit_monitor_verbose,
        )

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

        NOTE: If VllmConfig is incompatible, we raise an error.
        """
        current_platform.pre_register_and_update()

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

        envs.validate_environ(self.fail_on_environ_validation)

        # Check if the model is a speculator and override model/tokenizer/config
        # BEFORE creating ModelConfig, so the config is created with the target model
        # Skip speculator detection for cloud storage models (eg: S3, GCS) since
        # HuggingFace cannot load configs directly from S3 URLs. S3 models can still
        # use speculators with explicit --speculative-config.
        if not is_cloud_storage(self.model):
            (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,
                    hf_token=self.hf_token,
                )
            )

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

        self._check_feature_supported()
        self._set_default_chunked_prefill_and_prefix_caching_args(model_config)
        self._set_default_reasoning_config_args()
        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()

        # Resolve "auto" kv_cache_dtype to actual value from model config
        resolved_cache_dtype = resolve_kv_cache_dtype_string(
            self.kv_cache_dtype, model_config
        )

        assert self.enable_prefix_caching is not None, (
            "enable_prefix_caching must be set by this point"
        )

        cache_config = CacheConfig(
            block_size=self.block_size,  # type: ignore[arg-type]
            gpu_memory_utilization=self.gpu_memory_utilization,
            kv_cache_memory_bytes=self.kv_cache_memory_bytes,
            cache_dtype=resolved_cache_dtype,  # type: ignore[arg-type]
            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,
            calculate_kv_scales=self.calculate_kv_scales,
            kv_cache_dtype_skip_layers=self.kv_cache_dtype_skip_layers,
            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,
            mamba_block_size=self.mamba_block_size,
            mamba_cache_mode=self.mamba_cache_mode,
            kv_offloading_size=self.kv_offloading_size,
            kv_offloading_backend=self.kv_offloading_backend,
        )

        if resolved_cache_dtype.startswith("turboquant_"):
            from vllm.model_executor.layers.quantization.turboquant.config import (
                TurboQuantConfig,
            )

            boundary = TurboQuantConfig.get_boundary_skip_layers(model_config)
            existing = set(cache_config.kv_cache_dtype_skip_layers)
            cache_config.kv_cache_dtype_skip_layers = sorted(
                existing | set(boundary), key=int
            )

        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"
        )
        assert not (self.data_parallel_hybrid_lb and self.data_parallel_external_lb), (
            "data_parallel_hybrid_lb and data_parallel_external_lb cannot both be True."
        )
        assert self.data_parallel_backend == "mp" or self.nnodes == 1, (
            "nnodes > 1 is only supported with data_parallel_backend=mp"
        )
        inferred_data_parallel_rank = 0
        if self.nnodes > 1:
            world_size = (
                self.data_parallel_size
                * self.pipeline_parallel_size
                * self.tensor_parallel_size
            )
            world_size_within_dp = (
                self.pipeline_parallel_size * self.tensor_parallel_size
            )
            local_world_size = world_size // self.nnodes
            assert world_size % self.nnodes == 0, (
                f"world_size={world_size} must be divisible by nnodes={self.nnodes}."
            )
            assert self.node_rank < self.nnodes, (
                f"node_rank={self.node_rank} must be less than nnodes={self.nnodes}."
            )
            inferred_data_parallel_rank = (
                self.node_rank * local_world_size
            ) // world_size_within_dp
            if self.data_parallel_size > 1 and self.data_parallel_external_lb:
                self.data_parallel_rank = inferred_data_parallel_rank
                logger.info(
                    "Inferred data_parallel_rank %d from node_rank %d for external lb",
                    self.data_parallel_rank,
                    self.node_rank,
                )
            elif self.data_parallel_size_local is None:
                # Infer data parallel size local for internal dplb:
                self.data_parallel_size_local = max(
                    local_world_size // world_size_within_dp, 1
                )
        data_parallel_external_lb = (
            self.data_parallel_external_lb or self.data_parallel_rank is not None
        )
        if (
            self.data_parallel_size > 1
            and data_parallel_external_lb
            and not model_config.is_moe
        ):
            raise ValueError(
                "Non-MoE models do not support external data parallel mode. "
                "For external load balancing, launch independent vLLM "
                "instances without --data-parallel-* arguments."
            )
        # Local DP rank = 1, use pure-external LB.
        if data_parallel_external_lb:
            assert self.data_parallel_rank is not None, (
                "data_parallel_rank or node_rank must be specified if "
                "data_parallel_external_lb is enable."
            )
            assert self.data_parallel_size_local in (1, None), (
                "data_parallel_size_local must be 1 or None 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.
                logger.warning(
                    "data_parallel_hybrid_lb is not eligible when "
                    "data_parallel_size_local = 1, autoswitch to "
                    "data_parallel_external_lb."
                )
                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 inferred_data_parallel_rank
            )
            if self.nnodes > 1:
                logger.info(
                    "Inferred data_parallel_rank %d from node_rank %d",
                    self.data_parallel_rank,
                    self.node_rank,
                )
        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 = (
                    self.master_addr or 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.tokens_only and not model_config.skip_tokenizer_init:
            model_config.skip_tokenizer_init = True
            logger.info("Skipping tokenizer initialization for tokens-only mode.")

        parallel_config = ParallelConfig(
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            prefill_context_parallel_size=self.prefill_context_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,
            master_addr=self.master_addr,
            master_port=self.master_port,
            nnodes=self.nnodes,
            node_rank=self.node_rank,
            distributed_timeout_seconds=self.distributed_timeout_seconds,
            cpu_distributed_timeout_seconds=self.cpu_distributed_timeout_seconds,
            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,
            is_moe_model=model_config.is_moe,
            enable_expert_parallel=self.enable_expert_parallel,
            enable_ep_weight_filter=self.enable_ep_weight_filter,
            all2all_backend=self.all2all_backend,
            enable_elastic_ep=self.enable_elastic_ep,
            enable_dbo=self.enable_dbo,
            ubatch_size=self.ubatch_size,
            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,
            dcp_comm_backend=self.dcp_comm_backend,
            dcp_kv_cache_interleave_size=self.dcp_kv_cache_interleave_size,
            cp_kv_cache_interleave_size=self.cp_kv_cache_interleave_size,
            _api_process_count=self._api_process_count,
            _api_process_rank=self._api_process_rank,
            assigned_physical_gpu_ids=self._resolve_device_ids(),
            numa_bind=self.numa_bind,
            numa_bind_nodes=self.numa_bind_nodes,
            numa_bind_cpus=self.numa_bind_cpus,
        )

        speculative_config = self.create_speculative_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
        )
        diffusion_config = self.create_diffusion_config()

        self._set_default_max_num_seqs_and_batched_tokens_args(
            usage_context,
            model_config,
            parallel_config,
        )

        assert self.max_num_batched_tokens is not None, (
            "max_num_batched_tokens must be set by this point"
        )
        assert self.max_num_seqs is not None, "max_num_seqs must be set by this point"
        assert self.enable_chunked_prefill is not None, (
            "enable_chunked_prefill must be set by this point"
        )
        assert model_config.max_model_len is not None, (
            "max_model_len must be set by this point"
        )
        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,
            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,
            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,
            scheduler_reserve_full_isl=self.scheduler_reserve_full_isl,
            watermark=self.watermark,
            prefill_schedule_interval=self.prefill_schedule_interval,
            disable_hybrid_kv_cache_manager=self.disable_hybrid_kv_cache_manager,
            async_scheduling=self.async_scheduling,
            stream_interval=self.stream_interval,
        )

        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_dtype=self.lora_dtype,
                target_modules=self.lora_target_modules,
                enable_tower_connector_lora=self.enable_tower_connector_lora,
                specialize_active_lora=self.specialize_active_lora,
                enable_mixed_moe_lora_format=self.enable_mixed_moe_lora_format,
                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
        )

        if (
            lora_config is not None
            and speculative_config is not None
            and scheduler_config.max_num_batched_tokens
            < (
                scheduler_config.max_num_seqs
                * (speculative_config.num_speculative_tokens + 1)
            )
        ):
            raise ValueError(
                "Consider increasing max_num_batched_tokens or "
                "decreasing num_speculative_tokens"
            )

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

        # Attention config overrides
        attention_config = copy.deepcopy(self.attention_config)
        if self.attention_backend is not None:
            if attention_config.backend is not None:
                raise ValueError(
                    "attention_backend and attention_config.backend "
                    "are mutually exclusive"
                )
            # Reuse the validator to handle "auto" and string-to-enum conversion
            attention_config.backend = AttentionConfig.validate_backend_before(
                self.attention_backend
            )

        # TurboQuant requires FlashAttention 2 — FA3 boundary layers assert
        # FlashAttentionImpl which fails with TurboQuantAttentionImpl.
        if resolved_cache_dtype.startswith("turboquant_") and (
            attention_config.flash_attn_version is None
            or attention_config.flash_attn_version >= 3
        ):
            logger.warning(
                "TurboQuant is not yet compatible with FlashAttention >= 3. "
                "Overriding flash_attn_version to 2. To silence this "
                "warning, pass --attention-config.flash_attn_version=2"
            )
            attention_config.flash_attn_version = 2

        # Mamba config overrides
        mamba_config = copy.deepcopy(self.mamba_config)
        # Convert string to enum if needed (CLI parsing returns a string)
        if isinstance(self.mamba_backend, str):
            mamba_config.backend = MambaBackendEnum[self.mamba_backend.upper()]
        else:
            mamba_config.backend = self.mamba_backend
        if self.enable_mamba_cache_stochastic_rounding:
            mamba_config.enable_stochastic_rounding = (
                self.enable_mamba_cache_stochastic_rounding
            )
        if self.mamba_cache_philox_rounds:
            mamba_config.stochastic_rounding_philox_rounds = (
                self.mamba_cache_philox_rounds
            )

        # Kernel config overrides
        kernel_config = copy.deepcopy(self.kernel_config)
        if self.enable_flashinfer_autotune is not None:
            if kernel_config.enable_flashinfer_autotune is not None:
                raise ValueError(
                    "enable_flashinfer_autotune and "
                    "kernel_config.enable_flashinfer_autotune "
                    "are mutually exclusive"
                )
            kernel_config.enable_flashinfer_autotune = self.enable_flashinfer_autotune
        if self.moe_backend != "auto":
            kernel_config.moe_backend = self.moe_backend
        if self.linear_backend != "auto":
            kernel_config.linear_backend = self.linear_backend

        # Transfer top-level ir_op_priority into KernelConfig.ir_op_priority
        for op_name, op_priority in asdict(self.ir_op_priority).items():
            # Empty means unset
            if not op_priority:
                continue

            # Priority cannot be set 2x for the same op
            if getattr(kernel_config.ir_op_priority, op_name):
                raise ValueError(
                    f"Op priority for {op_name} specified via both ir_op_priority "
                    f"and KernelConfig.ir_op_priority, only one allowed at a time."
                )

            # Set the attribute
            setattr(kernel_config.ir_op_priority, op_name, op_priority)

        load_config = self.create_load_config()

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

        if self.reasoning_parser_plugin:
            self.structured_outputs_config.reasoning_parser_plugin = (
                self.reasoning_parser_plugin
            )

        observability_config = self.create_observability_config()

        # Compilation config overrides
        compilation_config = copy.deepcopy(self.compilation_config)
        if self.cudagraph_capture_sizes is not None:
            if compilation_config.cudagraph_capture_sizes is not None:
                raise ValueError(
                    "cudagraph_capture_sizes and compilation_config."
                    "cudagraph_capture_sizes are mutually exclusive"
                )
            compilation_config.cudagraph_capture_sizes = self.cudagraph_capture_sizes
        if self.max_cudagraph_capture_size is not None:
            if compilation_config.max_cudagraph_capture_size is not None:
                raise ValueError(
                    "max_cudagraph_capture_size and compilation_config."
                    "max_cudagraph_capture_size are mutually exclusive"
                )
            compilation_config.max_cudagraph_capture_size = (
                self.max_cudagraph_capture_size
            )

        offload_config = OffloadConfig(
            offload_backend=self.offload_backend,
            uva=UVAOffloadConfig(
                cpu_offload_gb=self.cpu_offload_gb,
                cpu_offload_params=self.cpu_offload_params,
            ),
            prefetch=PrefetchOffloadConfig(
                offload_group_size=self.offload_group_size,
                offload_num_in_group=self.offload_num_in_group,
                offload_prefetch_step=self.offload_prefetch_step,
                offload_params=self.offload_params,
            ),
        )

        if self.gdn_prefill_backend is not None:
            self.additional_config["gdn_prefill_backend"] = self.gdn_prefill_backend

        config = VllmConfig(
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            load_config=load_config,
            offload_config=offload_config,
            attention_config=attention_config,
            mamba_config=mamba_config,
            kernel_config=kernel_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            diffusion_config=diffusion_config,
            structured_outputs_config=self.structured_outputs_config,
            observability_config=observability_config,
            compilation_config=compilation_config,
            kv_transfer_config=self.kv_transfer_config,
            kv_events_config=self.kv_events_config,
            ec_transfer_config=self.ec_transfer_config,
            reasoning_config=self.reasoning_config,
            profiler_config=self.profiler_config,
            additional_config=self.additional_config,
            optimization_level=self.optimization_level,
            performance_mode=self.performance_mode,
            weight_transfer_config=self.weight_transfer_config,
            shutdown_timeout=self.shutdown_timeout,
        )

        return config

    def _check_feature_supported(self):
        """Raise an error if the feature is not supported."""
        # 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_unsupported_error(feature_name="Concurrent Partial Prefill")

        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_unsupported_error(feature_name=name)

    @classmethod
    def get_batch_defaults(
        cls,
        world_size: int,
    ) -> tuple[dict[UsageContext | None, int], dict[UsageContext | None, int]]:
        from vllm.usage.usage_lib import UsageContext

        default_max_num_batched_tokens: dict[UsageContext | None, int]
        default_max_num_seqs: dict[UsageContext | None, int]

        # 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
            device_name = ""

        # 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.
        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():
            chip_name = current_platform.get_device_name()

            if chip_name == "V6E":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 2048,
                    UsageContext.OPENAI_API_SERVER: 1024,
                }
            elif chip_name == "V5E":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 1024,
                    UsageContext.OPENAI_API_SERVER: 512,
                }
            elif chip_name == "V5P":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 512,
                    UsageContext.OPENAI_API_SERVER: 256,
                }

        # cpu specific default values.
        if current_platform.is_cpu():
            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,
            }

        return default_max_num_batched_tokens, default_max_num_seqs

    def _set_default_chunked_prefill_and_prefix_caching_args(
        self, model_config: ModelConfig
    ) -> None:
        default_chunked_prefill = model_config.is_chunked_prefill_supported
        default_prefix_caching = model_config.is_prefix_caching_supported

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

            logger.debug(
                "%s chunked prefill by default",
                "Enabling" if default_chunked_prefill else "Disabling",
            )
        elif (
            model_config.runner_type == "generate"
            and not self.enable_chunked_prefill
            and default_chunked_prefill
        ):
            logger.warning_once(
                "This model does not officially support disabling chunked prefill. "
                "Disabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
            )
        elif (
            model_config.runner_type == "pooling"
            and self.enable_chunked_prefill
            and not default_chunked_prefill
        ):
            logger.warning_once(
                "This model does not officially support chunked prefill. "
                "Enabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
            )

        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = default_prefix_caching

            logger.debug(
                "%s prefix caching by default",
                "Enabling" if default_prefix_caching else "Disabling",
            )
        elif (
            model_config.runner_type == "pooling"
            and self.enable_prefix_caching
            and not default_prefix_caching
        ):
            logger.warning_once(
                "This model does not officially support prefix caching. "
                "Enabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
            )

        # Disable chunked prefill and prefix caching for:
        # RISCV CPUs in V1
        if current_platform.is_cpu() and current_platform.get_cpu_architecture() in (
            CpuArchEnum.RISCV,
        ):
            logger.info(
                "Chunked prefill is not supported for"
                "RISC-V CPUs; "
                "disabling it for V1 backend."
            )
            self.enable_chunked_prefill = False
            logger.info(
                "Prefix caching is not supported for "
                "RISC-V CPUs; "
                "disabling it for V1 backend."
            )
            self.enable_prefix_caching = False

    def _set_default_reasoning_config_args(self):
        if not self.reasoning_parser:
            return
        if self.reasoning_config is None:
            self.reasoning_config = ReasoningConfig()
        self.reasoning_config.reasoning_parser = self.reasoning_parser

    @staticmethod
    def _get_min_mm_batched_tokens(
        model_config: ModelConfig,
    ) -> tuple[int, str] | None:
        """Get the minimum max_num_batched_tokens needed for a multimodal
        prefix-LM model to process at least one item of any supported modality.

        Returns (token_count, modality_name) for the most expensive modality,
        or None if the value cannot be determined at this stage.
        """
        try:
            from vllm.multimodal import MULTIMODAL_REGISTRY

            # get_processing_info returns the model's multimodal processing
            # metadata (supported modalities, token limits) without loading
            # model weights or generating dummy data.
            info = MULTIMODAL_REGISTRY.get_processing_info(model_config)
            mm_counts = {modality: 1 for modality in info.supported_mm_limits}
            # get_mm_max_tokens_per_item returns pre-computed per-item token
            # ceilings for models that override it (e.g., Gemma4), or None
            # for models that rely on dummy-input profiling. When None is
            # returned we bail out — no dummy generation is triggered here.
            max_tokens = info.get_mm_max_tokens_per_item(
                seq_len=model_config.max_model_len,
                mm_counts=mm_counts,
            )
            if max_tokens is not None:
                modality = max(max_tokens, key=max_tokens.__getitem__)
                return (max_tokens[modality], modality)
        except Exception as e:
            logger.warning("Failed to determine min multimodal batched tokens: %s", e)
        return None

    def _set_default_max_num_seqs_and_batched_tokens_args(
        self,
        usage_context: UsageContext | None,
        model_config: ModelConfig,
        parallel_config: ParallelConfig,
    ):
        world_size = self.pipeline_parallel_size * self.tensor_parallel_size
        (
            default_max_num_batched_tokens,
            default_max_num_seqs,
        ) = self.get_batch_defaults(world_size)

        orig_max_num_batched_tokens = self.max_num_batched_tokens
        orig_max_num_seqs = self.max_num_seqs

        if self.max_num_batched_tokens is None:
            if parallel_config.use_batched_dp_moe:
                self.max_num_batched_tokens = (
                    SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
                )
            else:
                self.max_num_batched_tokens = default_max_num_batched_tokens.get(
                    usage_context,
                    SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS,
                )

        if self.max_num_seqs is None:
            self.max_num_seqs = default_max_num_seqs.get(
                usage_context,
                SchedulerConfig.DEFAULT_MAX_NUM_SEQS,
            )

        # If throughput mode is set, double max_num_batched_tokens and max_num_seqs.
        if self.performance_mode == "throughput":
            if orig_max_num_batched_tokens is None:
                self.max_num_batched_tokens *= 2
            if orig_max_num_seqs is None:
                self.max_num_seqs *= 2

        if orig_max_num_batched_tokens is None:
            assert model_config.max_model_len is not None, (
                "max_model_len must be set by this point"
            )
            if not self.enable_chunked_prefill:
                # If max_model_len is too short, use the default for higher throughput.
                self.max_num_batched_tokens = max(
                    model_config.max_model_len,
                    self.max_num_batched_tokens,
                )

            # For multimodal prefix-LM models (e.g., Gemma 4) that disable
            # chunked MM input, a single multimodal item must fit in one batch.
            # Raise the floor to accommodate the largest per-item token count.
            if model_config.is_multimodal_model and model_config.is_mm_prefix_lm:
                result = self._get_min_mm_batched_tokens(model_config)
                if result is not None and result[0] > self.max_num_batched_tokens:
                    mm_min, modality = result
                    logger.info(
                        "Raising max_num_batched_tokens from %d to %d to "
                        "accommodate '%s' input for prefix-LM model %s.",
                        self.max_num_batched_tokens,
                        mm_min,
                        modality,
                        model_config.model,
                    )
                    self.max_num_batched_tokens = mm_min

            # When using default settings,
            # Ensure max_num_batched_tokens does not exceed model limit.
            # Some models (e.g., Whisper) have embeddings tied to max length.
            self.max_num_batched_tokens = min(
                self.max_num_seqs * model_config.max_model_len,
                self.max_num_batched_tokens,
            )

            logger.debug(
                "Defaulting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens,
                usage_context.value if usage_context else None,
            )

        if orig_max_num_seqs is None:
            assert self.max_num_batched_tokens is not None  # For type checking
            self.max_num_seqs = min(self.max_num_seqs, self.max_num_batched_tokens)

            logger.debug(
                "Defaulting max_num_seqs to %d for %s usage context.",
                self.max_num_seqs,
                usage_context.value if usage_context else None,
            )


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

    enable_log_requests: bool = False

    @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 request information, dependent on log level:\n"
            "- INFO: Request ID, parameters and LoRA request.\n"
            "- DEBUG: Prompt inputs (e.g: text, token IDs).\n"
            "You can set the minimum log level via `VLLM_LOGGING_LEVEL`.",
        )
        current_platform.pre_register_and_update(parser)
        return parser


def _raise_unsupported_error(feature_name: str):
    msg = (
        f"{feature_name} is not supported. We recommend to "
        f"remove {feature_name} from your config."
    )
    raise NotImplementedError(msg)
