arg_utils.py

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

# yapf: disable
import argparse
import copy
import dataclasses
import functools
import json
import sys
import threading
from dataclasses import MISSING, dataclass, fields, is_dataclass
from itertools import permutations
from typing import (TYPE_CHECKING, Annotated, Any, Callable, Dict, List,
                    Literal, Optional, Type, TypeVar, Union, cast, get_args,
                    get_origin)

import regex as re
import torch
from pydantic import TypeAdapter, ValidationError
from typing_extensions import TypeIs

import vllm.envs as envs
from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
                         ConfigFormat, ConfigType, ConvertOption,
                         DecodingConfig, DetailedTraceModules, Device,
                         DeviceConfig, DistributedExecutorBackend,
                         GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LogprobsMode,
                         LoRAConfig, ModelConfig, ModelDType, ModelImpl,
                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
                         PoolerConfig, PrefixCachingHashAlgo, RunnerOption,
                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
                         TaskOption, TokenizerMode, VllmConfig, get_attr_docs,
                         get_field)
from vllm.logger import init_logger
from vllm.platforms import CpuArchEnum, current_platform
from vllm.plugins import load_general_plugins
from vllm.reasoning import ReasoningParserManager
from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
from vllm.transformers_utils.utils import check_gguf_file
from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
                        GiB_bytes, get_ip, is_in_ray_actor)

# yapf: enable

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

logger = init_logger(__name__)

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

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

    return _optional_type


def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
    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 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 is Union:
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints


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


@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
    cls_docs = get_attr_docs(cls)
    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
        elif field.default_factory is not MISSING:
            default = field.default_factory()

        # Get the help text for the field
        name = field.name
        help = cls_docs[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. For example, the following sets of arguments are
equivalent:

- `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`\n
- `--json-arg.key1 value1 --json-arg.key2.key3 value2`

Additionally, list elements can be passed individually using `+`:

- `--json-arg '{"key4": ["value3", "value4", "value5"]}'`\n
- `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`"""
        if dataclass_cls is not None:

            def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
                try:
                    if hasattr(cls, "from_cli"):
                        return cls.from_cli(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"] += f"\n\n{json_tip}"
        elif contains_type(type_hints, bool):
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
            kwargs[name].update(literal_to_kwargs(type_hints))
        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
                f"type. Got {types}.")
            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
            assert len(types) == 1, (
                "List type must have exactly one type. Got "
                f"{type_hint} with types {types}")
            kwargs[name]["type"] = types[0]
            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
            # Special case for large integers
            if name in {"max_model_len", "max_num_batched_tokens"}:
                kwargs[name]["type"] = human_readable_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, Any]:
    """Return argparse kwargs for the given Config dataclass.

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


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

    num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
    multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode

    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input

    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager)

    guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern

    speculative_config: Optional[Dict[str, Any]] = None

    show_hidden_metrics_for_version: Optional[str] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls

    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
    compilation_config: CompilationConfig = \
        get_field(VllmConfig, "compilation_config")
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls

    kv_transfer_config: Optional[KVTransferConfig] = None
    kv_events_config: Optional[KVEventsConfig] = None

    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    model_impl: str = ModelConfig.model_impl
    override_attention_dtype: str = ModelConfig.override_attention_dtype

    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales

    additional_config: dict[str, Any] = \
        get_field(VllmConfig, "additional_config")
    reasoning_parser: str = DecodingConfig.reasoning_backend

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

    enable_multimodal_encoder_data_parallel: bool = \
        ParallelConfig.enable_multimodal_encoder_data_parallel

    async_scheduling: bool = SchedulerConfig.async_scheduling
    # DEPRECATED
    enable_prompt_adapter: bool = False

    kv_sharing_fast_prefill: bool = \
        CacheConfig.kv_sharing_fast_prefill

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

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

        # Model arguments
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
        if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
            model_group.add_argument("--model", **model_kwargs["model"])
        model_group.add_argument("--runner", **model_kwargs["runner"])
        model_group.add_argument("--convert", **model_kwargs["convert"])
        model_group.add_argument("--task",
                                 **model_kwargs["task"],
                                 deprecated=True)
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        model_group.add_argument("--hf-config-path",
                                 **model_kwargs["hf_config_path"])
        model_group.add_argument("--allowed-local-media-path",
                                 **model_kwargs["allowed_local_media_path"])
        model_group.add_argument("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        model_group.add_argument("--tokenizer-revision",
                                 **model_kwargs["tokenizer_revision"])
        model_group.add_argument("--max-model-len",
                                 **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q",
                                 **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager",
                                 **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
        model_group.add_argument("--logprobs-mode",
                                 **model_kwargs["logprobs_mode"])
        model_group.add_argument("--disable-sliding-window",
                                 **model_kwargs["disable_sliding_window"])
        model_group.add_argument("--disable-cascade-attn",
                                 **model_kwargs["disable_cascade_attn"])
        model_group.add_argument("--skip-tokenizer-init",
                                 **model_kwargs["skip_tokenizer_init"])
        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        model_group.add_argument("--hf-token",
                                 type=str,
                                 nargs="?",
                                 const=True,
                                 default=model_kwargs["hf_token"]["default"],
                                 help=model_kwargs["hf_token"]["help"])
        model_group.add_argument("--hf-overrides",
                                 **model_kwargs["hf_overrides"])
        model_group.add_argument("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        model_group.add_argument("--logits-processor-pattern",
                                 **model_kwargs["logits_processor_pattern"])
        model_group.add_argument("--generation-config",
                                 **model_kwargs["generation_config"])
        model_group.add_argument("--override-generation-config",
                                 **model_kwargs["override_generation_config"])
        model_group.add_argument("--enable-sleep-mode",
                                 **model_kwargs["enable_sleep_mode"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])
        model_group.add_argument("--override-attention-dtype",
                                 **model_kwargs["override_attention_dtype"])

        # 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("--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"])

        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-fallback",
            **guided_decoding_kwargs["disable_fallback"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-any-whitespace",
            **guided_decoding_kwargs["disable_any_whitespace"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-additional-properties",
            **guided_decoding_kwargs["disable_additional_properties"])
        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

        # Parallel arguments
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
            "--distributed-executor-backend",
            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
            "--pipeline-parallel-size", "-pp",
            **parallel_kwargs["pipeline_parallel_size"])
        parallel_group.add_argument("--tensor-parallel-size", "-tp",
                                    **parallel_kwargs["tensor_parallel_size"])
        parallel_group.add_argument("--data-parallel-size", "-dp",
                                    **parallel_kwargs["data_parallel_size"])
        parallel_group.add_argument(
            '--data-parallel-rank',
            '-dpn',
            type=int,
            help='Data parallel rank of this instance. '
            'When set, enables external load balancer mode.')
        parallel_group.add_argument('--data-parallel-start-rank',
                                    '-dpr',
                                    type=int,
                                    help='Starting data parallel rank '
                                    'for secondary nodes.')
        parallel_group.add_argument('--data-parallel-size-local',
                                    '-dpl',
                                    type=int,
                                    help='Number of data parallel replicas '
                                    'to run on this node.')
        parallel_group.add_argument('--data-parallel-address',
                                    '-dpa',
                                    type=str,
                                    help='Address of data parallel cluster '
                                    'head-node.')
        parallel_group.add_argument('--data-parallel-rpc-port',
                                    '-dpp',
                                    type=int,
                                    help='Port for data parallel RPC '
                                    'communication.')
        parallel_group.add_argument('--data-parallel-backend',
                                    '-dpb',
                                    type=str,
                                    default='mp',
                                    help='Backend for data parallel, either '
                                    '"mp" or "ray".')
        parallel_group.add_argument(
            "--data-parallel-hybrid-lb",
            **parallel_kwargs["data_parallel_hybrid_lb"])
        parallel_group.add_argument(
            "--enable-expert-parallel",
            **parallel_kwargs["enable_expert_parallel"])
        parallel_group.add_argument("--enable-eplb",
                                    **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--num-redundant-experts",
                                    **parallel_kwargs["num_redundant_experts"])
        parallel_group.add_argument("--eplb-window-size",
                                    **parallel_kwargs["eplb_window_size"])
        parallel_group.add_argument("--eplb-step-interval",
                                    **parallel_kwargs["eplb_step_interval"])
        parallel_group.add_argument("--eplb-log-balancedness",
                                    **parallel_kwargs["eplb_log_balancedness"])
        parallel_group.add_argument(
            "--max-parallel-loading-workers",
            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
            "--ray-workers-use-nsight",
            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
            "--disable-custom-all-reduce",
            **parallel_kwargs["disable_custom_all_reduce"])
        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
            **parallel_kwargs["enable_multimodal_encoder_data_parallel"])

        # 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("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
                                 **cache_kwargs["cache_dtype"])
        cache_group.add_argument("--num-gpu-blocks-override",
                                 **cache_kwargs["num_gpu_blocks_override"])
        cache_group.add_argument("--enable-prefix-caching",
                                 **cache_kwargs["enable_prefix_caching"])
        cache_group.add_argument("--prefix-caching-hash-algo",
                                 **cache_kwargs["prefix_caching_hash_algo"])
        cache_group.add_argument("--cpu-offload-gb",
                                 **cache_kwargs["cpu_offload_gb"])
        cache_group.add_argument("--calculate-kv-scales",
                                 **cache_kwargs["calculate_kv_scales"])
        cache_group.add_argument("--kv-sharing-fast-prefill",
                                 **cache_kwargs["kv_sharing_fast_prefill"])

        # Multimodal related configs
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
        multimodal_group.add_argument("--limit-mm-per-prompt",
                                      **multimodal_kwargs["limit_per_prompt"])
        multimodal_group.add_argument("--media-io-kwargs",
                                      **multimodal_kwargs["media_io_kwargs"])
        multimodal_group.add_argument(
            "--mm-processor-kwargs",
            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
            "--disable-mm-preprocessor-cache",
            **multimodal_kwargs["disable_mm_preprocessor_cache"])
        multimodal_group.add_argument(
            "--interleave-mm-strings",
            **multimodal_kwargs["interleave_mm_strings"])

        # 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("--enable-lora-bias",
                                **lora_kwargs["bias_enabled"])
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
                                **lora_kwargs["max_lora_rank"])
        lora_group.add_argument("--lora-extra-vocab-size",
                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
            "--lora-dtype",
            **lora_kwargs["lora_dtype"],
        )
        lora_group.add_argument("--max-cpu-loras",
                                **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument("--fully-sharded-loras",
                                **lora_kwargs["fully_sharded_loras"])
        lora_group.add_argument("--default-mm-loras",
                                **lora_kwargs["default_mm_loras"])

        # Speculative arguments
        speculative_group = parser.add_argument_group(
            title="SpeculativeConfig",
            description=SpeculativeConfig.__doc__,
        )
        speculative_group.add_argument(
            "--speculative-config",
            type=json.loads,
            default=None,
            help="The configurations for speculative decoding. Should be a "
            "JSON string.")

        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **observability_kwargs["collect_detailed_traces"])

        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
            "--max-num-batched-tokens",
            **scheduler_kwargs["max_num_batched_tokens"])
        scheduler_group.add_argument("--max-num-seqs",
                                     **scheduler_kwargs["max_num_seqs"])
        scheduler_group.add_argument(
            "--max-num-partial-prefills",
            **scheduler_kwargs["max_num_partial_prefills"])
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
            **scheduler_kwargs["max_long_partial_prefills"])
        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
        scheduler_group.add_argument("--num-lookahead-slots",
                                     **scheduler_kwargs["num_lookahead_slots"])
        scheduler_group.add_argument("--scheduler-delay-factor",
                                     **scheduler_kwargs["delay_factor"])
        scheduler_group.add_argument("--preemption-mode",
                                     **scheduler_kwargs["preemption_mode"])
        scheduler_group.add_argument("--num-scheduler-steps",
                                     **scheduler_kwargs["num_scheduler_steps"])
        scheduler_group.add_argument(
            "--multi-step-stream-outputs",
            **scheduler_kwargs["multi_step_stream_outputs"])
        scheduler_group.add_argument("--scheduling-policy",
                                     **scheduler_kwargs["policy"])
        scheduler_group.add_argument(
            "--enable-chunked-prefill",
            **scheduler_kwargs["enable_chunked_prefill"])
        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"])
        scheduler_group.add_argument("--async-scheduling",
                                     **scheduler_kwargs["async_scheduling"])

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

        # Other arguments
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
        parser.add_argument('--enable-prompt-adapter',
                            action='store_true',
                            deprecated=True,
                            help='[DEPRECATED] Prompt adapter has been '
                            'removed. Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')

        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})
        return engine_args

    def create_model_config(self) -> ModelConfig:
        # gguf file needs a specific model loader and doesn't use hf_repo
        if check_gguf_file(self.model):
            self.quantization = self.load_format = "gguf"

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

        return ModelConfig(
            model=self.model,
            hf_config_path=self.hf_config_path,
            runner=self.runner,
            convert=self.convert,
            task=self.task,
            tokenizer=self.tokenizer,
            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
            allowed_local_media_path=self.allowed_local_media_path,
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
            rope_theta=self.rope_theta,
            hf_token=self.hf_token,
            hf_overrides=self.hf_overrides,
            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            logprobs_mode=self.logprobs_mode,
            disable_sliding_window=self.disable_sliding_window,
            disable_cascade_attn=self.disable_cascade_attn,
            skip_tokenizer_init=self.skip_tokenizer_init,
            enable_prompt_embeds=self.enable_prompt_embeds,
            served_model_name=self.served_model_name,
            limit_mm_per_prompt=self.limit_mm_per_prompt,
            interleave_mm_strings=self.interleave_mm_strings,
            media_io_kwargs=self.media_io_kwargs,
            use_async_output_proc=not self.disable_async_output_proc,
            config_format=self.config_format,
            mm_processor_kwargs=self.mm_processor_kwargs,
            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
            logits_processor_pattern=self.logits_processor_pattern,
            generation_config=self.generation_config,
            override_generation_config=self.override_generation_config,
            enable_sleep_mode=self.enable_sleep_mode,
            model_impl=self.model_impl,
            override_attention_dtype=self.override_attention_dtype,
        )

    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,
            device="cpu"
            if is_online_quantization(self.quantization) else None,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
            use_tqdm_on_load=self.use_tqdm_on_load,
            pt_load_map_location=self.pt_load_map_location,
        )

    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
    ) -> Optional["SpeculativeConfig"]:
        """Initializes and returns a SpeculativeConfig object based on
        `speculative_config`.

        This function utilizes `speculative_config` to create a
        SpeculativeConfig object. The `speculative_config` can either be
        provided as a JSON string input via CLI arguments or directly as a
        dictionary from the engine.
        """
        if self.speculative_config is None:
            return None

        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
        self.speculative_config.update({
            "target_model_config": target_model_config,
            "target_parallel_config": target_parallel_config,
            "enable_chunked_prefill": enable_chunked_prefill,
            "disable_log_stats": disable_log_stats,
        })
        speculative_config = SpeculativeConfig.from_dict(
            self.speculative_config)

        return speculative_config

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

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.

        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
        current_platform.pre_register_and_update()

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

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

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

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

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

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

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

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

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

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

            if self.data_parallel_start_rank and not headless:
                # Infer hybrid LB mode.
                self.data_parallel_hybrid_lb = True

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

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

            self.data_parallel_rank = self.data_parallel_start_rank or 0
        else:
            assert not self.data_parallel_hybrid_lb, (
                "data_parallel_size_local must be set to use "
                "data_parallel_hybrid_lb.")

            # Local DP size defaults to global DP size if not set.
            data_parallel_size_local = self.data_parallel_size

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
        if self.data_parallel_address is None:
            if self.data_parallel_backend == "ray":
                host_ip = get_ip()
                logger.info(
                    "Using host IP %s as ray-based data parallel address",
                    host_ip)
                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
                    self.data_parallel_backend)
                data_parallel_address = ParallelConfig.data_parallel_master_ip
        else:
            data_parallel_address = self.data_parallel_address

        # This port is only used when there are remote data parallel engines,
        # otherwise the local IPC transport is used.
        data_parallel_rpc_port = self.data_parallel_rpc_port if (
            self.data_parallel_rpc_port
            is not None) else ParallelConfig.data_parallel_rpc_port

        if self.async_scheduling:
            # Async scheduling does not work with the uniprocess backend.
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "mp"
                logger.info("Using mp-based distributed executor backend "
                            "for async scheduling.")
            if self.distributed_executor_backend == "uni":
                raise ValueError("Async scheduling is not supported with "
                                 "uni-process backend.")
            if self.pipeline_parallel_size > 1:
                raise ValueError("Async scheduling is not supported with "
                                 "pipeline-parallel-size > 1.")

            # Currently, async scheduling does not support speculative decoding.
            # TODO(woosuk): Support it.
            if self.speculative_config is not None:
                raise ValueError(
                    "Currently, speculative decoding is not supported with "
                    "async scheduling.")

        parallel_config = ParallelConfig(
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            data_parallel_size=self.data_parallel_size,
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
            data_parallel_backend=self.data_parallel_backend,
            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
            enable_expert_parallel=self.enable_expert_parallel,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.num_redundant_experts,
            eplb_window_size=self.eplb_window_size,
            eplb_step_interval=self.eplb_step_interval,
            eplb_log_balancedness=self.eplb_log_balancedness,
            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,
            placement_group=placement_group,
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
            worker_extension_cls=self.worker_extension_cls,
            enable_multimodal_encoder_data_parallel=self.
            enable_multimodal_encoder_data_parallel,
        )

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

        # Reminder: Please update docs/features/compatibility_matrix.md
        # If the feature combo become valid
        if self.num_scheduler_steps > 1:
            if speculative_config is not None:
                raise ValueError("Speculative decoding is not supported with "
                                 "multi-step (--num-scheduler-steps > 1)")
            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                 "for pipeline-parallel-size > 1")
            if current_platform.is_cpu():
                logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
                               "currently not supported for CPUs and has been "
                               "disabled.")
                self.num_scheduler_steps = 1

        # make sure num_lookahead_slots is set the higher value depending on
        # if we are using speculative decoding or multi-step
        num_lookahead_slots = max(self.num_lookahead_slots,
                                  self.num_scheduler_steps - 1)
        num_lookahead_slots = num_lookahead_slots \
            if speculative_config is None \
            else speculative_config.num_lookahead_slots

        scheduler_config = SchedulerConfig(
            runner_type=model_config.runner_type,
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
            cuda_graph_sizes=self.cuda_graph_sizes,
            num_lookahead_slots=num_lookahead_slots,
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_chunked_mm_input=self.disable_chunked_mm_input,
            is_multimodal_model=model_config.is_multimodal_model,
            preemption_mode=self.preemption_mode,
            num_scheduler_steps=self.num_scheduler_steps,
            multi_step_stream_outputs=self.multi_step_stream_outputs,
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
            policy=self.scheduling_policy,
            scheduler_cls=self.scheduler_cls,
            max_num_partial_prefills=self.max_num_partial_prefills,
            max_long_partial_prefills=self.max_long_partial_prefills,
            long_prefill_token_threshold=self.long_prefill_token_threshold,
            disable_hybrid_kv_cache_manager=self.
            disable_hybrid_kv_cache_manager,
            async_scheduling=self.async_scheduling,
        )

        if not model_config.is_multimodal_model and self.default_mm_loras:
            raise ValueError(
                "Default modality-specific LoRA(s) were provided for a "
                "non multimodal model")

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

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

        load_config = self.create_load_config()

        decoding_config = DecodingConfig(
            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
            reasoning_backend=self.reasoning_parser
        )

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

        config = VllmConfig(
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
            decoding_config=decoding_config,
            observability_config=observability_config,
            compilation_config=self.compilation_config,
            kv_transfer_config=self.kv_transfer_config,
            kv_events_config=self.kv_events_config,
            additional_config=self.additional_config,
        )

        return config

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

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

        if self.load_format == "sharded_state":
            _raise_or_fallback(
                feature_name=f"--load_format {self.load_format}",
                recommend_to_remove=False)
            return False

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

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

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

        if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
            _raise_or_fallback(feature_name="--num-scheduler-steps",
                               recommend_to_remove=True)
            return False

        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

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

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

        # No text embedding inputs so far.
        if self.enable_prompt_embeds:
            _raise_or_fallback(feature_name="--enable-prompt-embeds",
                               recommend_to_remove=False)
            return False

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

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

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

        # No OTLP observability so far.
        if (self.otlp_traces_endpoint or self.collect_detailed_traces):
            _raise_or_fallback(feature_name="--otlp-traces-endpoint",
                               recommend_to_remove=False)
            return False

        # V1 supports N-gram, Medusa, and Eagle speculative decoding.
        if (self.speculative_config is not None
                and self.speculative_config.get("method") == "draft_model"):
            raise NotImplementedError(
                "Speculative decoding with draft model is not supported yet. "
                "Please consider using other speculative decoding methods "
                "such as ngram, medusa, eagle, or deepseek_mtp.")

        # No XFormers so far.
        V1_BACKENDS = [
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
            "CUTLASS_MLA_VLLM_V1",
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
            "ROCM_AITER_MLA",
            "TORCH_SDPA_VLLM_V1",
            "FLEX_ATTENTION",
        ]
        if (envs.is_set("VLLM_ATTENTION_BACKEND")
                and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):
            name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
            _raise_or_fallback(feature_name=name, recommend_to_remove=True)
            return False

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

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

        if (self.pipeline_parallel_size > 1
                and self.distributed_executor_backend
                not in (ParallelConfig.distributed_executor_backend, "ray",
                        "mp", "external_launcher")):
            name = "Pipeline Parallelism without Ray distributed executor " \
                    "or multiprocessing executor or external launcher"
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
            return False

        # The platform may be supported on V1, but off by default for now.
        if not current_platform.default_v1(  # noqa: SIM103
                model_config=model_config) and _warn_or_fallback(
                    current_platform.device_name):
            return False

        if (current_platform.is_cpu()
                and model_config.get_sliding_window() is not None):
            _raise_or_fallback(feature_name="sliding window (CPU backend)",
                               recommend_to_remove=False)
            return False

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

        return True

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

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

            # Enable chunked prefill by default for long context (> 32K)
            # models to avoid OOM errors in initial memory profiling phase.
            elif use_long_context:
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
                use_spec_decode = self.speculative_config is not None

                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

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

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

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

            # VLLM_V0 only supports builtin hash algo for prefix caching.
            if self.prefix_caching_hash_algo == "sha256":
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")

        # Set max_num_seqs to 256 for VLLM_V0.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256

    def _set_default_args_v1(self, usage_context: UsageContext,
                             model_config: ModelConfig) -> None:
        """Set Default Arguments for V1 Engine."""

        # V1 always uses chunked prefills and prefix caching
        # for non-pooling tasks.
        # For pooling tasks the default is False
        if model_config.runner_type != "pooling":
            self.enable_chunked_prefill = True
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = True
        else:

            pooling_type = model_config.pooler_config.pooling_type

            # TODO: when encoder models are supported we'll have to
            # check for causal attention here.
            incremental_prefill_supported = (pooling_type is not None and
                                             pooling_type.lower() == "last")

            action = "Enabling" if \
                incremental_prefill_supported else "Disabling"

            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = incremental_prefill_supported
                logger.info("(%s) chunked prefill by default", action)
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = incremental_prefill_supported
                logger.info("(%s) prefix caching by default", action)

        if not self.enable_chunked_prefill:
            self.max_num_batched_tokens = model_config.max_model_len

        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"

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

        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
        try:
            device_memory = current_platform.get_device_total_memory()
            device_name = current_platform.get_device_name().lower()
        except Exception:
            # This is only used to set default_max_num_batched_tokens
            device_memory = 0

        # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
        # throughput, see PR #17885 for more details.
        # So here we do an extra device name check to prevent such regression.
        from vllm.usage.usage_lib import UsageContext
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
            # For GPUs like H100 and MI300x, use larger default values.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }

        # tpu specific default values.
        if current_platform.is_tpu():
            default_max_num_batched_tokens_tpu = {
                UsageContext.LLM_CLASS: {
                    'V6E': 2048,
                    'V5E': 1024,
                    'V5P': 512,
                },
                UsageContext.OPENAI_API_SERVER: {
                    'V6E': 1024,
                    'V5E': 512,
                    'V5P': 256,
                }
            }

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

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

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

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


@dataclass
class AsyncEngineArgs(EngineArgs):
    """Arguments for asynchronous vLLM engine."""
    disable_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
        # adding 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('--disable-log-requests',
                            action='store_true',
                            help='Disable logging requests.')
        current_platform.pre_register_and_update(parser)
        return parser


def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        raise NotImplementedError(
            f"VLLM_USE_V1=1 is not supported with {feature_name}.")
    msg = f"{feature_name} is not supported by the V1 Engine. "
    msg += "Falling back to V0. "
    if recommend_to_remove:
        msg += f"We recommend to remove {feature_name} from your config "
        msg += "in favor of the V1 Engine."
    logger.warning(msg)


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


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

    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
    match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value)
    if match:
        decimal_multiplier = {
            'k': 10**3,
            'm': 10**6,
            'g': 10**9,
        }
        binary_multiplier = {
            'K': 2**10,
            'M': 2**20,
            'G': 2**30,
        }

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

    # Regular plain number.
    return int(value)


# These functions are used by sphinx to build the documentation
def _engine_args_parser():
    return EngineArgs.add_cli_args(FlexibleArgumentParser())


def _async_engine_args_parser():
    return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
                                        async_args_only=True)