arg_utils.py 77 KB
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# SPDX-License-Identifier: Apache-2.0

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# yapf: disable
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import os
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import argparse
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import dataclasses
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import json
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import re
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import threading
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from dataclasses import MISSING, dataclass, fields
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from typing import (Any, Callable, Dict, List, Literal, Optional, Type,
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                    TypeVar, Union, cast, get_args, get_origin)
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import torch
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from typing_extensions import TypeIs, deprecated
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import vllm.envs as envs
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from vllm import version
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from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
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                         ConfigFormat, ConfigType, DecodingConfig, Device,
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                         DeviceConfig, DistributedExecutorBackend,
                         GuidedDecodingBackendV1, HfOverrides,
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                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
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                         ModelConfig, ModelImpl, MultiModalConfig,
                         ObservabilityConfig, ParallelConfig, PoolerConfig,
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                         PrefixCachingHashAlgo, PromptAdapterConfig,
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                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
                         TaskOption, TokenizerPoolConfig, VllmConfig,
                         get_attr_docs, get_field)
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from vllm.executor.executor_base import ExecutorBase
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
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from vllm.plugins import load_general_plugins
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from vllm.reasoning import ReasoningParserManager
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from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import FlexibleArgumentParser, GiB_bytes, is_in_ray_actor
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# yapf: enable
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logger = init_logger(__name__)

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ALLOWED_DETAILED_TRACE_MODULES = ["model", "worker", "all"]
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models_path_prefix = os.getenv('VLLM_OPTEST_MODELS_PATH') or os.getenv("OPTEST_MODELS_PATH")
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# object is used to allow for special typing forms
T = TypeVar("T")
TypeHint = Union[type[Any], object]
TypeHintT = Union[type[T], object]
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def optional_type(
        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:
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    def _optional_type(val: str) -> Optional[T]:
        if val == "" or val == "None":
            return None
        try:
            if return_type is json.loads and not re.match("^{.*}$", val):
                return cast(T, nullable_kvs(val))
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
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    return _optional_type
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@deprecated(
    "Passing a JSON argument as a string containing comma separated key=value "
    "pairs is deprecated. This will be removed in v0.10.0. Please use a JSON "
    "string instead.")
def nullable_kvs(val: str) -> dict[str, int]:
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    """Parses a string containing comma separate key [str] to value [int]
    pairs into a dictionary.

    Args:
        val: String value to be parsed.

    Returns:
        Dictionary with parsed values.
    """
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    out_dict: dict[str, int] = {}
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    for item in val.split(","):
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        kv_parts = [part.lower().strip() for part in item.split("=")]
        if len(kv_parts) != 2:
            raise argparse.ArgumentTypeError(
                "Each item should be in the form KEY=VALUE")
        key, value = kv_parts
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        try:
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            parsed_value = int(value)
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        except ValueError as exc:
            msg = f"Failed to parse value of item {key}={value}"
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            raise argparse.ArgumentTypeError(msg) from exc

        if key in out_dict and out_dict[key] != parsed_value:
            raise argparse.ArgumentTypeError(
                f"Conflicting values specified for key: {key}")
        out_dict[key] = parsed_value
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    return out_dict


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def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]:
    """Check if the type hint is a specific type."""
    return type_hint is type or get_origin(type_hint) is type


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


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


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_kwargs(cls: ConfigType) -> dict[str, Any]:
    cls_docs = get_attr_docs(cls)
    kwargs = {}
    for field in fields(cls):
        # Get the default value of the field
        default = field.default
        if field.default_factory is not MISSING:
            default = field.default_factory()

        # Get the help text for the field
        name = field.name
        help = cls_docs[name]
        # Escape % for argparse
        help = help.replace("%", "%%")

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

        # Get the set of possible types for the field
        type_hints: set[TypeHint] = set()
        if get_origin(field.type) is Union:
            type_hints.update(get_args(field.type))
        else:
            type_hints.add(field.type)

        # Set other kwargs based on the type hints
        if contains_type(type_hints, bool):
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
            # Creates choices from Literal arguments
            type_hint = get_type(type_hints, Literal)
            choices = sorted(get_args(type_hint))
            kwargs[name]["choices"] = choices
            choice_type = type(choices[0])
            assert all(type(c) is choice_type for c in choices), (
                "All choices must be of the same type. "
                f"Got {choices} with types {[type(c) for c in choices]}")
            kwargs[name]["type"] = choice_type
        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
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
        elif contains_type(type_hints, dict):
            # Dict arguments will always be optional
            kwargs[name]["type"] = optional_type(json.loads)
        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 None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs
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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = os.path.join(models_path_prefix, 'facebook/opt-125m') if models_path_prefix is not None else 'facebook/opt-125m'
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    served_model_name: Optional[Union[str, List[str]]] = None
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    tokenizer: Optional[str] = None
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    hf_config_path: Optional[str] = None
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    task: TaskOption = "auto"
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    skip_tokenizer_init: bool = False
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    tokenizer_mode: str = 'auto'
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    trust_remote_code: bool = False
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    allowed_local_media_path: str = ""
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    download_dir: Optional[str] = LoadConfig.download_dir
    load_format: str = LoadConfig.load_format
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    config_format: ConfigFormat = ConfigFormat.AUTO
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    dtype: str = 'auto'
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    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
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    seed: Optional[int] = None
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    max_model_len: Optional[int] = None
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    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
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    distributed_executor_backend: Optional[Union[
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        DistributedExecutorBackend,
        Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
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    # number of P/D disaggregation (or other disaggregation) workers
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    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    data_parallel_size: int = ParallelConfig.data_parallel_size
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
    max_parallel_loading_workers: Optional[
        int] = ParallelConfig.max_parallel_loading_workers
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    block_size: Optional[BlockSize] = CacheConfig.block_size
    enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
    prefix_caching_hash_algo: PrefixCachingHashAlgo = \
        CacheConfig.prefix_caching_hash_algo
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    disable_sliding_window: bool = False
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    disable_cascade_attn: bool = False
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    use_v2_block_manager: bool = True
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    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
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    max_num_batched_tokens: Optional[
        int] = SchedulerConfig.max_num_batched_tokens
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
    long_prefill_token_threshold: int = \
        SchedulerConfig.long_prefill_token_threshold
    max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
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    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
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    disable_log_stats: bool = False
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    revision: Optional[str] = None
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    code_revision: Optional[str] = None
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    rope_scaling: Optional[Dict[str, Any]] = None
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    rope_theta: Optional[float] = None
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    hf_token: Optional[Union[bool, str]] = None
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    hf_overrides: Optional[HfOverrides] = None
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    tokenizer_revision: Optional[str] = None
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    quantization: Optional[str] = None
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    enforce_eager: Optional[bool] = None
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    max_seq_len_to_capture: int = 8192
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    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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    # The following three fields are deprecated and will be removed in a future
    # release. Setting them will have no effect. Please remove them from your
    # configurations.
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    tokenizer_pool_size: int = TokenizerPoolConfig.pool_size
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    tokenizer_pool_type: str = TokenizerPoolConfig.pool_type
    tokenizer_pool_extra_config: dict = \
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        get_field(TokenizerPoolConfig, "extra_config")
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    limit_mm_per_prompt: dict[str, int] = \
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        get_field(MultiModalConfig, "limit_per_prompt")
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    mm_processor_kwargs: Optional[Dict[str, Any]] = None
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    disable_mm_preprocessor_cache: bool = False
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    # LoRA fields
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    enable_lora: bool = False
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    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
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    lora_target_modules: Optional[List[str]] = LoRAConfig.lora_target_modules
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    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
    long_lora_scaling_factors: Optional[tuple[float, ...]] = \
        LoRAConfig.long_lora_scaling_factors
    # PromptAdapter fields
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    enable_prompt_adapter: bool = False
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    max_prompt_adapters: int = PromptAdapterConfig.max_prompt_adapters
    max_prompt_adapter_token: int = \
        PromptAdapterConfig.max_prompt_adapter_token
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    device: Device = DeviceConfig.device
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    num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
    multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
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    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
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    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
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    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
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    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
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    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
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    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
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    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
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    guided_decoding_backend: str = DecodingConfig.guided_decoding_backend
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    logits_processor_pattern: Optional[str] = None
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    speculative_config: Optional[Dict[str, Any]] = None
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    num_speculative_heads: Optional[int] = None
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    qlora_adapter_name_or_path: Optional[str] = None
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    show_hidden_metrics_for_version: Optional[str] = None
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    otlp_traces_endpoint: Optional[str] = None
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    collect_detailed_traces: Optional[str] = None
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    disable_async_output_proc: bool = False
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    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
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    override_neuron_config: Optional[Dict[str, Any]] = None
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    override_pooler_config: Optional[PoolerConfig] = None
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    compilation_config: Optional[CompilationConfig] = None
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    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
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    kv_transfer_config: Optional[KVTransferConfig] = None
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    generation_config: Optional[str] = "auto"
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    override_generation_config: Optional[Dict[str, Any]] = None
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    enable_sleep_mode: bool = False
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    model_impl: str = "auto"
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    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
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    additional_config: Optional[Dict[str, Any]] = None
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    enable_reasoning: Optional[bool] = None
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    reasoning_parser: Optional[str] = DecodingConfig.reasoning_backend
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    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
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    def __post_init__(self):
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        if not self.tokenizer:
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            self.tokenizer = self.model
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        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
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        if isinstance(self.compilation_config, (int, dict)):
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            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
        load_general_plugins()
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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        """Shared CLI arguments for vLLM engine."""
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        # Model arguments
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        parser.add_argument(
            '--model',
            type=str,
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            default=EngineArgs.model,
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            help='Name or path of the huggingface model to use.')
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        parser.add_argument(
            '--task',
            default=EngineArgs.task,
            choices=get_args(TaskOption),
            help='The task to use the model for. Each vLLM instance only '
            'supports one task, even if the same model can be used for '
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            'multiple tasks. When the model only supports one task, ``"auto"`` '
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            'can be used to select it; otherwise, you must specify explicitly '
            'which task to use.')
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        parser.add_argument(
            '--tokenizer',
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            type=optional_type(str),
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            default=EngineArgs.tokenizer,
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            help='Name or path of the huggingface tokenizer to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            "--hf-config-path",
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            type=optional_type(str),
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            default=EngineArgs.hf_config_path,
            help='Name or path of the huggingface config to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
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            help='Skip initialization of tokenizer and detokenizer. '
            'Expects valid prompt_token_ids and None for prompt from '
            'the input. The generated output will contain token ids.')
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        parser.add_argument(
            '--revision',
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            type=optional_type(str),
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            default=None,
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            help='The specific model version to use. It can be a branch '
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            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
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        parser.add_argument(
            '--code-revision',
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            type=optional_type(str),
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            default=None,
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            help='The specific revision to use for the model code on '
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            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-revision',
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            type=optional_type(str),
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            default=None,
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            help='Revision of the huggingface tokenizer to use. '
            'It can be a branch name, a tag name, or a commit id. '
            'If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
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            choices=['auto', 'cpm', 'slow', 'mistral', 'custom'],
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            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
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            'always use the slow tokenizer. \n* '
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            '"mistral" will always use the `mistral_common` tokenizer. \n* '
            '"custom" will use --tokenizer to select the '
            'preregistered tokenizer.')
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        parser.add_argument('--trust-remote-code',
                            action='store_true',
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                            help='Trust remote code from huggingface.')
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        parser.add_argument(
            '--allowed-local-media-path',
            type=str,
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            help="Allowing API requests to read local images or videos "
            "from directories specified by the server file system. "
            "This is a security risk. "
            "Should only be enabled in trusted environments.")
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        # 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',
                                choices=[f.value for f in LoadFormat],
                                **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('--use-tqdm-on-load',
                                **load_kwargs["use_tqdm_on_load"])

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        parser.add_argument(
            '--config-format',
            default=EngineArgs.config_format,
            choices=[f.value for f in ConfigFormat],
            help='The format of the model config to load.\n\n'
            '* "auto" will try to load the config in hf format '
            'if available else it will try to load in mistral format ')
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        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
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            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
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            help='Data type for model weights and activations.\n\n'
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            'BF16 precision for BF16 models.\n'
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.')
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        parser.add_argument('--max-model-len',
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                            type=human_readable_int,
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                            default=EngineArgs.max_model_len,
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                            help='Model context length. If unspecified, will '
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                            'be automatically derived from the model config. '
                            'Supports k/m/g/K/M/G in human-readable format.\n'
                            'Examples:\n'
                            '- 1k → 1000\n'
                            '- 1K → 1024\n')
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        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
        guided_decoding_group.add_argument(
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            '--guided-decoding-backend',
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            **guided_decoding_kwargs["guided_decoding_backend"])
        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

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        parser.add_argument(
            '--logits-processor-pattern',
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            type=optional_type(str),
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            default=None,
            help='Optional regex pattern specifying valid logits processor '
            'qualified names that can be passed with the `logits_processors` '
            'extra completion argument. Defaults to None, which allows no '
            'processors.')
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        parser.add_argument(
            '--model-impl',
            type=str,
            default=EngineArgs.model_impl,
            choices=[f.value for f in ModelImpl],
            help='Which implementation of the model to use.\n\n'
            '* "auto" will try to use the vLLM implementation if it exists '
            'and fall back to the Transformers implementation if no vLLM '
            'implementation is available.\n'
            '* "vllm" will use the vLLM model implementation.\n'
            '* "transformers" will use the Transformers model '
            'implementation.\n')
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        # Parallel arguments
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        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
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            '--distributed-executor-backend',
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            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
            '--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(
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            '--enable-expert-parallel',
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            **parallel_kwargs["enable_expert_parallel"])
        parallel_group.add_argument(
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            '--max-parallel-loading-workers',
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            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
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            '--ray-workers-use-nsight',
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            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
            '--disable-custom-all-reduce',
            **parallel_kwargs["disable_custom_all_reduce"])
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        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
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        )
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        cache_group.add_argument('--block-size', **cache_kwargs["block_size"])
        cache_group.add_argument('--gpu-memory-utilization',
                                 **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"])

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        parser.add_argument('--disable-sliding-window',
                            action='store_true',
                            help='Disables sliding window, '
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                            'capping to sliding window size.')
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        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
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                            default=True,
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                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
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        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
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                            help='Random seed for operations.')
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        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
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            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
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        parser.add_argument('--disable-log-stats',
                            action='store_true',
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                            help='Disable logging statistics.')
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        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
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                            type=optional_type(str),
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                            choices=[*QUANTIZATION_METHODS, None],
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                            default=EngineArgs.quantization,
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                            help='Method used to quantize the weights. If '
                            'None, we first check the `quantization_config` '
                            'attribute in the model config file. If that is '
                            'None, we assume the model weights are not '
                            'quantized and use `dtype` to determine the data '
                            'type of the weights.')
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        parser.add_argument(
            '--rope-scaling',
            default=None,
            type=json.loads,
            help='RoPE scaling configuration in JSON format. '
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            'For example, ``{"rope_type":"dynamic","factor":2.0}``')
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        parser.add_argument('--rope-theta',
                            default=None,
                            type=float,
                            help='RoPE theta. Use with `rope_scaling`. In '
                            'some cases, changing the RoPE theta improves the '
                            'performance of the scaled model.')
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        parser.add_argument(
            '--hf-token',
            type=str,
            nargs='?',
            const=True,
            default=None,
            help='The token to use as HTTP bearer authorization'
            ' for remote files. If `True`, will use the token '
            'generated when running `huggingface-cli login` '
            '(stored in `~/.huggingface`).')
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        parser.add_argument('--hf-overrides',
                            type=json.loads,
                            default=EngineArgs.hf_overrides,
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                            help='Extra arguments for the HuggingFace config. '
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                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
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        parser.add_argument('--enforce-eager',
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                            action='store_true',
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                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
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        parser.add_argument('--max-seq-len-to-capture',
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                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence length covered by CUDA '
                            'graphs. When a sequence has context length '
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                            'larger than this, we fall back to eager mode. '
                            'Additionally for encoder-decoder models, if the '
                            'sequence length of the encoder input is larger '
                            'than this, we fall back to the eager mode.')
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        # Tokenizer arguments
        tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
        tokenizer_group = parser.add_argument_group(
            title="TokenizerPoolConfig",
            description=TokenizerPoolConfig.__doc__,
        )
        tokenizer_group.add_argument('--tokenizer-pool-size',
                                     **tokenizer_kwargs["pool_size"])
        tokenizer_group.add_argument('--tokenizer-pool-type',
                                     **tokenizer_kwargs["pool_type"])
        tokenizer_group.add_argument('--tokenizer-pool-extra-config',
                                     **tokenizer_kwargs["extra_config"])
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        # Multimodal related configs
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        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
        multimodal_group.add_argument('--limit-mm-per-prompt',
                                      **multimodal_kwargs["limit_per_prompt"])

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        parser.add_argument(
            '--mm-processor-kwargs',
            default=None,
            type=json.loads,
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            help=('Overrides for the multi-modal processor obtained from '
                  '``AutoProcessor.from_pretrained``. The available overrides '
                  'depend on the model that is being run.'
                  'For example, for Phi-3-Vision: ``{"num_crops": 4}``.'))
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        parser.add_argument(
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            '--disable-mm-preprocessor-cache',
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            action='store_true',
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            help='If True, disable caching of the processed multi-modal '
            'inputs.')
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        # LoRA related configs
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        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
            '--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"])
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        lora_group.add_argument('--lora-target-modules',
                            **lora_kwargs["lora_target_modules"])
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        lora_group.add_argument('--lora-extra-vocab-size',
                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
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            '--lora-dtype',
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            **lora_kwargs["lora_dtype"],
        )
        lora_group.add_argument('--long-lora-scaling-factors',
                                **lora_kwargs["long_lora_scaling_factors"])
        lora_group.add_argument('--max-cpu-loras',
                                **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument('--fully-sharded-loras',
                                **lora_kwargs["fully_sharded_loras"])

        # PromptAdapter related configs
        prompt_adapter_kwargs = get_kwargs(PromptAdapterConfig)
        prompt_adapter_group = parser.add_argument_group(
            title="PromptAdapterConfig",
            description=PromptAdapterConfig.__doc__,
        )
        prompt_adapter_group.add_argument(
            '--enable-prompt-adapter',
            action=argparse.BooleanOptionalAction,
            help='If True, enable handling of PromptAdapters.')
        prompt_adapter_group.add_argument(
            '--max-prompt-adapters',
            **prompt_adapter_kwargs["max_prompt_adapters"])
        prompt_adapter_group.add_argument(
            '--max-prompt-adapter-token',
            **prompt_adapter_kwargs["max_prompt_adapter_token"])
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        # Device arguments
        device_kwargs = get_kwargs(DeviceConfig)
        device_group = parser.add_argument_group(
            title="DeviceConfig",
            description=DeviceConfig.__doc__,
        )
        device_group.add_argument("--device", **device_kwargs["device"])

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        # 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.')
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        parser.add_argument(
            '--num-speculative-heads',
            type=int,
            default=EngineArgs.num_speculative_heads,
            help='The number of speculative heads to sample from '
                 'the draft model in speculative decoding.')
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        parser.add_argument(
            '--ignore-patterns',
            action="append",
            type=str,
            default=[],
            help="The pattern(s) to ignore when loading the model."
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            "Default to `original/**/*` to avoid repeated loading of llama's "
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            "checkpoints.")
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        parser.add_argument(
            "--served-model-name",
            nargs="+",
            type=str,
            default=None,
            help="The model name(s) used in the API. If multiple "
            "names are provided, the server will respond to any "
            "of the provided names. The model name in the model "
            "field of a response will be the first name in this "
            "list. If not specified, the model name will be the "
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            "same as the ``--model`` argument. Noted that this name(s) "
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            "will also be used in `model_name` tag content of "
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            "prometheus metrics, if multiple names provided, metrics "
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            "tag will take the first one.")
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        parser.add_argument('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')
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        parser.add_argument('--show-hidden-metrics-for-version',
                            type=str,
                            default=None,
                            help='Enable deprecated Prometheus metrics that '
                            'have been hidden since the specified version. '
                            'For example, if a previously deprecated metric '
                            'has been hidden since the v0.7.0 release, you '
                            'use --show-hidden-metrics-for-version=0.7 as a '
                            'temporary escape hatch while you migrate to new '
                            'metrics. The metric is likely to be removed '
                            'completely in an upcoming release.')

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        parser.add_argument(
            '--otlp-traces-endpoint',
            type=str,
            default=None,
            help='Target URL to which OpenTelemetry traces will be sent.')
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        parser.add_argument(
            '--collect-detailed-traces',
            type=str,
            default=None,
            help="Valid choices are " +
            ",".join(ALLOWED_DETAILED_TRACE_MODULES) +
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            ". It makes sense to set this only if ``--otlp-traces-endpoint`` is"
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            " set. If set, it will collect detailed traces for the specified "
            "modules. This involves use of possibly costly and or blocking "
            "operations and hence might have a performance impact.")
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        parser.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.")
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        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
            '--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(
            "--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"])
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        scheduler_group.add_argument('--preemption-mode',
                                     **scheduler_kwargs["preemption_mode"])
        scheduler_group.add_argument('--num-scheduler-steps',
                                     **scheduler_kwargs["num_scheduler_steps"])
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        scheduler_group.add_argument(
            '--multi-step-stream-outputs',
            **scheduler_kwargs["multi_step_stream_outputs"])
        scheduler_group.add_argument('--scheduling-policy',
                                     **scheduler_kwargs["policy"])
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        scheduler_group.add_argument(
            '--enable-chunked-prefill',
            **scheduler_kwargs["enable_chunked_prefill"])
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        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
        parser.add_argument('--scheduler-cls',
                            **scheduler_kwargs["scheduler_cls"])
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        parser.add_argument(
            '--override-neuron-config',
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            type=json.loads,
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            default=None,
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            help="Override or set neuron device configuration. "
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            "e.g. ``{\"cast_logits_dtype\": \"bloat16\"}``.")
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        parser.add_argument(
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            '--override-pooler-config',
            type=PoolerConfig.from_json,
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            default=None,
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            help="Override or set the pooling method for pooling models. "
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            "e.g. ``{\"pooling_type\": \"mean\", \"normalize\": false}``.")
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        parser.add_argument('--compilation-config',
                            '-O',
                            type=CompilationConfig.from_cli,
                            default=None,
                            help='torch.compile configuration for the model.'
                            'When it is a number (0, 1, 2, 3), it will be '
                            'interpreted as the optimization level.\n'
                            'NOTE: level 0 is the default level without '
                            'any optimization. level 1 and 2 are for internal '
                            'testing only. level 3 is the recommended level '
                            'for production.\n'
                            'To specify the full compilation config, '
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                            'use a JSON string, e.g. ``{"level": 3, '
                            '"cudagraph_capture_sizes": [1, 2, 4, 8]}``\n'
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                            'Following the convention of traditional '
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                            'compilers, using ``-O`` without space is also '
                            'supported. ``-O3`` is equivalent to ``-O 3``.')
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        parser.add_argument('--kv-transfer-config',
                            type=KVTransferConfig.from_cli,
                            default=None,
                            help='The configurations for distributed KV cache '
                            'transfer. Should be a JSON string.')

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        parser.add_argument(
            '--worker-cls',
            type=str,
            default="auto",
            help='The worker class to use for distributed execution.')
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        parser.add_argument(
            '--worker-extension-cls',
            type=str,
            default="",
            help='The worker extension class on top of the worker cls, '
            'it is useful if you just want to add new functions to the worker '
            'class without changing the existing functions.')
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        parser.add_argument(
            "--generation-config",
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            type=optional_type(str),
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            default="auto",
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            help="The folder path to the generation config. "
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            "Defaults to 'auto', the generation config will be loaded from "
            "model path. If set to 'vllm', no generation config is loaded, "
            "vLLM defaults will be used. If set to a folder path, the "
            "generation config will be loaded from the specified folder path. "
            "If `max_new_tokens` is specified in generation config, then "
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            "it sets a server-wide limit on the number of output tokens "
            "for all requests.")

        parser.add_argument(
            "--override-generation-config",
            type=json.loads,
            default=None,
            help="Overrides or sets generation config in JSON format. "
            "e.g. ``{\"temperature\": 0.5}``. If used with "
            "--generation-config=auto, the override parameters will be merged "
            "with the default config from the model. If generation-config is "
            "None, only the override parameters are used.")
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        parser.add_argument("--enable-sleep-mode",
                            action="store_true",
                            default=False,
                            help="Enable sleep mode for the engine. "
                            "(only cuda platform is supported)")

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        parser.add_argument(
            "--additional-config",
            type=json.loads,
            default=None,
            help="Additional config for specified platform in JSON format. "
            "Different platforms may support different configs. Make sure the "
            "configs are valid for the platform you are using. The input format"
            " is like '{\"config_key\":\"config_value\"}'")
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        parser.add_argument(
            "--enable-reasoning",
            action="store_true",
            default=False,
            help="Whether to enable reasoning_content for the model. "
            "If enabled, the model will be able to generate reasoning content."
        )

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        parser.add_argument(
            "--disable-cascade-attn",
            action="store_true",
            default=False,
            help="Disable cascade attention for V1. While cascade attention "
            "does not change the mathematical correctness, disabling it "
            "could be useful for preventing potential numerical issues. "
            "Note that even if this is set to False, cascade attention will be "
            "only used when the heuristic tells that it's beneficial.")

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        return parser
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    @classmethod
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    def from_cli_args(cls, args: argparse.Namespace):
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        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
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        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args
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    def create_model_config(self) -> ModelConfig:
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        # gguf file needs a specific model loader and doesn't use hf_repo
        if check_gguf_file(self.model):
            self.quantization = self.load_format = "gguf"

        # NOTE: This is to allow model loading from S3 in CI
        if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3
                and self.model in MODELS_ON_S3
                and self.load_format == LoadFormat.AUTO):  # noqa: E501
            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
            self.load_format = LoadFormat.RUNAI_STREAMER

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        return ModelConfig(
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            model=self.model,
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            hf_config_path=self.hf_config_path,
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            task=self.task,
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            # We know this is not None because we set it in __post_init__
            tokenizer=cast(str, self.tokenizer),
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            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
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            allowed_local_media_path=self.allowed_local_media_path,
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            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
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            rope_theta=self.rope_theta,
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            hf_token=self.hf_token,
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            hf_overrides=self.hf_overrides,
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            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            disable_sliding_window=self.disable_sliding_window,
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            disable_cascade_attn=self.disable_cascade_attn,
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            skip_tokenizer_init=self.skip_tokenizer_init,
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            served_model_name=self.served_model_name,
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            limit_mm_per_prompt=self.limit_mm_per_prompt,
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            use_async_output_proc=not self.disable_async_output_proc,
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            config_format=self.config_format,
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            mm_processor_kwargs=self.mm_processor_kwargs,
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            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
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            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
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            logits_processor_pattern=self.logits_processor_pattern,
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            generation_config=self.generation_config,
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            override_generation_config=self.override_generation_config,
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            enable_sleep_mode=self.enable_sleep_mode,
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            model_impl=self.model_impl,
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        )
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    def create_load_config(self) -> LoadConfig:

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        if(self.qlora_adapter_name_or_path is not None) and \
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            self.quantization != "bitsandbytes":
            raise ValueError(
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                "QLoRA adapter only support "
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                f"'bitsandbytes' quantization, but got {self.quantization}")

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        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
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        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
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            use_tqdm_on_load=self.use_tqdm_on_load,
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        )

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

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

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        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
        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

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    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
    ) -> VllmConfig:
        """
        Create the VllmConfig.
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        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.
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        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.
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        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
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        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()
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        device_config = DeviceConfig(device=self.device)
        model_config = self.create_model_config()

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

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

        # Set default arguments for V0 or V1 Engine.
        if use_v1:
            self._set_default_args_v1(usage_context)
        else:
            self._set_default_args_v0(model_config)
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        assert self.enable_chunked_prefill is not None
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        cache_config = CacheConfig(
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            block_size=self.block_size,
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            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
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            is_attention_free=model_config.is_attention_free,
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            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
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            enable_prefix_caching=self.enable_prefix_caching,
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            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
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            cpu_offload_gb=self.cpu_offload_gb,
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            calculate_kv_scales=self.calculate_kv_scales,
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        )
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        # Get the current placement group if Ray is initialized and
        # we are in a Ray actor. If so, then the placement group will be
        # passed to spawned processes.
        placement_group = None
        if is_in_ray_actor():
            import ray

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

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        parallel_config = ParallelConfig(
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            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
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            data_parallel_size=self.data_parallel_size,
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            enable_expert_parallel=self.enable_expert_parallel,
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            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            ray_workers_use_nsight=self.ray_workers_use_nsight,
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            placement_group=placement_group,
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            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
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            worker_extension_cls=self.worker_extension_cls,
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        )
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        speculative_config = self.create_speculative_config(
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            target_model_config=model_config,
            target_parallel_config=parallel_config,
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            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_log_stats=self.disable_log_stats,
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        )

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        # Reminder: Please update docs/source/features/compatibility_matrix.md
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        # If the feature combo become valid
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        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)")
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            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")
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            from vllm.platforms import current_platform
            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
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        # 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

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        scheduler_config = SchedulerConfig(
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            runner_type=model_config.runner_type,
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            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
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            num_lookahead_slots=num_lookahead_slots,
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            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_chunked_mm_input=self.disable_chunked_mm_input,
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            is_multimodal_model=model_config.is_multimodal_model,
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            preemption_mode=self.preemption_mode,
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            num_scheduler_steps=self.num_scheduler_steps,
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            multi_step_stream_outputs=self.multi_step_stream_outputs,
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            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
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            policy=self.scheduling_policy,
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            scheduler_cls=self.scheduler_cls,
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            max_num_partial_prefills=self.max_num_partial_prefills,
            max_long_partial_prefills=self.max_long_partial_prefills,
            long_prefill_token_threshold=self.long_prefill_token_threshold,
        )
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        lora_config = LoRAConfig(
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            bias_enabled=self.enable_lora_bias,
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            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
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            fully_sharded_loras=self.fully_sharded_loras,
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            lora_extra_vocab_size=self.lora_extra_vocab_size,
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            long_lora_scaling_factors=self.long_lora_scaling_factors,
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            lora_dtype=self.lora_dtype,
            max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
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            and self.max_cpu_loras > 0 else None,
            lora_target_modules=self.lora_target_modules) if self.enable_lora else None
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        if self.qlora_adapter_name_or_path is not None and \
            self.qlora_adapter_name_or_path != "":
            self.model_loader_extra_config[
                "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path

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

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        load_config = self.create_load_config()
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        prompt_adapter_config = PromptAdapterConfig(
            max_prompt_adapters=self.max_prompt_adapters,
            max_prompt_adapter_token=self.max_prompt_adapter_token) \
                                        if self.enable_prompt_adapter else None

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        decoding_config = DecodingConfig(
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            guided_decoding_backend=self.guided_decoding_backend,
            reasoning_backend=self.reasoning_parser
            if self.enable_reasoning else None,
        )
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        show_hidden_metrics = False
        if self.show_hidden_metrics_for_version is not None:
            show_hidden_metrics = version._prev_minor_version_was(
                self.show_hidden_metrics_for_version)
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        detailed_trace_modules = []
        if self.collect_detailed_traces is not None:
            detailed_trace_modules = self.collect_detailed_traces.split(",")
        for m in detailed_trace_modules:
            if m not in ALLOWED_DETAILED_TRACE_MODULES:
                raise ValueError(
                    f"Invalid module {m} in collect_detailed_traces. "
                    f"Valid modules are {ALLOWED_DETAILED_TRACE_MODULES}")
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        observability_config = ObservabilityConfig(
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            show_hidden_metrics=show_hidden_metrics,
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            otlp_traces_endpoint=self.otlp_traces_endpoint,
            collect_model_forward_time="model" in detailed_trace_modules
            or "all" in detailed_trace_modules,
            collect_model_execute_time="worker" in detailed_trace_modules
            or "all" in detailed_trace_modules,
        )
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        config = VllmConfig(
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            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
            decoding_config=decoding_config,
            observability_config=observability_config,
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            prompt_adapter_config=prompt_adapter_config,
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            compilation_config=self.compilation_config,
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            kv_transfer_config=self.kv_transfer_config,
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            additional_config=self.additional_config,
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        )
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        return config

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

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

        if (self.load_format == LoadFormat.TENSORIZER.value
                or self.load_format == LoadFormat.SHARDED_STATE.value):
            _raise_or_fallback(
                feature_name=f"--load_format {self.load_format}",
                recommend_to_remove=False)
            return False

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

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

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        if self.scheduling_policy != SchedulerConfig.policy:
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            _raise_or_fallback(feature_name="--scheduling-policy",
                               recommend_to_remove=False)
            return False

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

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

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        # remove backend options when doing this check
        if self.guided_decoding_backend.split(':')[0] \
            not in get_args(GuidedDecodingBackendV1):
            _raise_or_fallback(
                feature_name=
                f"--guided-decoding-backend={self.guided_decoding_backend}",
                recommend_to_remove=False)
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            return False

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

        # No Fp8 KV cache so far.
        if self.kv_cache_dtype != "auto":
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            fp8_attention = self.kv_cache_dtype.startswith("fp8")
            will_use_fa = (
                current_platform.is_cuda()
                and not envs.is_set("VLLM_ATTENTION_BACKEND")
            ) or envs.VLLM_ATTENTION_BACKEND == "FLASH_ATTN_VLLM_V1"
            supported = False
            if fp8_attention and will_use_fa:
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                from vllm.attention.utils.fa_utils import (
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                    flash_attn_supports_fp8)
                supported = flash_attn_supports_fp8()
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            int8_attention = self.kv_cache_dtype.startswith("int8")
            if int8_attention:
                supported = True
                
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            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
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        # No Prompt Adapter so far.
        if self.enable_prompt_adapter:
            _raise_or_fallback(feature_name="--enable-prompt-adapter",
                               recommend_to_remove=False)
            return False

        # Only Fp16 and Bf16 dtypes since we only support FA.
        V1_SUPPORTED_DTYPES = [torch.bfloat16, torch.float16]
        if model_config.dtype not in V1_SUPPORTED_DTYPES:
            _raise_or_fallback(feature_name=f"--dtype {model_config.dtype}",
                               recommend_to_remove=False)
            return False

        # Some quantization is not compatible with torch.compile.
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        V1_UNSUPPORTED_QUANT = ["gguf"]
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        if model_config.quantization in V1_UNSUPPORTED_QUANT:
            _raise_or_fallback(
                feature_name=f"--quantization {model_config.quantization}",
                recommend_to_remove=False)
            return False

        # No Embedding Models so far.
        if model_config.task not in ["generate"]:
            _raise_or_fallback(feature_name=f"--task {model_config.task}",
                               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

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

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

        # Only Ngram speculative decoding so far.
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        is_ngram_enabled = False
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        is_eagle_enabled = False
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        if self.speculative_config is not None:
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            # This is supported but experimental (handled below).
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            speculative_method = self.speculative_config.get("method")
            if speculative_method:
                if speculative_method in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
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                elif speculative_method in ("eagle", "eagle3"):
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                    is_eagle_enabled = True
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            else:
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                speculative_model = self.speculative_config.get("model")
                if speculative_model in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
            if not (is_ngram_enabled or is_eagle_enabled):
                # Other speculative decoding methods are not supported yet.
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                _raise_or_fallback(feature_name="Speculative Decoding",
                                   recommend_to_remove=False)
                return False

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

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

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

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        # PP is supported on V1 with Ray distributed executor,
        # but off for MP distributed executor for now.
        if (self.pipeline_parallel_size > 1
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                and self.distributed_executor_backend != "ray"):
            name = "Pipeline Parallelism without Ray distributed executor"
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
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            return False

        # ngram is supported on V1, but off by default for now.
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        if is_ngram_enabled and _warn_or_fallback("ngram"):
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            return False

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        # Eagle is under development, so we don't support it yet.
        if is_eagle_enabled and _warn_or_fallback("Eagle"):
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            return False

        # Non-CUDA is supported on V1, but off by default for now.
        not_cuda = not current_platform.is_cuda()
        if not_cuda and _warn_or_fallback(  # noqa: SIM103
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                current_platform.device_name):
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            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:
                from vllm.platforms import current_platform
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
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                use_spec_decode = self.speculative_config is not None
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                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and not self.enable_prompt_adapter
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

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

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

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

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

    def _set_default_args_v1(self, usage_context: UsageContext) -> None:
        """Set Default Arguments for V1 Engine."""
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        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
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        # V1 enables prefix caching by default.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = True

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        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
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            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
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        # When no user override, set the default values based on the usage
        # context.
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        # Use different default values for different hardware.
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        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
        try:
            from vllm.platforms import current_platform
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            device_memory = current_platform.get_device_total_memory()
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        except Exception:
            # This is only used to set default_max_num_batched_tokens
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            device_memory = 0
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        if device_memory >= 70 * GiB_bytes:
            # For GPUs like H100 and MI300x, use larger default values.
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            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
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            default_max_num_seqs = 1024
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        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
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            default_max_num_seqs = 256
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        use_context_value = usage_context.value if usage_context else None
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        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
            self.max_num_batched_tokens = default_max_num_batched_tokens[
                usage_context]
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            logger.debug(
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                "Setting max_num_batched_tokens to %d for %s usage context.",
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                self.max_num_batched_tokens, use_context_value)
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        if self.max_num_seqs is None:
            self.max_num_seqs = default_max_num_seqs

            logger.debug("Setting max_num_seqs to %d for %s usage context.",
                         self.max_num_seqs, use_context_value)
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@dataclass
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class AsyncEngineArgs(EngineArgs):
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    """Arguments for asynchronous vLLM engine."""
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    disable_log_requests: bool = False
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
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        # Initialize plugin to update the parser, for example, The plugin may
        # adding a new kind of quantization method to --quantization argument or
        # a new device to --device argument.
        load_general_plugins()
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        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
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        parser.add_argument('--disable-log-requests',
                            action='store_true',
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                            help='Disable logging requests.')
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        from vllm.platforms import current_platform
        current_platform.pre_register_and_update(parser)
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        return parser
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def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        raise NotImplementedError(
            f"VLLM_USE_V1=1 is not supported with {feature_name}.")
    msg = f"{feature_name} is not supported by the V1 Engine. "
    msg += "Falling back to V0. "
    if recommend_to_remove:
        msg += f"We recommend to remove {feature_name} from your config "
        msg += "in favor of the V1 Engine."
    logger.warning(msg)


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


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


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# These functions are used by sphinx to build the documentation
def _engine_args_parser():
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    return EngineArgs.add_cli_args(FlexibleArgumentParser())
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def _async_engine_args_parser():
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    return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
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                                        async_args_only=True)