arg_utils.py 69.1 KB
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# SPDX-License-Identifier: Apache-2.0

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# yapf: disable
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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 itertools import permutations
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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.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
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                         ConfigFormat, ConfigType, DecodingConfig,
                         DetailedTraceModules, Device, DeviceConfig,
                         DistributedExecutorBackend, GuidedDecodingBackend,
                         GuidedDecodingBackendV1, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
                         ModelConfig, ModelDType, ModelImpl, MultiModalConfig,
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                         ObservabilityConfig, ParallelConfig, PoolerConfig,
                         PrefixCachingHashAlgo, PromptAdapterConfig,
                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
                         TaskOption, TokenizerMode, 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 QuantizationMethods
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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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# 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]:
    """Parses a string containing comma separate key [str] to value [int]
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    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)


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def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
    """Convert Literal type hints to argparse kwargs."""
    type_hint = get_type(type_hints, Literal)
    choices = get_args(type_hint)
    choice_type = type(choices[0])
    if not all(isinstance(choice, choice_type) for choice in choices):
        raise ValueError(
            "All choices must be of the same type. "
            f"Got {choices} with types {[type(c) for c in choices]}")
    return {"type": choice_type, "choices": sorted(choices)}


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


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):
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            kwargs[name].update(literal_to_kwargs(type_hints))
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        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
                f"type. Got {types}.")
            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
            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
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            # Special case for large integers
            if name in {"max_model_len"}:
                kwargs[name]["type"] = human_readable_int
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        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}.")

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        # If the type hint was a sequence of literals, use the helper function
        # to update the type and choices
        if get_origin(kwargs[name].get("type")) is Literal:
            kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]}))

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        # If None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs
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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = ModelConfig.model
    served_model_name: Optional[Union[
        str, List[str]]] = ModelConfig.served_model_name
    tokenizer: Optional[str] = ModelConfig.tokenizer
    hf_config_path: Optional[str] = ModelConfig.hf_config_path
    task: TaskOption = ModelConfig.task
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
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    download_dir: Optional[str] = LoadConfig.download_dir
    load_format: str = LoadConfig.load_format
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    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
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    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
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    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
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    # 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 = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
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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 = ModelConfig.max_logprobs
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    disable_log_stats: bool = False
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    revision: Optional[str] = ModelConfig.revision
    code_revision: Optional[str] = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: Optional[float] = ModelConfig.rope_theta
    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
    hf_overrides: Optional[HfOverrides] = \
        get_field(ModelConfig, "hf_overrides")
    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
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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]] = \
        MultiModalConfig.mm_processor_kwargs
    disable_mm_preprocessor_cache: bool = \
        MultiModalConfig.disable_mm_preprocessor_cache
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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
    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: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
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    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern
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    speculative_config: Optional[Dict[str, Any]] = None
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    qlora_adapter_name_or_path: Optional[str] = None
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    show_hidden_metrics_for_version: Optional[str] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
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    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
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    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
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    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
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    compilation_config: 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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    kv_events_config: Optional[KVEventsConfig] = None
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    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    model_impl: str = ModelConfig.model_impl
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    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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        # 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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        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
        model_group.add_argument("--model", **model_kwargs["model"])
        model_group.add_argument("--task", **model_kwargs["task"])
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        model_group.add_argument("--hf-config-path",
                                 **model_kwargs["hf_config_path"])
        model_group.add_argument("--allowed-local-media-path",
                                 **model_kwargs["allowed_local_media_path"])
        model_group.add_argument("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        model_group.add_argument("--tokenizer-revision",
                                 **model_kwargs["tokenizer_revision"])
        model_group.add_argument("--max-model-len",
                                 **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q",
                                 **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager",
                                 **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
        model_group.add_argument("--disable-sliding-window",
                                 **model_kwargs["disable_sliding_window"])
        model_group.add_argument("--disable-cascade-attn",
                                 **model_kwargs["disable_cascade_attn"])
        model_group.add_argument("--skip-tokenizer-init",
                                 **model_kwargs["skip_tokenizer_init"])
        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        model_group.add_argument("--hf-token",
                                 type=str,
                                 nargs="?",
                                 const=True,
                                 default=model_kwargs["hf_token"]["default"],
                                 help=model_kwargs["hf_token"]["help"])
        model_group.add_argument("--hf-overrides",
                                 **model_kwargs["hf_overrides"])
        model_group.add_argument("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        model_group.add_argument("--logits-processor-pattern",
                                 **model_kwargs["logits_processor_pattern"])
        model_group.add_argument("--generation-config",
                                 **model_kwargs["generation_config"])
        model_group.add_argument("--override-generation-config",
                                 **model_kwargs["override_generation_config"])
        model_group.add_argument("--enable-sleep-mode",
                                 **model_kwargs["enable_sleep_mode"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])

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

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        # Parallel arguments
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        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
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            '--distributed-executor-backend',
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            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
            '--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('--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('--disable-log-stats',
                            action='store_true',
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                            help='Disable logging statistics.')
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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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        multimodal_group.add_argument(
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            '--mm-processor-kwargs',
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            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
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            '--disable-mm-preprocessor-cache',
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            **multimodal_kwargs["disable_mm_preprocessor_cache"])
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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"])
        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(
            '--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('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')
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        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **observability_kwargs["collect_detailed_traces"])
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        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
            '--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('--compilation-config',
                            '-O',
                            type=CompilationConfig.from_cli,
                            default=None,
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                            help='torch.compile configuration for the model. '
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                            '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('--kv-events-config',
                            type=KVEventsConfig.from_cli,
                            default=None,
                            help='The configurations for event publishing.')
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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(
            "--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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        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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            tokenizer=self.tokenizer,
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            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
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            allowed_local_media_path=self.allowed_local_media_path,
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            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
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            rope_theta=self.rope_theta,
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            hf_token=self.hf_token,
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            hf_overrides=self.hf_overrides,
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            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            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.

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.
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        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
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        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()
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        device_config = DeviceConfig(device=self.device)
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        model_config = self.create_model_config()

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

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

        # Set default arguments for V0 or V1 Engine.
        if use_v1:
            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(),
973
            enable_prefix_caching=self.enable_prefix_caching,
974
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
975
            cpu_offload_gb=self.cpu_offload_gb,
976
            calculate_kv_scales=self.calculate_kv_scales,
977
        )
978
979
980
981
982
983
984
985
986
987
988
989

        # 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()

990
        parallel_config = ParallelConfig(
991
992
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
993
            data_parallel_size=self.data_parallel_size,
994
            enable_expert_parallel=self.enable_expert_parallel,
995
996
997
            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,
998
            placement_group=placement_group,
999
1000
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1001
            worker_extension_cls=self.worker_extension_cls,
1002
        )
1003

1004
        speculative_config = self.create_speculative_config(
1005
1006
            target_model_config=model_config,
            target_parallel_config=parallel_config,
1007
            enable_chunked_prefill=self.enable_chunked_prefill,
1008
            disable_log_stats=self.disable_log_stats,
1009
1010
        )

1011
        # Reminder: Please update docs/source/features/compatibility_matrix.md
1012
        # If the feature combo become valid
1013
1014
1015
1016
        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)")
1017
1018
1019
            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")
1020
1021
1022
1023
1024
1025
            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
1026
1027
1028
1029
1030
1031
1032
1033
1034

        # 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

1035
        scheduler_config = SchedulerConfig(
1036
            runner_type=model_config.runner_type,
1037
1038
1039
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1040
            num_lookahead_slots=num_lookahead_slots,
1041
1042
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1043
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1044
            is_multimodal_model=model_config.is_multimodal_model,
1045
            preemption_mode=self.preemption_mode,
1046
            num_scheduler_steps=self.num_scheduler_steps,
1047
            multi_step_stream_outputs=self.multi_step_stream_outputs,
1048
1049
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1050
            policy=self.scheduling_policy,
1051
            scheduler_cls=self.scheduler_cls,
1052
1053
1054
1055
            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,
        )
1056

1057
        lora_config = LoRAConfig(
1058
            bias_enabled=self.enable_lora_bias,
1059
1060
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1061
            fully_sharded_loras=self.fully_sharded_loras,
1062
            lora_extra_vocab_size=self.lora_extra_vocab_size,
1063
            long_lora_scaling_factors=self.long_lora_scaling_factors,
1064
1065
1066
            lora_dtype=self.lora_dtype,
            max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
            and self.max_cpu_loras > 0 else None) if self.enable_lora else None
1067

1068
1069
1070
1071
1072
        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

1073
1074
1075
1076
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1077
        load_config = self.create_load_config()
1078

1079
1080
1081
1082
1083
        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

1084
        decoding_config = DecodingConfig(
1085
1086
1087
1088
1089
            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
1090
1091
1092
            reasoning_backend=self.reasoning_parser
            if self.enable_reasoning else None,
        )
1093

1094
        observability_config = ObservabilityConfig(
1095
1096
            show_hidden_metrics_for_version=self.
            show_hidden_metrics_for_version,
1097
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1098
            collect_detailed_traces=self.collect_detailed_traces,
1099
        )
1100

1101
        config = VllmConfig(
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
            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,
1112
            prompt_adapter_config=prompt_adapter_config,
1113
            compilation_config=self.compilation_config,
1114
            kv_transfer_config=self.kv_transfer_config,
1115
            kv_events_config=self.kv_events_config,
1116
            additional_config=self.additional_config,
1117
        )
1118

1119
1120
        return config

1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
    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

1140
        if self.preemption_mode != SchedulerConfig.preemption_mode:
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
            _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

1151
        if self.scheduling_policy != SchedulerConfig.policy:
1152
1153
1154
1155
            _raise_or_fallback(feature_name="--scheduling-policy",
                               recommend_to_remove=False)
            return False

1156
        if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
1157
1158
1159
1160
            _raise_or_fallback(feature_name="--num-scheduler-steps",
                               recommend_to_remove=True)
            return False

1161
        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
1162
1163
1164
1165
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

1166
1167
        if self.guided_decoding_backend not in get_args(
                GuidedDecodingBackendV1):
1168
1169
1170
1171
            _raise_or_fallback(
                feature_name=
                f"--guided-decoding-backend={self.guided_decoding_backend}",
                recommend_to_remove=False)
1172
1173
1174
            return False

        # Need at least Ampere for now (FA support required).
1175
1176
1177
        # 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).
1178
1179
        from vllm.platforms import current_platform
        if (current_platform.is_cuda()
1180
                and current_platform.get_device_capability()
1181
1182
1183
1184
1185
1186
1187
                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":
1188
1189
1190
1191
1192
1193
1194
            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:
1195
                from vllm.attention.utils.fa_utils import (
1196
1197
1198
1199
1200
1201
                    flash_attn_supports_fp8)
                supported = flash_attn_supports_fp8()
            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216

        # 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.
1217
        V1_UNSUPPORTED_QUANT = ["gguf"]
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
        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
1238
                != SchedulerConfig.max_num_partial_prefills
1239
                or self.max_long_partial_prefills
1240
                != SchedulerConfig.max_long_partial_prefills):
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
            _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.
1252
        is_ngram_enabled = False
1253
        is_eagle_enabled = False
1254
        if self.speculative_config is not None:
1255
            # This is supported but experimental (handled below).
1256
1257
1258
1259
            speculative_method = self.speculative_config.get("method")
            if speculative_method:
                if speculative_method in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
1260
                elif speculative_method in ("eagle", "eagle3"):
1261
                    is_eagle_enabled = True
1262
            else:
1263
1264
1265
1266
1267
                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.
1268
1269
1270
1271
                _raise_or_fallback(feature_name="Speculative Decoding",
                                   recommend_to_remove=False)
                return False

1272
        # No XFormers so far.
1273
        V1_BACKENDS = [
1274
1275
1276
1277
1278
1279
1280
1281
1282
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
1283
1284
1285
1286
1287
1288
1289
        ]
        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

1290
1291
        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
1292
1293
1294
1295
            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
1296
1297
1298
        #############################################################
        # Experimental Features - allow users to opt in.

1299
1300
1301
1302
1303
        # Signal Handlers requires running in main thread.
        if (threading.current_thread() != threading.main_thread()
                and _warn_or_fallback("Engine in background thread")):
            return False

1304
1305
1306
        # PP is supported on V1 with Ray distributed executor,
        # but off for MP distributed executor for now.
        if (self.pipeline_parallel_size > 1
1307
1308
1309
                and self.distributed_executor_backend != "ray"):
            name = "Pipeline Parallelism without Ray distributed executor"
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
1310
1311
1312
            return False

        # ngram is supported on V1, but off by default for now.
1313
        if is_ngram_enabled and _warn_or_fallback("ngram"):
1314
1315
            return False

1316
1317
1318
1319
        # Eagle is under development, so we don't support it yet.
        if is_eagle_enabled and _warn_or_fallback("Eagle"):
            return False

1320
1321
1322
        # 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
1323
                current_platform.device_name):
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
            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)
1346
                use_spec_decode = self.speculative_config is not None
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373

                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)

1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
        # 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.
1384
            if self.prefix_caching_hash_algo == "sha256":
1385
1386
1387
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")
1388
1389
1390
1391
1392
1393
1394

        # 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."""
1395

1396
1397
        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
1398
1399
1400
1401
1402

        # V1 enables prefix caching by default.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = True

1403
1404
1405
        # 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:
1406
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
1407

1408
1409
        # When no user override, set the default values based on the usage
        # context.
1410
        # Use different default values for different hardware.
1411
1412
1413
1414
1415
1416
1417
1418

        # 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
1419
            device_memory = current_platform.get_device_total_memory()
1420
1421
        except Exception:
            # This is only used to set default_max_num_batched_tokens
1422
            device_memory = 0
1423

1424
1425
        if device_memory >= 70 * GiB_bytes:
            # For GPUs like H100 and MI300x, use larger default values.
1426
1427
1428
1429
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
1430
            default_max_num_seqs = 1024
1431
1432
1433
1434
1435
1436
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
1437
            default_max_num_seqs = 256
1438

1439
        use_context_value = usage_context.value if usage_context else None
1440
1441
1442
1443
        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]
1444
            logger.debug(
1445
                "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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Zhuohan Li committed
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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)