arg_utils.py 71.9 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("^{.*}$", val):
                return cast(T, nullable_kvs(val))
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
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    return _optional_type
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def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
    if not re.match("^{.*}$", val):
        return str(val)
    else:
        return optional_type(json.loads)(val)


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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
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        help = cls_docs[name].strip()
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        # 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
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        json_tip = "\n\nShould be a valid JSON string."
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        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
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        elif contains_type(type_hints,
                           dict) and (contains_type(type_hints, str) or any(
                               is_not_builtin(th) for th in type_hints)):
            kwargs[name]["type"] = union_dict_and_str
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        elif contains_type(type_hints, dict):
            # Dict arguments will always be optional
            kwargs[name]["type"] = optional_type(json.loads)
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            kwargs[name]["help"] += json_tip
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        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
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    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
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    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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    cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
                                            "cuda_graph_sizes")
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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  # DEPRECATED
    reasoning_parser: str = DecodingConfig.reasoning_backend

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    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
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    pt_load_map_location: str = LoadConfig.pt_load_map_location
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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"])
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        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
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        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__,
        )
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        load_group.add_argument("--load-format",
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                                choices=[f.value for f in LoadFormat],
                                **load_kwargs["load_format"])
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        load_group.add_argument("--download-dir",
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                                **load_kwargs["download_dir"])
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        load_group.add_argument("--model-loader-extra-config",
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                                **load_kwargs["model_loader_extra_config"])
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        load_group.add_argument("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
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                                **load_kwargs["use_tqdm_on_load"])
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        load_group.add_argument('--qlora-adapter-name-or-path',
                                type=str,
                                default=None,
                                help='Name or path of the QLoRA adapter.')
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        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])
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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(
            "--enable-reasoning",
            action=argparse.BooleanOptionalAction,
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            deprecated=True,
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            help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
            "of v0.8.6. Use `--reasoning-parser` to specify the reasoning "
            "parser backend insteadThis flag (`--enable-reasoning`) will be "
            "removed in v0.10.0. When `--reasoning-parser` is specified, "
            "reasoning mode is automatically enabled.")
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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(
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            "--pipeline-parallel-size", "-pp",
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            **parallel_kwargs["pipeline_parallel_size"])
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        parallel_group.add_argument("--tensor-parallel-size", "-tp",
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                                    **parallel_kwargs["tensor_parallel_size"])
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        parallel_group.add_argument("--data-parallel-size", "-dp",
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                                    **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(
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            "--disable-custom-all-reduce",
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            **parallel_kwargs["disable_custom_all_reduce"])
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        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
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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",
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                                 **cache_kwargs["gpu_memory_utilization"])
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        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
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                                 **cache_kwargs["cache_dtype"])
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        cache_group.add_argument("--num-gpu-blocks-override",
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                                 **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"])
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        cache_group.add_argument("--cpu-offload-gb",
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                                 **cache_kwargs["cpu_offload_gb"])
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        cache_group.add_argument("--calculate-kv-scales",
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                                 **cache_kwargs["calculate_kv_scales"])

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        # Tokenizer arguments
        tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
        tokenizer_group = parser.add_argument_group(
            title="TokenizerPoolConfig",
            description=TokenizerPoolConfig.__doc__,
        )
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        tokenizer_group.add_argument("--tokenizer-pool-size",
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                                     **tokenizer_kwargs["pool_size"])
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        tokenizer_group.add_argument("--tokenizer-pool-type",
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                                     **tokenizer_kwargs["pool_type"])
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        tokenizer_group.add_argument("--tokenizer-pool-extra-config",
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                                     **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__,
        )
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        multimodal_group.add_argument("--limit-mm-per-prompt",
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                                      **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(
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            "--enable-lora",
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            action=argparse.BooleanOptionalAction,
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            help="If True, enable handling of LoRA adapters.")
        lora_group.add_argument("--enable-lora-bias",
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                                **lora_kwargs["bias_enabled"])
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        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
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                                **lora_kwargs["max_lora_rank"])
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        lora_group.add_argument("--lora-extra-vocab-size",
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                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
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            "--lora-dtype",
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            **lora_kwargs["lora_dtype"],
        )
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        lora_group.add_argument("--long-lora-scaling-factors",
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                                **lora_kwargs["long_lora_scaling_factors"])
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        lora_group.add_argument("--max-cpu-loras",
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                                **lora_kwargs["max_cpu_loras"])
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        lora_group.add_argument("--fully-sharded-loras",
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                                **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(
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            "--enable-prompt-adapter",
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            action=argparse.BooleanOptionalAction,
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            help="If True, enable handling of PromptAdapters.")
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        prompt_adapter_group.add_argument(
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            "--max-prompt-adapters",
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            **prompt_adapter_kwargs["max_prompt_adapters"])
        prompt_adapter_group.add_argument(
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            "--max-prompt-adapter-token",
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            **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(
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            "--speculative-config",
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            type=json.loads,
            default=None,
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            help="The configurations for speculative decoding. Should be a "
            "JSON string.")
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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(
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            "--max-num-batched-tokens",
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            **scheduler_kwargs["max_num_batched_tokens"])
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        scheduler_group.add_argument("--max-num-seqs",
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                                     **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"])
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        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
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        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
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        scheduler_group.add_argument("--num-lookahead-slots",
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                                     **scheduler_kwargs["num_lookahead_slots"])
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        scheduler_group.add_argument("--scheduler-delay-factor",
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                                     **scheduler_kwargs["delay_factor"])
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        scheduler_group.add_argument("--preemption-mode",
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                                     **scheduler_kwargs["preemption_mode"])
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        scheduler_group.add_argument("--num-scheduler-steps",
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                                     **scheduler_kwargs["num_scheduler_steps"])
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        scheduler_group.add_argument(
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            "--multi-step-stream-outputs",
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            **scheduler_kwargs["multi_step_stream_outputs"])
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        scheduler_group.add_argument("--scheduling-policy",
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                                     **scheduler_kwargs["policy"])
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        scheduler_group.add_argument(
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            "--enable-chunked-prefill",
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            **scheduler_kwargs["enable_chunked_prefill"])
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        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
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        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])

        # Compilation arguments
        # compilation_kwargs = get_kwargs(CompilationConfig)
        compilation_group = parser.add_argument_group(
            title="CompilationConfig",
            description=CompilationConfig.__doc__,
        )
        compilation_group.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, "
            "use a JSON string, e.g. ``{\"level\": 3, "
            "\"cudagraph_capture_sizes\": [1, 2, 4, 8]}``\n"
            "Following the convention of traditional "
            "compilers, using ``-O`` without space is also "
            "supported. ``-O3`` is equivalent to ``-O 3``.")

        # KVTransfer arguments
        # kv_transfer_kwargs = get_kwargs(KVTransferConfig)
        kv_transfer_group = parser.add_argument_group(
            title="KVTransferConfig",
            description=KVTransferConfig.__doc__,
        )
        kv_transfer_group.add_argument(
            "--kv-transfer-config",
            type=KVTransferConfig.from_cli,
            default=None,
            help="The configurations for distributed KV cache "
            "transfer. Should be a JSON string.")
        kv_transfer_group.add_argument(
            '--kv-events-config',
            type=KVEventsConfig.from_cli,
            default=None,
            help='The configurations for event publishing.')

        # vLLM arguments
        # vllm_kwargs = get_kwargs(VllmConfig)
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
        vllm_group.add_argument(
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            "--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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        # Other arguments
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
                            default=True,
                            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.')
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
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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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            enable_prompt_embeds=self.enable_prompt_embeds,
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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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            pt_load_map_location=self.pt_load_map_location,
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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.
        """
966
967
        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()
968

969
        device_config = DeviceConfig(device=self.device)
970
971
        model_config = self.create_model_config()

972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
        # * 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)
994

995
996
        assert self.enable_chunked_prefill is not None

997
        cache_config = CacheConfig(
998
            block_size=self.block_size,
999
1000
1001
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
1002
            is_attention_free=model_config.is_attention_free,
1003
1004
            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
1005
            enable_prefix_caching=self.enable_prefix_caching,
1006
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
1007
            cpu_offload_gb=self.cpu_offload_gb,
1008
            calculate_kv_scales=self.calculate_kv_scales,
1009
        )
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021

        # 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()

1022
        parallel_config = ParallelConfig(
1023
1024
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
1025
            data_parallel_size=self.data_parallel_size,
1026
            enable_expert_parallel=self.enable_expert_parallel,
1027
1028
1029
            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,
1030
            placement_group=placement_group,
1031
1032
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1033
            worker_extension_cls=self.worker_extension_cls,
1034
        )
1035

1036
        speculative_config = self.create_speculative_config(
1037
1038
            target_model_config=model_config,
            target_parallel_config=parallel_config,
1039
            enable_chunked_prefill=self.enable_chunked_prefill,
1040
            disable_log_stats=self.disable_log_stats,
1041
1042
        )

1043
        # Reminder: Please update docs/source/features/compatibility_matrix.md
1044
        # If the feature combo become valid
1045
1046
1047
1048
        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)")
1049
1050
1051
            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")
1052
1053
1054
1055
1056
1057
            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
1058
1059
1060
1061
1062
1063
1064
1065
1066

        # 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

1067
        scheduler_config = SchedulerConfig(
1068
            runner_type=model_config.runner_type,
1069
1070
1071
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1072
            cuda_graph_sizes=self.cuda_graph_sizes,
1073
            num_lookahead_slots=num_lookahead_slots,
1074
1075
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1076
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1077
            is_multimodal_model=model_config.is_multimodal_model,
1078
            preemption_mode=self.preemption_mode,
1079
            num_scheduler_steps=self.num_scheduler_steps,
1080
            multi_step_stream_outputs=self.multi_step_stream_outputs,
1081
1082
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1083
            policy=self.scheduling_policy,
1084
            scheduler_cls=self.scheduler_cls,
1085
1086
1087
1088
            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,
        )
1089

1090
        lora_config = LoRAConfig(
1091
            bias_enabled=self.enable_lora_bias,
1092
1093
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1094
            fully_sharded_loras=self.fully_sharded_loras,
1095
            lora_extra_vocab_size=self.lora_extra_vocab_size,
1096
            long_lora_scaling_factors=self.long_lora_scaling_factors,
1097
1098
1099
            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
1100

1101
1102
1103
1104
1105
        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

1106
1107
1108
1109
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1110
        load_config = self.create_load_config()
1111

1112
1113
1114
1115
1116
        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

1117
        decoding_config = DecodingConfig(
1118
1119
1120
1121
1122
            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,
1123
1124
            reasoning_backend=self.reasoning_parser
        )
1125

1126
        observability_config = ObservabilityConfig(
1127
1128
            show_hidden_metrics_for_version=self.
            show_hidden_metrics_for_version,
1129
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1130
            collect_detailed_traces=self.collect_detailed_traces,
1131
        )
1132

1133
        config = VllmConfig(
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
            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,
1144
            prompt_adapter_config=prompt_adapter_config,
1145
            compilation_config=self.compilation_config,
1146
            kv_transfer_config=self.kv_transfer_config,
1147
            kv_events_config=self.kv_events_config,
1148
            additional_config=self.additional_config,
1149
        )
1150

1151
1152
        return config

1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
    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

1172
        if self.preemption_mode != SchedulerConfig.preemption_mode:
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
            _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

1183
        if self.scheduling_policy != SchedulerConfig.policy:
1184
1185
1186
1187
            _raise_or_fallback(feature_name="--scheduling-policy",
                               recommend_to_remove=False)
            return False

1188
        if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
1189
1190
1191
1192
            _raise_or_fallback(feature_name="--num-scheduler-steps",
                               recommend_to_remove=True)
            return False

1193
        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
1194
1195
1196
1197
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

1198
1199
        if self.guided_decoding_backend not in get_args(
                GuidedDecodingBackendV1):
1200
1201
1202
1203
            _raise_or_fallback(
                feature_name=
                f"--guided-decoding-backend={self.guided_decoding_backend}",
                recommend_to_remove=False)
1204
1205
1206
            return False

        # Need at least Ampere for now (FA support required).
1207
1208
1209
        # 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).
1210
1211
        from vllm.platforms import current_platform
        if (current_platform.is_cuda()
1212
                and current_platform.get_device_capability()
1213
1214
1215
1216
1217
1218
1219
                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":
1220
1221
1222
1223
1224
1225
1226
            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:
1227
                from vllm.attention.utils.fa_utils import (
1228
1229
1230
1231
1232
1233
                    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
1234
1235
1236
1237
1238
1239
1240

        # No Prompt Adapter so far.
        if self.enable_prompt_adapter:
            _raise_or_fallback(feature_name="--enable-prompt-adapter",
                               recommend_to_remove=False)
            return False

1241
1242
1243
1244
1245
1246
        # No text embedding inputs so far.
        if self.enable_prompt_embeds:
            _raise_or_fallback(feature_name="--enable-prompt-embeds",
                               recommend_to_remove=False)
            return False

1247
1248
1249
1250
1251
1252
1253
1254
        # 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.
1255
        V1_UNSUPPORTED_QUANT = ["gguf"]
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
        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
1276
                != SchedulerConfig.max_num_partial_prefills
1277
                or self.max_long_partial_prefills
1278
                != SchedulerConfig.max_long_partial_prefills):
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
            _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.
1290
        is_ngram_enabled = False
1291
        is_eagle_enabled = False
1292
        if self.speculative_config is not None:
1293
            # This is supported but experimental (handled below).
1294
1295
1296
1297
            speculative_method = self.speculative_config.get("method")
            if speculative_method:
                if speculative_method in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
1298
                elif speculative_method in ("eagle", "eagle3"):
1299
                    is_eagle_enabled = True
1300
            else:
1301
1302
1303
1304
1305
                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.
1306
1307
1308
1309
                _raise_or_fallback(feature_name="Speculative Decoding",
                                   recommend_to_remove=False)
                return False

1310
        # No XFormers so far.
1311
        V1_BACKENDS = [
1312
1313
1314
1315
1316
1317
1318
1319
1320
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
1321
1322
1323
1324
1325
1326
1327
        ]
        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

1328
1329
        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
1330
1331
1332
1333
            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
1334
1335
1336
        #############################################################
        # Experimental Features - allow users to opt in.

1337
1338
1339
1340
1341
        # Signal Handlers requires running in main thread.
        if (threading.current_thread() != threading.main_thread()
                and _warn_or_fallback("Engine in background thread")):
            return False

1342
        if (self.pipeline_parallel_size > 1
1343
1344
1345
                and self.distributed_executor_backend not in ["ray", "mp"]):
            name = "Pipeline Parallelism without Ray distributed executor " \
                    "or multiprocessing executor"
1346
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
1347
1348
1349
            return False

        # ngram is supported on V1, but off by default for now.
1350
        if is_ngram_enabled and _warn_or_fallback("ngram"):
1351
1352
            return False

1353
1354
1355
1356
        # Eagle is under development, so we don't support it yet.
        if is_eagle_enabled and _warn_or_fallback("Eagle"):
            return False

1357
1358
1359
1360
        # Non-[CUDA, TPU] may be supported on V1, but off by default for now.
        v0_hardware = not any(
            (current_platform.is_cuda(), current_platform.is_tpu()))
        if v0_hardware and _warn_or_fallback(  # noqa: SIM103
1361
                current_platform.device_name):
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
            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)
1384
                use_spec_decode = self.speculative_config is not None
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411

                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)

1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
        # 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.
1422
            if self.prefix_caching_hash_algo == "sha256":
1423
1424
1425
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")
1426
1427
1428
1429
1430
1431
1432

        # 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."""
1433

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

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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):
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            if current_platform.is_tpu():
                chip_name = current_platform.get_device_name()
                if chip_name in default_max_num_batched_tokens_tpu[
                        usage_context]:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens_tpu[
                            usage_context][chip_name]
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
            else:
                self.max_num_batched_tokens = default_max_num_batched_tokens[
                    usage_context]
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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
Zhuohan Li's avatar
Zhuohan Li committed
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class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
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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.
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    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)