arg_utils.py 81.9 KB
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# SPDX-License-Identifier: Apache-2.0

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import argparse
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import dataclasses
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import json
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import re
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import threading
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from dataclasses import MISSING, dataclass, fields
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from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional,
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                    Tuple, Type, Union, cast, get_args, get_origin)
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import torch

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import vllm.envs as envs
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from vllm import version
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from vllm.config import (CacheConfig, CompilationConfig, ConfigFormat,
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                         DecodingConfig, DeviceConfig, HfOverrides,
                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
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                         ModelConfig, ModelImpl, ObservabilityConfig,
                         ParallelConfig, PoolerConfig, PromptAdapterConfig,
                         SchedulerConfig, SpeculativeConfig, TaskOption,
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                         TokenizerPoolConfig, VllmConfig, get_attr_docs)
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from vllm.executor.executor_base import ExecutorBase
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
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from vllm.plugins import load_general_plugins
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from vllm.reasoning import ReasoningParserManager
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from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import FlexibleArgumentParser, StoreBoolean, is_in_ray_actor
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if TYPE_CHECKING:
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    from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
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logger = init_logger(__name__)

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ALLOWED_DETAILED_TRACE_MODULES = ["model", "worker", "all"]

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DEVICE_OPTIONS = [
    "auto",
    "cuda",
    "neuron",
    "cpu",
    "tpu",
    "xpu",
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    "hpu",
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]

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def nullable_str(val: str):
    if not val or val == "None":
        return None
    return val


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def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
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    """Parses a string containing comma separate key [str] to value [int]
    pairs into a dictionary.

    Args:
        val: String value to be parsed.

    Returns:
        Dictionary with parsed values.
    """
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    if len(val) == 0:
        return None

    out_dict: Dict[str, int] = {}
    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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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = 'facebook/opt-125m'
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    served_model_name: Optional[Union[str, List[str]]] = None
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    tokenizer: Optional[str] = None
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    hf_config_path: Optional[str] = None
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    task: TaskOption = "auto"
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    skip_tokenizer_init: bool = False
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    tokenizer_mode: str = 'auto'
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    trust_remote_code: bool = False
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    allowed_local_media_path: str = ""
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    download_dir: Optional[str] = LoadConfig.download_dir
    load_format: str = LoadConfig.load_format
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    config_format: ConfigFormat = ConfigFormat.AUTO
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    dtype: str = 'auto'
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    kv_cache_dtype: str = 'auto'
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    seed: Optional[int] = None
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    max_model_len: Optional[int] = None
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    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
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    distributed_executor_backend: Optional[Union[
        str, 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[int] = None
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    enable_prefix_caching: Optional[bool] = None
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    prefix_caching_hash_algo: str = "builtin"
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    disable_sliding_window: bool = False
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    disable_cascade_attn: bool = False
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    use_v2_block_manager: bool = True
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    swap_space: float = 4  # GiB
    cpu_offload_gb: float = 0  # GiB
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    gpu_memory_utilization: float = 0.90
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    max_num_batched_tokens: Optional[int] = None
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    max_num_partial_prefills: Optional[int] = 1
    max_long_partial_prefills: Optional[int] = 1
    long_prefill_token_threshold: Optional[int] = 0
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    max_num_seqs: Optional[int] = None
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    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
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    disable_log_stats: bool = False
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    revision: Optional[str] = None
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    code_revision: Optional[str] = None
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    rope_scaling: Optional[Dict[str, Any]] = None
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    rope_theta: Optional[float] = None
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    hf_token: Optional[Union[bool, str]] = None
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    hf_overrides: Optional[HfOverrides] = None
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    tokenizer_revision: Optional[str] = None
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    quantization: Optional[str] = None
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    enforce_eager: Optional[bool] = None
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    max_seq_len_to_capture: int = 8192
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    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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    tokenizer_pool_size: int = 0
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    # Note: Specifying a tokenizer pool by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
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    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
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    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
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    mm_processor_kwargs: Optional[Dict[str, Any]] = None
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    disable_mm_preprocessor_cache: bool = False
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    enable_lora: bool = False
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    enable_lora_bias: bool = False
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    max_loras: int = 1
    max_lora_rank: int = 16
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    enable_prompt_adapter: bool = False
    max_prompt_adapters: int = 1
    max_prompt_adapter_token: int = 0
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    fully_sharded_loras: bool = False
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    lora_extra_vocab_size: int = 256
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    long_lora_scaling_factors: Optional[Tuple[float]] = None
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    lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
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    max_cpu_loras: Optional[int] = None
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    merge_lora: bool = False
    lora_target_modules: Optional[List[str]] = None
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    device: str = 'auto'
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    num_scheduler_steps: int = 1
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    multi_step_stream_outputs: bool = True
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    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
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    num_gpu_blocks_override: Optional[int] = None
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    num_lookahead_slots: int = 0
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    model_loader_extra_config: Optional[
        dict] = LoadConfig.model_loader_extra_config
    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
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    preemption_mode: Optional[str] = None
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    scheduler_delay_factor: float = 0.0
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    enable_chunked_prefill: Optional[bool] = None
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    disable_chunked_mm_input: bool = False
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    guided_decoding_backend: str = DecodingConfig.guided_decoding_backend
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    logits_processor_pattern: Optional[str] = None
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    speculative_config: Optional[Dict[str, Any]] = None
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    num_speculative_heads: Optional[int] = None
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    qlora_adapter_name_or_path: Optional[str] = None
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    show_hidden_metrics_for_version: Optional[str] = None
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    otlp_traces_endpoint: Optional[str] = None
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    collect_detailed_traces: Optional[str] = None
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    disable_async_output_proc: bool = False
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    scheduling_policy: Literal["fcfs", "priority"] = "fcfs"
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    scheduler_cls: Union[str, Type[object]] = "vllm.core.scheduler.Scheduler"
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    override_neuron_config: Optional[Dict[str, Any]] = None
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    override_pooler_config: Optional[PoolerConfig] = None
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    compilation_config: Optional[CompilationConfig] = None
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    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
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    kv_transfer_config: Optional[KVTransferConfig] = None
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    generation_config: Optional[str] = "auto"
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    override_generation_config: Optional[Dict[str, Any]] = None
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    enable_sleep_mode: bool = False
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    model_impl: str = "auto"
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    calculate_kv_scales: Optional[bool] = None
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    additional_config: Optional[Dict[str, Any]] = None
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    enable_reasoning: Optional[bool] = None
    reasoning_parser: Optional[str] = None
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    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
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    def __post_init__(self):
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        if not self.tokenizer:
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            self.tokenizer = self.model
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        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
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        if isinstance(self.compilation_config, (int, dict)):
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            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
        load_general_plugins()
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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        """Shared CLI arguments for vLLM engine."""
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        def is_type_in_union(cls: type[Any], type: type[Any]) -> bool:
            """Check if the class is a type in a union type."""
            return get_origin(cls) is Union and type in get_args(cls)

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        def is_optional(cls: type[Any]) -> bool:
            """Check if the class is an optional type."""
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            return is_type_in_union(cls, type(None))
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        def get_kwargs(cls: type[Any]) -> Dict[str, Any]:
            cls_docs = get_attr_docs(cls)
            kwargs = {}
            for field in fields(cls):
                name = field.name
                # One of these will always be present
                default = (field.default_factory
                           if field.default is MISSING else field.default)
                kwargs[name] = {"default": default, "help": cls_docs[name]}
                # When using action="store_true"
                # add_argument doesn't accept type
                if field.type is bool:
                    continue
                # Handle optional fields
                if is_optional(field.type):
                    kwargs[name]["type"] = nullable_str
                    continue
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                # Handle str in union fields
                if is_type_in_union(field.type, str):
                    kwargs[name]["type"] = str
                    continue
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                kwargs[name]["type"] = field.type
            return kwargs

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        # Model arguments
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        parser.add_argument(
            '--model',
            type=str,
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            default=EngineArgs.model,
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            help='Name or path of the huggingface model to use.')
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        parser.add_argument(
            '--task',
            default=EngineArgs.task,
            choices=get_args(TaskOption),
            help='The task to use the model for. Each vLLM instance only '
            'supports one task, even if the same model can be used for '
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            'multiple tasks. When the model only supports one task, ``"auto"`` '
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            'can be used to select it; otherwise, you must specify explicitly '
            'which task to use.')
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        parser.add_argument(
            '--tokenizer',
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            type=nullable_str,
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            default=EngineArgs.tokenizer,
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            help='Name or path of the huggingface tokenizer to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            "--hf-config-path",
            type=nullable_str,
            default=EngineArgs.hf_config_path,
            help='Name or path of the huggingface config to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
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            help='Skip initialization of tokenizer and detokenizer. '
            'Expects valid prompt_token_ids and None for prompt from '
            'the input. The generated output will contain token ids.')
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        parser.add_argument(
            '--revision',
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            type=nullable_str,
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            default=None,
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            help='The specific model version to use. It can be a branch '
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            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
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        parser.add_argument(
            '--code-revision',
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            type=nullable_str,
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            default=None,
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            help='The specific revision to use for the model code on '
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            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-revision',
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            type=nullable_str,
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            default=None,
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            help='Revision of the huggingface tokenizer to use. '
            'It can be a branch name, a tag name, or a commit id. '
            'If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
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            choices=['auto', 'slow', 'mistral', 'custom'],
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            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
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            'always use the slow tokenizer. \n* '
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            '"mistral" will always use the `mistral_common` tokenizer. \n* '
            '"custom" will use --tokenizer to select the '
            'preregistered tokenizer.')
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        parser.add_argument('--trust-remote-code',
                            action='store_true',
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                            help='Trust remote code from huggingface.')
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        parser.add_argument(
            '--allowed-local-media-path',
            type=str,
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            help="Allowing API requests to read local images or videos "
            "from directories specified by the server file system. "
            "This is a security risk. "
            "Should only be enabled in trusted environments.")
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        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
        load_group.add_argument('--load-format',
                                choices=[f.value for f in LoadFormat],
                                **load_kwargs["load_format"])
        load_group.add_argument('--download-dir',
                                **load_kwargs["download_dir"])
        load_group.add_argument('--model-loader-extra-config',
                                **load_kwargs["model_loader_extra_config"])
        load_group.add_argument('--use-tqdm-on-load',
                                action=argparse.BooleanOptionalAction,
                                **load_kwargs["use_tqdm_on_load"])

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        parser.add_argument(
            '--config-format',
            default=EngineArgs.config_format,
            choices=[f.value for f in ConfigFormat],
            help='The format of the model config to load.\n\n'
            '* "auto" will try to load the config in hf format '
            'if available else it will try to load in mistral format ')
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        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
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            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
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            help='Data type for model weights and activations.\n\n'
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            'BF16 precision for BF16 models.\n'
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.')
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        parser.add_argument(
            '--kv-cache-dtype',
            type=str,
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            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
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            default=EngineArgs.kv_cache_dtype,
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            help='Data type for kv cache storage. If "auto", will use model '
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            'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
            'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
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        parser.add_argument('--max-model-len',
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                            type=human_readable_int,
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                            default=EngineArgs.max_model_len,
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                            help='Model context length. If unspecified, will '
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                            'be automatically derived from the model config. '
                            'Supports k/m/g/K/M/G in human-readable format.\n'
                            'Examples:\n'
                            '- 1k → 1000\n'
                            '- 1K → 1024\n')
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        parser.add_argument(
            '--guided-decoding-backend',
            type=str,
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            default=DecodingConfig.guided_decoding_backend,
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            help='Which engine will be used for guided decoding'
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            ' (JSON schema / regex etc) by default. Currently support '
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            'https://github.com/mlc-ai/xgrammar and '
            'https://github.com/guidance-ai/llguidance.'
            'Valid backend values are "xgrammar", "guidance", and "auto". '
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            'With "auto", we will make opinionated choices based on request '
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            'contents and what the backend libraries currently support, so '
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            'the behavior is subject to change in each release.')
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        parser.add_argument(
            '--logits-processor-pattern',
            type=nullable_str,
            default=None,
            help='Optional regex pattern specifying valid logits processor '
            'qualified names that can be passed with the `logits_processors` '
            'extra completion argument. Defaults to None, which allows no '
            'processors.')
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        parser.add_argument(
            '--model-impl',
            type=str,
            default=EngineArgs.model_impl,
            choices=[f.value for f in ModelImpl],
            help='Which implementation of the model to use.\n\n'
            '* "auto" will try to use the vLLM implementation if it exists '
            'and fall back to the Transformers implementation if no vLLM '
            'implementation is available.\n'
            '* "vllm" will use the vLLM model implementation.\n'
            '* "transformers" will use the Transformers model '
            'implementation.\n')
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        # Parallel arguments
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        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
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            '--distributed-executor-backend',
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            choices=['ray', 'mp', 'uni', 'external_launcher'],
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            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
            '--pipeline-parallel-size', '-pp',
            **parallel_kwargs["pipeline_parallel_size"])
        parallel_group.add_argument('--tensor-parallel-size', '-tp',
                                    **parallel_kwargs["tensor_parallel_size"])
        parallel_group.add_argument('--data-parallel-size', '-dp',
                                    **parallel_kwargs["data_parallel_size"])
        parallel_group.add_argument(
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            '--enable-expert-parallel',
            action='store_true',
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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',
            action='store_true',
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            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
            '--disable-custom-all-reduce',
            action='store_true',
            **parallel_kwargs["disable_custom_all_reduce"])
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        # KV cache arguments
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        parser.add_argument('--block-size',
                            type=int,
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                            default=EngineArgs.block_size,
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                            choices=[8, 16, 32, 64, 128],
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                            help='Token block size for contiguous chunks of '
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                            'tokens. This is ignored on neuron devices and '
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                            'set to ``--max-model-len``. On CUDA devices, '
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                            'only block sizes up to 32 are supported. '
                            'On HPU devices, block size defaults to 128.')
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        parser.add_argument(
            "--enable-prefix-caching",
            action=argparse.BooleanOptionalAction,
            default=EngineArgs.enable_prefix_caching,
            help="Enables automatic prefix caching. "
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            "Use ``--no-enable-prefix-caching`` to disable explicitly.",
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        )
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        parser.add_argument(
            "--prefix-caching-hash-algo",
            type=str,
            choices=["builtin", "sha256"],
            default=EngineArgs.prefix_caching_hash_algo,
            help="Set the hash algorithm for prefix caching. "
            "Options are 'builtin' (Python's built-in hash) or 'sha256' "
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            "(collision resistant but with certain overheads).",
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        )
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        parser.add_argument('--disable-sliding-window',
                            action='store_true',
                            help='Disables sliding window, '
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                            'capping to sliding window size.')
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        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
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                            default=True,
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                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
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        parser.add_argument(
            '--num-lookahead-slots',
            type=int,
            default=EngineArgs.num_lookahead_slots,
            help='Experimental scheduling config necessary for '
            'speculative decoding. This will be replaced by '
            'speculative config in the future; it is present '
            'to enable correctness tests until then.')
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        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
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                            help='Random seed for operations.')
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        parser.add_argument('--swap-space',
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                            type=float,
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                            default=EngineArgs.swap_space,
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                            help='CPU swap space size (GiB) per GPU.')
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        parser.add_argument(
            '--cpu-offload-gb',
            type=float,
            default=0,
            help='The space in GiB to offload to CPU, per GPU. '
            'Default is 0, which means no offloading. Intuitively, '
            'this argument can be seen as a virtual way to increase '
            'the GPU memory size. For example, if you have one 24 GB '
            'GPU and set this to 10, virtually you can think of it as '
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            'a 34 GB GPU. Then you can load a 13B model with BF16 weight, '
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            'which requires at least 26GB GPU memory. Note that this '
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            'requires fast CPU-GPU interconnect, as part of the model is '
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            'loaded from CPU memory to GPU memory on the fly in each '
            'model forward pass.')
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        parser.add_argument(
            '--gpu-memory-utilization',
            type=float,
            default=EngineArgs.gpu_memory_utilization,
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            help='The fraction of GPU memory to be used for the model '
            'executor, which can range from 0 to 1. For example, a value of '
            '0.5 would imply 50%% GPU memory utilization. If unspecified, '
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            'will use the default value of 0.9. This is a per-instance '
            'limit, and only applies to the current vLLM instance.'
            'It does not matter if you have another vLLM instance running '
            'on the same GPU. For example, if you have two vLLM instances '
            'running on the same GPU, you can set the GPU memory utilization '
            'to 0.5 for each instance.')
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        parser.add_argument(
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            '--num-gpu-blocks-override',
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            type=int,
            default=None,
            help='If specified, ignore GPU profiling result and use this number'
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            ' of GPU blocks. Used for testing preemption.')
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        parser.add_argument('--max-num-batched-tokens',
                            type=int,
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                            default=EngineArgs.max_num_batched_tokens,
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                            help='Maximum number of batched tokens per '
                            'iteration.')
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        parser.add_argument(
            "--max-num-partial-prefills",
            type=int,
            default=EngineArgs.max_num_partial_prefills,
            help="For chunked prefill, the max number of concurrent \
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            partial prefills.")
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        parser.add_argument(
            "--max-long-partial-prefills",
            type=int,
            default=EngineArgs.max_long_partial_prefills,
            help="For chunked prefill, the maximum number of prompts longer "
            "than --long-prefill-token-threshold that will be prefilled "
            "concurrently. Setting this less than --max-num-partial-prefills "
            "will allow shorter prompts to jump the queue in front of longer "
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            "prompts in some cases, improving latency.")
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        parser.add_argument(
            "--long-prefill-token-threshold",
            type=float,
            default=EngineArgs.long_prefill_token_threshold,
            help="For chunked prefill, a request is considered long if the "
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            "prompt is longer than this number of tokens.")
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        parser.add_argument('--max-num-seqs',
                            type=int,
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                            default=EngineArgs.max_num_seqs,
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                            help='Maximum number of sequences per iteration.')
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        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
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            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
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        parser.add_argument('--disable-log-stats',
                            action='store_true',
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                            help='Disable logging statistics.')
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        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
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                            type=nullable_str,
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                            choices=[*QUANTIZATION_METHODS, None],
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                            default=EngineArgs.quantization,
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                            help='Method used to quantize the weights. If '
                            'None, we first check the `quantization_config` '
                            'attribute in the model config file. If that is '
                            'None, we assume the model weights are not '
                            'quantized and use `dtype` to determine the data '
                            'type of the weights.')
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        parser.add_argument(
            '--rope-scaling',
            default=None,
            type=json.loads,
            help='RoPE scaling configuration in JSON format. '
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            'For example, ``{"rope_type":"dynamic","factor":2.0}``')
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        parser.add_argument('--rope-theta',
                            default=None,
                            type=float,
                            help='RoPE theta. Use with `rope_scaling`. In '
                            'some cases, changing the RoPE theta improves the '
                            'performance of the scaled model.')
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        parser.add_argument(
            '--hf-token',
            type=str,
            nargs='?',
            const=True,
            default=None,
            help='The token to use as HTTP bearer authorization'
            ' for remote files. If `True`, will use the token '
            'generated when running `huggingface-cli login` '
            '(stored in `~/.huggingface`).')
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        parser.add_argument('--hf-overrides',
                            type=json.loads,
                            default=EngineArgs.hf_overrides,
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                            help='Extra arguments for the HuggingFace config. '
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                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
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        parser.add_argument('--enforce-eager',
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                            action='store_true',
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                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
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        parser.add_argument('--max-seq-len-to-capture',
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                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence length covered by CUDA '
                            'graphs. When a sequence has context length '
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                            'larger than this, we fall back to eager mode. '
                            'Additionally for encoder-decoder models, if the '
                            'sequence length of the encoder input is larger '
                            'than this, we fall back to the eager mode.')
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        parser.add_argument('--tokenizer-pool-size',
                            type=int,
                            default=EngineArgs.tokenizer_pool_size,
                            help='Size of tokenizer pool to use for '
                            'asynchronous tokenization. If 0, will '
                            'use synchronous tokenization.')
        parser.add_argument('--tokenizer-pool-type',
                            type=str,
                            default=EngineArgs.tokenizer_pool_type,
                            help='Type of tokenizer pool to use for '
                            'asynchronous tokenization. Ignored '
                            'if tokenizer_pool_size is 0.')
        parser.add_argument('--tokenizer-pool-extra-config',
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                            type=nullable_str,
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                            default=EngineArgs.tokenizer_pool_extra_config,
                            help='Extra config for tokenizer pool. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary. Ignored if '
                            'tokenizer_pool_size is 0.')
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        # Multimodal related configs
        parser.add_argument(
            '--limit-mm-per-prompt',
            type=nullable_kvs,
            default=EngineArgs.limit_mm_per_prompt,
            # The default value is given in
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            # MultiModalConfig.get_default_limit_per_prompt
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            help=('For each multimodal plugin, limit how many '
                  'input instances to allow for each prompt. '
                  'Expects a comma-separated list of items, '
                  'e.g.: `image=16,video=2` allows a maximum of 16 '
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                  'images and 2 videos per prompt. Defaults to '
                  '1 (V0) or 999 (V1) for each modality.'))
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        parser.add_argument(
            '--mm-processor-kwargs',
            default=None,
            type=json.loads,
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            help=('Overrides for the multimodal input mapping/processing, '
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                  'e.g., image processor. For example: ``{"num_crops": 4}``.'))
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        parser.add_argument(
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            '--disable-mm-preprocessor-cache',
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            action='store_true',
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            help='If true, then disables caching of the multi-modal '
            'preprocessor/mapper. (not recommended)')
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        # LoRA related configs
        parser.add_argument('--enable-lora',
                            action='store_true',
                            help='If True, enable handling of LoRA adapters.')
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        parser.add_argument('--enable-lora-bias',
                            action='store_true',
                            help='If True, enable bias for LoRA adapters.')
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        parser.add_argument('--max-loras',
                            type=int,
                            default=EngineArgs.max_loras,
                            help='Max number of LoRAs in a single batch.')
        parser.add_argument('--max-lora-rank',
                            type=int,
                            default=EngineArgs.max_lora_rank,
                            help='Max LoRA rank.')
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        parser.add_argument('--merge-lora',
                            type=bool,
                            default=False,
                            help='If set to True, the weights of the base layer will be merged with the weights of Lora.')
        parser.add_argument('--lora-target-modules',
                            nargs='*',
                            default=None,
                            help='List of lora module name, If not specified, modules will be chosen according to the model architecture.')
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        parser.add_argument(
            '--lora-extra-vocab-size',
            type=int,
            default=EngineArgs.lora_extra_vocab_size,
            help=('Maximum size of extra vocabulary that can be '
                  'present in a LoRA adapter (added to the base '
                  'model vocabulary).'))
        parser.add_argument(
            '--lora-dtype',
            type=str,
            default=EngineArgs.lora_dtype,
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            choices=['auto', 'float16', 'bfloat16'],
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            help=('Data type for LoRA. If auto, will default to '
                  'base model dtype.'))
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        parser.add_argument(
            '--long-lora-scaling-factors',
            type=nullable_str,
            default=EngineArgs.long_lora_scaling_factors,
            help=('Specify multiple scaling factors (which can '
                  'be different from base model scaling factor '
                  '- see eg. Long LoRA) to allow for multiple '
                  'LoRA adapters trained with those scaling '
                  'factors to be used at the same time. If not '
                  'specified, only adapters trained with the '
                  'base model scaling factor are allowed.'))
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        parser.add_argument(
            '--max-cpu-loras',
            type=int,
            default=EngineArgs.max_cpu_loras,
            help=('Maximum number of LoRAs to store in CPU memory. '
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                  'Must be >= than max_loras.'))
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        parser.add_argument(
            '--fully-sharded-loras',
            action='store_true',
            help=('By default, only half of the LoRA computation is '
                  'sharded with tensor parallelism. '
                  'Enabling this will use the fully sharded layers. '
                  'At high sequence length, max rank or '
                  'tensor parallel size, this is likely faster.'))
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        parser.add_argument('--enable-prompt-adapter',
                            action='store_true',
                            help='If True, enable handling of PromptAdapters.')
        parser.add_argument('--max-prompt-adapters',
                            type=int,
                            default=EngineArgs.max_prompt_adapters,
                            help='Max number of PromptAdapters in a batch.')
        parser.add_argument('--max-prompt-adapter-token',
                            type=int,
                            default=EngineArgs.max_prompt_adapter_token,
                            help='Max number of PromptAdapters tokens')
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        parser.add_argument("--device",
                            type=str,
                            default=EngineArgs.device,
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                            choices=DEVICE_OPTIONS,
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                            help='Device type for vLLM execution.')
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        parser.add_argument('--num-scheduler-steps',
                            type=int,
                            default=1,
                            help=('Maximum number of forward steps per '
                                  'scheduler call.'))
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        parser.add_argument(
            '--multi-step-stream-outputs',
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            action=StoreBoolean,
            default=EngineArgs.multi_step_stream_outputs,
            nargs="?",
            const="True",
            help='If False, then multi-step will stream outputs at the end '
            'of all steps')
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        parser.add_argument(
            '--scheduler-delay-factor',
            type=float,
            default=EngineArgs.scheduler_delay_factor,
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            help='Apply a delay (of delay factor multiplied by previous '
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            'prompt latency) before scheduling next prompt.')
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        parser.add_argument(
            '--enable-chunked-prefill',
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            action=StoreBoolean,
            default=EngineArgs.enable_chunked_prefill,
            nargs="?",
            const="True",
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            help='If set, the prefill requests can be chunked based on the '
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            'max_num_batched_tokens.')
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        parser.add_argument('--speculative-config',
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                            type=json.loads,
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                            default=None,
                            help='The configurations for speculative decoding.'
                            ' Should be a JSON string.')
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        parser.add_argument(
            '--num-speculative-heads',
            type=int,
            default=EngineArgs.num_speculative_heads,
            help='The number of speculative heads to sample from '
                 'the draft model in speculative decoding.')
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        parser.add_argument('--model-loader-extra-config',
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                            type=nullable_str,
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                            default=EngineArgs.model_loader_extra_config,
                            help='Extra config for model loader. '
                            'This will be passed to the model loader '
                            'corresponding to the chosen load_format. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
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        parser.add_argument(
            '--ignore-patterns',
            action="append",
            type=str,
            default=[],
            help="The pattern(s) to ignore when loading the model."
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            "Default to `original/**/*` to avoid repeated loading of llama's "
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            "checkpoints.")
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        parser.add_argument(
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            '--preemption-mode',
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            type=str,
            default=None,
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            help='If \'recompute\', the engine performs preemption by '
            'recomputing; If \'swap\', the engine performs preemption by '
            'block swapping.')
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        parser.add_argument(
            "--served-model-name",
            nargs="+",
            type=str,
            default=None,
            help="The model name(s) used in the API. If multiple "
            "names are provided, the server will respond to any "
            "of the provided names. The model name in the model "
            "field of a response will be the first name in this "
            "list. If not specified, the model name will be the "
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            "same as the ``--model`` argument. Noted that this name(s) "
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            "will also be used in `model_name` tag content of "
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            "prometheus metrics, if multiple names provided, metrics "
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            "tag will take the first one.")
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        parser.add_argument('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')
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        parser.add_argument('--show-hidden-metrics-for-version',
                            type=str,
                            default=None,
                            help='Enable deprecated Prometheus metrics that '
                            'have been hidden since the specified version. '
                            'For example, if a previously deprecated metric '
                            'has been hidden since the v0.7.0 release, you '
                            'use --show-hidden-metrics-for-version=0.7 as a '
                            'temporary escape hatch while you migrate to new '
                            'metrics. The metric is likely to be removed '
                            'completely in an upcoming release.')

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        parser.add_argument(
            '--otlp-traces-endpoint',
            type=str,
            default=None,
            help='Target URL to which OpenTelemetry traces will be sent.')
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        parser.add_argument(
            '--collect-detailed-traces',
            type=str,
            default=None,
            help="Valid choices are " +
            ",".join(ALLOWED_DETAILED_TRACE_MODULES) +
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            ". It makes sense to set this only if ``--otlp-traces-endpoint`` is"
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            " set. If set, it will collect detailed traces for the specified "
            "modules. This involves use of possibly costly and or blocking "
            "operations and hence might have a performance impact.")
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        parser.add_argument(
            '--disable-async-output-proc',
            action='store_true',
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
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        parser.add_argument(
            '--scheduling-policy',
            choices=['fcfs', 'priority'],
            default="fcfs",
            help='The scheduling policy to use. "fcfs" (first come first served'
            ', i.e. requests are handled in order of arrival; default) '
            'or "priority" (requests are handled based on given '
            'priority (lower value means earlier handling) and time of '
            'arrival deciding any ties).')

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        parser.add_argument(
            '--scheduler-cls',
            default=EngineArgs.scheduler_cls,
            help='The scheduler class to use. "vllm.core.scheduler.Scheduler" '
            'is the default scheduler. Can be a class directly or the path to '
            'a class of form "mod.custom_class".')

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        parser.add_argument(
            '--override-neuron-config',
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            type=json.loads,
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            default=None,
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            help="Override or set neuron device configuration. "
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        parser.add_argument(
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            '--override-pooler-config',
            type=PoolerConfig.from_json,
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            help="Override or set the pooling method for pooling models. "
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        parser.add_argument('--compilation-config',
                            '-O',
                            type=CompilationConfig.from_cli,
                            default=None,
                            help='torch.compile configuration for the model.'
                            'When it is a number (0, 1, 2, 3), it will be '
                            'interpreted as the optimization level.\n'
                            'NOTE: level 0 is the default level without '
                            'any optimization. level 1 and 2 are for internal '
                            'testing only. level 3 is the recommended level '
                            'for production.\n'
                            'To specify the full compilation config, '
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                            'use a JSON string.\n'
                            'Following the convention of traditional '
                            'compilers, using -O without space is also '
                            'supported. -O3 is equivalent to -O 3.')
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        parser.add_argument('--kv-transfer-config',
                            type=KVTransferConfig.from_cli,
                            default=None,
                            help='The configurations for distributed KV cache '
                            'transfer. Should be a JSON string.')

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

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

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        parser.add_argument(
            '--calculate-kv-scales',
            action='store_true',
            help='This enables dynamic calculation of '
            'k_scale and v_scale when kv-cache-dtype is fp8. '
            'If calculate-kv-scales is false, the scales will '
            'be loaded from the model checkpoint if available. '
            'Otherwise, the scales will default to 1.0.')
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        parser.add_argument(
            "--additional-config",
            type=json.loads,
            default=None,
            help="Additional config for specified platform in JSON format. "
            "Different platforms may support different configs. Make sure the "
            "configs are valid for the platform you are using. The input format"
            " is like '{\"config_key\":\"config_value\"}'")
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        parser.add_argument(
            "--enable-reasoning",
            action="store_true",
            default=False,
            help="Whether to enable reasoning_content for the model. "
            "If enabled, the model will be able to generate reasoning content."
        )

        parser.add_argument(
            "--reasoning-parser",
            type=str,
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            choices=list(ReasoningParserManager.reasoning_parsers),
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            default=None,
            help=
            "Select the reasoning parser depending on the model that you're "
            "using. This is used to parse the reasoning content into OpenAI "
            "API format. Required for ``--enable-reasoning``.")

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

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        parser.add_argument(
            "--disable-chunked-mm-input",
            action=StoreBoolean,
            default=EngineArgs.disable_chunked_mm_input,
            nargs="?",
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            const="True",
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            help="Disable multimodal input chunking attention for V1. "
            "If set to true and chunked prefill is enabled, we do not want to"
            " partially schedule a multimodal item. This ensures that if a "
            "request has a mixed prompt (like text tokens TTTT followed by "
            "image tokens IIIIIIIIII) where only some image tokens can be "
            "scheduled (like TTTTIIIII, leaving IIIII), it will be scheduled "
            "as TTTT in one step and IIIIIIIIII in the next.")

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

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

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

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

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

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    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
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        num_speculative_heads: Optional[int],
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    ) -> 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,
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            "num_speculative_heads": num_speculative_heads,
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        })
        speculative_config = SpeculativeConfig.from_dict(
            self.speculative_config)

        return speculative_config

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

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

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

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

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

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        parallel_config = ParallelConfig(
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            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
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            data_parallel_size=self.data_parallel_size,
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            enable_expert_parallel=self.enable_expert_parallel,
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            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            tokenizer_pool_config=TokenizerPoolConfig.create_config(
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                self.tokenizer_pool_size,
                self.tokenizer_pool_type,
                self.tokenizer_pool_extra_config,
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            ),
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            ray_workers_use_nsight=self.ray_workers_use_nsight,
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            placement_group=placement_group,
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            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
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            worker_extension_cls=self.worker_extension_cls,
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        )
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        speculative_config = self.create_speculative_config(
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            target_model_config=model_config,
            target_parallel_config=parallel_config,
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            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_log_stats=self.disable_log_stats,    
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            num_speculative_heads=self.num_speculative_heads
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        )

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        # Reminder: Please update docs/source/features/compatibility_matrix.md
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        # If the feature combo become valid
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        if self.num_scheduler_steps > 1:
            if speculative_config is not None:
                raise ValueError("Speculative decoding is not supported with "
                                 "multi-step (--num-scheduler-steps > 1)")
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            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                 "for pipeline-parallel-size > 1")
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            from vllm.platforms import current_platform
            if current_platform.is_cpu():
                logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
                               "currently not supported for CPUs and has been "
                               "disabled.")
                self.num_scheduler_steps = 1
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        # make sure num_lookahead_slots is set the higher value depending on
        # if we are using speculative decoding or multi-step
        num_lookahead_slots = max(self.num_lookahead_slots,
                                  self.num_scheduler_steps - 1)
        num_lookahead_slots = num_lookahead_slots \
            if speculative_config is None \
            else speculative_config.num_lookahead_slots

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

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

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

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

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

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

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

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

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

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

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

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

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

        if self.additional_config != EngineArgs.additional_config:
            _raise_or_fallback(feature_name="--additional-config",
                               recommend_to_remove=False)
            return False

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        # Xgrammar and Guidance are supported.
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        SUPPORTED_GUIDED_DECODING = [
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            "xgrammar", "xgrammar:disable-any-whitespace", "guidance",
            "guidance:disable-any-whitespace", "auto"
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        ]
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        if self.guided_decoding_backend not in SUPPORTED_GUIDED_DECODING:
            _raise_or_fallback(feature_name="--guided-decoding-backend",
                               recommend_to_remove=False)
            return False

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

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

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

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

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

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

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

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

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

        # No Disaggregated Prefill so far.
        if self.kv_transfer_config != EngineArgs.kv_transfer_config:
            _raise_or_fallback(feature_name="--kv-transfer-config",
                               recommend_to_remove=False)
            return False

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

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

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

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

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

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

        # Non-CUDA is supported on V1, but off by default for now.
        not_cuda = not current_platform.is_cuda()
        if not_cuda and _warn_or_fallback(  # noqa: SIM103
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                current_platform.device_name):
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            return False
        #############################################################

        return True

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

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

            # Enable chunked prefill by default for long context (> 32K)
            # models to avoid OOM errors in initial memory profiling phase.
            elif use_long_context:
                from vllm.platforms import current_platform
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
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                use_spec_decode = self.speculative_config is not None
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                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and not self.enable_prompt_adapter
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

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

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

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

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

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

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        # if using prefix caching, we must set a hash algo
        if self.enable_prefix_caching and self.prefix_caching_hash_algo is None:
            self.prefix_caching_hash_algo = "builtin"

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        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
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            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
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        # When no user override, set the default values based on the usage
        # context.
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        # Use different default values for different hardware.
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        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
        try:
            from vllm.platforms import current_platform
            device_name = current_platform.get_device_name().lower()
        except Exception:
            # This is only used to set default_max_num_batched_tokens
            device_name = "no-device"

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        if "h100" in device_name or "h200" in device_name:
            # For H100 and H200, we use larger default values.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
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            default_max_num_seqs = 1024
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        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
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            default_max_num_seqs = 256
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        use_context_value = usage_context.value if usage_context else None
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        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
            self.max_num_batched_tokens = default_max_num_batched_tokens[
                usage_context]
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            logger.debug(
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                "Setting max_num_batched_tokens to %d for %s usage context.",
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                self.max_num_batched_tokens, use_context_value)
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        if self.max_num_seqs is None:
            self.max_num_seqs = default_max_num_seqs

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


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


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def human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
    
    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
    match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value)
    if match:
        decimal_multiplier = {
            'k': 10**3,
            'm': 10**6,
            'g': 10**9,
        }
        binary_multiplier = {
            'K': 2**10,
            'M': 2**20,
            'G': 2**30,
        }

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

    # Regular plain number.
    return int(value)


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