arg_utils.py

import argparse
import dataclasses
import json
from dataclasses import dataclass
from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional,
                    Tuple, Type, Union, cast, get_args)

import torch

import vllm.envs as envs
from vllm.config import (CacheConfig, CompilationConfig, ConfigFormat,
                         DecodingConfig, DeviceConfig, HfOverrides,
                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
                         ModelConfig, ObservabilityConfig, ParallelConfig,
                         PoolerConfig, PromptAdapterConfig, SchedulerConfig,
                         SpeculativeConfig, TaskOption, TokenizerPoolConfig,
                         VllmConfig)
from vllm.executor.executor_base import ExecutorBase
from vllm.logger import init_logger
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
from vllm.platforms import current_platform
from vllm.transformers_utils.utils import check_gguf_file
from vllm.usage.usage_lib import UsageContext
from vllm.utils import FlexibleArgumentParser, StoreBoolean

if TYPE_CHECKING:
    from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup

logger = init_logger(__name__)

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

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


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


def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
    """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.
    """
    if len(val) == 0:
        return None

    out_dict: Dict[str, int] = {}
    for item in val.split(","):
        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

        try:
            parsed_value = int(value)
        except ValueError as exc:
            msg = f"Failed to parse value of item {key}={value}"
            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

    return out_dict


@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""
    model: str = 'facebook/opt-125m'
    served_model_name: Optional[Union[str, List[str]]] = None
    tokenizer: Optional[str] = None
    task: TaskOption = "auto"
    skip_tokenizer_init: bool = False
    tokenizer_mode: str = 'auto'
    trust_remote_code: bool = False
    allowed_local_media_path: str = ""
    download_dir: Optional[str] = None
    load_format: str = 'auto'
    config_format: ConfigFormat = ConfigFormat.AUTO
    dtype: str = 'auto'
    kv_cache_dtype: str = 'auto'
    quantization_param_path: Optional[str] = None
    seed: int = 0
    max_model_len: Optional[int] = None
    worker_use_ray: bool = False
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    distributed_executor_backend: Optional[Union[str,
                                                 Type[ExecutorBase]]] = None
    # number of P/D disaggregation (or other disaggregation) workers
    pipeline_parallel_size: int = 1
    tensor_parallel_size: int = 1
    max_parallel_loading_workers: Optional[int] = None
    # NOTE(kzawora): default block size for Gaudi should be 128
    # smaller sizes still work, but very inefficiently
    block_size: int = 16 if not current_platform.is_hpu() else 128
    enable_prefix_caching: Optional[bool] = None
    disable_sliding_window: bool = False
    use_v2_block_manager: bool = True
    swap_space: float = 4  # GiB
    cpu_offload_gb: float = 0  # GiB
    gpu_memory_utilization: float = 0.90
    max_num_batched_tokens: Optional[int] = None
    max_num_seqs: Optional[int] = None
    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
    disable_log_stats: bool = False
    revision: Optional[str] = None
    code_revision: Optional[str] = None
    rope_scaling: Optional[Dict[str, Any]] = None
    rope_theta: Optional[float] = None
    hf_overrides: Optional[HfOverrides] = None
    tokenizer_revision: Optional[str] = None
    quantization: Optional[str] = None
    enforce_eager: Optional[bool] = None
    max_seq_len_to_capture: int = 8192
    disable_custom_all_reduce: bool = False
    tokenizer_pool_size: int = 0
    # 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"
    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
    mm_processor_kwargs: Optional[Dict[str, Any]] = None
    mm_cache_preprocessor: bool = False
    enable_lora: bool = False
    enable_lora_bias: bool = False
    max_loras: int = 1
    max_lora_rank: int = 16
    enable_prompt_adapter: bool = False
    max_prompt_adapters: int = 1
    max_prompt_adapter_token: int = 0
    fully_sharded_loras: bool = False
    lora_extra_vocab_size: int = 256
    long_lora_scaling_factors: Optional[Tuple[float]] = None
    lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
    max_cpu_loras: Optional[int] = None
    device: str = 'auto'
    num_scheduler_steps: int = 1
    multi_step_stream_outputs: bool = True
    ray_workers_use_nsight: bool = False
    num_gpu_blocks_override: Optional[int] = None
    num_lookahead_slots: int = 0
    model_loader_extra_config: Optional[dict] = None
    ignore_patterns: Optional[Union[str, List[str]]] = None
    preemption_mode: Optional[str] = None

    scheduler_delay_factor: float = 0.0
    enable_chunked_prefill: Optional[bool] = None

    guided_decoding_backend: str = 'xgrammar'
    logits_processor_pattern: Optional[str] = None
    # Speculative decoding configuration.
    speculative_model: Optional[str] = None
    speculative_model_quantization: Optional[str] = None
    speculative_draft_tensor_parallel_size: Optional[int] = None
    num_speculative_tokens: Optional[int] = None
    speculative_disable_mqa_scorer: Optional[bool] = False
    speculative_max_model_len: Optional[int] = None
    speculative_disable_by_batch_size: Optional[int] = None
    ngram_prompt_lookup_max: Optional[int] = None
    ngram_prompt_lookup_min: Optional[int] = None
    spec_decoding_acceptance_method: str = 'rejection_sampler'
    typical_acceptance_sampler_posterior_threshold: Optional[float] = None
    typical_acceptance_sampler_posterior_alpha: Optional[float] = None
    qlora_adapter_name_or_path: Optional[str] = None
    disable_logprobs_during_spec_decoding: Optional[bool] = None

    otlp_traces_endpoint: Optional[str] = None
    collect_detailed_traces: Optional[str] = None
    disable_async_output_proc: bool = False
    scheduling_policy: Literal["fcfs", "priority"] = "fcfs"

    override_neuron_config: Optional[Dict[str, Any]] = None
    override_pooler_config: Optional[PoolerConfig] = None
    compilation_config: Optional[CompilationConfig] = None
    worker_cls: str = "auto"

    kv_transfer_config: Optional[KVTransferConfig] = None

    def __post_init__(self):
        if not self.tokenizer:
            self.tokenizer = self.model

        # Override the default value of enable_prefix_caching if it's not set
        # by user.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = bool(envs.VLLM_USE_V1)
        # Override max_num_seqs if it's not set by user.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024

        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
        if isinstance(self.compilation_config, (int, dict)):
            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))

        # Setup plugins
        from vllm.plugins import load_general_plugins
        load_general_plugins()

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

        # Model arguments
        parser.add_argument(
            '--model',
            type=str,
            default=EngineArgs.model,
            help='Name or path of the huggingface model to use.')
        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 '
            'multiple tasks. When the model only supports one task, "auto" '
            'can be used to select it; otherwise, you must specify explicitly '
            'which task to use.')
        parser.add_argument(
            '--tokenizer',
            type=nullable_str,
            default=EngineArgs.tokenizer,
            help='Name or path of the huggingface tokenizer to use. '
            'If unspecified, model name or path will be used.')
        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
            help='Skip initialization of tokenizer and detokenizer')
        parser.add_argument(
            '--revision',
            type=nullable_str,
            default=None,
            help='The specific model version to use. It can be a branch '
            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
        parser.add_argument(
            '--code-revision',
            type=nullable_str,
            default=None,
            help='The specific revision to use for the model code on '
            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
        parser.add_argument(
            '--tokenizer-revision',
            type=nullable_str,
            default=None,
            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.')
        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
            choices=['auto', 'slow', 'mistral'],
            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
            'always use the slow tokenizer. \n* '
            '"mistral" will always use the `mistral_common` tokenizer.')
        parser.add_argument('--trust-remote-code',
                            action='store_true',
                            help='Trust remote code from huggingface.')
        parser.add_argument(
            '--allowed-local-media-path',
            type=str,
            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.")
        parser.add_argument('--download-dir',
                            type=nullable_str,
                            default=EngineArgs.download_dir,
                            help='Directory to download and load the weights, '
                            'default to the default cache dir of '
                            'huggingface.')
        parser.add_argument(
            '--load-format',
            type=str,
            default=EngineArgs.load_format,
            choices=[f.value for f in LoadFormat],
            help='The format of the model weights to load.\n\n'
            '* "auto" will try to load the weights in the safetensors format '
            'and fall back to the pytorch bin format if safetensors format '
            'is not available.\n'
            '* "pt" will load the weights in the pytorch bin format.\n'
            '* "safetensors" will load the weights in the safetensors format.\n'
            '* "npcache" will load the weights in pytorch format and store '
            'a numpy cache to speed up the loading.\n'
            '* "dummy" will initialize the weights with random values, '
            'which is mainly for profiling.\n'
            '* "tensorizer" will load the weights using tensorizer from '
            'CoreWeave. See the Tensorize vLLM Model script in the Examples '
            'section for more information.\n'
            '* "bitsandbytes" will load the weights using bitsandbytes '
            'quantization.\n')
        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 ')
        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
            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.')
        parser.add_argument(
            '--kv-cache-dtype',
            type=str,
            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
            default=EngineArgs.kv_cache_dtype,
            help='Data type for kv cache storage. If "auto", will use model '
            'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
            'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
        parser.add_argument(
            '--quantization-param-path',
            type=nullable_str,
            default=None,
            help='Path to the JSON file containing the KV cache '
            'scaling factors. This should generally be supplied, when '
            'KV cache dtype is FP8. Otherwise, KV cache scaling factors '
            'default to 1.0, which may cause accuracy issues. '
            'FP8_E5M2 (without scaling) is only supported on cuda version '
            'greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
            'supported for common inference criteria.')
        parser.add_argument('--max-model-len',
                            type=int,
                            default=EngineArgs.max_model_len,
                            help='Model context length. If unspecified, will '
                            'be automatically derived from the model config.')
        parser.add_argument(
            '--guided-decoding-backend',
            type=str,
            default='xgrammar',
            choices=['outlines', 'lm-format-enforcer', 'xgrammar'],
            help='Which engine will be used for guided decoding'
            ' (JSON schema / regex etc) by default. Currently support '
            'https://github.com/outlines-dev/outlines,'
            'https://github.com/mlc-ai/xgrammar, and '
            'https://github.com/noamgat/lm-format-enforcer.'
            ' Can be overridden per request via guided_decoding_backend'
            ' parameter.')
        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.')
        # Parallel arguments
        parser.add_argument(
            '--distributed-executor-backend',
            choices=['ray', 'mp'],
            default=EngineArgs.distributed_executor_backend,
            help='Backend to use for distributed model '
            'workers, either "ray" or "mp" (multiprocessing). If the product '
            'of pipeline_parallel_size and tensor_parallel_size is less than '
            'or equal to the number of GPUs available, "mp" will be used to '
            'keep processing on a single host. Otherwise, this will default '
            'to "ray" if Ray is installed and fail otherwise. Note that tpu '
            'and hpu only support Ray for distributed inference.')

        parser.add_argument(
            '--worker-use-ray',
            action='store_true',
            help='Deprecated, use --distributed-executor-backend=ray.')
        parser.add_argument('--pipeline-parallel-size',
                            '-pp',
                            type=int,
                            default=EngineArgs.pipeline_parallel_size,
                            help='Number of pipeline stages.')
        parser.add_argument('--tensor-parallel-size',
                            '-tp',
                            type=int,
                            default=EngineArgs.tensor_parallel_size,
                            help='Number of tensor parallel replicas.')
        parser.add_argument(
            '--max-parallel-loading-workers',
            type=int,
            default=EngineArgs.max_parallel_loading_workers,
            help='Load model sequentially in multiple batches, '
            'to avoid RAM OOM when using tensor '
            'parallel and large models.')
        parser.add_argument(
            '--ray-workers-use-nsight',
            action='store_true',
            help='If specified, use nsight to profile Ray workers.')
        # KV cache arguments
        parser.add_argument('--block-size',
                            type=int,
                            default=EngineArgs.block_size,
                            choices=[8, 16, 32, 64, 128],
                            help='Token block size for contiguous chunks of '
                            'tokens. This is ignored on neuron devices and '
                            'set to max-model-len')

        parser.add_argument(
            "--enable-prefix-caching",
            action=argparse.BooleanOptionalAction,
            default=EngineArgs.enable_prefix_caching,
            help="Enables automatic prefix caching. "
            "Use --no-enable-prefix-caching to disable explicitly.",
        )
        parser.add_argument('--disable-sliding-window',
                            action='store_true',
                            help='Disables sliding window, '
                            'capping to sliding window size')
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
                            default=True,
                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
        parser.add_argument(
            '--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.')

        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
                            help='Random seed for operations.')
        parser.add_argument('--swap-space',
                            type=float,
                            default=EngineArgs.swap_space,
                            help='CPU swap space size (GiB) per GPU.')
        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 '
            'a 34 GB GPU. Then you can load a 13B model with BF16 weight, '
            'which requires at least 26GB GPU memory. Note that this '
            'requires fast CPU-GPU interconnect, as part of the model is '
            'loaded from CPU memory to GPU memory on the fly in each '
            'model forward pass.')
        parser.add_argument(
            '--gpu-memory-utilization',
            type=float,
            default=EngineArgs.gpu_memory_utilization,
            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, '
            'will use the default value of 0.9. This is a global gpu memory '
            'utilization limit, for example if 50%% of the gpu memory is '
            'already used before vLLM starts and --gpu-memory-utilization is '
            'set to 0.9, then only 40%% of the gpu memory will be allocated '
            'to the model executor.')
        parser.add_argument(
            '--num-gpu-blocks-override',
            type=int,
            default=None,
            help='If specified, ignore GPU profiling result and use this number'
            ' of GPU blocks. Used for testing preemption.')
        parser.add_argument('--max-num-batched-tokens',
                            type=int,
                            default=EngineArgs.max_num_batched_tokens,
                            help='Maximum number of batched tokens per '
                            'iteration.')
        parser.add_argument('--max-num-seqs',
                            type=int,
                            default=EngineArgs.max_num_seqs,
                            help='Maximum number of sequences per iteration.')
        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
                            type=nullable_str,
                            choices=[*QUANTIZATION_METHODS, None],
                            default=EngineArgs.quantization,
                            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.')
        parser.add_argument(
            '--rope-scaling',
            default=None,
            type=json.loads,
            help='RoPE scaling configuration in JSON format. '
            'For example, {"rope_type":"dynamic","factor":2.0}')
        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.')
        parser.add_argument('--hf-overrides',
                            type=json.loads,
                            default=EngineArgs.hf_overrides,
                            help='Extra arguments for the HuggingFace config. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
        parser.add_argument('--enforce-eager',
                            action='store_true',
                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
        parser.add_argument('--max-seq-len-to-capture',
                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence length covered by CUDA '
                            'graphs. When a sequence has context length '
                            '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.')
        parser.add_argument('--disable-custom-all-reduce',
                            action='store_true',
                            default=EngineArgs.disable_custom_all_reduce,
                            help='See ParallelConfig.')
        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',
                            type=nullable_str,
                            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.')

        # 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
            # MultiModalRegistry.init_mm_limits_per_prompt
            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 '
                  'images and 2 videos per prompt. Defaults to 1 for '
                  'each modality.'))
        parser.add_argument(
            '--mm-processor-kwargs',
            default=None,
            type=json.loads,
            help=('Overrides for the multimodal input mapping/processing, '
                  'e.g., image processor. For example: {"num_crops": 4}.'))
        parser.add_argument(
            '--mm-cache-preprocessor',
            action='store_true',
            help='If true, then enables caching of the multi-modal '
            'preprocessor/mapper. Otherwise, the mapper executes each time'
            ', and for better performance consider enabling frontend process.')

        # LoRA related configs
        parser.add_argument('--enable-lora',
                            action='store_true',
                            help='If True, enable handling of LoRA adapters.')
        parser.add_argument('--enable-lora-bias',
                            action='store_true',
                            help='If True, enable bias for LoRA adapters.')
        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.')
        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,
            choices=['auto', 'float16', 'bfloat16'],
            help=('Data type for LoRA. If auto, will default to '
                  'base model dtype.'))
        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.'))
        parser.add_argument(
            '--max-cpu-loras',
            type=int,
            default=EngineArgs.max_cpu_loras,
            help=('Maximum number of LoRAs to store in CPU memory. '
                  'Must be >= than max_loras. '
                  'Defaults to max_loras.'))
        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.'))
        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')
        parser.add_argument("--device",
                            type=str,
                            default=EngineArgs.device,
                            choices=DEVICE_OPTIONS,
                            help='Device type for vLLM execution.')
        parser.add_argument('--num-scheduler-steps',
                            type=int,
                            default=1,
                            help=('Maximum number of forward steps per '
                                  'scheduler call.'))

        parser.add_argument(
            '--multi-step-stream-outputs',
            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')
        parser.add_argument(
            '--scheduler-delay-factor',
            type=float,
            default=EngineArgs.scheduler_delay_factor,
            help='Apply a delay (of delay factor multiplied by previous '
            'prompt latency) before scheduling next prompt.')
        parser.add_argument(
            '--enable-chunked-prefill',
            action=StoreBoolean,
            default=EngineArgs.enable_chunked_prefill,
            nargs="?",
            const="True",
            help='If set, the prefill requests can be chunked based on the '
            'max_num_batched_tokens.')

        parser.add_argument(
            '--speculative-model',
            type=nullable_str,
            default=EngineArgs.speculative_model,
            help=
            'The name of the draft model to be used in speculative decoding.')
        # Quantization settings for speculative model.
        parser.add_argument(
            '--speculative-model-quantization',
            type=nullable_str,
            choices=[*QUANTIZATION_METHODS, None],
            default=EngineArgs.speculative_model_quantization,
            help='Method used to quantize the weights of speculative model. '
            '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.')
        parser.add_argument(
            '--num-speculative-tokens',
            type=int,
            default=EngineArgs.num_speculative_tokens,
            help='The number of speculative tokens to sample from '
            'the draft model in speculative decoding.')
        parser.add_argument(
            '--speculative-disable-mqa-scorer',
            action='store_true',
            help=
            'If set to True, the MQA scorer will be disabled in speculative '
            ' and fall back to batch expansion')
        parser.add_argument(
            '--speculative-draft-tensor-parallel-size',
            '-spec-draft-tp',
            type=int,
            default=EngineArgs.speculative_draft_tensor_parallel_size,
            help='Number of tensor parallel replicas for '
            'the draft model in speculative decoding.')

        parser.add_argument(
            '--speculative-max-model-len',
            type=int,
            default=EngineArgs.speculative_max_model_len,
            help='The maximum sequence length supported by the '
            'draft model. Sequences over this length will skip '
            'speculation.')

        parser.add_argument(
            '--speculative-disable-by-batch-size',
            type=int,
            default=EngineArgs.speculative_disable_by_batch_size,
            help='Disable speculative decoding for new incoming requests '
            'if the number of enqueue requests is larger than this value.')

        parser.add_argument(
            '--ngram-prompt-lookup-max',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_max,
            help='Max size of window for ngram prompt lookup in speculative '
            'decoding.')

        parser.add_argument(
            '--ngram-prompt-lookup-min',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_min,
            help='Min size of window for ngram prompt lookup in speculative '
            'decoding.')

        parser.add_argument(
            '--spec-decoding-acceptance-method',
            type=str,
            default=EngineArgs.spec_decoding_acceptance_method,
            choices=['rejection_sampler', 'typical_acceptance_sampler'],
            help='Specify the acceptance method to use during draft token '
            'verification in speculative decoding. Two types of acceptance '
            'routines are supported: '
            '1) RejectionSampler which does not allow changing the '
            'acceptance rate of draft tokens, '
            '2) TypicalAcceptanceSampler which is configurable, allowing for '
            'a higher acceptance rate at the cost of lower quality, '
            'and vice versa.')

        parser.add_argument(
            '--typical-acceptance-sampler-posterior-threshold',
            type=float,
            default=EngineArgs.typical_acceptance_sampler_posterior_threshold,
            help='Set the lower bound threshold for the posterior '
            'probability of a token to be accepted. This threshold is '
            'used by the TypicalAcceptanceSampler to make sampling decisions '
            'during speculative decoding. Defaults to 0.09')

        parser.add_argument(
            '--typical-acceptance-sampler-posterior-alpha',
            type=float,
            default=EngineArgs.typical_acceptance_sampler_posterior_alpha,
            help='A scaling factor for the entropy-based threshold for token '
            'acceptance in the TypicalAcceptanceSampler. Typically defaults '
            'to sqrt of --typical-acceptance-sampler-posterior-threshold '
            'i.e. 0.3')

        parser.add_argument(
            '--disable-logprobs-during-spec-decoding',
            action=StoreBoolean,
            default=EngineArgs.disable_logprobs_during_spec_decoding,
            nargs="?",
            const="True",
            help='If set to True, token log probabilities are not returned '
            'during speculative decoding. If set to False, log probabilities '
            'are returned according to the settings in SamplingParams. If '
            'not specified, it defaults to True. Disabling log probabilities '
            'during speculative decoding reduces latency by skipping logprob '
            'calculation in proposal sampling, target sampling, and after '
            'accepted tokens are determined.')

        parser.add_argument('--model-loader-extra-config',
                            type=nullable_str,
                            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.')
        parser.add_argument(
            '--ignore-patterns',
            action="append",
            type=str,
            default=[],
            help="The pattern(s) to ignore when loading the model."
            "Default to `original/**/*` to avoid repeated loading of llama's "
            "checkpoints.")
        parser.add_argument(
            '--preemption-mode',
            type=str,
            default=None,
            help='If \'recompute\', the engine performs preemption by '
            'recomputing; If \'swap\', the engine performs preemption by '
            'block swapping.')

        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 "
            "same as the `--model` argument. Noted that this name(s) "
            "will also be used in `model_name` tag content of "
            "prometheus metrics, if multiple names provided, metrics "
            "tag will take the first one.")
        parser.add_argument('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')

        parser.add_argument(
            '--otlp-traces-endpoint',
            type=str,
            default=None,
            help='Target URL to which OpenTelemetry traces will be sent.')
        parser.add_argument(
            '--collect-detailed-traces',
            type=str,
            default=None,
            help="Valid choices are " +
            ",".join(ALLOWED_DETAILED_TRACE_MODULES) +
            ". It makes sense to set this only if --otlp-traces-endpoint is"
            " 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.")

        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.")

        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).')

        parser.add_argument(
            '--override-neuron-config',
            type=json.loads,
            default=None,
            help="Override or set neuron device configuration. "
            "e.g. {\"cast_logits_dtype\": \"bloat16\"}.'")
        parser.add_argument(
            '--override-pooler-config',
            type=PoolerConfig.from_json,
            default=None,
            help="Override or set the pooling method for pooling models. "
            "e.g. {\"pooling_type\": \"mean\", \"normalize\": false}.'")

        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, '
                            'use a JSON string.\n'
                            'Following the convention of traditional '
                            'compilers, using -O without space is also '
                            'supported. -O3 is equivalent to -O 3.')

        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.')

        parser.add_argument(
            '--worker-cls',
            type=str,
            default="auto",
            help='The worker class to use for distributed execution.')

        return parser

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace):
        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args

    def create_model_config(self) -> ModelConfig:
        return ModelConfig(
            model=self.model,
            task=self.task,
            # We know this is not None because we set it in __post_init__
            tokenizer=cast(str, self.tokenizer),
            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
            allowed_local_media_path=self.allowed_local_media_path,
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
            rope_theta=self.rope_theta,
            hf_overrides=self.hf_overrides,
            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            quantization_param_path=self.quantization_param_path,
            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,
            skip_tokenizer_init=self.skip_tokenizer_init,
            served_model_name=self.served_model_name,
            limit_mm_per_prompt=self.limit_mm_per_prompt,
            use_async_output_proc=not self.disable_async_output_proc,
            config_format=self.config_format,
            mm_processor_kwargs=self.mm_processor_kwargs,
            mm_cache_preprocessor=self.mm_cache_preprocessor,
            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
            logits_processor_pattern=self.logits_processor_pattern)

    def create_load_config(self) -> LoadConfig:
        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,
        )

    def create_engine_config(self,
                             usage_context: Optional[UsageContext] = None
                             ) -> VllmConfig:
        if envs.VLLM_USE_V1:
            self._override_v1_engine_args(usage_context)

        # 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"

        # bitsandbytes quantization needs a specific model loader
        # so we make sure the quant method and the load format are consistent
        if (self.quantization == "bitsandbytes" or
           self.qlora_adapter_name_or_path is not None) and \
           self.load_format != "bitsandbytes":
            raise ValueError(
                "BitsAndBytes quantization and QLoRA adapter only support "
                f"'bitsandbytes' load format, but got {self.load_format}")

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

        assert self.cpu_offload_gb >= 0, (
            "CPU offload space must be non-negative"
            f", but got {self.cpu_offload_gb}")

        device_config = DeviceConfig(device=self.device)
        model_config = self.create_model_config()

        if model_config.is_multimodal_model:
            if self.enable_prefix_caching:
                logger.warning(
                    "--enable-prefix-caching is currently not "
                    "supported for multimodal models and has been disabled.")
            self.enable_prefix_caching = False

        cache_config = CacheConfig(
            # neuron needs block_size = max_model_len
            block_size=self.block_size if self.device != "neuron" else
            (self.max_model_len if self.max_model_len is not None else 0),
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
            is_attention_free=model_config.is_attention_free,
            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
            enable_prefix_caching=self.enable_prefix_caching,
            cpu_offload_gb=self.cpu_offload_gb,
        )
        parallel_config = ParallelConfig(
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            worker_use_ray=self.worker_use_ray,
            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            tokenizer_pool_config=TokenizerPoolConfig.create_config(
                self.tokenizer_pool_size,
                self.tokenizer_pool_type,
                self.tokenizer_pool_extra_config,
            ),
            ray_workers_use_nsight=self.ray_workers_use_nsight,
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
        )

        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # If not explicitly set, enable chunked prefill by default for
            # long context (> 32K) models. This is to avoid OOM errors in the
            # initial memory profiling phase.

            # For multimodal models, chunked prefill is disabled by default in
            # V0, but enabled by design in V1
            if model_config.is_multimodal_model:
                self.enable_chunked_prefill = bool(envs.VLLM_USE_V1)

            elif use_long_context:
                is_gpu = device_config.device_type == "cuda"
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
                use_spec_decode = self.speculative_model is not None
                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"
                        and not current_platform.is_rocm()):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models with "
                        "max_model_len > 32K. Currently, chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable chunked prefill "
                        "by setting --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 "
                "errors during the initial memory profiling phase, or result "
                "in low performance due to small KV cache space. 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)


        speculative_config = SpeculativeConfig.maybe_create_spec_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
            target_dtype=self.dtype,
            speculative_model=self.speculative_model,
            speculative_model_quantization = \
                self.speculative_model_quantization,
            speculative_draft_tensor_parallel_size = \
                self.speculative_draft_tensor_parallel_size,
            num_speculative_tokens=self.num_speculative_tokens,
            speculative_disable_mqa_scorer=self.speculative_disable_mqa_scorer,
            speculative_disable_by_batch_size=self.
            speculative_disable_by_batch_size,
            speculative_max_model_len=self.speculative_max_model_len,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_log_stats=self.disable_log_stats,
            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
            draft_token_acceptance_method=\
                self.spec_decoding_acceptance_method,
            typical_acceptance_sampler_posterior_threshold=self.
            typical_acceptance_sampler_posterior_threshold,
            typical_acceptance_sampler_posterior_alpha=self.
            typical_acceptance_sampler_posterior_alpha,
            disable_logprobs=self.disable_logprobs_during_spec_decoding,
        )

        # Reminder: Please update docs/source/usage/compatibility_matrix.rst
        # If the feature combo become valid
        if self.num_scheduler_steps > 1:
            if speculative_config is not None:
                raise ValueError("Speculative decoding is not supported with "
                                 "multi-step (--num-scheduler-steps > 1)")
            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                 "for pipeline-parallel-size > 1")

        # 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

        if not self.use_v2_block_manager:
            logger.warning(
                "[DEPRECATED] Block manager v1 has been removed, "
                "and setting --use-v2-block-manager to True or False has "
                "no effect on vLLM behavior. Please remove "
                "--use-v2-block-manager in your engine argument. "
                "If your use case is not supported by "
                "SelfAttnBlockSpaceManager (i.e. block manager v2),"
                " please file an issue with detailed information.")

        scheduler_config = SchedulerConfig(
            runner_type=model_config.runner_type,
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
            num_lookahead_slots=num_lookahead_slots,
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
            is_multimodal_model=model_config.is_multimodal_model,
            preemption_mode=self.preemption_mode,
            num_scheduler_steps=self.num_scheduler_steps,
            multi_step_stream_outputs=self.multi_step_stream_outputs,
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
            policy=self.scheduling_policy)
        lora_config = LoRAConfig(
            bias_enabled=self.enable_lora_bias,
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
            fully_sharded_loras=self.fully_sharded_loras,
            lora_extra_vocab_size=self.lora_extra_vocab_size,
            long_lora_scaling_factors=self.long_lora_scaling_factors,
            lora_dtype=self.lora_dtype,
            max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
            and self.max_cpu_loras > 0 else None) if self.enable_lora else None

        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

        load_config = self.create_load_config()

        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

        decoding_config = DecodingConfig(
            guided_decoding_backend=self.guided_decoding_backend)

        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}")
        observability_config = ObservabilityConfig(
            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,
        )

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

        if envs.VLLM_USE_V1:
            self._override_v1_engine_config(config)
        return config

    def _override_v1_engine_args(self, usage_context: UsageContext) -> None:
        """
        Override the EngineArgs's args based on the usage context for V1.
        """
        assert envs.VLLM_USE_V1, "V1 is not enabled"

        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
        # When no user override, set the default values based on the usage
        # context.
        # TODO(woosuk): Tune the default values for different hardware.
        if self.max_num_batched_tokens is None:
            if usage_context == UsageContext.LLM_CLASS:
                self.max_num_batched_tokens = 8192
            elif usage_context == UsageContext.OPENAI_API_SERVER:
                self.max_num_batched_tokens = 2048
            logger.warning(
                "Setting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens, usage_context.value)

    def _override_v1_engine_config(self, engine_config: VllmConfig) -> None:
        """
        Override the EngineConfig's configs based on the usage context for V1.
        """
        assert envs.VLLM_USE_V1, "V1 is not enabled"
        if engine_config.model_config.is_multimodal_model:
            # TODO (ywang96): Enable APC by default when VLM supports it.
            assert not engine_config.cache_config.enable_prefix_caching


@dataclass
class AsyncEngineArgs(EngineArgs):
    """Arguments for asynchronous vLLM engine."""
    disable_log_requests: bool = False

    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
        parser.add_argument('--disable-log-requests',
                            action='store_true',
                            help='Disable logging requests.')
        return parser


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


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