vllm.engine.arg_utils

T module-attribute

T = TypeVar('T')

TypeHint module-attribute

TypeHint = Union[type[Any], object]

TypeHintT module-attribute

TypeHintT = Union[type[T], object]

logger module-attribute

logger = init_logger(__name__)

AsyncEngineArgs dataclass

Bases: EngineArgs

Arguments for asynchronous vLLM engine.

Source code in vllm/engine/arg_utils.py

@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:
        # 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()
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
        parser.add_argument('--disable-log-requests',
                            action='store_true',
                            help='Disable logging requests.')
        from vllm.platforms import current_platform
        current_platform.pre_register_and_update(parser)
        return parser

disable_log_requests class-attribute instance-attribute

disable_log_requests: bool = False

__init__

__init__(
    model: str = model,
    served_model_name: Optional[
        Union[str, List[str]]
    ] = served_model_name,
    tokenizer: Optional[str] = tokenizer,
    hf_config_path: Optional[str] = hf_config_path,
    task: TaskOption = task,
    skip_tokenizer_init: bool = skip_tokenizer_init,
    enable_prompt_embeds: bool = enable_prompt_embeds,
    tokenizer_mode: TokenizerMode = tokenizer_mode,
    trust_remote_code: bool = trust_remote_code,
    allowed_local_media_path: str = allowed_local_media_path,
    download_dir: Optional[str] = download_dir,
    load_format: str = load_format,
    config_format: str = config_format,
    dtype: ModelDType = dtype,
    kv_cache_dtype: CacheDType = cache_dtype,
    seed: Optional[int] = seed,
    max_model_len: Optional[int] = max_model_len,
    cuda_graph_sizes: list[int] = get_field(
        SchedulerConfig, "cuda_graph_sizes"
    ),
    distributed_executor_backend: Optional[
        Union[
            DistributedExecutorBackend, Type[ExecutorBase]
        ]
    ] = distributed_executor_backend,
    pipeline_parallel_size: int = pipeline_parallel_size,
    tensor_parallel_size: int = tensor_parallel_size,
    data_parallel_size: int = data_parallel_size,
    data_parallel_rank: Optional[int] = None,
    data_parallel_size_local: Optional[int] = None,
    data_parallel_address: Optional[str] = None,
    data_parallel_rpc_port: Optional[int] = None,
    data_parallel_backend: str = data_parallel_backend,
    enable_expert_parallel: bool = enable_expert_parallel,
    enable_eplb: bool = enable_eplb,
    num_redundant_experts: int = num_redundant_experts,
    eplb_window_size: int = eplb_window_size,
    eplb_step_interval: int = eplb_step_interval,
    eplb_log_balancedness: bool = eplb_log_balancedness,
    max_parallel_loading_workers: Optional[
        int
    ] = max_parallel_loading_workers,
    block_size: Optional[BlockSize] = block_size,
    enable_prefix_caching: Optional[
        bool
    ] = enable_prefix_caching,
    prefix_caching_hash_algo: PrefixCachingHashAlgo = prefix_caching_hash_algo,
    disable_sliding_window: bool = disable_sliding_window,
    disable_cascade_attn: bool = disable_cascade_attn,
    use_v2_block_manager: bool = True,
    swap_space: float = swap_space,
    cpu_offload_gb: float = cpu_offload_gb,
    gpu_memory_utilization: float = gpu_memory_utilization,
    max_num_batched_tokens: Optional[
        int
    ] = max_num_batched_tokens,
    max_num_partial_prefills: int = max_num_partial_prefills,
    max_long_partial_prefills: int = max_long_partial_prefills,
    long_prefill_token_threshold: int = long_prefill_token_threshold,
    max_num_seqs: Optional[int] = max_num_seqs,
    max_logprobs: int = max_logprobs,
    disable_log_stats: bool = False,
    revision: Optional[str] = revision,
    code_revision: Optional[str] = code_revision,
    rope_scaling: dict[str, Any] = get_field(
        ModelConfig, "rope_scaling"
    ),
    rope_theta: Optional[float] = rope_theta,
    hf_token: Optional[Union[bool, str]] = hf_token,
    hf_overrides: HfOverrides = get_field(
        ModelConfig, "hf_overrides"
    ),
    tokenizer_revision: Optional[str] = tokenizer_revision,
    quantization: Optional[
        QuantizationMethods
    ] = quantization,
    enforce_eager: bool = enforce_eager,
    max_seq_len_to_capture: int = max_seq_len_to_capture,
    disable_custom_all_reduce: bool = disable_custom_all_reduce,
    tokenizer_pool_size: int = pool_size,
    tokenizer_pool_type: str = pool_type,
    tokenizer_pool_extra_config: dict = get_field(
        TokenizerPoolConfig, "extra_config"
    ),
    limit_mm_per_prompt: dict[str, int] = get_field(
        MultiModalConfig, "limit_per_prompt"
    ),
    media_io_kwargs: dict[str, dict[str, Any]] = get_field(
        MultiModalConfig, "media_io_kwargs"
    ),
    mm_processor_kwargs: Optional[
        Dict[str, Any]
    ] = mm_processor_kwargs,
    disable_mm_preprocessor_cache: bool = disable_mm_preprocessor_cache,
    enable_lora: bool = False,
    enable_lora_bias: bool = bias_enabled,
    max_loras: int = max_loras,
    max_lora_rank: int = max_lora_rank,
    fully_sharded_loras: bool = fully_sharded_loras,
    max_cpu_loras: Optional[int] = max_cpu_loras,
    lora_dtype: Optional[Union[str, dtype]] = lora_dtype,
    lora_extra_vocab_size: int = lora_extra_vocab_size,
    long_lora_scaling_factors: Optional[
        tuple[float, ...]
    ] = long_lora_scaling_factors,
    enable_prompt_adapter: bool = False,
    max_prompt_adapters: int = max_prompt_adapters,
    max_prompt_adapter_token: int = max_prompt_adapter_token,
    device: Device = device,
    num_scheduler_steps: int = num_scheduler_steps,
    multi_step_stream_outputs: bool = multi_step_stream_outputs,
    ray_workers_use_nsight: bool = ray_workers_use_nsight,
    num_gpu_blocks_override: Optional[
        int
    ] = num_gpu_blocks_override,
    num_lookahead_slots: int = num_lookahead_slots,
    model_loader_extra_config: dict = get_field(
        LoadConfig, "model_loader_extra_config"
    ),
    ignore_patterns: Optional[
        Union[str, List[str]]
    ] = ignore_patterns,
    preemption_mode: Optional[str] = preemption_mode,
    scheduler_delay_factor: float = delay_factor,
    enable_chunked_prefill: Optional[
        bool
    ] = enable_chunked_prefill,
    disable_chunked_mm_input: bool = disable_chunked_mm_input,
    disable_hybrid_kv_cache_manager: bool = disable_hybrid_kv_cache_manager,
    guided_decoding_backend: GuidedDecodingBackend = backend,
    guided_decoding_disable_fallback: bool = disable_fallback,
    guided_decoding_disable_any_whitespace: bool = disable_any_whitespace,
    guided_decoding_disable_additional_properties: bool = disable_additional_properties,
    logits_processor_pattern: Optional[
        str
    ] = logits_processor_pattern,
    speculative_config: Optional[Dict[str, Any]] = None,
    qlora_adapter_name_or_path: Optional[str] = None,
    show_hidden_metrics_for_version: Optional[
        str
    ] = show_hidden_metrics_for_version,
    otlp_traces_endpoint: Optional[
        str
    ] = otlp_traces_endpoint,
    collect_detailed_traces: Optional[
        list[DetailedTraceModules]
    ] = collect_detailed_traces,
    disable_async_output_proc: bool = not use_async_output_proc,
    scheduling_policy: SchedulerPolicy = policy,
    scheduler_cls: Union[str, Type[object]] = scheduler_cls,
    override_neuron_config: dict[str, Any] = get_field(
        ModelConfig, "override_neuron_config"
    ),
    override_pooler_config: Optional[
        Union[dict, PoolerConfig]
    ] = override_pooler_config,
    compilation_config: CompilationConfig = get_field(
        VllmConfig, "compilation_config"
    ),
    worker_cls: str = worker_cls,
    worker_extension_cls: str = worker_extension_cls,
    kv_transfer_config: Optional[KVTransferConfig] = None,
    kv_events_config: Optional[KVEventsConfig] = None,
    generation_config: str = generation_config,
    enable_sleep_mode: bool = enable_sleep_mode,
    override_generation_config: dict[str, Any] = get_field(
        ModelConfig, "override_generation_config"
    ),
    model_impl: str = model_impl,
    override_attention_dtype: str = override_attention_dtype,
    calculate_kv_scales: bool = calculate_kv_scales,
    additional_config: dict[str, Any] = get_field(
        VllmConfig, "additional_config"
    ),
    enable_reasoning: Optional[bool] = None,
    reasoning_parser: str = reasoning_backend,
    use_tqdm_on_load: bool = use_tqdm_on_load,
    pt_load_map_location: str = pt_load_map_location,
    enable_multimodal_encoder_data_parallel: bool = enable_multimodal_encoder_data_parallel,
    disable_log_requests: bool = False,
) -> None

add_cli_args staticmethod

add_cli_args(
    parser: FlexibleArgumentParser,
    async_args_only: bool = False,
) -> FlexibleArgumentParser

Source code in vllm/engine/arg_utils.py

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

EngineArgs dataclass

Arguments for vLLM engine.

Source code in vllm/engine/arg_utils.py

@dataclass
class EngineArgs:
    """Arguments for vLLM engine."""
    model: str = ModelConfig.model
    served_model_name: Optional[Union[
        str, List[str]]] = ModelConfig.served_model_name
    tokenizer: Optional[str] = ModelConfig.tokenizer
    hf_config_path: Optional[str] = ModelConfig.hf_config_path
    task: TaskOption = ModelConfig.task
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
    download_dir: Optional[str] = LoadConfig.download_dir
    load_format: str = LoadConfig.load_format
    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
    cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
                                            "cuda_graph_sizes")
    # 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[
        DistributedExecutorBackend,
        Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
    # number of P/D disaggregation (or other disaggregation) workers
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    data_parallel_size: int = ParallelConfig.data_parallel_size
    data_parallel_rank: Optional[int] = None
    data_parallel_size_local: Optional[int] = None
    data_parallel_address: Optional[str] = None
    data_parallel_rpc_port: Optional[int] = None
    data_parallel_backend: str = ParallelConfig.data_parallel_backend
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
    enable_eplb: bool = ParallelConfig.enable_eplb
    num_redundant_experts: int = ParallelConfig.num_redundant_experts
    eplb_window_size: int = ParallelConfig.eplb_window_size
    eplb_step_interval: int = ParallelConfig.eplb_step_interval
    eplb_log_balancedness: bool = ParallelConfig.eplb_log_balancedness
    max_parallel_loading_workers: Optional[
        int] = ParallelConfig.max_parallel_loading_workers
    block_size: Optional[BlockSize] = CacheConfig.block_size
    enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
    prefix_caching_hash_algo: PrefixCachingHashAlgo = \
        CacheConfig.prefix_caching_hash_algo
    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
    use_v2_block_manager: bool = True
    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
    max_num_batched_tokens: Optional[
        int] = SchedulerConfig.max_num_batched_tokens
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
    long_prefill_token_threshold: int = \
        SchedulerConfig.long_prefill_token_threshold
    max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
    max_logprobs: int = ModelConfig.max_logprobs
    disable_log_stats: bool = False
    revision: Optional[str] = ModelConfig.revision
    code_revision: Optional[str] = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: Optional[float] = ModelConfig.rope_theta
    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
    # The following three fields are deprecated and will be removed in a future
    # release. Setting them will have no effect. Please remove them from your
    # configurations.
    tokenizer_pool_size: int = TokenizerPoolConfig.pool_size
    tokenizer_pool_type: str = TokenizerPoolConfig.pool_type
    tokenizer_pool_extra_config: dict = \
        get_field(TokenizerPoolConfig, "extra_config")
    limit_mm_per_prompt: dict[str, int] = \
        get_field(MultiModalConfig, "limit_per_prompt")
    media_io_kwargs: dict[str, dict[str,
                                    Any]] = get_field(MultiModalConfig,
                                                      "media_io_kwargs")
    mm_processor_kwargs: Optional[Dict[str, Any]] = \
        MultiModalConfig.mm_processor_kwargs
    disable_mm_preprocessor_cache: bool = \
        MultiModalConfig.disable_mm_preprocessor_cache
    # LoRA fields
    enable_lora: bool = False
    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
    long_lora_scaling_factors: Optional[tuple[float, ...]] = \
        LoRAConfig.long_lora_scaling_factors
    # PromptAdapter fields
    enable_prompt_adapter: bool = False
    max_prompt_adapters: int = PromptAdapterConfig.max_prompt_adapters
    max_prompt_adapter_token: int = \
        PromptAdapterConfig.max_prompt_adapter_token

    device: Device = DeviceConfig.device
    num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
    multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode

    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input

    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager)

    guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern

    speculative_config: Optional[Dict[str, Any]] = None

    qlora_adapter_name_or_path: Optional[str] = None
    show_hidden_metrics_for_version: Optional[str] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls

    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
    compilation_config: CompilationConfig = \
        get_field(VllmConfig, "compilation_config")
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls

    kv_transfer_config: Optional[KVTransferConfig] = None
    kv_events_config: Optional[KVEventsConfig] = None

    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    model_impl: str = ModelConfig.model_impl
    override_attention_dtype: str = ModelConfig.override_attention_dtype

    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales

    additional_config: dict[str, Any] = \
        get_field(VllmConfig, "additional_config")
    enable_reasoning: Optional[bool] = None  # DEPRECATED
    reasoning_parser: str = DecodingConfig.reasoning_backend

    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
    pt_load_map_location: str = LoadConfig.pt_load_map_location

    enable_multimodal_encoder_data_parallel: bool = \
        ParallelConfig.enable_multimodal_encoder_data_parallel

    def __post_init__(self):
        # 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))
        if self.qlora_adapter_name_or_path is not None:
            warnings.warn(
                "The `qlora_adapter_name_or_path` is deprecated "
                "and will be removed in v0.10.0. ",
                DeprecationWarning,
                stacklevel=2,
            )
        # 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
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
        if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
            model_group.add_argument("--model", **model_kwargs["model"])
        model_group.add_argument("--task", **model_kwargs["task"])
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        model_group.add_argument("--hf-config-path",
                                 **model_kwargs["hf_config_path"])
        model_group.add_argument("--allowed-local-media-path",
                                 **model_kwargs["allowed_local_media_path"])
        model_group.add_argument("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        model_group.add_argument("--tokenizer-revision",
                                 **model_kwargs["tokenizer_revision"])
        model_group.add_argument("--max-model-len",
                                 **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q",
                                 **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager",
                                 **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
        model_group.add_argument("--disable-sliding-window",
                                 **model_kwargs["disable_sliding_window"])
        model_group.add_argument("--disable-cascade-attn",
                                 **model_kwargs["disable_cascade_attn"])
        model_group.add_argument("--skip-tokenizer-init",
                                 **model_kwargs["skip_tokenizer_init"])
        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        model_group.add_argument("--hf-token",
                                 type=str,
                                 nargs="?",
                                 const=True,
                                 default=model_kwargs["hf_token"]["default"],
                                 help=model_kwargs["hf_token"]["help"])
        model_group.add_argument("--hf-overrides",
                                 **model_kwargs["hf_overrides"])
        model_group.add_argument("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        model_group.add_argument("--logits-processor-pattern",
                                 **model_kwargs["logits_processor_pattern"])
        model_group.add_argument("--generation-config",
                                 **model_kwargs["generation_config"])
        model_group.add_argument("--override-generation-config",
                                 **model_kwargs["override_generation_config"])
        model_group.add_argument("--enable-sleep-mode",
                                 **model_kwargs["enable_sleep_mode"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])
        model_group.add_argument("--override-attention-dtype",
                                 **model_kwargs["override_attention_dtype"])

        # 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("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
                                **load_kwargs["use_tqdm_on_load"])
        load_group.add_argument(
            "--qlora-adapter-name-or-path",
            type=str,
            default=None,
            help="The `--qlora-adapter-name-or-path` has no effect, do not set"
            " it, and it  will be removed in v0.10.0.",
            deprecated=True,
        )
        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])

        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-fallback",
            **guided_decoding_kwargs["disable_fallback"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-any-whitespace",
            **guided_decoding_kwargs["disable_any_whitespace"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-additional-properties",
            **guided_decoding_kwargs["disable_additional_properties"])
        guided_decoding_group.add_argument(
            "--enable-reasoning",
            action=argparse.BooleanOptionalAction,
            deprecated=True,
            help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
            "of v0.9.0. Use `--reasoning-parser` to specify the reasoning "
            "parser backend instead. This flag (`--enable-reasoning`) will be "
            "removed in v0.10.0. When `--reasoning-parser` is specified, "
            "reasoning mode is automatically enabled.")
        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

        # Parallel arguments
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
            "--distributed-executor-backend",
            **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(
            '--data-parallel-rank',
            '-dpn',
            type=int,
            help='Data parallel rank of this instance. '
            'When set, enables external load balancer mode.')
        parallel_group.add_argument('--data-parallel-size-local',
                                    '-dpl',
                                    type=int,
                                    help='Number of data parallel replicas '
                                    'to run on this node.')
        parallel_group.add_argument('--data-parallel-address',
                                    '-dpa',
                                    type=str,
                                    help='Address of data parallel cluster '
                                    'head-node.')
        parallel_group.add_argument('--data-parallel-rpc-port',
                                    '-dpp',
                                    type=int,
                                    help='Port for data parallel RPC '
                                    'communication.')
        parallel_group.add_argument('--data-parallel-backend',
                                    '-dpb',
                                    type=str,
                                    default='mp',
                                    help='Backend for data parallel, either '
                                    '"mp" or "ray".')
        parallel_group.add_argument(
            "--enable-expert-parallel",
            **parallel_kwargs["enable_expert_parallel"])
        parallel_group.add_argument("--enable-eplb",
                                    **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--num-redundant-experts",
                                    **parallel_kwargs["num_redundant_experts"])
        parallel_group.add_argument("--eplb-window-size",
                                    **parallel_kwargs["eplb_window_size"])
        parallel_group.add_argument("--eplb-step-interval",
                                    **parallel_kwargs["eplb_step_interval"])
        parallel_group.add_argument("--eplb-log-balancedness",
                                    **parallel_kwargs["eplb_log_balancedness"])
        parallel_group.add_argument(
            "--max-parallel-loading-workers",
            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
            "--ray-workers-use-nsight",
            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
            "--disable-custom-all-reduce",
            **parallel_kwargs["disable_custom_all_reduce"])
        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
            **parallel_kwargs["enable_multimodal_encoder_data_parallel"])

        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
        )
        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
        cache_group.add_argument("--gpu-memory-utilization",
                                 **cache_kwargs["gpu_memory_utilization"])
        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
                                 **cache_kwargs["cache_dtype"])
        cache_group.add_argument("--num-gpu-blocks-override",
                                 **cache_kwargs["num_gpu_blocks_override"])
        cache_group.add_argument("--enable-prefix-caching",
                                 **cache_kwargs["enable_prefix_caching"])
        cache_group.add_argument("--prefix-caching-hash-algo",
                                 **cache_kwargs["prefix_caching_hash_algo"])
        cache_group.add_argument("--cpu-offload-gb",
                                 **cache_kwargs["cpu_offload_gb"])
        cache_group.add_argument("--calculate-kv-scales",
                                 **cache_kwargs["calculate_kv_scales"])

        # Tokenizer arguments
        tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
        tokenizer_group = parser.add_argument_group(
            title="TokenizerPoolConfig",
            description=TokenizerPoolConfig.__doc__,
        )
        tokenizer_group.add_argument("--tokenizer-pool-size",
                                     **tokenizer_kwargs["pool_size"])
        tokenizer_group.add_argument("--tokenizer-pool-type",
                                     **tokenizer_kwargs["pool_type"])
        tokenizer_group.add_argument("--tokenizer-pool-extra-config",
                                     **tokenizer_kwargs["extra_config"])

        # Multimodal related configs
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
        multimodal_group.add_argument("--limit-mm-per-prompt",
                                      **multimodal_kwargs["limit_per_prompt"])
        multimodal_group.add_argument("--media-io-kwargs",
                                      **multimodal_kwargs["media_io_kwargs"])
        multimodal_group.add_argument(
            "--mm-processor-kwargs",
            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
            "--disable-mm-preprocessor-cache",
            **multimodal_kwargs["disable_mm_preprocessor_cache"])

        # LoRA related configs
        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
            "--enable-lora",
            action=argparse.BooleanOptionalAction,
            help="If True, enable handling of LoRA adapters.")
        lora_group.add_argument("--enable-lora-bias",
                                **lora_kwargs["bias_enabled"])
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
                                **lora_kwargs["max_lora_rank"])
        lora_group.add_argument("--lora-extra-vocab-size",
                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
            "--lora-dtype",
            **lora_kwargs["lora_dtype"],
        )
        lora_group.add_argument("--long-lora-scaling-factors",
                                **lora_kwargs["long_lora_scaling_factors"])
        lora_group.add_argument("--max-cpu-loras",
                                **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument("--fully-sharded-loras",
                                **lora_kwargs["fully_sharded_loras"])

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

        # Device arguments
        device_kwargs = get_kwargs(DeviceConfig)
        device_group = parser.add_argument_group(
            title="DeviceConfig",
            description=DeviceConfig.__doc__,
        )
        device_group.add_argument("--device",
                                  **device_kwargs["device"],
                                  deprecated=True)

        # Speculative arguments
        speculative_group = parser.add_argument_group(
            title="SpeculativeConfig",
            description=SpeculativeConfig.__doc__,
        )
        speculative_group.add_argument(
            "--speculative-config",
            type=json.loads,
            default=None,
            help="The configurations for speculative decoding. Should be a "
            "JSON string.")

        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **observability_kwargs["collect_detailed_traces"])

        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
            "--max-num-batched-tokens",
            **scheduler_kwargs["max_num_batched_tokens"])
        scheduler_group.add_argument("--max-num-seqs",
                                     **scheduler_kwargs["max_num_seqs"])
        scheduler_group.add_argument(
            "--max-num-partial-prefills",
            **scheduler_kwargs["max_num_partial_prefills"])
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
            **scheduler_kwargs["max_long_partial_prefills"])
        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
        scheduler_group.add_argument("--num-lookahead-slots",
                                     **scheduler_kwargs["num_lookahead_slots"])
        scheduler_group.add_argument("--scheduler-delay-factor",
                                     **scheduler_kwargs["delay_factor"])
        scheduler_group.add_argument("--preemption-mode",
                                     **scheduler_kwargs["preemption_mode"])
        scheduler_group.add_argument("--num-scheduler-steps",
                                     **scheduler_kwargs["num_scheduler_steps"])
        scheduler_group.add_argument(
            "--multi-step-stream-outputs",
            **scheduler_kwargs["multi_step_stream_outputs"])
        scheduler_group.add_argument("--scheduling-policy",
                                     **scheduler_kwargs["policy"])
        scheduler_group.add_argument(
            "--enable-chunked-prefill",
            **scheduler_kwargs["enable_chunked_prefill"])
        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"])

        # vLLM arguments
        vllm_kwargs = get_kwargs(VllmConfig)
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
        vllm_group.add_argument("--kv-transfer-config",
                                **vllm_kwargs["kv_transfer_config"])
        vllm_group.add_argument('--kv-events-config',
                                **vllm_kwargs["kv_events_config"])
        vllm_group.add_argument("--compilation-config", "-O",
                                **vllm_kwargs["compilation_config"])
        vllm_group.add_argument("--additional-config",
                                **vllm_kwargs["additional_config"])

        # Other arguments
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
                            default=True,
                            deprecated=True,
                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')

        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:
        # 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

        return ModelConfig(
            model=self.model,
            hf_config_path=self.hf_config_path,
            task=self.task,
            tokenizer=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_token=self.hf_token,
            hf_overrides=self.hf_overrides,
            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,
            disable_cascade_attn=self.disable_cascade_attn,
            skip_tokenizer_init=self.skip_tokenizer_init,
            enable_prompt_embeds=self.enable_prompt_embeds,
            served_model_name=self.served_model_name,
            limit_mm_per_prompt=self.limit_mm_per_prompt,
            media_io_kwargs=self.media_io_kwargs,
            use_async_output_proc=not self.disable_async_output_proc,
            config_format=self.config_format,
            mm_processor_kwargs=self.mm_processor_kwargs,
            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
            logits_processor_pattern=self.logits_processor_pattern,
            generation_config=self.generation_config,
            override_generation_config=self.override_generation_config,
            enable_sleep_mode=self.enable_sleep_mode,
            model_impl=self.model_impl,
            override_attention_dtype=self.override_attention_dtype,
        )

    def create_load_config(self) -> LoadConfig:

        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"

        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,
            use_tqdm_on_load=self.use_tqdm_on_load,
            pt_load_map_location=self.pt_load_map_location,
        )

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

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

        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
        self.speculative_config.update({
            "target_model_config": target_model_config,
            "target_parallel_config": target_parallel_config,
            "enable_chunked_prefill": enable_chunked_prefill,
            "disable_log_stats": disable_log_stats,
        })
        speculative_config = SpeculativeConfig.from_dict(
            self.speculative_config)

        return speculative_config

    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
    ) -> VllmConfig:
        """
        Create the VllmConfig.

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.

        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()

        device_config = DeviceConfig(
            device=cast(Device, current_platform.device_type))
        model_config = self.create_model_config()

        # * 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, model_config)
        else:
            self._set_default_args_v0(model_config)

        assert self.enable_chunked_prefill is not None

        if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
            assert self.enforce_eager, (
                "Cuda graph is not supported with DualChunkFlashAttention. "
                "To run the model in eager mode, set 'enforce_eager=True' "
                "or use '--enforce-eager' in the CLI.")
            assert current_platform.is_cuda(), (
                "DualChunkFlashAttention is only supported on CUDA platform.")
            assert not use_v1, (
                "DualChunkFlashAttention is not supported on V1 engine. "
                "To run the model in V0 engine, try set 'VLLM_USE_V1=0'")

        cache_config = CacheConfig(
            block_size=self.block_size,
            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,
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
            cpu_offload_gb=self.cpu_offload_gb,
            calculate_kv_scales=self.calculate_kv_scales,
        )

        # 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()

        data_parallel_external_lb = self.data_parallel_rank is not None
        if data_parallel_external_lb:
            assert self.data_parallel_size_local in (1, None), (
                "data_parallel_size_local must be 1 when data_parallel_rank "
                "is set")
            data_parallel_size_local = 1
        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local
        else:
            # Local DP size defaults to global DP size if not set.
            data_parallel_size_local = self.data_parallel_size

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
        if self.data_parallel_address is None:
            if self.data_parallel_backend == "ray":
                host_ip = get_ip()
                logger.info(
                    "Using host IP %s as ray-based data parallel address",
                    host_ip)
                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
                    self.data_parallel_backend)
                data_parallel_address = ParallelConfig.data_parallel_master_ip
        else:
            data_parallel_address = self.data_parallel_address

        # This port is only used when there are remote data parallel engines,
        # otherwise the local IPC transport is used.
        data_parallel_rpc_port = self.data_parallel_rpc_port if (
            self.data_parallel_rpc_port
            is not None) else ParallelConfig.data_parallel_rpc_port

        parallel_config = ParallelConfig(
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            data_parallel_size=self.data_parallel_size,
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
            data_parallel_backend=self.data_parallel_backend,
            enable_expert_parallel=self.enable_expert_parallel,
            enable_eplb=self.enable_eplb,
            num_redundant_experts=self.num_redundant_experts,
            eplb_window_size=self.eplb_window_size,
            eplb_step_interval=self.eplb_step_interval,
            eplb_log_balancedness=self.eplb_log_balancedness,
            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            ray_workers_use_nsight=self.ray_workers_use_nsight,
            placement_group=placement_group,
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
            worker_extension_cls=self.worker_extension_cls,
            enable_multimodal_encoder_data_parallel=self.
            enable_multimodal_encoder_data_parallel,
        )

        speculative_config = self.create_speculative_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_log_stats=self.disable_log_stats,
        )

        # Reminder: Please update docs/features/compatibility_matrix.md
        # 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")
            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

        # 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

        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,
            cuda_graph_sizes=self.cuda_graph_sizes,
            num_lookahead_slots=num_lookahead_slots,
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
            disable_chunked_mm_input=self.disable_chunked_mm_input,
            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,
            scheduler_cls=self.scheduler_cls,
            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,
            disable_hybrid_kv_cache_manager=self.
            disable_hybrid_kv_cache_manager,
        )

        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

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

        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(
            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
            reasoning_backend=self.reasoning_parser
        )

        observability_config = ObservabilityConfig(
            show_hidden_metrics_for_version=self.
            show_hidden_metrics_for_version,
            otlp_traces_endpoint=self.otlp_traces_endpoint,
            collect_detailed_traces=self.collect_detailed_traces,
        )

        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,
            kv_events_config=self.kv_events_config,
            additional_config=self.additional_config,
        )

        return config

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

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

        if self.guided_decoding_backend not in get_args(
                GuidedDecodingBackendV1):
            _raise_or_fallback(
                feature_name=
                f"--guided-decoding-backend={self.guided_decoding_backend}",
                recommend_to_remove=False)
            return False

        # Need at least Ampere for now (FA support required).
        # 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).
        from vllm.platforms import current_platform
        if (current_platform.is_cuda()
                and current_platform.get_device_capability()
                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":
            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 current_platform.is_rocm():
                supported = True
            elif fp8_attention and will_use_fa:
                from vllm.attention.utils.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

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

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

        # 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

        # 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

        # V1 mamba models are unoptimized.
        if model_config.has_inner_state and _warn_or_fallback(
                feature_name="Mamba"):
            return False

        # No Concurrent Partial Prefills so far.
        if (self.max_num_partial_prefills
                != SchedulerConfig.max_num_partial_prefills
                or self.max_long_partial_prefills
                != SchedulerConfig.max_long_partial_prefills):
            _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

        # V1 supports N-gram, Medusa, and Eagle speculative decoding.
        is_ngram_enabled = False
        is_eagle_enabled = False
        is_medusa_enabled = False
        if self.speculative_config is not None:
            # This is supported but experimental (handled below).
            speculative_method = self.speculative_config.get("method")
            if speculative_method:
                if speculative_method in ("ngram", "[ngram]"):
                    is_ngram_enabled = True
                elif speculative_method == "medusa":
                    is_medusa_enabled = True
                elif speculative_method in ("eagle", "eagle3", "deepseek_mtp"):
                    is_eagle_enabled = True
            else:
                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 or is_medusa_enabled):
                # Other speculative decoding methods are not supported yet.
                _raise_or_fallback(feature_name="Speculative Decoding",
                                   recommend_to_remove=False)
                return False

        # No XFormers so far.
        V1_BACKENDS = [
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
            "CUTLASS_MLA_VLLM_V1",
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
            "ROCM_AITER_MLA",
            "TORCH_SDPA_VLLM_V1",
            "FLEX_ATTENTION",
        ]
        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

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

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

        if (self.pipeline_parallel_size > 1
                and self.distributed_executor_backend
                not in (ParallelConfig.distributed_executor_backend, "ray",
                        "mp", "external_launcher")):
            name = "Pipeline Parallelism without Ray distributed executor " \
                    "or multiprocessing executor or external launcher"
            _raise_or_fallback(feature_name=name, recommend_to_remove=False)
            return False

        # The platform may be supported on V1, but off by default for now.
        if not current_platform.default_v1(  # noqa: SIM103
                model_config=model_config) and _warn_or_fallback(
                    current_platform.device_name):
            return False

        if (current_platform.is_cpu()
                and model_config.get_sliding_window() is not None):
            _raise_or_fallback(feature_name="sliding window (CPU backend)",
                               recommend_to_remove=False)
            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)
                use_spec_decode = self.speculative_config 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"):
                    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)

        # 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 == "sha256":
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")

        # 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,
                             model_config: ModelConfig) -> None:
        """Set Default Arguments for V1 Engine."""

        # V1 always uses chunked prefills and prefix caching
        # for non-pooling tasks.
        # For pooling tasks the default is False
        if model_config.runner_type != "pooling":
            self.enable_chunked_prefill = True
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = True
        else:

            pooling_type = model_config.pooler_config.pooling_type

            # TODO: when encoder models are supported we'll have to
            # check for causal attention here.
            incremental_prefill_supported = (pooling_type is not None and
                                             pooling_type.lower() == "last")

            action = "Enabling" if \
                incremental_prefill_supported else "Disabling"

            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = incremental_prefill_supported
                logger.info("(%s) chunked prefill by default", action)
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = incremental_prefill_supported
                logger.info("(%s) prefix caching by default", action)

        if not self.enable_chunked_prefill:
            self.max_num_batched_tokens = model_config.max_model_len

        # 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:
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"

        # When no user override, set the default values based on the usage
        # context.
        # Use different default values for different hardware.

        # 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.
        from vllm.platforms import current_platform
        try:
            device_memory = current_platform.get_device_total_memory()
            device_name = current_platform.get_device_name().lower()
        except Exception:
            # This is only used to set default_max_num_batched_tokens
            device_memory = 0

        # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
        # throughput, see PR #17885 for more details.
        # So here we do an extra device name check to prevent such regression.
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
            # For GPUs like H100 and MI300x, use larger default values.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }

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

        # cpu specific default values.
        if current_platform.is_cpu():
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 4096,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 128,
                UsageContext.OPENAI_API_SERVER: 32,
            }

        use_context_value = usage_context.value if usage_context else None
        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
            if current_platform.is_tpu():
                chip_name = current_platform.get_device_name()
                if chip_name in default_max_num_batched_tokens_tpu[
                        usage_context]:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens_tpu[
                            usage_context][chip_name]
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
            else:
                self.max_num_batched_tokens = default_max_num_batched_tokens[
                    usage_context]
            logger.debug(
                "Setting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens, use_context_value)

        if (self.max_num_seqs is None
                and usage_context in default_max_num_seqs):
            self.max_num_seqs = default_max_num_seqs[usage_context]

            logger.debug("Setting max_num_seqs to %d for %s usage context.",
                         self.max_num_seqs, use_context_value)

additional_config class-attribute instance-attribute

additional_config: dict[str, Any] = get_field(
    VllmConfig, "additional_config"
)

allowed_local_media_path class-attribute instance-attribute

allowed_local_media_path: str = allowed_local_media_path

block_size class-attribute instance-attribute

block_size: Optional[BlockSize] = block_size

calculate_kv_scales class-attribute instance-attribute

calculate_kv_scales: bool = calculate_kv_scales

code_revision class-attribute instance-attribute

code_revision: Optional[str] = code_revision

collect_detailed_traces class-attribute instance-attribute

collect_detailed_traces: Optional[
    list[DetailedTraceModules]
] = collect_detailed_traces

compilation_config class-attribute instance-attribute

compilation_config: CompilationConfig = get_field(
    VllmConfig, "compilation_config"
)

config_format class-attribute instance-attribute

config_format: str = config_format

cpu_offload_gb class-attribute instance-attribute

cpu_offload_gb: float = cpu_offload_gb

cuda_graph_sizes class-attribute instance-attribute

cuda_graph_sizes: list[int] = get_field(
    SchedulerConfig, "cuda_graph_sizes"
)

data_parallel_address class-attribute instance-attribute

data_parallel_address: Optional[str] = None

data_parallel_backend class-attribute instance-attribute

data_parallel_backend: str = data_parallel_backend

data_parallel_rank class-attribute instance-attribute

data_parallel_rank: Optional[int] = None

data_parallel_rpc_port class-attribute instance-attribute

data_parallel_rpc_port: Optional[int] = None

data_parallel_size class-attribute instance-attribute

data_parallel_size: int = data_parallel_size

data_parallel_size_local class-attribute instance-attribute

data_parallel_size_local: Optional[int] = None

device class-attribute instance-attribute

device: Device = device

disable_async_output_proc class-attribute instance-attribute

disable_async_output_proc: bool = not use_async_output_proc

disable_cascade_attn class-attribute instance-attribute

disable_cascade_attn: bool = disable_cascade_attn

disable_chunked_mm_input class-attribute instance-attribute

disable_chunked_mm_input: bool = disable_chunked_mm_input

disable_custom_all_reduce class-attribute instance-attribute

disable_custom_all_reduce: bool = disable_custom_all_reduce

disable_hybrid_kv_cache_manager class-attribute instance-attribute

disable_hybrid_kv_cache_manager: bool = (
    disable_hybrid_kv_cache_manager
)

disable_log_stats class-attribute instance-attribute

disable_log_stats: bool = False

disable_mm_preprocessor_cache class-attribute instance-attribute

disable_mm_preprocessor_cache: bool = (
    disable_mm_preprocessor_cache
)

disable_sliding_window class-attribute instance-attribute

disable_sliding_window: bool = disable_sliding_window

distributed_executor_backend class-attribute instance-attribute

distributed_executor_backend: Optional[
    Union[DistributedExecutorBackend, Type[ExecutorBase]]
] = distributed_executor_backend

download_dir class-attribute instance-attribute

download_dir: Optional[str] = download_dir

dtype class-attribute instance-attribute

dtype: ModelDType = dtype

enable_chunked_prefill class-attribute instance-attribute

enable_chunked_prefill: Optional[bool] = (
    enable_chunked_prefill
)

enable_eplb class-attribute instance-attribute

enable_eplb: bool = enable_eplb

enable_expert_parallel class-attribute instance-attribute

enable_expert_parallel: bool = enable_expert_parallel

enable_lora class-attribute instance-attribute

enable_lora: bool = False

enable_lora_bias class-attribute instance-attribute

enable_lora_bias: bool = bias_enabled

enable_multimodal_encoder_data_parallel class-attribute instance-attribute

enable_multimodal_encoder_data_parallel: bool = (
    enable_multimodal_encoder_data_parallel
)

enable_prefix_caching class-attribute instance-attribute

enable_prefix_caching: Optional[bool] = (
    enable_prefix_caching
)

enable_prompt_adapter class-attribute instance-attribute

enable_prompt_adapter: bool = False

enable_prompt_embeds class-attribute instance-attribute

enable_prompt_embeds: bool = enable_prompt_embeds

enable_reasoning class-attribute instance-attribute

enable_reasoning: Optional[bool] = None

enable_sleep_mode class-attribute instance-attribute

enable_sleep_mode: bool = enable_sleep_mode

enforce_eager class-attribute instance-attribute

enforce_eager: bool = enforce_eager

eplb_log_balancedness class-attribute instance-attribute

eplb_log_balancedness: bool = eplb_log_balancedness

eplb_step_interval class-attribute instance-attribute

eplb_step_interval: int = eplb_step_interval

eplb_window_size class-attribute instance-attribute

eplb_window_size: int = eplb_window_size

fully_sharded_loras class-attribute instance-attribute

fully_sharded_loras: bool = fully_sharded_loras

generation_config class-attribute instance-attribute

generation_config: str = generation_config

gpu_memory_utilization class-attribute instance-attribute

gpu_memory_utilization: float = gpu_memory_utilization

guided_decoding_backend class-attribute instance-attribute

guided_decoding_backend: GuidedDecodingBackend = backend

guided_decoding_disable_additional_properties class-attribute instance-attribute

guided_decoding_disable_additional_properties: bool = (
    disable_additional_properties
)

guided_decoding_disable_any_whitespace class-attribute instance-attribute

guided_decoding_disable_any_whitespace: bool = (
    disable_any_whitespace
)

guided_decoding_disable_fallback class-attribute instance-attribute

guided_decoding_disable_fallback: bool = disable_fallback

hf_config_path class-attribute instance-attribute

hf_config_path: Optional[str] = hf_config_path

hf_overrides class-attribute instance-attribute

hf_overrides: HfOverrides = get_field(
    ModelConfig, "hf_overrides"
)

hf_token class-attribute instance-attribute

hf_token: Optional[Union[bool, str]] = hf_token

ignore_patterns class-attribute instance-attribute

ignore_patterns: Optional[Union[str, List[str]]] = (
    ignore_patterns
)

kv_cache_dtype class-attribute instance-attribute

kv_cache_dtype: CacheDType = cache_dtype

kv_events_config class-attribute instance-attribute

kv_events_config: Optional[KVEventsConfig] = None

kv_transfer_config class-attribute instance-attribute

kv_transfer_config: Optional[KVTransferConfig] = None

limit_mm_per_prompt class-attribute instance-attribute

limit_mm_per_prompt: dict[str, int] = get_field(
    MultiModalConfig, "limit_per_prompt"
)

load_format class-attribute instance-attribute

load_format: str = load_format

logits_processor_pattern class-attribute instance-attribute

logits_processor_pattern: Optional[str] = (
    logits_processor_pattern
)

long_lora_scaling_factors class-attribute instance-attribute

long_lora_scaling_factors: Optional[tuple[float, ...]] = (
    long_lora_scaling_factors
)

long_prefill_token_threshold class-attribute instance-attribute

long_prefill_token_threshold: int = (
    long_prefill_token_threshold
)

lora_dtype class-attribute instance-attribute

lora_dtype: Optional[Union[str, dtype]] = lora_dtype

lora_extra_vocab_size class-attribute instance-attribute

lora_extra_vocab_size: int = lora_extra_vocab_size

max_cpu_loras class-attribute instance-attribute

max_cpu_loras: Optional[int] = max_cpu_loras

max_logprobs class-attribute instance-attribute

max_logprobs: int = max_logprobs

max_long_partial_prefills class-attribute instance-attribute

max_long_partial_prefills: int = max_long_partial_prefills

max_lora_rank class-attribute instance-attribute

max_lora_rank: int = max_lora_rank

max_loras class-attribute instance-attribute

max_loras: int = max_loras

max_model_len class-attribute instance-attribute

max_model_len: Optional[int] = max_model_len

max_num_batched_tokens class-attribute instance-attribute

max_num_batched_tokens: Optional[int] = (
    max_num_batched_tokens
)

max_num_partial_prefills class-attribute instance-attribute

max_num_partial_prefills: int = max_num_partial_prefills

max_num_seqs class-attribute instance-attribute

max_num_seqs: Optional[int] = max_num_seqs

max_parallel_loading_workers class-attribute instance-attribute

max_parallel_loading_workers: Optional[int] = (
    max_parallel_loading_workers
)

max_prompt_adapter_token class-attribute instance-attribute

max_prompt_adapter_token: int = max_prompt_adapter_token

max_prompt_adapters class-attribute instance-attribute

max_prompt_adapters: int = max_prompt_adapters

max_seq_len_to_capture class-attribute instance-attribute

max_seq_len_to_capture: int = max_seq_len_to_capture

media_io_kwargs class-attribute instance-attribute

media_io_kwargs: dict[str, dict[str, Any]] = get_field(
    MultiModalConfig, "media_io_kwargs"
)

mm_processor_kwargs class-attribute instance-attribute

mm_processor_kwargs: Optional[Dict[str, Any]] = (
    mm_processor_kwargs
)

model class-attribute instance-attribute

model: str = model

model_impl class-attribute instance-attribute

model_impl: str = model_impl

model_loader_extra_config class-attribute instance-attribute

model_loader_extra_config: dict = get_field(
    LoadConfig, "model_loader_extra_config"
)

multi_step_stream_outputs class-attribute instance-attribute

multi_step_stream_outputs: bool = multi_step_stream_outputs

num_gpu_blocks_override class-attribute instance-attribute

num_gpu_blocks_override: Optional[int] = (
    num_gpu_blocks_override
)

num_lookahead_slots class-attribute instance-attribute

num_lookahead_slots: int = num_lookahead_slots

num_redundant_experts class-attribute instance-attribute

num_redundant_experts: int = num_redundant_experts

num_scheduler_steps class-attribute instance-attribute

num_scheduler_steps: int = num_scheduler_steps

otlp_traces_endpoint class-attribute instance-attribute

otlp_traces_endpoint: Optional[str] = otlp_traces_endpoint

override_attention_dtype class-attribute instance-attribute

override_attention_dtype: str = override_attention_dtype

override_generation_config class-attribute instance-attribute

override_generation_config: dict[str, Any] = get_field(
    ModelConfig, "override_generation_config"
)

override_neuron_config class-attribute instance-attribute

override_neuron_config: dict[str, Any] = get_field(
    ModelConfig, "override_neuron_config"
)

override_pooler_config class-attribute instance-attribute

override_pooler_config: Optional[
    Union[dict, PoolerConfig]
] = override_pooler_config

pipeline_parallel_size class-attribute instance-attribute

pipeline_parallel_size: int = pipeline_parallel_size

preemption_mode class-attribute instance-attribute

preemption_mode: Optional[str] = preemption_mode

prefix_caching_hash_algo class-attribute instance-attribute

prefix_caching_hash_algo: PrefixCachingHashAlgo = (
    prefix_caching_hash_algo
)

pt_load_map_location class-attribute instance-attribute

pt_load_map_location: str = pt_load_map_location

qlora_adapter_name_or_path class-attribute instance-attribute

qlora_adapter_name_or_path: Optional[str] = None

quantization class-attribute instance-attribute

quantization: Optional[QuantizationMethods] = quantization

ray_workers_use_nsight class-attribute instance-attribute

ray_workers_use_nsight: bool = ray_workers_use_nsight

reasoning_parser class-attribute instance-attribute

reasoning_parser: str = reasoning_backend

revision class-attribute instance-attribute

revision: Optional[str] = revision

rope_scaling class-attribute instance-attribute

rope_scaling: dict[str, Any] = get_field(
    ModelConfig, "rope_scaling"
)

rope_theta class-attribute instance-attribute

rope_theta: Optional[float] = rope_theta

scheduler_cls class-attribute instance-attribute

scheduler_cls: Union[str, Type[object]] = scheduler_cls

scheduler_delay_factor class-attribute instance-attribute

scheduler_delay_factor: float = delay_factor

scheduling_policy class-attribute instance-attribute

scheduling_policy: SchedulerPolicy = policy

seed class-attribute instance-attribute

seed: Optional[int] = seed

served_model_name class-attribute instance-attribute

served_model_name: Optional[Union[str, List[str]]] = (
    served_model_name
)

show_hidden_metrics_for_version class-attribute instance-attribute

show_hidden_metrics_for_version: Optional[str] = (
    show_hidden_metrics_for_version
)

skip_tokenizer_init class-attribute instance-attribute

skip_tokenizer_init: bool = skip_tokenizer_init

speculative_config class-attribute instance-attribute

speculative_config: Optional[Dict[str, Any]] = None

swap_space class-attribute instance-attribute

swap_space: float = swap_space

task class-attribute instance-attribute

task: TaskOption = task

tensor_parallel_size class-attribute instance-attribute

tensor_parallel_size: int = tensor_parallel_size

tokenizer class-attribute instance-attribute

tokenizer: Optional[str] = tokenizer

tokenizer_mode class-attribute instance-attribute

tokenizer_mode: TokenizerMode = tokenizer_mode

tokenizer_pool_extra_config class-attribute instance-attribute

tokenizer_pool_extra_config: dict = get_field(
    TokenizerPoolConfig, "extra_config"
)

tokenizer_pool_size class-attribute instance-attribute

tokenizer_pool_size: int = pool_size

tokenizer_pool_type class-attribute instance-attribute

tokenizer_pool_type: str = pool_type

tokenizer_revision class-attribute instance-attribute

tokenizer_revision: Optional[str] = tokenizer_revision

trust_remote_code class-attribute instance-attribute

trust_remote_code: bool = trust_remote_code

use_tqdm_on_load class-attribute instance-attribute

use_tqdm_on_load: bool = use_tqdm_on_load

use_v2_block_manager class-attribute instance-attribute

use_v2_block_manager: bool = True

worker_cls class-attribute instance-attribute

worker_cls: str = worker_cls

worker_extension_cls class-attribute instance-attribute

worker_extension_cls: str = worker_extension_cls

__init__

__init__(
    model: str = model,
    served_model_name: Optional[
        Union[str, List[str]]
    ] = served_model_name,
    tokenizer: Optional[str] = tokenizer,
    hf_config_path: Optional[str] = hf_config_path,
    task: TaskOption = task,
    skip_tokenizer_init: bool = skip_tokenizer_init,
    enable_prompt_embeds: bool = enable_prompt_embeds,
    tokenizer_mode: TokenizerMode = tokenizer_mode,
    trust_remote_code: bool = trust_remote_code,
    allowed_local_media_path: str = allowed_local_media_path,
    download_dir: Optional[str] = download_dir,
    load_format: str = load_format,
    config_format: str = config_format,
    dtype: ModelDType = dtype,
    kv_cache_dtype: CacheDType = cache_dtype,
    seed: Optional[int] = seed,
    max_model_len: Optional[int] = max_model_len,
    cuda_graph_sizes: list[int] = get_field(
        SchedulerConfig, "cuda_graph_sizes"
    ),
    distributed_executor_backend: Optional[
        Union[
            DistributedExecutorBackend, Type[ExecutorBase]
        ]
    ] = distributed_executor_backend,
    pipeline_parallel_size: int = pipeline_parallel_size,
    tensor_parallel_size: int = tensor_parallel_size,
    data_parallel_size: int = data_parallel_size,
    data_parallel_rank: Optional[int] = None,
    data_parallel_size_local: Optional[int] = None,
    data_parallel_address: Optional[str] = None,
    data_parallel_rpc_port: Optional[int] = None,
    data_parallel_backend: str = data_parallel_backend,
    enable_expert_parallel: bool = enable_expert_parallel,
    enable_eplb: bool = enable_eplb,
    num_redundant_experts: int = num_redundant_experts,
    eplb_window_size: int = eplb_window_size,
    eplb_step_interval: int = eplb_step_interval,
    eplb_log_balancedness: bool = eplb_log_balancedness,
    max_parallel_loading_workers: Optional[
        int
    ] = max_parallel_loading_workers,
    block_size: Optional[BlockSize] = block_size,
    enable_prefix_caching: Optional[
        bool
    ] = enable_prefix_caching,
    prefix_caching_hash_algo: PrefixCachingHashAlgo = prefix_caching_hash_algo,
    disable_sliding_window: bool = disable_sliding_window,
    disable_cascade_attn: bool = disable_cascade_attn,
    use_v2_block_manager: bool = True,
    swap_space: float = swap_space,
    cpu_offload_gb: float = cpu_offload_gb,
    gpu_memory_utilization: float = gpu_memory_utilization,
    max_num_batched_tokens: Optional[
        int
    ] = max_num_batched_tokens,
    max_num_partial_prefills: int = max_num_partial_prefills,
    max_long_partial_prefills: int = max_long_partial_prefills,
    long_prefill_token_threshold: int = long_prefill_token_threshold,
    max_num_seqs: Optional[int] = max_num_seqs,
    max_logprobs: int = max_logprobs,
    disable_log_stats: bool = False,
    revision: Optional[str] = revision,
    code_revision: Optional[str] = code_revision,
    rope_scaling: dict[str, Any] = get_field(
        ModelConfig, "rope_scaling"
    ),
    rope_theta: Optional[float] = rope_theta,
    hf_token: Optional[Union[bool, str]] = hf_token,
    hf_overrides: HfOverrides = get_field(
        ModelConfig, "hf_overrides"
    ),
    tokenizer_revision: Optional[str] = tokenizer_revision,
    quantization: Optional[
        QuantizationMethods
    ] = quantization,
    enforce_eager: bool = enforce_eager,
    max_seq_len_to_capture: int = max_seq_len_to_capture,
    disable_custom_all_reduce: bool = disable_custom_all_reduce,
    tokenizer_pool_size: int = pool_size,
    tokenizer_pool_type: str = pool_type,
    tokenizer_pool_extra_config: dict = get_field(
        TokenizerPoolConfig, "extra_config"
    ),
    limit_mm_per_prompt: dict[str, int] = get_field(
        MultiModalConfig, "limit_per_prompt"
    ),
    media_io_kwargs: dict[str, dict[str, Any]] = get_field(
        MultiModalConfig, "media_io_kwargs"
    ),
    mm_processor_kwargs: Optional[
        Dict[str, Any]
    ] = mm_processor_kwargs,
    disable_mm_preprocessor_cache: bool = disable_mm_preprocessor_cache,
    enable_lora: bool = False,
    enable_lora_bias: bool = bias_enabled,
    max_loras: int = max_loras,
    max_lora_rank: int = max_lora_rank,
    fully_sharded_loras: bool = fully_sharded_loras,
    max_cpu_loras: Optional[int] = max_cpu_loras,
    lora_dtype: Optional[Union[str, dtype]] = lora_dtype,
    lora_extra_vocab_size: int = lora_extra_vocab_size,
    long_lora_scaling_factors: Optional[
        tuple[float, ...]
    ] = long_lora_scaling_factors,
    enable_prompt_adapter: bool = False,
    max_prompt_adapters: int = max_prompt_adapters,
    max_prompt_adapter_token: int = max_prompt_adapter_token,
    device: Device = device,
    num_scheduler_steps: int = num_scheduler_steps,
    multi_step_stream_outputs: bool = multi_step_stream_outputs,
    ray_workers_use_nsight: bool = ray_workers_use_nsight,
    num_gpu_blocks_override: Optional[
        int
    ] = num_gpu_blocks_override,
    num_lookahead_slots: int = num_lookahead_slots,
    model_loader_extra_config: dict = get_field(
        LoadConfig, "model_loader_extra_config"
    ),
    ignore_patterns: Optional[
        Union[str, List[str]]
    ] = ignore_patterns,
    preemption_mode: Optional[str] = preemption_mode,
    scheduler_delay_factor: float = delay_factor,
    enable_chunked_prefill: Optional[
        bool
    ] = enable_chunked_prefill,
    disable_chunked_mm_input: bool = disable_chunked_mm_input,
    disable_hybrid_kv_cache_manager: bool = disable_hybrid_kv_cache_manager,
    guided_decoding_backend: GuidedDecodingBackend = backend,
    guided_decoding_disable_fallback: bool = disable_fallback,
    guided_decoding_disable_any_whitespace: bool = disable_any_whitespace,
    guided_decoding_disable_additional_properties: bool = disable_additional_properties,
    logits_processor_pattern: Optional[
        str
    ] = logits_processor_pattern,
    speculative_config: Optional[Dict[str, Any]] = None,
    qlora_adapter_name_or_path: Optional[str] = None,
    show_hidden_metrics_for_version: Optional[
        str
    ] = show_hidden_metrics_for_version,
    otlp_traces_endpoint: Optional[
        str
    ] = otlp_traces_endpoint,
    collect_detailed_traces: Optional[
        list[DetailedTraceModules]
    ] = collect_detailed_traces,
    disable_async_output_proc: bool = not use_async_output_proc,
    scheduling_policy: SchedulerPolicy = policy,
    scheduler_cls: Union[str, Type[object]] = scheduler_cls,
    override_neuron_config: dict[str, Any] = get_field(
        ModelConfig, "override_neuron_config"
    ),
    override_pooler_config: Optional[
        Union[dict, PoolerConfig]
    ] = override_pooler_config,
    compilation_config: CompilationConfig = get_field(
        VllmConfig, "compilation_config"
    ),
    worker_cls: str = worker_cls,
    worker_extension_cls: str = worker_extension_cls,
    kv_transfer_config: Optional[KVTransferConfig] = None,
    kv_events_config: Optional[KVEventsConfig] = None,
    generation_config: str = generation_config,
    enable_sleep_mode: bool = enable_sleep_mode,
    override_generation_config: dict[str, Any] = get_field(
        ModelConfig, "override_generation_config"
    ),
    model_impl: str = model_impl,
    override_attention_dtype: str = override_attention_dtype,
    calculate_kv_scales: bool = calculate_kv_scales,
    additional_config: dict[str, Any] = get_field(
        VllmConfig, "additional_config"
    ),
    enable_reasoning: Optional[bool] = None,
    reasoning_parser: str = reasoning_backend,
    use_tqdm_on_load: bool = use_tqdm_on_load,
    pt_load_map_location: str = pt_load_map_location,
    enable_multimodal_encoder_data_parallel: bool = enable_multimodal_encoder_data_parallel,
) -> None

__post_init__

__post_init__()

Source code in vllm/engine/arg_utils.py

def __post_init__(self):
    # 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))
    if self.qlora_adapter_name_or_path is not None:
        warnings.warn(
            "The `qlora_adapter_name_or_path` is deprecated "
            "and will be removed in v0.10.0. ",
            DeprecationWarning,
            stacklevel=2,
        )
    # Setup plugins
    from vllm.plugins import load_general_plugins
    load_general_plugins()

_is_v1_supported_oracle

_is_v1_supported_oracle(model_config: ModelConfig) -> bool

Oracle for whether to use V0 or V1 Engine by default.

Source code in vllm/engine/arg_utils.py

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

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

    if self.guided_decoding_backend not in get_args(
            GuidedDecodingBackendV1):
        _raise_or_fallback(
            feature_name=
            f"--guided-decoding-backend={self.guided_decoding_backend}",
            recommend_to_remove=False)
        return False

    # Need at least Ampere for now (FA support required).
    # 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).
    from vllm.platforms import current_platform
    if (current_platform.is_cuda()
            and current_platform.get_device_capability()
            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":
        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 current_platform.is_rocm():
            supported = True
        elif fp8_attention and will_use_fa:
            from vllm.attention.utils.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

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

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

    # 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

    # 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

    # V1 mamba models are unoptimized.
    if model_config.has_inner_state and _warn_or_fallback(
            feature_name="Mamba"):
        return False

    # No Concurrent Partial Prefills so far.
    if (self.max_num_partial_prefills
            != SchedulerConfig.max_num_partial_prefills
            or self.max_long_partial_prefills
            != SchedulerConfig.max_long_partial_prefills):
        _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

    # V1 supports N-gram, Medusa, and Eagle speculative decoding.
    is_ngram_enabled = False
    is_eagle_enabled = False
    is_medusa_enabled = False
    if self.speculative_config is not None:
        # This is supported but experimental (handled below).
        speculative_method = self.speculative_config.get("method")
        if speculative_method:
            if speculative_method in ("ngram", "[ngram]"):
                is_ngram_enabled = True
            elif speculative_method == "medusa":
                is_medusa_enabled = True
            elif speculative_method in ("eagle", "eagle3", "deepseek_mtp"):
                is_eagle_enabled = True
        else:
            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 or is_medusa_enabled):
            # Other speculative decoding methods are not supported yet.
            _raise_or_fallback(feature_name="Speculative Decoding",
                               recommend_to_remove=False)
            return False

    # No XFormers so far.
    V1_BACKENDS = [
        "FLASH_ATTN_VLLM_V1",
        "FLASH_ATTN",
        "PALLAS",
        "PALLAS_VLLM_V1",
        "TRITON_ATTN_VLLM_V1",
        "TRITON_MLA",
        "CUTLASS_MLA_VLLM_V1",
        "FLASHMLA",
        "FLASHINFER",
        "FLASHINFER_VLLM_V1",
        "ROCM_AITER_MLA",
        "TORCH_SDPA_VLLM_V1",
        "FLEX_ATTENTION",
    ]
    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

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

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

    if (self.pipeline_parallel_size > 1
            and self.distributed_executor_backend
            not in (ParallelConfig.distributed_executor_backend, "ray",
                    "mp", "external_launcher")):
        name = "Pipeline Parallelism without Ray distributed executor " \
                "or multiprocessing executor or external launcher"
        _raise_or_fallback(feature_name=name, recommend_to_remove=False)
        return False

    # The platform may be supported on V1, but off by default for now.
    if not current_platform.default_v1(  # noqa: SIM103
            model_config=model_config) and _warn_or_fallback(
                current_platform.device_name):
        return False

    if (current_platform.is_cpu()
            and model_config.get_sliding_window() is not None):
        _raise_or_fallback(feature_name="sliding window (CPU backend)",
                           recommend_to_remove=False)
        return False

    #############################################################

    return True

_set_default_args_v0

_set_default_args_v0(model_config: ModelConfig) -> None

Set Default Arguments for V0 Engine.

Source code in vllm/engine/arg_utils.py

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)
            use_spec_decode = self.speculative_config 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"):
                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)

    # 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 == "sha256":
            raise ValueError(
                "sha256 is not supported for prefix caching in V0 engine. "
                "Please use 'builtin'.")

    # Set max_num_seqs to 256 for VLLM_V0.
    if self.max_num_seqs is None:
        self.max_num_seqs = 256

_set_default_args_v1

_set_default_args_v1(
    usage_context: UsageContext, model_config: ModelConfig
) -> None

Set Default Arguments for V1 Engine.

Source code in vllm/engine/arg_utils.py

def _set_default_args_v1(self, usage_context: UsageContext,
                         model_config: ModelConfig) -> None:
    """Set Default Arguments for V1 Engine."""

    # V1 always uses chunked prefills and prefix caching
    # for non-pooling tasks.
    # For pooling tasks the default is False
    if model_config.runner_type != "pooling":
        self.enable_chunked_prefill = True
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = True
    else:

        pooling_type = model_config.pooler_config.pooling_type

        # TODO: when encoder models are supported we'll have to
        # check for causal attention here.
        incremental_prefill_supported = (pooling_type is not None and
                                         pooling_type.lower() == "last")

        action = "Enabling" if \
            incremental_prefill_supported else "Disabling"

        if self.enable_chunked_prefill is None:
            self.enable_chunked_prefill = incremental_prefill_supported
            logger.info("(%s) chunked prefill by default", action)
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = incremental_prefill_supported
            logger.info("(%s) prefix caching by default", action)

    if not self.enable_chunked_prefill:
        self.max_num_batched_tokens = model_config.max_model_len

    # 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:
        self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"

    # When no user override, set the default values based on the usage
    # context.
    # Use different default values for different hardware.

    # 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.
    from vllm.platforms import current_platform
    try:
        device_memory = current_platform.get_device_total_memory()
        device_name = current_platform.get_device_name().lower()
    except Exception:
        # This is only used to set default_max_num_batched_tokens
        device_memory = 0

    # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
    # throughput, see PR #17885 for more details.
    # So here we do an extra device name check to prevent such regression.
    if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
        # For GPUs like H100 and MI300x, use larger default values.
        default_max_num_batched_tokens = {
            UsageContext.LLM_CLASS: 16384,
            UsageContext.OPENAI_API_SERVER: 8192,
        }
        default_max_num_seqs = {
            UsageContext.LLM_CLASS: 1024,
            UsageContext.OPENAI_API_SERVER: 1024,
        }
    else:
        # TODO(woosuk): Tune the default values for other hardware.
        default_max_num_batched_tokens = {
            UsageContext.LLM_CLASS: 8192,
            UsageContext.OPENAI_API_SERVER: 2048,
        }
        default_max_num_seqs = {
            UsageContext.LLM_CLASS: 256,
            UsageContext.OPENAI_API_SERVER: 256,
        }

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

    # cpu specific default values.
    if current_platform.is_cpu():
        default_max_num_batched_tokens = {
            UsageContext.LLM_CLASS: 4096,
            UsageContext.OPENAI_API_SERVER: 2048,
        }
        default_max_num_seqs = {
            UsageContext.LLM_CLASS: 128,
            UsageContext.OPENAI_API_SERVER: 32,
        }

    use_context_value = usage_context.value if usage_context else None
    if (self.max_num_batched_tokens is None
            and usage_context in default_max_num_batched_tokens):
        if current_platform.is_tpu():
            chip_name = current_platform.get_device_name()
            if chip_name in default_max_num_batched_tokens_tpu[
                    usage_context]:
                self.max_num_batched_tokens = \
                    default_max_num_batched_tokens_tpu[
                        usage_context][chip_name]
            else:
                self.max_num_batched_tokens = \
                    default_max_num_batched_tokens[usage_context]
        else:
            self.max_num_batched_tokens = default_max_num_batched_tokens[
                usage_context]
        logger.debug(
            "Setting max_num_batched_tokens to %d for %s usage context.",
            self.max_num_batched_tokens, use_context_value)

    if (self.max_num_seqs is None
            and usage_context in default_max_num_seqs):
        self.max_num_seqs = default_max_num_seqs[usage_context]

        logger.debug("Setting max_num_seqs to %d for %s usage context.",
                     self.max_num_seqs, use_context_value)

add_cli_args staticmethod

add_cli_args(
    parser: FlexibleArgumentParser,
) -> FlexibleArgumentParser

Shared CLI arguments for vLLM engine.

Source code in vllm/engine/arg_utils.py

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

    # Model arguments
    model_kwargs = get_kwargs(ModelConfig)
    model_group = parser.add_argument_group(
        title="ModelConfig",
        description=ModelConfig.__doc__,
    )
    if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
        model_group.add_argument("--model", **model_kwargs["model"])
    model_group.add_argument("--task", **model_kwargs["task"])
    model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
    model_group.add_argument("--tokenizer-mode",
                             **model_kwargs["tokenizer_mode"])
    model_group.add_argument("--trust-remote-code",
                             **model_kwargs["trust_remote_code"])
    model_group.add_argument("--dtype", **model_kwargs["dtype"])
    model_group.add_argument("--seed", **model_kwargs["seed"])
    model_group.add_argument("--hf-config-path",
                             **model_kwargs["hf_config_path"])
    model_group.add_argument("--allowed-local-media-path",
                             **model_kwargs["allowed_local_media_path"])
    model_group.add_argument("--revision", **model_kwargs["revision"])
    model_group.add_argument("--code-revision",
                             **model_kwargs["code_revision"])
    model_group.add_argument("--rope-scaling",
                             **model_kwargs["rope_scaling"])
    model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
    model_group.add_argument("--tokenizer-revision",
                             **model_kwargs["tokenizer_revision"])
    model_group.add_argument("--max-model-len",
                             **model_kwargs["max_model_len"])
    model_group.add_argument("--quantization", "-q",
                             **model_kwargs["quantization"])
    model_group.add_argument("--enforce-eager",
                             **model_kwargs["enforce_eager"])
    model_group.add_argument("--max-seq-len-to-capture",
                             **model_kwargs["max_seq_len_to_capture"])
    model_group.add_argument("--max-logprobs",
                             **model_kwargs["max_logprobs"])
    model_group.add_argument("--disable-sliding-window",
                             **model_kwargs["disable_sliding_window"])
    model_group.add_argument("--disable-cascade-attn",
                             **model_kwargs["disable_cascade_attn"])
    model_group.add_argument("--skip-tokenizer-init",
                             **model_kwargs["skip_tokenizer_init"])
    model_group.add_argument("--enable-prompt-embeds",
                             **model_kwargs["enable_prompt_embeds"])
    model_group.add_argument("--served-model-name",
                             **model_kwargs["served_model_name"])
    # This one is a special case because it is the
    # opposite of ModelConfig.use_async_output_proc
    model_group.add_argument(
        "--disable-async-output-proc",
        action="store_true",
        default=EngineArgs.disable_async_output_proc,
        help="Disable async output processing. This may result in "
        "lower performance.")
    model_group.add_argument("--config-format",
                             choices=[f.value for f in ConfigFormat],
                             **model_kwargs["config_format"])
    # This one is a special case because it can bool
    # or str. TODO: Handle this in get_kwargs
    model_group.add_argument("--hf-token",
                             type=str,
                             nargs="?",
                             const=True,
                             default=model_kwargs["hf_token"]["default"],
                             help=model_kwargs["hf_token"]["help"])
    model_group.add_argument("--hf-overrides",
                             **model_kwargs["hf_overrides"])
    model_group.add_argument("--override-neuron-config",
                             **model_kwargs["override_neuron_config"])
    model_group.add_argument("--override-pooler-config",
                             **model_kwargs["override_pooler_config"])
    model_group.add_argument("--logits-processor-pattern",
                             **model_kwargs["logits_processor_pattern"])
    model_group.add_argument("--generation-config",
                             **model_kwargs["generation_config"])
    model_group.add_argument("--override-generation-config",
                             **model_kwargs["override_generation_config"])
    model_group.add_argument("--enable-sleep-mode",
                             **model_kwargs["enable_sleep_mode"])
    model_group.add_argument("--model-impl",
                             choices=[f.value for f in ModelImpl],
                             **model_kwargs["model_impl"])
    model_group.add_argument("--override-attention-dtype",
                             **model_kwargs["override_attention_dtype"])

    # 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("--ignore-patterns",
                            **load_kwargs["ignore_patterns"])
    load_group.add_argument("--use-tqdm-on-load",
                            **load_kwargs["use_tqdm_on_load"])
    load_group.add_argument(
        "--qlora-adapter-name-or-path",
        type=str,
        default=None,
        help="The `--qlora-adapter-name-or-path` has no effect, do not set"
        " it, and it  will be removed in v0.10.0.",
        deprecated=True,
    )
    load_group.add_argument('--pt-load-map-location',
                            **load_kwargs["pt_load_map_location"])

    # Guided decoding arguments
    guided_decoding_kwargs = get_kwargs(DecodingConfig)
    guided_decoding_group = parser.add_argument_group(
        title="DecodingConfig",
        description=DecodingConfig.__doc__,
    )
    guided_decoding_group.add_argument("--guided-decoding-backend",
                                       **guided_decoding_kwargs["backend"])
    guided_decoding_group.add_argument(
        "--guided-decoding-disable-fallback",
        **guided_decoding_kwargs["disable_fallback"])
    guided_decoding_group.add_argument(
        "--guided-decoding-disable-any-whitespace",
        **guided_decoding_kwargs["disable_any_whitespace"])
    guided_decoding_group.add_argument(
        "--guided-decoding-disable-additional-properties",
        **guided_decoding_kwargs["disable_additional_properties"])
    guided_decoding_group.add_argument(
        "--enable-reasoning",
        action=argparse.BooleanOptionalAction,
        deprecated=True,
        help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
        "of v0.9.0. Use `--reasoning-parser` to specify the reasoning "
        "parser backend instead. This flag (`--enable-reasoning`) will be "
        "removed in v0.10.0. When `--reasoning-parser` is specified, "
        "reasoning mode is automatically enabled.")
    guided_decoding_group.add_argument(
        "--reasoning-parser",
        # This choices is a special case because it's not static
        choices=list(ReasoningParserManager.reasoning_parsers),
        **guided_decoding_kwargs["reasoning_backend"])

    # Parallel arguments
    parallel_kwargs = get_kwargs(ParallelConfig)
    parallel_group = parser.add_argument_group(
        title="ParallelConfig",
        description=ParallelConfig.__doc__,
    )
    parallel_group.add_argument(
        "--distributed-executor-backend",
        **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(
        '--data-parallel-rank',
        '-dpn',
        type=int,
        help='Data parallel rank of this instance. '
        'When set, enables external load balancer mode.')
    parallel_group.add_argument('--data-parallel-size-local',
                                '-dpl',
                                type=int,
                                help='Number of data parallel replicas '
                                'to run on this node.')
    parallel_group.add_argument('--data-parallel-address',
                                '-dpa',
                                type=str,
                                help='Address of data parallel cluster '
                                'head-node.')
    parallel_group.add_argument('--data-parallel-rpc-port',
                                '-dpp',
                                type=int,
                                help='Port for data parallel RPC '
                                'communication.')
    parallel_group.add_argument('--data-parallel-backend',
                                '-dpb',
                                type=str,
                                default='mp',
                                help='Backend for data parallel, either '
                                '"mp" or "ray".')
    parallel_group.add_argument(
        "--enable-expert-parallel",
        **parallel_kwargs["enable_expert_parallel"])
    parallel_group.add_argument("--enable-eplb",
                                **parallel_kwargs["enable_eplb"])
    parallel_group.add_argument("--num-redundant-experts",
                                **parallel_kwargs["num_redundant_experts"])
    parallel_group.add_argument("--eplb-window-size",
                                **parallel_kwargs["eplb_window_size"])
    parallel_group.add_argument("--eplb-step-interval",
                                **parallel_kwargs["eplb_step_interval"])
    parallel_group.add_argument("--eplb-log-balancedness",
                                **parallel_kwargs["eplb_log_balancedness"])
    parallel_group.add_argument(
        "--max-parallel-loading-workers",
        **parallel_kwargs["max_parallel_loading_workers"])
    parallel_group.add_argument(
        "--ray-workers-use-nsight",
        **parallel_kwargs["ray_workers_use_nsight"])
    parallel_group.add_argument(
        "--disable-custom-all-reduce",
        **parallel_kwargs["disable_custom_all_reduce"])
    parallel_group.add_argument("--worker-cls",
                                **parallel_kwargs["worker_cls"])
    parallel_group.add_argument("--worker-extension-cls",
                                **parallel_kwargs["worker_extension_cls"])
    parallel_group.add_argument(
        "--enable-multimodal-encoder-data-parallel",
        **parallel_kwargs["enable_multimodal_encoder_data_parallel"])

    # KV cache arguments
    cache_kwargs = get_kwargs(CacheConfig)
    cache_group = parser.add_argument_group(
        title="CacheConfig",
        description=CacheConfig.__doc__,
    )
    cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
    cache_group.add_argument("--gpu-memory-utilization",
                             **cache_kwargs["gpu_memory_utilization"])
    cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
    cache_group.add_argument("--kv-cache-dtype",
                             **cache_kwargs["cache_dtype"])
    cache_group.add_argument("--num-gpu-blocks-override",
                             **cache_kwargs["num_gpu_blocks_override"])
    cache_group.add_argument("--enable-prefix-caching",
                             **cache_kwargs["enable_prefix_caching"])
    cache_group.add_argument("--prefix-caching-hash-algo",
                             **cache_kwargs["prefix_caching_hash_algo"])
    cache_group.add_argument("--cpu-offload-gb",
                             **cache_kwargs["cpu_offload_gb"])
    cache_group.add_argument("--calculate-kv-scales",
                             **cache_kwargs["calculate_kv_scales"])

    # Tokenizer arguments
    tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
    tokenizer_group = parser.add_argument_group(
        title="TokenizerPoolConfig",
        description=TokenizerPoolConfig.__doc__,
    )
    tokenizer_group.add_argument("--tokenizer-pool-size",
                                 **tokenizer_kwargs["pool_size"])
    tokenizer_group.add_argument("--tokenizer-pool-type",
                                 **tokenizer_kwargs["pool_type"])
    tokenizer_group.add_argument("--tokenizer-pool-extra-config",
                                 **tokenizer_kwargs["extra_config"])

    # Multimodal related configs
    multimodal_kwargs = get_kwargs(MultiModalConfig)
    multimodal_group = parser.add_argument_group(
        title="MultiModalConfig",
        description=MultiModalConfig.__doc__,
    )
    multimodal_group.add_argument("--limit-mm-per-prompt",
                                  **multimodal_kwargs["limit_per_prompt"])
    multimodal_group.add_argument("--media-io-kwargs",
                                  **multimodal_kwargs["media_io_kwargs"])
    multimodal_group.add_argument(
        "--mm-processor-kwargs",
        **multimodal_kwargs["mm_processor_kwargs"])
    multimodal_group.add_argument(
        "--disable-mm-preprocessor-cache",
        **multimodal_kwargs["disable_mm_preprocessor_cache"])

    # LoRA related configs
    lora_kwargs = get_kwargs(LoRAConfig)
    lora_group = parser.add_argument_group(
        title="LoRAConfig",
        description=LoRAConfig.__doc__,
    )
    lora_group.add_argument(
        "--enable-lora",
        action=argparse.BooleanOptionalAction,
        help="If True, enable handling of LoRA adapters.")
    lora_group.add_argument("--enable-lora-bias",
                            **lora_kwargs["bias_enabled"])
    lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
    lora_group.add_argument("--max-lora-rank",
                            **lora_kwargs["max_lora_rank"])
    lora_group.add_argument("--lora-extra-vocab-size",
                            **lora_kwargs["lora_extra_vocab_size"])
    lora_group.add_argument(
        "--lora-dtype",
        **lora_kwargs["lora_dtype"],
    )
    lora_group.add_argument("--long-lora-scaling-factors",
                            **lora_kwargs["long_lora_scaling_factors"])
    lora_group.add_argument("--max-cpu-loras",
                            **lora_kwargs["max_cpu_loras"])
    lora_group.add_argument("--fully-sharded-loras",
                            **lora_kwargs["fully_sharded_loras"])

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

    # Device arguments
    device_kwargs = get_kwargs(DeviceConfig)
    device_group = parser.add_argument_group(
        title="DeviceConfig",
        description=DeviceConfig.__doc__,
    )
    device_group.add_argument("--device",
                              **device_kwargs["device"],
                              deprecated=True)

    # Speculative arguments
    speculative_group = parser.add_argument_group(
        title="SpeculativeConfig",
        description=SpeculativeConfig.__doc__,
    )
    speculative_group.add_argument(
        "--speculative-config",
        type=json.loads,
        default=None,
        help="The configurations for speculative decoding. Should be a "
        "JSON string.")

    # Observability arguments
    observability_kwargs = get_kwargs(ObservabilityConfig)
    observability_group = parser.add_argument_group(
        title="ObservabilityConfig",
        description=ObservabilityConfig.__doc__,
    )
    observability_group.add_argument(
        "--show-hidden-metrics-for-version",
        **observability_kwargs["show_hidden_metrics_for_version"])
    observability_group.add_argument(
        "--otlp-traces-endpoint",
        **observability_kwargs["otlp_traces_endpoint"])
    # TODO: generalise this special case
    choices = observability_kwargs["collect_detailed_traces"]["choices"]
    metavar = f"{{{','.join(choices)}}}"
    observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
    observability_kwargs["collect_detailed_traces"]["choices"] += [
        ",".join(p)
        for p in permutations(get_args(DetailedTraceModules), r=2)
    ]
    observability_group.add_argument(
        "--collect-detailed-traces",
        **observability_kwargs["collect_detailed_traces"])

    # Scheduler arguments
    scheduler_kwargs = get_kwargs(SchedulerConfig)
    scheduler_group = parser.add_argument_group(
        title="SchedulerConfig",
        description=SchedulerConfig.__doc__,
    )
    scheduler_group.add_argument(
        "--max-num-batched-tokens",
        **scheduler_kwargs["max_num_batched_tokens"])
    scheduler_group.add_argument("--max-num-seqs",
                                 **scheduler_kwargs["max_num_seqs"])
    scheduler_group.add_argument(
        "--max-num-partial-prefills",
        **scheduler_kwargs["max_num_partial_prefills"])
    scheduler_group.add_argument(
        "--max-long-partial-prefills",
        **scheduler_kwargs["max_long_partial_prefills"])
    scheduler_group.add_argument('--cuda-graph-sizes',
                                 **scheduler_kwargs["cuda_graph_sizes"])
    scheduler_group.add_argument(
        "--long-prefill-token-threshold",
        **scheduler_kwargs["long_prefill_token_threshold"])
    scheduler_group.add_argument("--num-lookahead-slots",
                                 **scheduler_kwargs["num_lookahead_slots"])
    scheduler_group.add_argument("--scheduler-delay-factor",
                                 **scheduler_kwargs["delay_factor"])
    scheduler_group.add_argument("--preemption-mode",
                                 **scheduler_kwargs["preemption_mode"])
    scheduler_group.add_argument("--num-scheduler-steps",
                                 **scheduler_kwargs["num_scheduler_steps"])
    scheduler_group.add_argument(
        "--multi-step-stream-outputs",
        **scheduler_kwargs["multi_step_stream_outputs"])
    scheduler_group.add_argument("--scheduling-policy",
                                 **scheduler_kwargs["policy"])
    scheduler_group.add_argument(
        "--enable-chunked-prefill",
        **scheduler_kwargs["enable_chunked_prefill"])
    scheduler_group.add_argument(
        "--disable-chunked-mm-input",
        **scheduler_kwargs["disable_chunked_mm_input"])
    scheduler_group.add_argument("--scheduler-cls",
                                 **scheduler_kwargs["scheduler_cls"])
    scheduler_group.add_argument(
        "--disable-hybrid-kv-cache-manager",
        **scheduler_kwargs["disable_hybrid_kv_cache_manager"])

    # vLLM arguments
    vllm_kwargs = get_kwargs(VllmConfig)
    vllm_group = parser.add_argument_group(
        title="VllmConfig",
        description=VllmConfig.__doc__,
    )
    vllm_group.add_argument("--kv-transfer-config",
                            **vllm_kwargs["kv_transfer_config"])
    vllm_group.add_argument('--kv-events-config',
                            **vllm_kwargs["kv_events_config"])
    vllm_group.add_argument("--compilation-config", "-O",
                            **vllm_kwargs["compilation_config"])
    vllm_group.add_argument("--additional-config",
                            **vllm_kwargs["additional_config"])

    # Other arguments
    parser.add_argument('--use-v2-block-manager',
                        action='store_true',
                        default=True,
                        deprecated=True,
                        help='[DEPRECATED] block manager v1 has been '
                        'removed and SelfAttnBlockSpaceManager (i.e. '
                        'block manager v2) is now the default. '
                        'Setting this flag to True or False'
                        ' has no effect on vLLM behavior.')
    parser.add_argument('--disable-log-stats',
                        action='store_true',
                        help='Disable logging statistics.')

    return parser

create_engine_config

create_engine_config(
    usage_context: Optional[UsageContext] = None,
) -> VllmConfig

Create the VllmConfig.

NOTE: for autoselection of V0 vs V1 engine, we need to create the ModelConfig first, since ModelConfig's attrs (e.g. the model arch) are needed to make the decision.

This function set VLLM_USE_V1=X if VLLM_USE_V1 is unspecified by the user.

If VLLM_USE_V1 is specified by the user but the VllmConfig is incompatible, we raise an error.

Source code in vllm/engine/arg_utils.py

def create_engine_config(
    self,
    usage_context: Optional[UsageContext] = None,
) -> VllmConfig:
    """
    Create the VllmConfig.

    NOTE: for autoselection of V0 vs V1 engine, we need to
    create the ModelConfig first, since ModelConfig's attrs
    (e.g. the model arch) are needed to make the decision.

    This function set VLLM_USE_V1=X if VLLM_USE_V1 is
    unspecified by the user.

    If VLLM_USE_V1 is specified by the user but the VllmConfig
    is incompatible, we raise an error.
    """
    from vllm.platforms import current_platform
    current_platform.pre_register_and_update()

    device_config = DeviceConfig(
        device=cast(Device, current_platform.device_type))
    model_config = self.create_model_config()

    # * 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, model_config)
    else:
        self._set_default_args_v0(model_config)

    assert self.enable_chunked_prefill is not None

    if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
        assert self.enforce_eager, (
            "Cuda graph is not supported with DualChunkFlashAttention. "
            "To run the model in eager mode, set 'enforce_eager=True' "
            "or use '--enforce-eager' in the CLI.")
        assert current_platform.is_cuda(), (
            "DualChunkFlashAttention is only supported on CUDA platform.")
        assert not use_v1, (
            "DualChunkFlashAttention is not supported on V1 engine. "
            "To run the model in V0 engine, try set 'VLLM_USE_V1=0'")

    cache_config = CacheConfig(
        block_size=self.block_size,
        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,
        prefix_caching_hash_algo=self.prefix_caching_hash_algo,
        cpu_offload_gb=self.cpu_offload_gb,
        calculate_kv_scales=self.calculate_kv_scales,
    )

    # 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()

    data_parallel_external_lb = self.data_parallel_rank is not None
    if data_parallel_external_lb:
        assert self.data_parallel_size_local in (1, None), (
            "data_parallel_size_local must be 1 when data_parallel_rank "
            "is set")
        data_parallel_size_local = 1
    elif self.data_parallel_size_local is not None:
        data_parallel_size_local = self.data_parallel_size_local
    else:
        # Local DP size defaults to global DP size if not set.
        data_parallel_size_local = self.data_parallel_size

    # DP address, used in multi-node case for torch distributed group
    # and ZMQ sockets.
    if self.data_parallel_address is None:
        if self.data_parallel_backend == "ray":
            host_ip = get_ip()
            logger.info(
                "Using host IP %s as ray-based data parallel address",
                host_ip)
            data_parallel_address = host_ip
        else:
            assert self.data_parallel_backend == "mp", (
                "data_parallel_backend can only be ray or mp, got %s",
                self.data_parallel_backend)
            data_parallel_address = ParallelConfig.data_parallel_master_ip
    else:
        data_parallel_address = self.data_parallel_address

    # This port is only used when there are remote data parallel engines,
    # otherwise the local IPC transport is used.
    data_parallel_rpc_port = self.data_parallel_rpc_port if (
        self.data_parallel_rpc_port
        is not None) else ParallelConfig.data_parallel_rpc_port

    parallel_config = ParallelConfig(
        pipeline_parallel_size=self.pipeline_parallel_size,
        tensor_parallel_size=self.tensor_parallel_size,
        data_parallel_size=self.data_parallel_size,
        data_parallel_rank=self.data_parallel_rank or 0,
        data_parallel_external_lb=data_parallel_external_lb,
        data_parallel_size_local=data_parallel_size_local,
        data_parallel_master_ip=data_parallel_address,
        data_parallel_rpc_port=data_parallel_rpc_port,
        data_parallel_backend=self.data_parallel_backend,
        enable_expert_parallel=self.enable_expert_parallel,
        enable_eplb=self.enable_eplb,
        num_redundant_experts=self.num_redundant_experts,
        eplb_window_size=self.eplb_window_size,
        eplb_step_interval=self.eplb_step_interval,
        eplb_log_balancedness=self.eplb_log_balancedness,
        max_parallel_loading_workers=self.max_parallel_loading_workers,
        disable_custom_all_reduce=self.disable_custom_all_reduce,
        ray_workers_use_nsight=self.ray_workers_use_nsight,
        placement_group=placement_group,
        distributed_executor_backend=self.distributed_executor_backend,
        worker_cls=self.worker_cls,
        worker_extension_cls=self.worker_extension_cls,
        enable_multimodal_encoder_data_parallel=self.
        enable_multimodal_encoder_data_parallel,
    )

    speculative_config = self.create_speculative_config(
        target_model_config=model_config,
        target_parallel_config=parallel_config,
        enable_chunked_prefill=self.enable_chunked_prefill,
        disable_log_stats=self.disable_log_stats,
    )

    # Reminder: Please update docs/features/compatibility_matrix.md
    # 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")
        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

    # 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

    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,
        cuda_graph_sizes=self.cuda_graph_sizes,
        num_lookahead_slots=num_lookahead_slots,
        delay_factor=self.scheduler_delay_factor,
        enable_chunked_prefill=self.enable_chunked_prefill,
        disable_chunked_mm_input=self.disable_chunked_mm_input,
        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,
        scheduler_cls=self.scheduler_cls,
        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,
        disable_hybrid_kv_cache_manager=self.
        disable_hybrid_kv_cache_manager,
    )

    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

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

    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(
        backend=self.guided_decoding_backend,
        disable_fallback=self.guided_decoding_disable_fallback,
        disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
        disable_additional_properties=\
            self.guided_decoding_disable_additional_properties,
        reasoning_backend=self.reasoning_parser
    )

    observability_config = ObservabilityConfig(
        show_hidden_metrics_for_version=self.
        show_hidden_metrics_for_version,
        otlp_traces_endpoint=self.otlp_traces_endpoint,
        collect_detailed_traces=self.collect_detailed_traces,
    )

    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,
        kv_events_config=self.kv_events_config,
        additional_config=self.additional_config,
    )

    return config

create_load_config

create_load_config() -> LoadConfig

Source code in vllm/engine/arg_utils.py

def create_load_config(self) -> LoadConfig:

    if self.quantization == "bitsandbytes":
        self.load_format = "bitsandbytes"

    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,
        use_tqdm_on_load=self.use_tqdm_on_load,
        pt_load_map_location=self.pt_load_map_location,
    )

create_model_config

create_model_config() -> ModelConfig

Source code in vllm/engine/arg_utils.py

def create_model_config(self) -> ModelConfig:
    # 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

    return ModelConfig(
        model=self.model,
        hf_config_path=self.hf_config_path,
        task=self.task,
        tokenizer=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_token=self.hf_token,
        hf_overrides=self.hf_overrides,
        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,
        disable_cascade_attn=self.disable_cascade_attn,
        skip_tokenizer_init=self.skip_tokenizer_init,
        enable_prompt_embeds=self.enable_prompt_embeds,
        served_model_name=self.served_model_name,
        limit_mm_per_prompt=self.limit_mm_per_prompt,
        media_io_kwargs=self.media_io_kwargs,
        use_async_output_proc=not self.disable_async_output_proc,
        config_format=self.config_format,
        mm_processor_kwargs=self.mm_processor_kwargs,
        disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
        override_neuron_config=self.override_neuron_config,
        override_pooler_config=self.override_pooler_config,
        logits_processor_pattern=self.logits_processor_pattern,
        generation_config=self.generation_config,
        override_generation_config=self.override_generation_config,
        enable_sleep_mode=self.enable_sleep_mode,
        model_impl=self.model_impl,
        override_attention_dtype=self.override_attention_dtype,
    )

create_speculative_config

create_speculative_config(
    target_model_config: ModelConfig,
    target_parallel_config: ParallelConfig,
    enable_chunked_prefill: bool,
    disable_log_stats: bool,
) -> Optional[SpeculativeConfig]

Initializes and returns a SpeculativeConfig object based on speculative_config.

This function utilizes speculative_config to create a SpeculativeConfig object. The speculative_config can either be provided as a JSON string input via CLI arguments or directly as a dictionary from the engine.

Source code in vllm/engine/arg_utils.py

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

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

    # Note(Shangming): These parameters are not obtained from the cli arg
    # '--speculative-config' and must be passed in when creating the engine
    # config.
    self.speculative_config.update({
        "target_model_config": target_model_config,
        "target_parallel_config": target_parallel_config,
        "enable_chunked_prefill": enable_chunked_prefill,
        "disable_log_stats": disable_log_stats,
    })
    speculative_config = SpeculativeConfig.from_dict(
        self.speculative_config)

    return speculative_config

from_cli_args classmethod

from_cli_args(args: Namespace)

Source code in vllm/engine/arg_utils.py

@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

_async_engine_args_parser

_async_engine_args_parser()

Source code in vllm/engine/arg_utils.py

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

_compute_kwargs cached

_compute_kwargs(cls: ConfigType) -> dict[str, Any]

Source code in vllm/engine/arg_utils.py

@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
    cls_docs = get_attr_docs(cls)
    kwargs = {}
    for field in fields(cls):
        # Get the set of possible types for the field
        type_hints: set[TypeHint] = get_type_hints(field.type)

        # If the field is a dataclass, we can use the model_validate_json
        generator = (th for th in type_hints if is_dataclass(th))
        dataclass_cls = next(generator, None)

        # Get the default value of the field
        if field.default is not MISSING:
            default = field.default
        elif field.default_factory is not MISSING:
            default = field.default_factory()

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

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

        # Set other kwargs based on the type hints
        json_tip = """\n\nShould either be a valid JSON string or JSON keys
        passed individually. For example, the following sets of arguments are
        equivalent:\n\n
        - `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`\n
        - `--json-arg.key1 value1 --json-arg.key2.key3 value2`\n
        Additionally, list elements can be passed individually using '+':
        - `--json-arg '{"key4": ["value3", "value4", "value5"]}'`\n
        - `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`\n\n"""
        if dataclass_cls is not None:

            def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
                try:
                    if hasattr(cls, "from_cli"):
                        return cls.from_cli(val)
                    return TypeAdapter(cls).validate_json(val)
                except ValidationError as e:
                    raise argparse.ArgumentTypeError(repr(e)) from e

            kwargs[name]["type"] = parse_dataclass
            kwargs[name]["help"] += json_tip
        elif contains_type(type_hints, bool):
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
            kwargs[name].update(literal_to_kwargs(type_hints))
        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
                f"type. Got {types}.")
            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
            assert len(types) == 1, (
                "List type must have exactly one type. Got "
                f"{type_hint} with types {types}")
            kwargs[name]["type"] = types[0]
            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
            # Special case for large integers
            if name in {"max_model_len", "max_num_batched_tokens"}:
                kwargs[name]["type"] = human_readable_int
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
        elif (contains_type(type_hints, dict)
              and (contains_type(type_hints, str)
                   or any(is_not_builtin(th) for th in type_hints))):
            kwargs[name]["type"] = union_dict_and_str
        elif contains_type(type_hints, dict):
            kwargs[name]["type"] = parse_type(json.loads)
            kwargs[name]["help"] += json_tip
        elif (contains_type(type_hints, str)
              or any(is_not_builtin(th) for th in type_hints)):
            kwargs[name]["type"] = str
        else:
            raise ValueError(
                f"Unsupported type {type_hints} for argument {name}.")

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

        # If None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs

_engine_args_parser

_engine_args_parser()

Source code in vllm/engine/arg_utils.py

def _engine_args_parser():
    return EngineArgs.add_cli_args(FlexibleArgumentParser())

_raise_or_fallback

_raise_or_fallback(
    feature_name: str, recommend_to_remove: bool
)

Source code in vllm/engine/arg_utils.py

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)

_warn_or_fallback

_warn_or_fallback(feature_name: str) -> bool

Source code in vllm/engine/arg_utils.py

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

contains_type

contains_type(
    type_hints: set[TypeHint], type: TypeHintT
) -> bool

Check if the type hints contain a specific type.

Source code in vllm/engine/arg_utils.py

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

get_kwargs

get_kwargs(cls: ConfigType) -> dict[str, Any]

Return argparse kwargs for the given Config dataclass.

The heavy computation is cached via functools.lru_cache, and a deep copy is returned so callers can mutate the dictionary without affecting the cached version.

Source code in vllm/engine/arg_utils.py

def get_kwargs(cls: ConfigType) -> dict[str, Any]:
    """Return argparse kwargs for the given Config dataclass.

    The heavy computation is cached via functools.lru_cache, and a deep copy
    is returned so callers can mutate the dictionary without affecting the
    cached version.
    """
    return copy.deepcopy(_compute_kwargs(cls))

get_type

get_type(
    type_hints: set[TypeHint], type: TypeHintT
) -> TypeHintT

Get the specific type from the type hints.

Source code in vllm/engine/arg_utils.py

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

get_type_hints

get_type_hints(type_hint: TypeHint) -> set[TypeHint]

Extract type hints from Annotated or Union type hints.

Source code in vllm/engine/arg_utils.py

def get_type_hints(type_hint: TypeHint) -> set[TypeHint]:
    """Extract type hints from Annotated or Union type hints."""
    type_hints: set[TypeHint] = set()
    origin = get_origin(type_hint)
    args = get_args(type_hint)

    if origin is Annotated:
        type_hints.update(get_type_hints(args[0]))
    elif origin is Union:
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints

human_readable_int

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

Source code in vllm/engine/arg_utils.py

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)

is_not_builtin

is_not_builtin(type_hint: TypeHint) -> bool

Check if the class is not a built-in type.

Source code in vllm/engine/arg_utils.py

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

is_type

is_type(
    type_hint: TypeHint, type: TypeHintT
) -> TypeIs[TypeHintT]

Check if the type hint is a specific type.

Source code in vllm/engine/arg_utils.py

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

literal_to_kwargs

literal_to_kwargs(
    type_hints: set[TypeHint],
) -> dict[str, Any]

Convert Literal type hints to argparse kwargs.

Source code in vllm/engine/arg_utils.py

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

nullable_kvs

nullable_kvs(val: str) -> dict[str, int]

Parses a string containing comma separate key [str] to value [int] pairs into a dictionary.

Parameters:

Name	Type	Description	Default
val	str	String value to be parsed.	required

Returns:

Type	Description
dict[str, int]	Dictionary with parsed values.

Source code in vllm/engine/arg_utils.py

@deprecated(
    "Passing a JSON argument as a string containing comma separated key=value "
    "pairs is deprecated. This will be removed in v0.10.0. Please use a JSON "
    "string instead.")
def nullable_kvs(val: str) -> dict[str, int]:
    """Parses a string containing comma separate key [str] to value [int]
    pairs into a dictionary.

    Args:
        val: String value to be parsed.

    Returns:
        Dictionary with parsed values.
    """
    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

optional_type

optional_type(
    return_type: Callable[[str], T],
) -> Callable[[str], Optional[T]]

Source code in vllm/engine/arg_utils.py

def optional_type(
        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:

    def _optional_type(val: str) -> Optional[T]:
        if val == "" or val == "None":
            return None
        return parse_type(return_type)(val)

    return _optional_type

parse_type

parse_type(
    return_type: Callable[[str], T],
) -> Callable[[str], T]

Source code in vllm/engine/arg_utils.py

def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:

    def _parse_type(val: str) -> T:
        try:
            if return_type is json.loads and not re.match("^{.*}$", val):
                return cast(T, nullable_kvs(val))
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e

    return _parse_type

union_dict_and_str

union_dict_and_str(
    val: str,
) -> Optional[Union[str, dict[str, str]]]

Source code in vllm/engine/arg_utils.py

def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
    if not re.match("^{.*}$", val):
        return str(val)
    return optional_type(json.loads)(val)