arg_utils.py 69.3 KB
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# SPDX-License-Identifier: Apache-2.0

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

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

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

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

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


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

    Args:
        val: String value to be parsed.

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

    out_dict: Dict[str, int] = {}
    for item in val.split(","):
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        kv_parts = [part.lower().strip() for part in item.split("=")]
        if len(kv_parts) != 2:
            raise argparse.ArgumentTypeError(
                "Each item should be in the form KEY=VALUE")
        key, value = kv_parts
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        try:
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            parsed_value = int(value)
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        except ValueError as exc:
            msg = f"Failed to parse value of item {key}={value}"
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            raise argparse.ArgumentTypeError(msg) from exc

        if key in out_dict and out_dict[key] != parsed_value:
            raise argparse.ArgumentTypeError(
                f"Conflicting values specified for key: {key}")
        out_dict[key] = parsed_value
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    return out_dict


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@dataclass
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class EngineArgs:
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    """Arguments for vLLM engine."""
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    model: str = 'facebook/opt-125m'
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    served_model_name: Optional[Union[str, List[str]]] = None
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    tokenizer: Optional[str] = None
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    hf_config_path: Optional[str] = None
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    task: TaskOption = "auto"
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    skip_tokenizer_init: bool = False
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    tokenizer_mode: str = 'auto'
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    trust_remote_code: bool = False
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    allowed_local_media_path: str = ""
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    download_dir: Optional[str] = None
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    load_format: str = 'auto'
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    config_format: ConfigFormat = ConfigFormat.AUTO
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    dtype: str = 'auto'
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    kv_cache_dtype: str = 'auto'
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    seed: Optional[int] = None
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    max_model_len: Optional[int] = None
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    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    distributed_executor_backend: Optional[Union[str,
                                                 Type[ExecutorBase]]] = None
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    # number of P/D disaggregation (or other disaggregation) workers
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    pipeline_parallel_size: int = 1
    tensor_parallel_size: int = 1
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    enable_expert_parallel: bool = False
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    max_parallel_loading_workers: Optional[int] = None
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    block_size: Optional[int] = None
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    enable_prefix_caching: Optional[bool] = None
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    disable_sliding_window: bool = False
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    use_v2_block_manager: bool = True
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    swap_space: float = 4  # GiB
    cpu_offload_gb: float = 0  # GiB
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    gpu_memory_utilization: float = 0.90
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    max_num_batched_tokens: Optional[int] = None
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    max_num_partial_prefills: Optional[int] = 1
    max_long_partial_prefills: Optional[int] = 1
    long_prefill_token_threshold: Optional[int] = 0
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    max_num_seqs: Optional[int] = None
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    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
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    disable_log_stats: bool = False
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    revision: Optional[str] = None
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    code_revision: Optional[str] = None
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    rope_scaling: Optional[Dict[str, Any]] = None
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    rope_theta: Optional[float] = None
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    hf_overrides: Optional[HfOverrides] = None
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    tokenizer_revision: Optional[str] = None
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    quantization: Optional[str] = None
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    enforce_eager: Optional[bool] = None
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    max_seq_len_to_capture: int = 8192
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    disable_custom_all_reduce: bool = False
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    tokenizer_pool_size: int = 0
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    # Note: Specifying a tokenizer pool by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
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    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
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    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
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    mm_processor_kwargs: Optional[Dict[str, Any]] = None
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    disable_mm_preprocessor_cache: bool = False
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    enable_lora: bool = False
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    enable_lora_bias: bool = False
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    max_loras: int = 1
    max_lora_rank: int = 16
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    enable_prompt_adapter: bool = False
    max_prompt_adapters: int = 1
    max_prompt_adapter_token: int = 0
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    fully_sharded_loras: bool = False
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    lora_extra_vocab_size: int = 256
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    long_lora_scaling_factors: Optional[Tuple[float]] = None
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    lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
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    max_cpu_loras: Optional[int] = None
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    device: str = 'auto'
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    num_scheduler_steps: int = 1
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    multi_step_stream_outputs: bool = True
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    ray_workers_use_nsight: bool = False
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    num_gpu_blocks_override: Optional[int] = None
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    num_lookahead_slots: int = 0
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    model_loader_extra_config: Optional[dict] = None
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    ignore_patterns: Optional[Union[str, List[str]]] = None
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    preemption_mode: Optional[str] = None
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    scheduler_delay_factor: float = 0.0
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    enable_chunked_prefill: Optional[bool] = None
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    guided_decoding_backend: str = 'xgrammar'
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    logits_processor_pattern: Optional[str] = None
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    # Speculative decoding configuration.
    speculative_model: Optional[str] = None
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    speculative_model_quantization: Optional[str] = None
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    speculative_draft_tensor_parallel_size: Optional[int] = None
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    num_speculative_tokens: Optional[int] = None
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    speculative_disable_mqa_scorer: Optional[bool] = False
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    speculative_max_model_len: Optional[int] = None
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    speculative_disable_by_batch_size: Optional[int] = None
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    ngram_prompt_lookup_max: Optional[int] = None
    ngram_prompt_lookup_min: Optional[int] = None
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    spec_decoding_acceptance_method: str = 'rejection_sampler'
    typical_acceptance_sampler_posterior_threshold: Optional[float] = None
    typical_acceptance_sampler_posterior_alpha: Optional[float] = None
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    qlora_adapter_name_or_path: Optional[str] = None
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    disable_logprobs_during_spec_decoding: Optional[bool] = None
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    show_hidden_metrics_for_version: Optional[str] = None
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    otlp_traces_endpoint: Optional[str] = None
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    collect_detailed_traces: Optional[str] = None
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    disable_async_output_proc: bool = False
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    scheduling_policy: Literal["fcfs", "priority"] = "fcfs"
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    scheduler_cls: Union[str, Type[object]] = "vllm.core.scheduler.Scheduler"
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    override_neuron_config: Optional[Dict[str, Any]] = None
    override_pooler_config: Optional[PoolerConfig] = None
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    compilation_config: Optional[CompilationConfig] = None
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    worker_cls: str = "auto"
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    worker_extension_cls: str = ""
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    kv_transfer_config: Optional[KVTransferConfig] = None

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    generation_config: Optional[str] = "auto"
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    override_generation_config: Optional[Dict[str, Any]] = None
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    enable_sleep_mode: bool = False
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    model_impl: str = "auto"
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    calculate_kv_scales: Optional[bool] = None

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    additional_config: Optional[Dict[str, Any]] = None
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    enable_reasoning: Optional[bool] = None
    reasoning_parser: Optional[str] = None
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    use_tqdm_on_load: bool = True
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    def __post_init__(self):
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        if not self.tokenizer:
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            self.tokenizer = self.model
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        # Override the default value of enable_prefix_caching if it's not set
        # by user.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = bool(envs.VLLM_USE_V1)
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        # Override max_num_seqs if it's not set by user.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024
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        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
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        if isinstance(self.compilation_config, (int, dict)):
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            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))
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        # Setup plugins
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        from vllm.plugins import load_general_plugins
        load_general_plugins()
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
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        """Shared CLI arguments for vLLM engine."""
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        # Model arguments
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        parser.add_argument(
            '--model',
            type=str,
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            default=EngineArgs.model,
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            help='Name or path of the huggingface model to use.')
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        parser.add_argument(
            '--task',
            default=EngineArgs.task,
            choices=get_args(TaskOption),
            help='The task to use the model for. Each vLLM instance only '
            'supports one task, even if the same model can be used for '
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            'multiple tasks. When the model only supports one task, ``"auto"`` '
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            'can be used to select it; otherwise, you must specify explicitly '
            'which task to use.')
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        parser.add_argument(
            '--tokenizer',
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            type=nullable_str,
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            default=EngineArgs.tokenizer,
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            help='Name or path of the huggingface tokenizer to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            "--hf-config-path",
            type=nullable_str,
            default=EngineArgs.hf_config_path,
            help='Name or path of the huggingface config to use. '
            'If unspecified, model name or path will be used.')
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        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
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            help='Skip initialization of tokenizer and detokenizer. '
            'Expects valid prompt_token_ids and None for prompt from '
            'the input. The generated output will contain token ids.')
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        parser.add_argument(
            '--revision',
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            type=nullable_str,
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            default=None,
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            help='The specific model version to use. It can be a branch '
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            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
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        parser.add_argument(
            '--code-revision',
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            type=nullable_str,
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            default=None,
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            help='The specific revision to use for the model code on '
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            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-revision',
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            type=nullable_str,
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            default=None,
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            help='Revision of the huggingface tokenizer to use. '
            'It can be a branch name, a tag name, or a commit id. '
            'If unspecified, will use the default version.')
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        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
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            choices=['auto', 'slow', 'mistral', 'custom'],
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            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
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            'always use the slow tokenizer. \n* '
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            '"mistral" will always use the `mistral_common` tokenizer. \n* '
            '"custom" will use --tokenizer to select the '
            'preregistered tokenizer.')
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        parser.add_argument('--trust-remote-code',
                            action='store_true',
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                            help='Trust remote code from huggingface.')
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        parser.add_argument(
            '--allowed-local-media-path',
            type=str,
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            help="Allowing API requests to read local images or videos "
            "from directories specified by the server file system. "
            "This is a security risk. "
            "Should only be enabled in trusted environments.")
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        parser.add_argument('--download-dir',
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                            type=nullable_str,
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                            default=EngineArgs.download_dir,
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                            help='Directory to download and load the weights, '
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                            'default to the default cache dir of '
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                            'huggingface.')
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        parser.add_argument(
            '--load-format',
            type=str,
            default=EngineArgs.load_format,
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            choices=[f.value for f in LoadFormat],
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            help='The format of the model weights to load.\n\n'
            '* "auto" will try to load the weights in the safetensors format '
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            'and fall back to the pytorch bin format if safetensors format '
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            'is not available.\n'
            '* "pt" will load the weights in the pytorch bin format.\n'
            '* "safetensors" will load the weights in the safetensors format.\n'
            '* "npcache" will load the weights in pytorch format and store '
            'a numpy cache to speed up the loading.\n'
            '* "dummy" will initialize the weights with random values, '
            'which is mainly for profiling.\n'
            '* "tensorizer" will load the weights using tensorizer from '
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            'CoreWeave. See the Tensorize vLLM Model script in the Examples '
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            'section for more information.\n'
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            '* "runai_streamer" will load the Safetensors weights using Run:ai'
            'Model Streamer \n'
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            '* "bitsandbytes" will load the weights using bitsandbytes '
            'quantization.\n')
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        parser.add_argument(
            '--config-format',
            default=EngineArgs.config_format,
            choices=[f.value for f in ConfigFormat],
            help='The format of the model config to load.\n\n'
            '* "auto" will try to load the config in hf format '
            'if available else it will try to load in mistral format ')
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        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
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            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
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            help='Data type for model weights and activations.\n\n'
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            'BF16 precision for BF16 models.\n'
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.')
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        parser.add_argument(
            '--kv-cache-dtype',
            type=str,
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            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
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            default=EngineArgs.kv_cache_dtype,
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            help='Data type for kv cache storage. If "auto", will use model '
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            'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
            'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
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        parser.add_argument('--max-model-len',
                            type=int,
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                            default=EngineArgs.max_model_len,
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                            help='Model context length. If unspecified, will '
                            'be automatically derived from the model config.')
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        parser.add_argument(
            '--guided-decoding-backend',
            type=str,
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            default='xgrammar',
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            help='Which engine will be used for guided decoding'
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            ' (JSON schema / regex etc) by default. Currently support '
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            'https://github.com/outlines-dev/outlines, '
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            'https://github.com/mlc-ai/xgrammar, and '
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            'https://github.com/noamgat/lm-format-enforcer.'
            ' Can be overridden per request via guided_decoding_backend'
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            ' parameter.\n'
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            'Backend-specific options can be supplied in a comma-separated '
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            'list following a colon after the backend name. Valid backends and '
            'all available options are: [xgrammar:no-fallback, '
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            'xgrammar:disable-any-whitespace, '
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            'outlines:no-fallback, lm-format-enforcer:no-fallback]')
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        parser.add_argument(
            '--logits-processor-pattern',
            type=nullable_str,
            default=None,
            help='Optional regex pattern specifying valid logits processor '
            'qualified names that can be passed with the `logits_processors` '
            'extra completion argument. Defaults to None, which allows no '
            'processors.')
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        parser.add_argument(
            '--model-impl',
            type=str,
            default=EngineArgs.model_impl,
            choices=[f.value for f in ModelImpl],
            help='Which implementation of the model to use.\n\n'
            '* "auto" will try to use the vLLM implementation if it exists '
            'and fall back to the Transformers implementation if no vLLM '
            'implementation is available.\n'
            '* "vllm" will use the vLLM model implementation.\n'
            '* "transformers" will use the Transformers model '
            'implementation.\n')
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        # Parallel arguments
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        parser.add_argument(
            '--distributed-executor-backend',
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            choices=['ray', 'mp', 'uni', 'external_launcher'],
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            default=EngineArgs.distributed_executor_backend,
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            help='Backend to use for distributed model '
            'workers, either "ray" or "mp" (multiprocessing). If the product '
            'of pipeline_parallel_size and tensor_parallel_size is less than '
            'or equal to the number of GPUs available, "mp" will be used to '
            'keep processing on a single host. Otherwise, this will default '
            'to "ray" if Ray is installed and fail otherwise. Note that tpu '
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            'only supports Ray for distributed inference.')
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        parser.add_argument('--pipeline-parallel-size',
                            '-pp',
                            type=int,
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                            default=EngineArgs.pipeline_parallel_size,
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                            help='Number of pipeline stages.')
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        parser.add_argument('--tensor-parallel-size',
                            '-tp',
                            type=int,
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                            default=EngineArgs.tensor_parallel_size,
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                            help='Number of tensor parallel replicas.')
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        parser.add_argument(
            '--enable-expert-parallel',
            action='store_true',
            help='Use expert parallelism instead of tensor parallelism '
            'for MoE layers.')
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        parser.add_argument(
            '--max-parallel-loading-workers',
            type=int,
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            default=EngineArgs.max_parallel_loading_workers,
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            help='Load model sequentially in multiple batches, '
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            'to avoid RAM OOM when using tensor '
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            'parallel and large models.')
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        parser.add_argument(
            '--ray-workers-use-nsight',
            action='store_true',
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            help='If specified, use nsight to profile Ray workers.')
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        # KV cache arguments
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        parser.add_argument('--block-size',
                            type=int,
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                            default=EngineArgs.block_size,
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                            choices=[8, 16, 32, 64, 128],
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                            help='Token block size for contiguous chunks of '
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                            'tokens. This is ignored on neuron devices and '
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                            'set to ``--max-model-len``. On CUDA devices, '
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                            'only block sizes up to 32 are supported. '
                            'On HPU devices, block size defaults to 128.')
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        parser.add_argument(
            "--enable-prefix-caching",
            action=argparse.BooleanOptionalAction,
            default=EngineArgs.enable_prefix_caching,
            help="Enables automatic prefix caching. "
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            "Use ``--no-enable-prefix-caching`` to disable explicitly.",
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        )
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        parser.add_argument('--disable-sliding-window',
                            action='store_true',
                            help='Disables sliding window, '
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                            'capping to sliding window size.')
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        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
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                            default=True,
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                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
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        parser.add_argument(
            '--num-lookahead-slots',
            type=int,
            default=EngineArgs.num_lookahead_slots,
            help='Experimental scheduling config necessary for '
            'speculative decoding. This will be replaced by '
            'speculative config in the future; it is present '
            'to enable correctness tests until then.')
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        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
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                            help='Random seed for operations.')
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        parser.add_argument('--swap-space',
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                            type=float,
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                            default=EngineArgs.swap_space,
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                            help='CPU swap space size (GiB) per GPU.')
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        parser.add_argument(
            '--cpu-offload-gb',
            type=float,
            default=0,
            help='The space in GiB to offload to CPU, per GPU. '
            'Default is 0, which means no offloading. Intuitively, '
            'this argument can be seen as a virtual way to increase '
            'the GPU memory size. For example, if you have one 24 GB '
            'GPU and set this to 10, virtually you can think of it as '
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            'a 34 GB GPU. Then you can load a 13B model with BF16 weight, '
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            'which requires at least 26GB GPU memory. Note that this '
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            'requires fast CPU-GPU interconnect, as part of the model is '
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            'loaded from CPU memory to GPU memory on the fly in each '
            'model forward pass.')
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        parser.add_argument(
            '--gpu-memory-utilization',
            type=float,
            default=EngineArgs.gpu_memory_utilization,
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            help='The fraction of GPU memory to be used for the model '
            'executor, which can range from 0 to 1. For example, a value of '
            '0.5 would imply 50%% GPU memory utilization. If unspecified, '
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            'will use the default value of 0.9. This is a per-instance '
            'limit, and only applies to the current vLLM instance.'
            'It does not matter if you have another vLLM instance running '
            'on the same GPU. For example, if you have two vLLM instances '
            'running on the same GPU, you can set the GPU memory utilization '
            'to 0.5 for each instance.')
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        parser.add_argument(
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            '--num-gpu-blocks-override',
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            type=int,
            default=None,
            help='If specified, ignore GPU profiling result and use this number'
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            ' of GPU blocks. Used for testing preemption.')
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        parser.add_argument('--max-num-batched-tokens',
                            type=int,
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                            default=EngineArgs.max_num_batched_tokens,
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                            help='Maximum number of batched tokens per '
                            'iteration.')
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        parser.add_argument(
            "--max-num-partial-prefills",
            type=int,
            default=EngineArgs.max_num_partial_prefills,
            help="For chunked prefill, the max number of concurrent \
            partial prefills."
            "Defaults to 1",
        )
        parser.add_argument(
            "--max-long-partial-prefills",
            type=int,
            default=EngineArgs.max_long_partial_prefills,
            help="For chunked prefill, the maximum number of prompts longer "
            "than --long-prefill-token-threshold that will be prefilled "
            "concurrently. Setting this less than --max-num-partial-prefills "
            "will allow shorter prompts to jump the queue in front of longer "
            "prompts in some cases, improving latency. Defaults to 1.")
        parser.add_argument(
            "--long-prefill-token-threshold",
            type=float,
            default=EngineArgs.long_prefill_token_threshold,
            help="For chunked prefill, a request is considered long if the "
            "prompt is longer than this number of tokens. Defaults to 4%% of "
            "the model's context length.",
        )
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        parser.add_argument('--max-num-seqs',
                            type=int,
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                            default=EngineArgs.max_num_seqs,
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                            help='Maximum number of sequences per iteration.')
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        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
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            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
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        parser.add_argument('--disable-log-stats',
                            action='store_true',
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                            help='Disable logging statistics.')
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        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
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                            type=nullable_str,
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                            choices=[*QUANTIZATION_METHODS, None],
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                            default=EngineArgs.quantization,
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                            help='Method used to quantize the weights. If '
                            'None, we first check the `quantization_config` '
                            'attribute in the model config file. If that is '
                            'None, we assume the model weights are not '
                            'quantized and use `dtype` to determine the data '
                            'type of the weights.')
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        parser.add_argument(
            '--rope-scaling',
            default=None,
            type=json.loads,
            help='RoPE scaling configuration in JSON format. '
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            'For example, ``{"rope_type":"dynamic","factor":2.0}``')
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        parser.add_argument('--rope-theta',
                            default=None,
                            type=float,
                            help='RoPE theta. Use with `rope_scaling`. In '
                            'some cases, changing the RoPE theta improves the '
                            'performance of the scaled model.')
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        parser.add_argument('--hf-overrides',
                            type=json.loads,
                            default=EngineArgs.hf_overrides,
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                            help='Extra arguments for the HuggingFace config. '
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                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
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        parser.add_argument('--enforce-eager',
                            action='store_true',
                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
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        parser.add_argument('--max-seq-len-to-capture',
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                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence length covered by CUDA '
                            'graphs. When a sequence has context length '
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                            'larger than this, we fall back to eager mode. '
                            'Additionally for encoder-decoder models, if the '
                            'sequence length of the encoder input is larger '
                            'than this, we fall back to the eager mode.')
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        parser.add_argument('--disable-custom-all-reduce',
                            action='store_true',
                            default=EngineArgs.disable_custom_all_reduce,
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                            help='See ParallelConfig.')
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        parser.add_argument('--tokenizer-pool-size',
                            type=int,
                            default=EngineArgs.tokenizer_pool_size,
                            help='Size of tokenizer pool to use for '
                            'asynchronous tokenization. If 0, will '
                            'use synchronous tokenization.')
        parser.add_argument('--tokenizer-pool-type',
                            type=str,
                            default=EngineArgs.tokenizer_pool_type,
                            help='Type of tokenizer pool to use for '
                            'asynchronous tokenization. Ignored '
                            'if tokenizer_pool_size is 0.')
        parser.add_argument('--tokenizer-pool-extra-config',
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                            type=nullable_str,
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                            default=EngineArgs.tokenizer_pool_extra_config,
                            help='Extra config for tokenizer pool. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary. Ignored if '
                            'tokenizer_pool_size is 0.')
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        # Multimodal related configs
        parser.add_argument(
            '--limit-mm-per-prompt',
            type=nullable_kvs,
            default=EngineArgs.limit_mm_per_prompt,
            # The default value is given in
            # MultiModalRegistry.init_mm_limits_per_prompt
            help=('For each multimodal plugin, limit how many '
                  'input instances to allow for each prompt. '
                  'Expects a comma-separated list of items, '
                  'e.g.: `image=16,video=2` allows a maximum of 16 '
                  'images and 2 videos per prompt. Defaults to 1 for '
                  'each modality.'))
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        parser.add_argument(
            '--mm-processor-kwargs',
            default=None,
            type=json.loads,
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            help=('Overrides for the multimodal input mapping/processing, '
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                  'e.g., image processor. For example: ``{"num_crops": 4}``.'))
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        parser.add_argument(
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            '--disable-mm-preprocessor-cache',
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            action='store_true',
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            help='If true, then disables caching of the multi-modal '
            'preprocessor/mapper. (not recommended)')
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        # LoRA related configs
        parser.add_argument('--enable-lora',
                            action='store_true',
                            help='If True, enable handling of LoRA adapters.')
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        parser.add_argument('--enable-lora-bias',
                            action='store_true',
                            help='If True, enable bias for LoRA adapters.')
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        parser.add_argument('--max-loras',
                            type=int,
                            default=EngineArgs.max_loras,
                            help='Max number of LoRAs in a single batch.')
        parser.add_argument('--max-lora-rank',
                            type=int,
                            default=EngineArgs.max_lora_rank,
                            help='Max LoRA rank.')
        parser.add_argument(
            '--lora-extra-vocab-size',
            type=int,
            default=EngineArgs.lora_extra_vocab_size,
            help=('Maximum size of extra vocabulary that can be '
                  'present in a LoRA adapter (added to the base '
                  'model vocabulary).'))
        parser.add_argument(
            '--lora-dtype',
            type=str,
            default=EngineArgs.lora_dtype,
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            choices=['auto', 'float16', 'bfloat16'],
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            help=('Data type for LoRA. If auto, will default to '
                  'base model dtype.'))
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        parser.add_argument(
            '--long-lora-scaling-factors',
            type=nullable_str,
            default=EngineArgs.long_lora_scaling_factors,
            help=('Specify multiple scaling factors (which can '
                  'be different from base model scaling factor '
                  '- see eg. Long LoRA) to allow for multiple '
                  'LoRA adapters trained with those scaling '
                  'factors to be used at the same time. If not '
                  'specified, only adapters trained with the '
                  'base model scaling factor are allowed.'))
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        parser.add_argument(
            '--max-cpu-loras',
            type=int,
            default=EngineArgs.max_cpu_loras,
            help=('Maximum number of LoRAs to store in CPU memory. '
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                  'Must be >= than max_loras. '
                  'Defaults to max_loras.'))
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        parser.add_argument(
            '--fully-sharded-loras',
            action='store_true',
            help=('By default, only half of the LoRA computation is '
                  'sharded with tensor parallelism. '
                  'Enabling this will use the fully sharded layers. '
                  'At high sequence length, max rank or '
                  'tensor parallel size, this is likely faster.'))
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        parser.add_argument('--enable-prompt-adapter',
                            action='store_true',
                            help='If True, enable handling of PromptAdapters.')
        parser.add_argument('--max-prompt-adapters',
                            type=int,
                            default=EngineArgs.max_prompt_adapters,
                            help='Max number of PromptAdapters in a batch.')
        parser.add_argument('--max-prompt-adapter-token',
                            type=int,
                            default=EngineArgs.max_prompt_adapter_token,
                            help='Max number of PromptAdapters tokens')
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        parser.add_argument("--device",
                            type=str,
                            default=EngineArgs.device,
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                            choices=DEVICE_OPTIONS,
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                            help='Device type for vLLM execution.')
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        parser.add_argument('--num-scheduler-steps',
                            type=int,
                            default=1,
                            help=('Maximum number of forward steps per '
                                  'scheduler call.'))
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        parser.add_argument(
            '--use-tqdm-on-load',
            dest='use_tqdm_on_load',
            action=argparse.BooleanOptionalAction,
            default=EngineArgs.use_tqdm_on_load,
            help='Whether to enable/disable progress bar '
            'when loading model weights.',
        )
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        parser.add_argument(
            '--multi-step-stream-outputs',
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            action=StoreBoolean,
            default=EngineArgs.multi_step_stream_outputs,
            nargs="?",
            const="True",
            help='If False, then multi-step will stream outputs at the end '
            'of all steps')
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        parser.add_argument(
            '--scheduler-delay-factor',
            type=float,
            default=EngineArgs.scheduler_delay_factor,
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            help='Apply a delay (of delay factor multiplied by previous '
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            'prompt latency) before scheduling next prompt.')
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        parser.add_argument(
            '--enable-chunked-prefill',
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            action=StoreBoolean,
            default=EngineArgs.enable_chunked_prefill,
            nargs="?",
            const="True",
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            help='If set, the prefill requests can be chunked based on the '
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            'max_num_batched_tokens.')
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        parser.add_argument(
            '--speculative-model',
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            type=nullable_str,
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            default=EngineArgs.speculative_model,
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            help=
            'The name of the draft model to be used in speculative decoding.')
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        # Quantization settings for speculative model.
        parser.add_argument(
            '--speculative-model-quantization',
            type=nullable_str,
            choices=[*QUANTIZATION_METHODS, None],
            default=EngineArgs.speculative_model_quantization,
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            help='Method used to quantize the weights of speculative model. '
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            'If None, we first check the `quantization_config` '
            'attribute in the model config file. If that is '
            'None, we assume the model weights are not '
            'quantized and use `dtype` to determine the data '
            'type of the weights.')
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        parser.add_argument(
            '--num-speculative-tokens',
            type=int,
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            default=EngineArgs.num_speculative_tokens,
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            help='The number of speculative tokens to sample from '
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            'the draft model in speculative decoding.')
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        parser.add_argument(
            '--speculative-disable-mqa-scorer',
            action='store_true',
            help=
            'If set to True, the MQA scorer will be disabled in speculative '
            ' and fall back to batch expansion')
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        parser.add_argument(
            '--speculative-draft-tensor-parallel-size',
            '-spec-draft-tp',
            type=int,
            default=EngineArgs.speculative_draft_tensor_parallel_size,
            help='Number of tensor parallel replicas for '
            'the draft model in speculative decoding.')
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        parser.add_argument(
            '--speculative-max-model-len',
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            type=int,
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            default=EngineArgs.speculative_max_model_len,
            help='The maximum sequence length supported by the '
            'draft model. Sequences over this length will skip '
            'speculation.')

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

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

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

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

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

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

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

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        parser.add_argument('--model-loader-extra-config',
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                            type=nullable_str,
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                            default=EngineArgs.model_loader_extra_config,
                            help='Extra config for model loader. '
                            'This will be passed to the model loader '
                            'corresponding to the chosen load_format. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
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        parser.add_argument(
            '--ignore-patterns',
            action="append",
            type=str,
            default=[],
            help="The pattern(s) to ignore when loading the model."
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            "Default to `original/**/*` to avoid repeated loading of llama's "
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            "checkpoints.")
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            type=str,
            default=None,
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            help='If \'recompute\', the engine performs preemption by '
            'recomputing; If \'swap\', the engine performs preemption by '
            'block swapping.')
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        parser.add_argument(
            "--served-model-name",
            nargs="+",
            type=str,
            default=None,
            help="The model name(s) used in the API. If multiple "
            "names are provided, the server will respond to any "
            "of the provided names. The model name in the model "
            "field of a response will be the first name in this "
            "list. If not specified, the model name will be the "
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            "same as the ``--model`` argument. Noted that this name(s) "
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            "will also be used in `model_name` tag content of "
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            "prometheus metrics, if multiple names provided, metrics "
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            "tag will take the first one.")
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        parser.add_argument('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')
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        parser.add_argument('--show-hidden-metrics-for-version',
                            type=str,
                            default=None,
                            help='Enable deprecated Prometheus metrics that '
                            'have been hidden since the specified version. '
                            'For example, if a previously deprecated metric '
                            'has been hidden since the v0.7.0 release, you '
                            'use --show-hidden-metrics-for-version=0.7 as a '
                            'temporary escape hatch while you migrate to new '
                            'metrics. The metric is likely to be removed '
                            'completely in an upcoming release.')

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

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

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

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

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

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        parser.add_argument(
            '--calculate-kv-scales',
            action='store_true',
            help='This enables dynamic calculation of '
            'k_scale and v_scale when kv-cache-dtype is fp8. '
            'If calculate-kv-scales is false, the scales will '
            'be loaded from the model checkpoint if available. '
            'Otherwise, the scales will default to 1.0.')

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        parser.add_argument(
            "--additional-config",
            type=json.loads,
            default=None,
            help="Additional config for specified platform in JSON format. "
            "Different platforms may support different configs. Make sure the "
            "configs are valid for the platform you are using. The input format"
            " is like '{\"config_key\":\"config_value\"}'")
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        parser.add_argument(
            "--enable-reasoning",
            action="store_true",
            default=False,
            help="Whether to enable reasoning_content for the model. "
            "If enabled, the model will be able to generate reasoning content."
        )

        parser.add_argument(
            "--reasoning-parser",
            type=str,
            choices=["deepseek_r1"],
            default=None,
            help=
            "Select the reasoning parser depending on the model that you're "
            "using. This is used to parse the reasoning content into OpenAI "
            "API format. Required for ``--enable-reasoning``.")

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

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

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        return ModelConfig(
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            model=self.model,
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            hf_config_path=self.hf_config_path,
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            task=self.task,
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            # We know this is not None because we set it in __post_init__
            tokenizer=cast(str, self.tokenizer),
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            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
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            allowed_local_media_path=self.allowed_local_media_path,
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            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
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            rope_theta=self.rope_theta,
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            hf_overrides=self.hf_overrides,
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            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            disable_sliding_window=self.disable_sliding_window,
            skip_tokenizer_init=self.skip_tokenizer_init,
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            served_model_name=self.served_model_name,
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            limit_mm_per_prompt=self.limit_mm_per_prompt,
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            use_async_output_proc=not self.disable_async_output_proc,
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            config_format=self.config_format,
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            mm_processor_kwargs=self.mm_processor_kwargs,
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            disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
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            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
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            logits_processor_pattern=self.logits_processor_pattern,
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            generation_config=self.generation_config,
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            override_generation_config=self.override_generation_config,
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            enable_sleep_mode=self.enable_sleep_mode,
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            model_impl=self.model_impl,
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        )
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    def create_load_config(self) -> LoadConfig:
        # bitsandbytes quantization needs a specific model loader
        # so we make sure the quant method and the load format are consistent
        if (self.quantization == "bitsandbytes" or
           self.qlora_adapter_name_or_path is not None) and \
           self.load_format != "bitsandbytes":
            raise ValueError(
                "BitsAndBytes quantization and QLoRA adapter only support "
                f"'bitsandbytes' load format, but got {self.load_format}")

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

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        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
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            use_tqdm_on_load=self.use_tqdm_on_load,
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        )
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    def create_engine_config(self,
                             usage_context: Optional[UsageContext] = None
                             ) -> VllmConfig:
        from vllm.platforms import current_platform
        current_platform.pre_register_and_update()
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        if envs.VLLM_USE_V1:
            self._override_v1_engine_args(usage_context)
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        device_config = DeviceConfig(device=self.device)
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        model_config = self.create_model_config()

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        if (model_config.is_multimodal_model and not envs.VLLM_USE_V1
                and self.enable_prefix_caching):
            logger.warning("--enable-prefix-caching is currently not "
                           "supported for multimodal models in v0 and "
                           "has been disabled.")
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            self.enable_prefix_caching = False

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        cache_config = CacheConfig(
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            block_size=self.block_size,
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            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
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            is_attention_free=model_config.is_attention_free,
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            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
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            enable_prefix_caching=self.enable_prefix_caching,
            cpu_offload_gb=self.cpu_offload_gb,
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            calculate_kv_scales=self.calculate_kv_scales,
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        )
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        parallel_config = ParallelConfig(
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            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
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            enable_expert_parallel=self.enable_expert_parallel,
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            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            tokenizer_pool_config=TokenizerPoolConfig.create_config(
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                self.tokenizer_pool_size,
                self.tokenizer_pool_type,
                self.tokenizer_pool_extra_config,
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            ),
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            ray_workers_use_nsight=self.ray_workers_use_nsight,
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            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
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            worker_extension_cls=self.worker_extension_cls,
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        )
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        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # If not explicitly set, enable chunked prefill by default for
            # long context (> 32K) models. This is to avoid OOM errors in the
            # initial memory profiling phase.
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            # For multimodal models and models with MLA, chunked prefill is
            # disabled by default in V0, but enabled by design in V1
            if model_config.is_multimodal_model or model_config.use_mla:
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                self.enable_chunked_prefill = bool(envs.VLLM_USE_V1)

            elif use_long_context:
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                is_gpu = device_config.device_type == "cuda"
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
                use_spec_decode = self.speculative_model is not None
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                from vllm.platforms import current_platform
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                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
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                        and not self.enable_prompt_adapter
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                        and model_config.runner_type != "pooling"
                        and not current_platform.is_rocm()):
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                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models with "
                        "max_model_len > 32K. Currently, chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable chunked prefill "
                        "by setting --enable-chunked-prefill=False.")
            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = False

        if not self.enable_chunked_prefill and use_long_context:
            logger.warning(
                "The model has a long context length (%s). This may cause OOM "
                "errors during the initial memory profiling phase, or result "
                "in low performance due to small KV cache space. Consider "
                "setting --max-model-len to a smaller value.", max_model_len)
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        elif (self.enable_chunked_prefill
              and model_config.runner_type == "pooling"):
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            msg = "Chunked prefill is not supported for pooling models"
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            raise ValueError(msg)
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        speculative_config = SpeculativeConfig.maybe_create_spec_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
            target_dtype=self.dtype,
            speculative_model=self.speculative_model,
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            speculative_model_quantization = \
                self.speculative_model_quantization,
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            speculative_draft_tensor_parallel_size = \
                self.speculative_draft_tensor_parallel_size,
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            num_speculative_tokens=self.num_speculative_tokens,
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            speculative_disable_mqa_scorer=self.speculative_disable_mqa_scorer,
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            speculative_disable_by_batch_size=self.
            speculative_disable_by_batch_size,
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            speculative_max_model_len=self.speculative_max_model_len,
            enable_chunked_prefill=self.enable_chunked_prefill,
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            disable_log_stats=self.disable_log_stats,
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            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
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            draft_token_acceptance_method=\
                self.spec_decoding_acceptance_method,
            typical_acceptance_sampler_posterior_threshold=self.
            typical_acceptance_sampler_posterior_threshold,
            typical_acceptance_sampler_posterior_alpha=self.
            typical_acceptance_sampler_posterior_alpha,
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            disable_logprobs=self.disable_logprobs_during_spec_decoding,
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        )

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

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

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

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        decoding_config = DecodingConfig(
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            guided_decoding_backend=self.guided_decoding_backend,
            reasoning_backend=self.reasoning_parser
            if self.enable_reasoning else None,
        )
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        show_hidden_metrics = False
        if self.show_hidden_metrics_for_version is not None:
            show_hidden_metrics = version._prev_minor_version_was(
                self.show_hidden_metrics_for_version)

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        detailed_trace_modules = []
        if self.collect_detailed_traces is not None:
            detailed_trace_modules = self.collect_detailed_traces.split(",")
        for m in detailed_trace_modules:
            if m not in ALLOWED_DETAILED_TRACE_MODULES:
                raise ValueError(
                    f"Invalid module {m} in collect_detailed_traces. "
                    f"Valid modules are {ALLOWED_DETAILED_TRACE_MODULES}")
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        observability_config = ObservabilityConfig(
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            show_hidden_metrics=show_hidden_metrics,
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            otlp_traces_endpoint=self.otlp_traces_endpoint,
            collect_model_forward_time="model" in detailed_trace_modules
            or "all" in detailed_trace_modules,
            collect_model_execute_time="worker" in detailed_trace_modules
            or "all" in detailed_trace_modules,
        )
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        config = VllmConfig(
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            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
            decoding_config=decoding_config,
            observability_config=observability_config,
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            prompt_adapter_config=prompt_adapter_config,
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            compilation_config=self.compilation_config,
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            kv_transfer_config=self.kv_transfer_config,
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            additional_config=self.additional_config,
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        )
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        if envs.VLLM_USE_V1:
            self._override_v1_engine_config(config)
        return config

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

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        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
        # When no user override, set the default values based on the usage
        # context.
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        # Use different default values for different hardware.
        from vllm.platforms import current_platform
        device_name = current_platform.get_device_name().lower()
        if "h100" in device_name or "h200" in device_name:
            # For H100 and H200, we use larger default values.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }

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        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
            self.max_num_batched_tokens = default_max_num_batched_tokens[
                usage_context]
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            logger.warning(
                "Setting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens, usage_context.value)
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    def _override_v1_engine_config(self, engine_config: VllmConfig) -> None:
        """
        Override the EngineConfig's configs based on the usage context for V1.
        """
        assert envs.VLLM_USE_V1, "V1 is not enabled"

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@dataclass
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Zhuohan Li committed
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class AsyncEngineArgs(EngineArgs):
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    """Arguments for asynchronous vLLM engine."""
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    disable_log_requests: bool = False
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    @staticmethod
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    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
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        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
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        parser.add_argument('--disable-log-requests',
                            action='store_true',
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                            help='Disable logging requests.')
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        # 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()
        from vllm.platforms import current_platform
        current_platform.pre_register_and_update(parser)
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        return parser
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# These functions are used by sphinx to build the documentation
def _engine_args_parser():
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    return EngineArgs.add_cli_args(FlexibleArgumentParser())
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def _async_engine_args_parser():
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    return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
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                                        async_args_only=True)