# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # yapf: disable import argparse import copy import dataclasses import functools import json import sys import threading from dataclasses import MISSING, dataclass, fields, is_dataclass from itertools import permutations from typing import (TYPE_CHECKING, Annotated, Any, Callable, Dict, List, Literal, Optional, Type, TypeVar, Union, cast, get_args, get_origin) import regex as re import torch from pydantic import TypeAdapter, ValidationError from typing_extensions import TypeIs, deprecated import vllm.envs as envs from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig, ConfigFormat, ConfigType, ConvertOption, DecodingConfig, DetailedTraceModules, Device, DeviceConfig, DistributedExecutorBackend, GuidedDecodingBackend, HfOverrides, KVEventsConfig, KVTransferConfig, LoadConfig, LogprobsMode, LoRAConfig, MambaDType, ModelConfig, ModelDType, ModelImpl, MultiModalConfig, ObservabilityConfig, ParallelConfig, PoolerConfig, PrefixCachingHashAlgo, RunnerOption, SchedulerConfig, SchedulerPolicy, SpeculativeConfig, TaskOption, TokenizerMode, VllmConfig, get_attr_docs, get_field) from vllm.logger import init_logger from vllm.platforms import CpuArchEnum, current_platform from vllm.plugins import load_general_plugins from vllm.ray.lazy_utils import is_ray_initialized from vllm.reasoning import ReasoningParserManager from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3 from vllm.transformers_utils.config import is_interleaved from vllm.transformers_utils.utils import check_gguf_file from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser, GiB_bytes, get_ip, is_in_ray_actor) from vllm.v1.sample.logits_processor import LogitsProcessor # yapf: enable if TYPE_CHECKING: from vllm.executor.executor_base import ExecutorBase from vllm.model_executor.layers.quantization import QuantizationMethods from vllm.model_executor.model_loader import LoadFormats from vllm.usage.usage_lib import UsageContext else: ExecutorBase = Any QuantizationMethods = Any LoadFormats = Any UsageContext = Any logger = init_logger(__name__) # object is used to allow for special typing forms T = TypeVar("T") TypeHint = Union[type[Any], object] TypeHintT = Union[type[T], object] def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]: def _parse_type(val: str) -> T: try: return return_type(val) except ValueError as e: raise argparse.ArgumentTypeError( f"Value {val} cannot be converted to {return_type}.") from e return _parse_type def optional_type( return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]: def _optional_type(val: str) -> Optional[T]: if val == "" or val == "None": return None return parse_type(return_type)(val) return _optional_type def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]: if not re.match(r"(?s)^\s*{.*}\s*$", val): return str(val) return optional_type(json.loads)(val) def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]: """Check if the type hint is a specific type.""" return type_hint is type or get_origin(type_hint) is type def contains_type(type_hints: set[TypeHint], type: TypeHintT) -> bool: """Check if the type hints contain a specific type.""" return any(is_type(type_hint, type) for type_hint in type_hints) def get_type(type_hints: set[TypeHint], type: TypeHintT) -> TypeHintT: """Get the specific type from the type hints.""" return next((th for th in type_hints if is_type(th, type)), None) def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]: """Get the `type` and `choices` from a `Literal` type hint in `type_hints`. If `type_hints` also contains `str`, we use `metavar` instead of `choices`. """ type_hint = get_type(type_hints, Literal) options = get_args(type_hint) option_type = type(options[0]) if not all(isinstance(option, option_type) for option in options): raise ValueError( "All options must be of the same type. " f"Got {options} with types {[type(c) for c in options]}") kwarg = "metavar" if contains_type(type_hints, str) else "choices" return {"type": option_type, kwarg: sorted(options)} def is_not_builtin(type_hint: TypeHint) -> bool: """Check if the class is not a built-in type.""" return type_hint.__module__ != "builtins" def get_type_hints(type_hint: TypeHint) -> set[TypeHint]: """Extract type hints from Annotated or Union type hints.""" type_hints: set[TypeHint] = set() origin = get_origin(type_hint) args = get_args(type_hint) if origin is Annotated: type_hints.update(get_type_hints(args[0])) elif origin is Union: for arg in args: type_hints.update(get_type_hints(arg)) else: type_hints.add(type_hint) return type_hints def is_online_quantization(quantization: Any) -> bool: return quantization in ["inc"] @functools.lru_cache(maxsize=30) def _compute_kwargs(cls: ConfigType) -> dict[str, Any]: cls_docs = get_attr_docs(cls) kwargs = {} for field in fields(cls): # Get the set of possible types for the field type_hints: set[TypeHint] = get_type_hints(field.type) # If the field is a dataclass, we can use the model_validate_json generator = (th for th in type_hints if is_dataclass(th)) dataclass_cls = next(generator, None) # Get the default value of the field if field.default is not MISSING: default = field.default elif field.default_factory is not MISSING: default = field.default_factory() # Get the help text for the field name = field.name help = cls_docs[name].strip() # Escape % for argparse help = help.replace("%", "%%") # Initialise the kwargs dictionary for the field kwargs[name] = {"default": default, "help": help} # Set other kwargs based on the type hints json_tip = ("Should either be a valid JSON string or JSON keys passed " "individually.") if dataclass_cls is not None: def parse_dataclass(val: str, cls=dataclass_cls) -> Any: try: return TypeAdapter(cls).validate_json(val) except ValidationError as e: raise argparse.ArgumentTypeError(repr(e)) from e kwargs[name]["type"] = parse_dataclass kwargs[name]["help"] += f"\n\n{json_tip}" elif contains_type(type_hints, bool): # Creates --no- and -- flags kwargs[name]["action"] = argparse.BooleanOptionalAction elif contains_type(type_hints, Literal): kwargs[name].update(literal_to_kwargs(type_hints)) elif contains_type(type_hints, tuple): type_hint = get_type(type_hints, tuple) types = get_args(type_hint) tuple_type = types[0] assert all(t is tuple_type for t in types if t is not Ellipsis), ( "All non-Ellipsis tuple elements must be of the same " f"type. Got {types}.") kwargs[name]["type"] = tuple_type kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types) elif contains_type(type_hints, list): type_hint = get_type(type_hints, list) types = get_args(type_hint) list_type = types[0] if get_origin(list_type) is Union: msg = "List type must contain str if it is a Union." assert str in get_args(list_type), msg list_type = str kwargs[name]["type"] = list_type kwargs[name]["nargs"] = "+" elif contains_type(type_hints, int): kwargs[name]["type"] = int # Special case for large integers if name in {"max_model_len", "max_num_batched_tokens"}: kwargs[name]["type"] = human_readable_int elif contains_type(type_hints, float): kwargs[name]["type"] = float elif (contains_type(type_hints, dict) and (contains_type(type_hints, str) or any(is_not_builtin(th) for th in type_hints))): kwargs[name]["type"] = union_dict_and_str elif contains_type(type_hints, dict): kwargs[name]["type"] = parse_type(json.loads) kwargs[name]["help"] += f"\n\n{json_tip}" elif (contains_type(type_hints, str) or any(is_not_builtin(th) for th in type_hints)): kwargs[name]["type"] = str else: raise ValueError( f"Unsupported type {type_hints} for argument {name}.") # If the type hint was a sequence of literals, use the helper function # to update the type and choices if get_origin(kwargs[name].get("type")) is Literal: kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]})) # If None is in type_hints, make the argument optional. # But not if it's a bool, argparse will handle this better. if type(None) in type_hints and not contains_type(type_hints, bool): kwargs[name]["type"] = optional_type(kwargs[name]["type"]) if kwargs[name].get("choices"): kwargs[name]["choices"].append("None") return kwargs def get_kwargs(cls: ConfigType) -> dict[str, Any]: """Return argparse kwargs for the given Config dataclass. The heavy computation is cached via functools.lru_cache, and a deep copy is returned so callers can mutate the dictionary without affecting the cached version. """ return copy.deepcopy(_compute_kwargs(cls)) @dataclass class EngineArgs: """Arguments for vLLM engine.""" model: str = ModelConfig.model served_model_name: Optional[Union[ str, List[str]]] = ModelConfig.served_model_name tokenizer: Optional[str] = ModelConfig.tokenizer hf_config_path: Optional[str] = ModelConfig.hf_config_path runner: RunnerOption = ModelConfig.runner convert: ConvertOption = ModelConfig.convert task: Optional[TaskOption] = ModelConfig.task skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode trust_remote_code: bool = ModelConfig.trust_remote_code allowed_local_media_path: str = ModelConfig.allowed_local_media_path download_dir: Optional[str] = LoadConfig.download_dir load_format: Union[str, LoadFormats] = LoadConfig.load_format config_format: str = ModelConfig.config_format dtype: ModelDType = ModelConfig.dtype kv_cache_dtype: CacheDType = CacheConfig.cache_dtype seed: Optional[int] = ModelConfig.seed max_model_len: Optional[int] = ModelConfig.max_model_len cuda_graph_sizes: list[int] = get_field(SchedulerConfig, "cuda_graph_sizes") # Note: Specifying a custom executor backend by passing a class # is intended for expert use only. The API may change without # notice. distributed_executor_backend: Optional[Union[ DistributedExecutorBackend, Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend # number of P/D disaggregation (or other disaggregation) workers pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size tensor_parallel_size: int = ParallelConfig.tensor_parallel_size data_parallel_size: int = ParallelConfig.data_parallel_size data_parallel_rank: Optional[int] = None data_parallel_start_rank: Optional[int] = None data_parallel_size_local: Optional[int] = None data_parallel_address: Optional[str] = None data_parallel_rpc_port: Optional[int] = None data_parallel_hybrid_lb: bool = False data_parallel_backend: str = ParallelConfig.data_parallel_backend enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel enable_eplb: bool = ParallelConfig.enable_eplb num_redundant_experts: int = ParallelConfig.num_redundant_experts eplb_window_size: int = ParallelConfig.eplb_window_size eplb_step_interval: int = ParallelConfig.eplb_step_interval eplb_log_balancedness: bool = ParallelConfig.eplb_log_balancedness max_parallel_loading_workers: Optional[ int] = ParallelConfig.max_parallel_loading_workers block_size: Optional[BlockSize] = CacheConfig.block_size enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching prefix_caching_hash_algo: PrefixCachingHashAlgo = \ CacheConfig.prefix_caching_hash_algo disable_sliding_window: bool = ModelConfig.disable_sliding_window disable_cascade_attn: bool = ModelConfig.disable_cascade_attn swap_space: float = CacheConfig.swap_space cpu_offload_gb: float = CacheConfig.cpu_offload_gb gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization max_num_batched_tokens: Optional[ int] = SchedulerConfig.max_num_batched_tokens max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills long_prefill_token_threshold: int = \ SchedulerConfig.long_prefill_token_threshold max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs max_logprobs: int = ModelConfig.max_logprobs logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode disable_log_stats: bool = False revision: Optional[str] = ModelConfig.revision code_revision: Optional[str] = ModelConfig.code_revision rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling") rope_theta: Optional[float] = ModelConfig.rope_theta hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides") tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision quantization: Optional[QuantizationMethods] = ModelConfig.quantization enforce_eager: bool = ModelConfig.enforce_eager max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce limit_mm_per_prompt: dict[str, int] = \ get_field(MultiModalConfig, "limit_per_prompt") interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings media_io_kwargs: dict[str, dict[str, Any]] = get_field(MultiModalConfig, "media_io_kwargs") mm_processor_kwargs: Optional[Dict[str, Any]] = \ MultiModalConfig.mm_processor_kwargs disable_mm_preprocessor_cache: bool = False # DEPRECATED mm_processor_cache_gb: int = MultiModalConfig.mm_processor_cache_gb skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling # LoRA fields enable_lora: bool = False enable_lora_bias: bool = LoRAConfig.bias_enabled max_loras: int = LoRAConfig.max_loras max_lora_rank: int = LoRAConfig.max_lora_rank default_mm_loras: Optional[Dict[str, str]] = \ LoRAConfig.default_mm_loras fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight num_gpu_blocks_override: Optional[ int] = CacheConfig.num_gpu_blocks_override num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots model_loader_extra_config: dict = \ get_field(LoadConfig, "model_loader_extra_config") ignore_patterns: Optional[Union[str, List[str]]] = LoadConfig.ignore_patterns preemption_mode: Optional[str] = SchedulerConfig.preemption_mode scheduler_delay_factor: float = SchedulerConfig.delay_factor enable_chunked_prefill: Optional[ bool] = SchedulerConfig.enable_chunked_prefill disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input disable_hybrid_kv_cache_manager: bool = ( SchedulerConfig.disable_hybrid_kv_cache_manager) guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback guided_decoding_disable_any_whitespace: bool = \ DecodingConfig.disable_any_whitespace guided_decoding_disable_additional_properties: bool = \ DecodingConfig.disable_additional_properties logits_processor_pattern: Optional[ str] = ModelConfig.logits_processor_pattern speculative_config: Optional[Dict[str, Any]] = None show_hidden_metrics_for_version: Optional[str] = \ ObservabilityConfig.show_hidden_metrics_for_version otlp_traces_endpoint: Optional[str] = \ ObservabilityConfig.otlp_traces_endpoint collect_detailed_traces: Optional[list[DetailedTraceModules]] = \ ObservabilityConfig.collect_detailed_traces disable_async_output_proc: bool = not ModelConfig.use_async_output_proc scheduling_policy: SchedulerPolicy = SchedulerConfig.policy scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls override_neuron_config: dict[str, Any] = \ get_field(ModelConfig, "override_neuron_config") override_pooler_config: Optional[Union[dict, PoolerConfig]] = \ ModelConfig.override_pooler_config compilation_config: CompilationConfig = \ get_field(VllmConfig, "compilation_config") worker_cls: str = ParallelConfig.worker_cls worker_extension_cls: str = ParallelConfig.worker_extension_cls kv_transfer_config: Optional[KVTransferConfig] = None kv_events_config: Optional[KVEventsConfig] = None generation_config: str = ModelConfig.generation_config enable_sleep_mode: bool = ModelConfig.enable_sleep_mode override_generation_config: dict[str, Any] = \ get_field(ModelConfig, "override_generation_config") model_impl: str = ModelConfig.model_impl override_attention_dtype: str = ModelConfig.override_attention_dtype calculate_kv_scales: bool = CacheConfig.calculate_kv_scales mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype additional_config: dict[str, Any] = \ get_field(VllmConfig, "additional_config") reasoning_parser: str = DecodingConfig.reasoning_backend use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load pt_load_map_location: str = LoadConfig.pt_load_map_location enable_multimodal_encoder_data_parallel: bool = \ ParallelConfig.enable_multimodal_encoder_data_parallel logits_processors: Optional[list[Union[ str, type[LogitsProcessor]]]] = ModelConfig.logits_processors """Custom logitproc types""" async_scheduling: bool = SchedulerConfig.async_scheduling # DEPRECATED enable_prompt_adapter: bool = False kv_sharing_fast_prefill: bool = \ CacheConfig.kv_sharing_fast_prefill def __post_init__(self): # support `EngineArgs(compilation_config={...})` # without having to manually construct a # CompilationConfig object if isinstance(self.compilation_config, dict): self.compilation_config = CompilationConfig( **self.compilation_config) # Setup plugins from vllm.plugins import load_general_plugins load_general_plugins() @staticmethod def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser: """Shared CLI arguments for vLLM engine.""" # Model arguments model_kwargs = get_kwargs(ModelConfig) model_group = parser.add_argument_group( title="ModelConfig", description=ModelConfig.__doc__, ) if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]): model_group.add_argument("--model", **model_kwargs["model"]) model_group.add_argument("--runner", **model_kwargs["runner"]) model_group.add_argument("--convert", **model_kwargs["convert"]) model_group.add_argument("--task", **model_kwargs["task"], deprecated=True) model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"]) model_group.add_argument("--tokenizer-mode", **model_kwargs["tokenizer_mode"]) model_group.add_argument("--trust-remote-code", **model_kwargs["trust_remote_code"]) model_group.add_argument("--dtype", **model_kwargs["dtype"]) model_group.add_argument("--seed", **model_kwargs["seed"]) model_group.add_argument("--hf-config-path", **model_kwargs["hf_config_path"]) model_group.add_argument("--allowed-local-media-path", **model_kwargs["allowed_local_media_path"]) model_group.add_argument("--revision", **model_kwargs["revision"]) model_group.add_argument("--code-revision", **model_kwargs["code_revision"]) model_group.add_argument("--rope-scaling", **model_kwargs["rope_scaling"]) model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"]) model_group.add_argument("--tokenizer-revision", **model_kwargs["tokenizer_revision"]) model_group.add_argument("--max-model-len", **model_kwargs["max_model_len"]) model_group.add_argument("--quantization", "-q", **model_kwargs["quantization"]) model_group.add_argument("--enforce-eager", **model_kwargs["enforce_eager"]) model_group.add_argument("--max-seq-len-to-capture", **model_kwargs["max_seq_len_to_capture"]) model_group.add_argument("--max-logprobs", **model_kwargs["max_logprobs"]) model_group.add_argument("--logprobs-mode", **model_kwargs["logprobs_mode"]) model_group.add_argument("--disable-sliding-window", **model_kwargs["disable_sliding_window"]) model_group.add_argument("--disable-cascade-attn", **model_kwargs["disable_cascade_attn"]) model_group.add_argument("--skip-tokenizer-init", **model_kwargs["skip_tokenizer_init"]) model_group.add_argument("--enable-prompt-embeds", **model_kwargs["enable_prompt_embeds"]) model_group.add_argument("--served-model-name", **model_kwargs["served_model_name"]) # This one is a special case because it is the # opposite of ModelConfig.use_async_output_proc model_group.add_argument( "--disable-async-output-proc", action="store_true", default=EngineArgs.disable_async_output_proc, help="Disable async output processing. This may result in " "lower performance.") model_group.add_argument("--config-format", choices=[f.value for f in ConfigFormat], **model_kwargs["config_format"]) # This one is a special case because it can bool # or str. TODO: Handle this in get_kwargs model_group.add_argument("--hf-token", type=str, nargs="?", const=True, default=model_kwargs["hf_token"]["default"], help=model_kwargs["hf_token"]["help"]) model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"]) model_group.add_argument("--override-neuron-config", **model_kwargs["override_neuron_config"]) model_group.add_argument("--override-pooler-config", **model_kwargs["override_pooler_config"]) model_group.add_argument("--logits-processor-pattern", **model_kwargs["logits_processor_pattern"]) model_group.add_argument("--generation-config", **model_kwargs["generation_config"]) model_group.add_argument("--override-generation-config", **model_kwargs["override_generation_config"]) model_group.add_argument("--enable-sleep-mode", **model_kwargs["enable_sleep_mode"]) model_group.add_argument("--model-impl", choices=[f.value for f in ModelImpl], **model_kwargs["model_impl"]) model_group.add_argument("--override-attention-dtype", **model_kwargs["override_attention_dtype"]) model_group.add_argument("--logits-processors", **model_kwargs["logits_processors"]) # Model loading arguments load_kwargs = get_kwargs(LoadConfig) load_group = parser.add_argument_group( title="LoadConfig", description=LoadConfig.__doc__, ) load_group.add_argument("--load-format", **load_kwargs["load_format"]) load_group.add_argument("--download-dir", **load_kwargs["download_dir"]) load_group.add_argument("--model-loader-extra-config", **load_kwargs["model_loader_extra_config"]) load_group.add_argument("--ignore-patterns", **load_kwargs["ignore_patterns"]) load_group.add_argument("--use-tqdm-on-load", **load_kwargs["use_tqdm_on_load"]) load_group.add_argument('--pt-load-map-location', **load_kwargs["pt_load_map_location"]) # Guided decoding arguments guided_decoding_kwargs = get_kwargs(DecodingConfig) guided_decoding_group = parser.add_argument_group( title="DecodingConfig", description=DecodingConfig.__doc__, ) guided_decoding_group.add_argument("--guided-decoding-backend", **guided_decoding_kwargs["backend"]) guided_decoding_group.add_argument( "--guided-decoding-disable-fallback", **guided_decoding_kwargs["disable_fallback"]) guided_decoding_group.add_argument( "--guided-decoding-disable-any-whitespace", **guided_decoding_kwargs["disable_any_whitespace"]) guided_decoding_group.add_argument( "--guided-decoding-disable-additional-properties", **guided_decoding_kwargs["disable_additional_properties"]) guided_decoding_group.add_argument( "--reasoning-parser", # This choices is a special case because it's not static choices=list(ReasoningParserManager.reasoning_parsers), **guided_decoding_kwargs["reasoning_backend"]) # Parallel arguments parallel_kwargs = get_kwargs(ParallelConfig) parallel_group = parser.add_argument_group( title="ParallelConfig", description=ParallelConfig.__doc__, ) parallel_group.add_argument( "--distributed-executor-backend", **parallel_kwargs["distributed_executor_backend"]) parallel_group.add_argument( "--pipeline-parallel-size", "-pp", **parallel_kwargs["pipeline_parallel_size"]) parallel_group.add_argument("--tensor-parallel-size", "-tp", **parallel_kwargs["tensor_parallel_size"]) parallel_group.add_argument("--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]) parallel_group.add_argument( '--data-parallel-rank', '-dpn', type=int, help='Data parallel rank of this instance. ' 'When set, enables external load balancer mode.') parallel_group.add_argument('--data-parallel-start-rank', '-dpr', type=int, help='Starting data parallel rank ' 'for secondary nodes.') parallel_group.add_argument('--data-parallel-size-local', '-dpl', type=int, help='Number of data parallel replicas ' 'to run on this node.') parallel_group.add_argument('--data-parallel-address', '-dpa', type=str, help='Address of data parallel cluster ' 'head-node.') parallel_group.add_argument('--data-parallel-rpc-port', '-dpp', type=int, help='Port for data parallel RPC ' 'communication.') parallel_group.add_argument('--data-parallel-backend', '-dpb', type=str, default='mp', help='Backend for data parallel, either ' '"mp" or "ray".') parallel_group.add_argument( "--data-parallel-hybrid-lb", **parallel_kwargs["data_parallel_hybrid_lb"]) parallel_group.add_argument( "--enable-expert-parallel", **parallel_kwargs["enable_expert_parallel"]) parallel_group.add_argument("--enable-eplb", **parallel_kwargs["enable_eplb"]) parallel_group.add_argument("--num-redundant-experts", **parallel_kwargs["num_redundant_experts"]) parallel_group.add_argument("--eplb-window-size", **parallel_kwargs["eplb_window_size"]) parallel_group.add_argument("--eplb-step-interval", **parallel_kwargs["eplb_step_interval"]) parallel_group.add_argument("--eplb-log-balancedness", **parallel_kwargs["eplb_log_balancedness"]) parallel_group.add_argument( "--max-parallel-loading-workers", **parallel_kwargs["max_parallel_loading_workers"]) parallel_group.add_argument( "--ray-workers-use-nsight", **parallel_kwargs["ray_workers_use_nsight"]) parallel_group.add_argument( "--disable-custom-all-reduce", **parallel_kwargs["disable_custom_all_reduce"]) parallel_group.add_argument("--worker-cls", **parallel_kwargs["worker_cls"]) parallel_group.add_argument("--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]) parallel_group.add_argument( "--enable-multimodal-encoder-data-parallel", **parallel_kwargs["enable_multimodal_encoder_data_parallel"]) # KV cache arguments cache_kwargs = get_kwargs(CacheConfig) cache_group = parser.add_argument_group( title="CacheConfig", description=CacheConfig.__doc__, ) cache_group.add_argument("--block-size", **cache_kwargs["block_size"]) cache_group.add_argument("--gpu-memory-utilization", **cache_kwargs["gpu_memory_utilization"]) cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"]) cache_group.add_argument("--kv-cache-dtype", **cache_kwargs["cache_dtype"]) cache_group.add_argument("--num-gpu-blocks-override", **cache_kwargs["num_gpu_blocks_override"]) cache_group.add_argument("--enable-prefix-caching", **cache_kwargs["enable_prefix_caching"]) cache_group.add_argument("--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]) cache_group.add_argument("--cpu-offload-gb", **cache_kwargs["cpu_offload_gb"]) cache_group.add_argument("--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]) cache_group.add_argument("--kv-sharing-fast-prefill", **cache_kwargs["kv_sharing_fast_prefill"]) cache_group.add_argument("--mamba-cache-dtype", **cache_kwargs["mamba_cache_dtype"]) cache_group.add_argument("--mamba-ssm-cache-dtype", **cache_kwargs["mamba_ssm_cache_dtype"]) # Multimodal related configs multimodal_kwargs = get_kwargs(MultiModalConfig) multimodal_group = parser.add_argument_group( title="MultiModalConfig", description=MultiModalConfig.__doc__, ) multimodal_group.add_argument("--limit-mm-per-prompt", **multimodal_kwargs["limit_per_prompt"]) multimodal_group.add_argument("--media-io-kwargs", **multimodal_kwargs["media_io_kwargs"]) multimodal_group.add_argument( "--mm-processor-kwargs", **multimodal_kwargs["mm_processor_kwargs"]) multimodal_group.add_argument( "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]) multimodal_group.add_argument("--disable-mm-preprocessor-cache", action="store_true", deprecated=True) multimodal_group.add_argument( "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]) multimodal_group.add_argument("--skip-mm-profiling", **multimodal_kwargs["skip_mm_profiling"]) # LoRA related configs lora_kwargs = get_kwargs(LoRAConfig) lora_group = parser.add_argument_group( title="LoRAConfig", description=LoRAConfig.__doc__, ) lora_group.add_argument( "--enable-lora", action=argparse.BooleanOptionalAction, help="If True, enable handling of LoRA adapters.") lora_group.add_argument("--enable-lora-bias", **lora_kwargs["bias_enabled"]) lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"]) lora_group.add_argument("--max-lora-rank", **lora_kwargs["max_lora_rank"]) lora_group.add_argument("--lora-extra-vocab-size", **lora_kwargs["lora_extra_vocab_size"]) lora_group.add_argument( "--lora-dtype", **lora_kwargs["lora_dtype"], ) lora_group.add_argument("--max-cpu-loras", **lora_kwargs["max_cpu_loras"]) lora_group.add_argument("--fully-sharded-loras", **lora_kwargs["fully_sharded_loras"]) lora_group.add_argument("--default-mm-loras", **lora_kwargs["default_mm_loras"]) # Observability arguments observability_kwargs = get_kwargs(ObservabilityConfig) observability_group = parser.add_argument_group( title="ObservabilityConfig", description=ObservabilityConfig.__doc__, ) observability_group.add_argument( "--show-hidden-metrics-for-version", **observability_kwargs["show_hidden_metrics_for_version"]) observability_group.add_argument( "--otlp-traces-endpoint", **observability_kwargs["otlp_traces_endpoint"]) # TODO: generalise this special case choices = observability_kwargs["collect_detailed_traces"]["choices"] metavar = f"{{{','.join(choices)}}}" observability_kwargs["collect_detailed_traces"]["metavar"] = metavar observability_kwargs["collect_detailed_traces"]["choices"] += [ ",".join(p) for p in permutations(get_args(DetailedTraceModules), r=2) ] observability_group.add_argument( "--collect-detailed-traces", **observability_kwargs["collect_detailed_traces"]) # Scheduler arguments scheduler_kwargs = get_kwargs(SchedulerConfig) scheduler_group = parser.add_argument_group( title="SchedulerConfig", description=SchedulerConfig.__doc__, ) scheduler_group.add_argument( "--max-num-batched-tokens", **scheduler_kwargs["max_num_batched_tokens"]) scheduler_group.add_argument("--max-num-seqs", **scheduler_kwargs["max_num_seqs"]) scheduler_group.add_argument( "--max-num-partial-prefills", **scheduler_kwargs["max_num_partial_prefills"]) scheduler_group.add_argument( "--max-long-partial-prefills", **scheduler_kwargs["max_long_partial_prefills"]) scheduler_group.add_argument('--cuda-graph-sizes', **scheduler_kwargs["cuda_graph_sizes"]) scheduler_group.add_argument( "--long-prefill-token-threshold", **scheduler_kwargs["long_prefill_token_threshold"]) scheduler_group.add_argument("--num-lookahead-slots", **scheduler_kwargs["num_lookahead_slots"]) scheduler_group.add_argument("--scheduler-delay-factor", **scheduler_kwargs["delay_factor"]) scheduler_group.add_argument("--preemption-mode", **scheduler_kwargs["preemption_mode"]) # multi-step scheduling has been removed; corresponding arguments # are no longer supported. scheduler_group.add_argument("--scheduling-policy", **scheduler_kwargs["policy"]) scheduler_group.add_argument( "--enable-chunked-prefill", **scheduler_kwargs["enable_chunked_prefill"]) scheduler_group.add_argument( "--disable-chunked-mm-input", **scheduler_kwargs["disable_chunked_mm_input"]) scheduler_group.add_argument("--scheduler-cls", **scheduler_kwargs["scheduler_cls"]) scheduler_group.add_argument( "--disable-hybrid-kv-cache-manager", **scheduler_kwargs["disable_hybrid_kv_cache_manager"]) scheduler_group.add_argument("--async-scheduling", **scheduler_kwargs["async_scheduling"]) # vLLM arguments vllm_kwargs = get_kwargs(VllmConfig) vllm_group = parser.add_argument_group( title="VllmConfig", description=VllmConfig.__doc__, ) # We construct SpeculativeConfig using fields from other configs in # create_engine_config. So we set the type to a JSON string here to # delay the Pydantic validation that comes with SpeculativeConfig. vllm_kwargs["speculative_config"]["type"] = optional_type(json.loads) vllm_group.add_argument("--speculative-config", **vllm_kwargs["speculative_config"]) vllm_group.add_argument("--kv-transfer-config", **vllm_kwargs["kv_transfer_config"]) vllm_group.add_argument('--kv-events-config', **vllm_kwargs["kv_events_config"]) vllm_group.add_argument("--compilation-config", "-O", **vllm_kwargs["compilation_config"]) vllm_group.add_argument("--additional-config", **vllm_kwargs["additional_config"]) # Other arguments parser.add_argument('--disable-log-stats', action='store_true', help='Disable logging statistics.') parser.add_argument('--enable-prompt-adapter', action='store_true', deprecated=True, help='[DEPRECATED] Prompt adapter has been ' 'removed. Setting this flag to True or False' ' has no effect on vLLM behavior.') return parser @classmethod def from_cli_args(cls, args: argparse.Namespace): # Get the list of attributes of this dataclass. attrs = [attr.name for attr in dataclasses.fields(cls)] # Set the attributes from the parsed arguments. engine_args = cls(**{attr: getattr(args, attr) for attr in attrs}) return engine_args def create_model_config(self) -> ModelConfig: # gguf file needs a specific model loader and doesn't use hf_repo if check_gguf_file(self.model): self.quantization = self.load_format = "gguf" # NOTE: This is to allow model loading from S3 in CI if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3 and self.model in MODELS_ON_S3 and self.load_format == "auto"): self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}" self.load_format = "runai_streamer" if self.disable_mm_preprocessor_cache: logger.warning( "`--disable-mm-preprocessor-cache` is deprecated " "and will be removed in v0.13. " "Please use `--mm-processor-cache-gb 0` instead.", ) self.mm_processor_cache_gb = 0 elif envs.VLLM_MM_INPUT_CACHE_GIB != 4: logger.warning( "VLLM_MM_INPUT_CACHE_GIB` is deprecated " "and will be removed in v0.13. " "Please use `--mm-processor-cache-gb %d` instead.", envs.VLLM_MM_INPUT_CACHE_GIB, ) self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB return ModelConfig( model=self.model, hf_config_path=self.hf_config_path, runner=self.runner, convert=self.convert, task=self.task, tokenizer=self.tokenizer, tokenizer_mode=self.tokenizer_mode, trust_remote_code=self.trust_remote_code, allowed_local_media_path=self.allowed_local_media_path, dtype=self.dtype, seed=self.seed, revision=self.revision, code_revision=self.code_revision, rope_scaling=self.rope_scaling, rope_theta=self.rope_theta, hf_token=self.hf_token, hf_overrides=self.hf_overrides, tokenizer_revision=self.tokenizer_revision, max_model_len=self.max_model_len, quantization=self.quantization, enforce_eager=self.enforce_eager, max_seq_len_to_capture=self.max_seq_len_to_capture, max_logprobs=self.max_logprobs, logprobs_mode=self.logprobs_mode, disable_sliding_window=self.disable_sliding_window, disable_cascade_attn=self.disable_cascade_attn, skip_tokenizer_init=self.skip_tokenizer_init, enable_prompt_embeds=self.enable_prompt_embeds, served_model_name=self.served_model_name, limit_mm_per_prompt=self.limit_mm_per_prompt, interleave_mm_strings=self.interleave_mm_strings, media_io_kwargs=self.media_io_kwargs, skip_mm_profiling=self.skip_mm_profiling, use_async_output_proc=not self.disable_async_output_proc, config_format=self.config_format, mm_processor_kwargs=self.mm_processor_kwargs, mm_processor_cache_gb=self.mm_processor_cache_gb, override_neuron_config=self.override_neuron_config, override_pooler_config=self.override_pooler_config, logits_processor_pattern=self.logits_processor_pattern, generation_config=self.generation_config, override_generation_config=self.override_generation_config, enable_sleep_mode=self.enable_sleep_mode, model_impl=self.model_impl, override_attention_dtype=self.override_attention_dtype, logits_processors=self.logits_processors, ) def validate_tensorizer_args(self): from vllm.model_executor.model_loader.tensorizer import ( TensorizerConfig) for key in self.model_loader_extra_config: if key in TensorizerConfig._fields: self.model_loader_extra_config["tensorizer_config"][ key] = self.model_loader_extra_config[key] def create_load_config(self) -> LoadConfig: if self.quantization == "bitsandbytes": self.load_format = "bitsandbytes" if self.load_format == "tensorizer": if hasattr(self.model_loader_extra_config, "to_serializable"): self.model_loader_extra_config = ( self.model_loader_extra_config.to_serializable()) self.model_loader_extra_config["tensorizer_config"] = {} self.model_loader_extra_config["tensorizer_config"][ "tensorizer_dir"] = self.model self.validate_tensorizer_args() return LoadConfig( load_format=self.load_format, download_dir=self.download_dir, device="cpu" if is_online_quantization(self.quantization) else None, model_loader_extra_config=self.model_loader_extra_config, ignore_patterns=self.ignore_patterns, use_tqdm_on_load=self.use_tqdm_on_load, pt_load_map_location=self.pt_load_map_location, ) def create_speculative_config( self, target_model_config: ModelConfig, target_parallel_config: ParallelConfig, enable_chunked_prefill: bool, disable_log_stats: bool, ) -> Optional["SpeculativeConfig"]: """Initializes and returns a SpeculativeConfig object based on `speculative_config`. This function utilizes `speculative_config` to create a SpeculativeConfig object. The `speculative_config` can either be provided as a JSON string input via CLI arguments or directly as a dictionary from the engine. """ from vllm.transformers_utils.config import get_config from vllm.transformers_utils.configs.speculators.base import ( SpeculatorsConfig) if self.speculative_config is None: hf_config = get_config(self.hf_config_path or self.model, self.trust_remote_code, self.revision, self.code_revision, self.config_format) # if loading a SpeculatorsConfig, load the specualtive_config # details from the config directly # no user input required / expected if isinstance(hf_config, SpeculatorsConfig): # We create one since we dont create one self.speculative_config = {} self.speculative_config[ "num_speculative_tokens"] = hf_config.num_lookahead_tokens self.speculative_config["model"] = self.model self.speculative_config["method"] = hf_config.method else: return None # Note(Shangming): These parameters are not obtained from the cli arg # '--speculative-config' and must be passed in when creating the engine # config. self.speculative_config.update({ "target_model_config": target_model_config, "target_parallel_config": target_parallel_config, "enable_chunked_prefill": enable_chunked_prefill, "disable_log_stats": disable_log_stats, }) return SpeculativeConfig(**self.speculative_config) def create_engine_config( self, usage_context: Optional[UsageContext] = None, headless: bool = False, ) -> VllmConfig: """ Create the VllmConfig. NOTE: for autoselection of V0 vs V1 engine, we need to create the ModelConfig first, since ModelConfig's attrs (e.g. the model arch) are needed to make the decision. This function set VLLM_USE_V1=X if VLLM_USE_V1 is unspecified by the user. If VLLM_USE_V1 is specified by the user but the VllmConfig is incompatible, we raise an error. """ current_platform.pre_register_and_update() device_config = DeviceConfig( device=cast(Device, current_platform.device_type)) model_config = self.create_model_config() # * If VLLM_USE_V1 is unset, we enable V1 for "supported features" # and fall back to V0 for experimental or unsupported features. # * If VLLM_USE_V1=1, we enable V1 for supported + experimental # features and raise error for unsupported features. # * If VLLM_USE_V1=0, we disable V1. use_v1 = False try_v1 = envs.VLLM_USE_V1 or not envs.is_set("VLLM_USE_V1") if try_v1 and self._is_v1_supported_oracle(model_config): use_v1 = True # If user explicitly set VLLM_USE_V1, sanity check we respect it. if envs.is_set("VLLM_USE_V1"): assert use_v1 == envs.VLLM_USE_V1 # Otherwise, set the VLLM_USE_V1 variable globally. else: envs.set_vllm_use_v1(use_v1) # Set default arguments for V0 or V1 Engine. if use_v1: self._set_default_args_v1(usage_context, model_config) # Disable chunked prefill for POWER (ppc64le)/ARM CPUs in V1 if current_platform.is_cpu( ) and current_platform.get_cpu_architecture() in ( CpuArchEnum.POWERPC, CpuArchEnum.ARM): logger.info( "Chunked prefill is not supported for ARM and POWER CPUs; " "disabling it for V1 backend.") self.enable_chunked_prefill = False else: self._set_default_args_v0(model_config) assert self.enable_chunked_prefill is not None if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]: assert self.enforce_eager, ( "Cuda graph is not supported with DualChunkFlashAttention. " "To run the model in eager mode, set 'enforce_eager=True' " "or use '--enforce-eager' in the CLI.") assert current_platform.is_cuda(), ( "DualChunkFlashAttention is only supported on CUDA platform.") assert not use_v1, ( "DualChunkFlashAttention is not supported on V1 engine. " "To run the model in V0 engine, try set 'VLLM_USE_V1=0'") sliding_window: Optional[int] = None if not is_interleaved(model_config.hf_text_config): # Only set CacheConfig.sliding_window if the model is all sliding # window. Otherwise CacheConfig.sliding_window will override the # global layers in interleaved sliding window models. sliding_window = model_config.get_sliding_window() cache_config = CacheConfig( block_size=self.block_size, gpu_memory_utilization=self.gpu_memory_utilization, swap_space=self.swap_space, cache_dtype=self.kv_cache_dtype, is_attention_free=model_config.is_attention_free, num_gpu_blocks_override=self.num_gpu_blocks_override, sliding_window=sliding_window, enable_prefix_caching=self.enable_prefix_caching, prefix_caching_hash_algo=self.prefix_caching_hash_algo, cpu_offload_gb=self.cpu_offload_gb, calculate_kv_scales=self.calculate_kv_scales, kv_sharing_fast_prefill=self.kv_sharing_fast_prefill, mamba_cache_dtype=self.mamba_cache_dtype, mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype, ) ray_runtime_env = None if is_ray_initialized(): # Ray Serve LLM calls `create_engine_config` in the context # of a Ray task, therefore we check is_ray_initialized() # as opposed to is_in_ray_actor(). import ray ray_runtime_env = ray.get_runtime_context().runtime_env logger.info("Using ray runtime env: %s", ray_runtime_env) # Get the current placement group if Ray is initialized and # we are in a Ray actor. If so, then the placement group will be # passed to spawned processes. placement_group = None if is_in_ray_actor(): import ray # This call initializes Ray automatically if it is not initialized, # but we should not do this here. placement_group = ray.util.get_current_placement_group() assert not headless or not self.data_parallel_hybrid_lb, ( "data_parallel_hybrid_lb is not applicable in " "headless mode") data_parallel_external_lb = self.data_parallel_rank is not None # Local DP rank = 1, use pure-external LB. if data_parallel_external_lb: assert self.data_parallel_size_local in (1, None), ( "data_parallel_size_local must be 1 when data_parallel_rank " "is set") data_parallel_size_local = 1 # Use full external lb if we have local_size of 1. self.data_parallel_hybrid_lb = False elif self.data_parallel_size_local is not None: data_parallel_size_local = self.data_parallel_size_local if self.data_parallel_start_rank and not headless: # Infer hybrid LB mode. self.data_parallel_hybrid_lb = True if self.data_parallel_hybrid_lb and data_parallel_size_local == 1: # Use full external lb if we have local_size of 1. data_parallel_external_lb = True self.data_parallel_hybrid_lb = False if data_parallel_size_local == self.data_parallel_size: # Disable hybrid LB mode if set for a single node self.data_parallel_hybrid_lb = False self.data_parallel_rank = self.data_parallel_start_rank or 0 else: assert not self.data_parallel_hybrid_lb, ( "data_parallel_size_local must be set to use " "data_parallel_hybrid_lb.") # Local DP size defaults to global DP size if not set. data_parallel_size_local = self.data_parallel_size # DP address, used in multi-node case for torch distributed group # and ZMQ sockets. if self.data_parallel_address is None: if self.data_parallel_backend == "ray": host_ip = get_ip() logger.info( "Using host IP %s as ray-based data parallel address", host_ip) data_parallel_address = host_ip else: assert self.data_parallel_backend == "mp", ( "data_parallel_backend can only be ray or mp, got %s", self.data_parallel_backend) data_parallel_address = ParallelConfig.data_parallel_master_ip else: data_parallel_address = self.data_parallel_address # This port is only used when there are remote data parallel engines, # otherwise the local IPC transport is used. data_parallel_rpc_port = self.data_parallel_rpc_port if ( self.data_parallel_rpc_port is not None) else ParallelConfig.data_parallel_rpc_port if self.async_scheduling: # Async scheduling does not work with the uniprocess backend. if self.distributed_executor_backend is None: self.distributed_executor_backend = "mp" logger.info("Using mp-based distributed executor backend " "for async scheduling.") if self.distributed_executor_backend == "uni": raise ValueError("Async scheduling is not supported with " "uni-process backend.") if self.pipeline_parallel_size > 1: raise ValueError("Async scheduling is not supported with " "pipeline-parallel-size > 1.") # Currently, async scheduling does not support speculative decoding. # TODO(woosuk): Support it. if self.speculative_config is not None: raise ValueError( "Currently, speculative decoding is not supported with " "async scheduling.") parallel_config = ParallelConfig( pipeline_parallel_size=self.pipeline_parallel_size, tensor_parallel_size=self.tensor_parallel_size, data_parallel_size=self.data_parallel_size, data_parallel_rank=self.data_parallel_rank or 0, data_parallel_external_lb=data_parallel_external_lb, data_parallel_size_local=data_parallel_size_local, data_parallel_master_ip=data_parallel_address, data_parallel_rpc_port=data_parallel_rpc_port, data_parallel_backend=self.data_parallel_backend, data_parallel_hybrid_lb=self.data_parallel_hybrid_lb, enable_expert_parallel=self.enable_expert_parallel, enable_eplb=self.enable_eplb, num_redundant_experts=self.num_redundant_experts, eplb_window_size=self.eplb_window_size, eplb_step_interval=self.eplb_step_interval, eplb_log_balancedness=self.eplb_log_balancedness, max_parallel_loading_workers=self.max_parallel_loading_workers, disable_custom_all_reduce=self.disable_custom_all_reduce, ray_workers_use_nsight=self.ray_workers_use_nsight, ray_runtime_env=ray_runtime_env, placement_group=placement_group, distributed_executor_backend=self.distributed_executor_backend, worker_cls=self.worker_cls, worker_extension_cls=self.worker_extension_cls, enable_multimodal_encoder_data_parallel=self. enable_multimodal_encoder_data_parallel, ) if model_config.is_multimodal_model: dp_supports_mm_processor_cache = (self.data_parallel_size == 1 or data_parallel_external_lb) if (not dp_supports_mm_processor_cache and model_config.mm_processor_cache_gb > 0): logger.warning( "Multi-modal processor cache is disabled because " "it is not compatible with data parallelism when " "there does not exist a one-to-one correspondance " "between API and engine core processes.") model_config.set_mm_processor_cache_gb(0) speculative_config = self.create_speculative_config( target_model_config=model_config, target_parallel_config=parallel_config, enable_chunked_prefill=self.enable_chunked_prefill, disable_log_stats=self.disable_log_stats, ) # make sure num_lookahead_slots is set appropriately depending on # whether speculative decoding is enabled num_lookahead_slots = self.num_lookahead_slots if speculative_config is not None: num_lookahead_slots = speculative_config.num_lookahead_slots scheduler_config = SchedulerConfig( runner_type=model_config.runner_type, max_num_batched_tokens=self.max_num_batched_tokens, max_num_seqs=self.max_num_seqs, max_model_len=model_config.max_model_len, cuda_graph_sizes=self.cuda_graph_sizes, num_lookahead_slots=num_lookahead_slots, delay_factor=self.scheduler_delay_factor, enable_chunked_prefill=self.enable_chunked_prefill, disable_chunked_mm_input=self.disable_chunked_mm_input, is_multimodal_model=model_config.is_multimodal_model, preemption_mode=self.preemption_mode, send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER and parallel_config.use_ray), policy=self.scheduling_policy, scheduler_cls=self.scheduler_cls, max_num_partial_prefills=self.max_num_partial_prefills, max_long_partial_prefills=self.max_long_partial_prefills, long_prefill_token_threshold=self.long_prefill_token_threshold, disable_hybrid_kv_cache_manager=self. disable_hybrid_kv_cache_manager, async_scheduling=self.async_scheduling, ) if not model_config.is_multimodal_model and self.default_mm_loras: raise ValueError( "Default modality-specific LoRA(s) were provided for a " "non multimodal model") lora_config = LoRAConfig( bias_enabled=self.enable_lora_bias, max_lora_rank=self.max_lora_rank, max_loras=self.max_loras, default_mm_loras=self.default_mm_loras, fully_sharded_loras=self.fully_sharded_loras, lora_extra_vocab_size=self.lora_extra_vocab_size, lora_dtype=self.lora_dtype, max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras and self.max_cpu_loras > 0 else None) if self.enable_lora else None # bitsandbytes pre-quantized model need a specific model loader if model_config.quantization == "bitsandbytes": self.quantization = self.load_format = "bitsandbytes" load_config = self.create_load_config() decoding_config = DecodingConfig( backend=self.guided_decoding_backend, disable_fallback=self.guided_decoding_disable_fallback, disable_any_whitespace=self.guided_decoding_disable_any_whitespace, disable_additional_properties=\ self.guided_decoding_disable_additional_properties, reasoning_backend=self.reasoning_parser ) observability_config = ObservabilityConfig( show_hidden_metrics_for_version=( self.show_hidden_metrics_for_version), otlp_traces_endpoint=self.otlp_traces_endpoint, collect_detailed_traces=self.collect_detailed_traces, ) config = VllmConfig( model_config=model_config, cache_config=cache_config, parallel_config=parallel_config, scheduler_config=scheduler_config, device_config=device_config, lora_config=lora_config, speculative_config=speculative_config, load_config=load_config, decoding_config=decoding_config, observability_config=observability_config, compilation_config=self.compilation_config, kv_transfer_config=self.kv_transfer_config, kv_events_config=self.kv_events_config, additional_config=self.additional_config, ) return config def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool: """Oracle for whether to use V0 or V1 Engine by default.""" ############################################################# # Unsupported Feature Flags on V1. if self.load_format == "sharded_state": _raise_or_fallback( feature_name=f"--load_format {self.load_format}", recommend_to_remove=False) return False if (self.logits_processor_pattern != EngineArgs.logits_processor_pattern): _raise_or_fallback(feature_name="--logits-processor-pattern", recommend_to_remove=False) return False if self.preemption_mode != SchedulerConfig.preemption_mode: _raise_or_fallback(feature_name="--preemption-mode", recommend_to_remove=True) return False if (self.disable_async_output_proc != EngineArgs.disable_async_output_proc): _raise_or_fallback(feature_name="--disable-async-output-proc", recommend_to_remove=True) return False if self.scheduler_delay_factor != SchedulerConfig.delay_factor: _raise_or_fallback(feature_name="--scheduler-delay-factor", recommend_to_remove=True) return False # Need at least Ampere for now (FA support required). # Skip this check if we are running on a non-GPU platform, # or if the device capability is not available # (e.g. in a Ray actor without GPUs). if (current_platform.is_cuda() and current_platform.get_device_capability() and current_platform.get_device_capability().major < 8): _raise_or_fallback(feature_name="Compute Capability < 8.0", recommend_to_remove=False) return False # No Fp8 KV cache so far. if self.kv_cache_dtype != "auto": supported = current_platform.is_kv_cache_dtype_supported( self.kv_cache_dtype) if not supported: _raise_or_fallback(feature_name="--kv-cache-dtype", recommend_to_remove=False) return False # No text embedding inputs so far. if self.enable_prompt_embeds: _raise_or_fallback(feature_name="--enable-prompt-embeds", recommend_to_remove=False) return False # No Mamba or Encoder-Decoder so far. if not model_config.is_v1_compatible: _raise_or_fallback(feature_name=model_config.architectures, recommend_to_remove=False) return False # V1 mamba models are unoptimized. if model_config.has_inner_state and _warn_or_fallback( feature_name="Mamba"): return False # No Concurrent Partial Prefills so far. if (self.max_num_partial_prefills != SchedulerConfig.max_num_partial_prefills or self.max_long_partial_prefills != SchedulerConfig.max_long_partial_prefills): _raise_or_fallback(feature_name="Concurrent Partial Prefill", recommend_to_remove=False) return False # No OTLP observability so far. if (self.otlp_traces_endpoint or self.collect_detailed_traces): _raise_or_fallback(feature_name="--otlp-traces-endpoint", recommend_to_remove=False) return False # V1 supports N-gram, Medusa, and Eagle speculative decoding. if (self.speculative_config is not None and self.speculative_config.get("method") == "draft_model"): raise NotImplementedError( "Speculative decoding with draft model is not supported yet. " "Please consider using other speculative decoding methods " "such as ngram, medusa, eagle, or deepseek_mtp.") V1_BACKENDS = [ "FLASH_ATTN_VLLM_V1", "FLASH_ATTN", "PALLAS", "PALLAS_VLLM_V1", "TRITON_ATTN_VLLM_V1", "TRITON_MLA", "CUTLASS_MLA", "FLASHMLA", "FLASHINFER", "FLASHINFER_VLLM_V1", "ROCM_AITER_MLA", "TORCH_SDPA_VLLM_V1", "FLEX_ATTENTION", "TREE_ATTN", "XFORMERS_VLLM_V1", ] if (envs.is_set("VLLM_ATTENTION_BACKEND") and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS): name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}" _raise_or_fallback(feature_name=name, recommend_to_remove=True) return False # Platforms must decide if they can support v1 for this model if not current_platform.supports_v1(model_config=model_config): _raise_or_fallback( feature_name=f"device type={current_platform.device_type}", recommend_to_remove=False) return False ############################################################# # Experimental Features - allow users to opt in. # Signal Handlers requires running in main thread. if (threading.current_thread() != threading.main_thread() and _warn_or_fallback("Engine in background thread")): return False if self.pipeline_parallel_size > 1: supports_pp = getattr(self.distributed_executor_backend, 'supports_pp', False) if not supports_pp and self.distributed_executor_backend not in ( ParallelConfig.distributed_executor_backend, "ray", "mp", "external_launcher"): name = "Pipeline Parallelism without Ray distributed " \ "executor or multiprocessing executor or external " \ "launcher" _raise_or_fallback(feature_name=name, recommend_to_remove=False) return False # The platform may be supported on V1, but off by default for now. if not current_platform.default_v1( # noqa: SIM103 model_config=model_config) and _warn_or_fallback( current_platform.device_name): return False if (current_platform.is_cpu() and model_config.get_sliding_window() is not None): _raise_or_fallback(feature_name="sliding window (CPU backend)", recommend_to_remove=False) return False ############################################################# return True def _set_default_args_v0(self, model_config: ModelConfig) -> None: """Set Default Arguments for V0 Engine.""" max_model_len = model_config.max_model_len use_long_context = max_model_len > 32768 if self.enable_chunked_prefill is None: # Chunked prefill not supported for Multimodal or MLA in V0. if model_config.is_multimodal_model or model_config.use_mla: self.enable_chunked_prefill = False # Enable chunked prefill by default for long context (> 32K) # models to avoid OOM errors in initial memory profiling phase. elif use_long_context: is_gpu = current_platform.is_cuda() use_sliding_window = (model_config.get_sliding_window() is not None) use_spec_decode = self.speculative_config is not None if (is_gpu and not use_sliding_window and not use_spec_decode and not self.enable_lora and model_config.runner_type != "pooling"): self.enable_chunked_prefill = True logger.warning( "Chunked prefill is enabled by default for models " "with max_model_len > 32K. Chunked prefill might " "not work with some features or models. If you " "encounter any issues, please disable by launching " "with --enable-chunked-prefill=False.") if self.enable_chunked_prefill is None: self.enable_chunked_prefill = False if not self.enable_chunked_prefill and use_long_context: logger.warning( "The model has a long context length (%s). This may cause" "OOM during the initial memory profiling phase, or result " "in low performance due to small KV cache size. Consider " "setting --max-model-len to a smaller value.", max_model_len) elif (self.enable_chunked_prefill and model_config.runner_type == "pooling"): msg = "Chunked prefill is not supported for pooling models" raise ValueError(msg) # if using prefix caching, we must set a hash algo if self.enable_prefix_caching: # Disable prefix caching for multimodal models for VLLM_V0. if model_config.is_multimodal_model: logger.warning( "--enable-prefix-caching is not supported for multimodal " "models in V0 and has been disabled.") self.enable_prefix_caching = False # VLLM_V0 only supports builtin hash algo for prefix caching. if self.prefix_caching_hash_algo == "sha256": raise ValueError( "sha256 is not supported for prefix caching in V0 engine. " "Please use 'builtin'.") # Set max_num_seqs to 256 for VLLM_V0. if self.max_num_seqs is None: self.max_num_seqs = 256 def _set_default_args_v1(self, usage_context: UsageContext, model_config: ModelConfig) -> None: """Set Default Arguments for V1 Engine.""" # V1 always uses chunked prefills and prefix caching # for non-pooling tasks. # For pooling tasks the default is False if model_config.runner_type != "pooling": self.enable_chunked_prefill = True if self.enable_prefix_caching is None: self.enable_prefix_caching = True else: pooling_type = model_config.pooler_config.pooling_type is_causal = getattr(model_config.hf_config, "is_causal", True) incremental_prefill_supported = (pooling_type is not None and pooling_type.lower() == "last" and is_causal) action = "Enabling" if \ incremental_prefill_supported else "Disabling" if self.enable_chunked_prefill is None: self.enable_chunked_prefill = incremental_prefill_supported logger.info("(%s) chunked prefill by default", action) if self.enable_prefix_caching is None: self.enable_prefix_caching = incremental_prefill_supported logger.info("(%s) prefix caching by default", action) # V1 should use the new scheduler by default. # Swap it only if this arg is set to the original V0 default if self.scheduler_cls == EngineArgs.scheduler_cls: self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler" # When no user override, set the default values based on the usage # context. # Use different default values for different hardware. # Try to query the device name on the current platform. If it fails, # it may be because the platform that imports vLLM is not the same # as the platform that vLLM is running on (e.g. the case of scaling # vLLM with Ray) and has no GPUs. In this case we use the default # values for non-H100/H200 GPUs. try: device_memory = current_platform.get_device_total_memory() device_name = current_platform.get_device_name().lower() except Exception: # This is only used to set default_max_num_batched_tokens device_memory = 0 # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces # throughput, see PR #17885 for more details. # So here we do an extra device name check to prevent such regression. from vllm.usage.usage_lib import UsageContext if device_memory >= 70 * GiB_bytes and "a100" not in device_name: # For GPUs like H100 and MI300x, use larger default values. default_max_num_batched_tokens = { UsageContext.LLM_CLASS: 16384, UsageContext.OPENAI_API_SERVER: 8192, } default_max_num_seqs = { UsageContext.LLM_CLASS: 1024, UsageContext.OPENAI_API_SERVER: 1024, } else: # TODO(woosuk): Tune the default values for other hardware. default_max_num_batched_tokens = { UsageContext.LLM_CLASS: 8192, UsageContext.OPENAI_API_SERVER: 2048, } default_max_num_seqs = { UsageContext.LLM_CLASS: 256, UsageContext.OPENAI_API_SERVER: 256, } # tpu specific default values. if current_platform.is_tpu(): default_max_num_batched_tokens_tpu = { UsageContext.LLM_CLASS: { 'V6E': 2048, 'V5E': 1024, 'V5P': 512, }, UsageContext.OPENAI_API_SERVER: { 'V6E': 1024, 'V5E': 512, 'V5P': 256, } } # cpu specific default values. if current_platform.is_cpu(): world_size = self.pipeline_parallel_size * self.tensor_parallel_size default_max_num_batched_tokens = { UsageContext.LLM_CLASS: 4096 * world_size, UsageContext.OPENAI_API_SERVER: 2048 * world_size, } default_max_num_seqs = { UsageContext.LLM_CLASS: 256 * world_size, UsageContext.OPENAI_API_SERVER: 128 * world_size, } use_context_value = usage_context.value if usage_context else None if (self.max_num_batched_tokens is None and usage_context in default_max_num_batched_tokens): if current_platform.is_tpu(): chip_name = current_platform.get_device_name() if chip_name in default_max_num_batched_tokens_tpu[ usage_context]: self.max_num_batched_tokens = \ default_max_num_batched_tokens_tpu[ usage_context][chip_name] else: self.max_num_batched_tokens = \ default_max_num_batched_tokens[usage_context] else: if not self.enable_chunked_prefill: self.max_num_batched_tokens = model_config.max_model_len else: self.max_num_batched_tokens = \ default_max_num_batched_tokens[usage_context] logger.debug( "Setting max_num_batched_tokens to %d for %s usage context.", self.max_num_batched_tokens, use_context_value) if (self.max_num_seqs is None and usage_context in default_max_num_seqs): self.max_num_seqs = min(default_max_num_seqs[usage_context], self.max_num_batched_tokens or sys.maxsize) logger.debug("Setting max_num_seqs to %d for %s usage context.", self.max_num_seqs, use_context_value) @dataclass class AsyncEngineArgs(EngineArgs): """Arguments for asynchronous vLLM engine.""" enable_log_requests: bool = False @property @deprecated( "`disable_log_requests` is deprecated and has been replaced with " "`enable_log_requests`. This will be removed in v0.12.0. Please use " "`enable_log_requests` instead.") def disable_log_requests(self) -> bool: return not self.enable_log_requests @disable_log_requests.setter @deprecated( "`disable_log_requests` is deprecated and has been replaced with " "`enable_log_requests`. This will be removed in v0.12.0. Please use " "`enable_log_requests` instead.") def disable_log_requests(self, value: bool): self.enable_log_requests = not value @staticmethod def add_cli_args(parser: FlexibleArgumentParser, async_args_only: bool = False) -> FlexibleArgumentParser: # Initialize plugin to update the parser, for example, The plugin may # adding a new kind of quantization method to --quantization argument or # a new device to --device argument. load_general_plugins() if not async_args_only: parser = EngineArgs.add_cli_args(parser) parser.add_argument('--enable-log-requests', action=argparse.BooleanOptionalAction, default=AsyncEngineArgs.enable_log_requests, help='Enable logging requests.') parser.add_argument('--disable-log-requests', action=argparse.BooleanOptionalAction, default=not AsyncEngineArgs.enable_log_requests, help='[DEPRECATED] Disable logging requests.', deprecated=True) current_platform.pre_register_and_update(parser) return parser def _raise_or_fallback(feature_name: str, recommend_to_remove: bool): if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1: raise NotImplementedError( f"VLLM_USE_V1=1 is not supported with {feature_name}.") msg = f"{feature_name} is not supported by the V1 Engine. " msg += "Falling back to V0. " if recommend_to_remove: msg += f"We recommend to remove {feature_name} from your config " msg += "in favor of the V1 Engine." logger.warning(msg) def _warn_or_fallback(feature_name: str) -> bool: if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1: logger.warning( "Detected VLLM_USE_V1=1 with %s. Usage should " "be considered experimental. Please report any " "issues on Github.", feature_name) should_exit = False else: logger.info( "%s is experimental on VLLM_USE_V1=1. " "Falling back to V0 Engine.", feature_name) should_exit = True return should_exit def human_readable_int(value): """Parse human-readable integers like '1k', '2M', etc. Including decimal values with decimal multipliers. Examples: - '1k' -> 1,000 - '1K' -> 1,024 - '25.6k' -> 25,600 """ value = value.strip() match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value) if match: decimal_multiplier = { 'k': 10**3, 'm': 10**6, 'g': 10**9, } binary_multiplier = { 'K': 2**10, 'M': 2**20, 'G': 2**30, } number, suffix = match.groups() if suffix in decimal_multiplier: mult = decimal_multiplier[suffix] return int(float(number) * mult) elif suffix in binary_multiplier: mult = binary_multiplier[suffix] # Do not allow decimals with binary multipliers try: return int(number) * mult except ValueError as e: raise argparse.ArgumentTypeError("Decimals are not allowed " \ f"with binary suffixes like {suffix}. Did you mean to use " \ f"{number}{suffix.lower()} instead?") from e # Regular plain number. return int(value)