1653 lines
74 KiB
Python
1653 lines
74 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# yapf: disable
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import argparse
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import dataclasses
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import json
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import sys
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import threading
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import warnings
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from dataclasses import MISSING, dataclass, fields, is_dataclass
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from itertools import permutations
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from typing import (Annotated, Any, Callable, Dict, List, Literal, Optional,
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Type, TypeVar, Union, cast, get_args, get_origin)
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import regex as re
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import torch
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from pydantic import SkipValidation, TypeAdapter, ValidationError
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from typing_extensions import TypeIs, deprecated
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import vllm.envs as envs
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from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
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ConfigFormat, ConfigType, DecodingConfig,
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DetailedTraceModules, Device, DeviceConfig,
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DistributedExecutorBackend, GuidedDecodingBackend,
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GuidedDecodingBackendV1, HfOverrides, KVEventsConfig,
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KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
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ModelConfig, ModelDType, ModelImpl, MultiModalConfig,
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ObservabilityConfig, ParallelConfig, PoolerConfig,
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PrefixCachingHashAlgo, PromptAdapterConfig,
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SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
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TaskOption, TokenizerMode, TokenizerPoolConfig,
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VllmConfig, get_attr_docs, get_field)
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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 QuantizationMethods
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from vllm.plugins import load_general_plugins
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from vllm.reasoning import ReasoningParserManager
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from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
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from vllm.transformers_utils.utils import check_gguf_file
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from vllm.usage.usage_lib import UsageContext
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from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
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GiB_bytes, is_in_ray_actor)
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# yapf: enable
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logger = init_logger(__name__)
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# object is used to allow for special typing forms
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T = TypeVar("T")
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TypeHint = Union[type[Any], object]
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TypeHintT = Union[type[T], object]
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def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
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def _parse_type(val: str) -> T:
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try:
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if return_type is json.loads and not re.match("^{.*}$", val):
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return cast(T, nullable_kvs(val))
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return return_type(val)
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except ValueError as e:
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raise argparse.ArgumentTypeError(
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f"Value {val} cannot be converted to {return_type}.") from e
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return _parse_type
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def optional_type(
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return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:
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def _optional_type(val: str) -> Optional[T]:
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if val == "" or val == "None":
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return None
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return parse_type(return_type)(val)
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return _optional_type
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def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
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if not re.match("^{.*}$", val):
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return str(val)
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return optional_type(json.loads)(val)
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@deprecated(
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"Passing a JSON argument as a string containing comma separated key=value "
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"pairs is deprecated. This will be removed in v0.10.0. Please use a JSON "
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"string instead.")
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def nullable_kvs(val: str) -> dict[str, int]:
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"""Parses a string containing comma separate key [str] to value [int]
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pairs into a dictionary.
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Args:
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val: String value to be parsed.
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Returns:
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Dictionary with parsed values.
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"""
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out_dict: dict[str, int] = {}
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for item in val.split(","):
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kv_parts = [part.lower().strip() for part in item.split("=")]
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if len(kv_parts) != 2:
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raise argparse.ArgumentTypeError(
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"Each item should be in the form KEY=VALUE")
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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:
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msg = f"Failed to parse value of item {key}={value}"
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raise argparse.ArgumentTypeError(msg) from exc
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if key in out_dict and out_dict[key] != parsed_value:
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raise argparse.ArgumentTypeError(
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f"Conflicting values specified for key: {key}")
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out_dict[key] = parsed_value
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return out_dict
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def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]:
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"""Check if the type hint is a specific type."""
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return type_hint is type or get_origin(type_hint) is type
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def contains_type(type_hints: set[TypeHint], type: TypeHintT) -> bool:
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"""Check if the type hints contain a specific type."""
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return any(is_type(type_hint, type) for type_hint in type_hints)
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def get_type(type_hints: set[TypeHint], type: TypeHintT) -> TypeHintT:
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"""Get the specific type from the type hints."""
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return next((th for th in type_hints if is_type(th, type)), None)
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def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
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"""Convert Literal type hints to argparse kwargs."""
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type_hint = get_type(type_hints, Literal)
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choices = get_args(type_hint)
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choice_type = type(choices[0])
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if not all(isinstance(choice, choice_type) for choice in choices):
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raise ValueError(
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"All choices must be of the same type. "
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f"Got {choices} with types {[type(c) for c in choices]}")
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return {"type": choice_type, "choices": sorted(choices)}
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def is_not_builtin(type_hint: TypeHint) -> bool:
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"""Check if the class is not a built-in type."""
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return type_hint.__module__ != "builtins"
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def get_kwargs(cls: ConfigType) -> dict[str, Any]:
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cls_docs = get_attr_docs(cls)
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kwargs = {}
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for field in fields(cls):
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# Get the set of possible types for the field
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type_hints: set[TypeHint] = set()
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if get_origin(field.type) in {Union, Annotated}:
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predicate = lambda arg: not isinstance(arg, SkipValidation)
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type_hints.update(filter(predicate, get_args(field.type)))
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else:
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type_hints.add(field.type)
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# If the field is a dataclass, we can use the model_validate_json
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generator = (th for th in type_hints if is_dataclass(th))
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dataclass_cls = next(generator, None)
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# Get the default value of the field
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if field.default is not MISSING:
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default = field.default
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elif field.default_factory is not MISSING:
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default = field.default_factory()
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# Get the help text for the field
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name = field.name
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help = cls_docs[name].strip()
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# Escape % for argparse
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help = help.replace("%", "%%")
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# Initialise the kwargs dictionary for the field
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kwargs[name] = {"default": default, "help": help}
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# Set other kwargs based on the type hints
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json_tip = """\n\nShould either be a valid JSON string or JSON keys
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passed individually. For example, the following sets of arguments are
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equivalent:\n\n
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- `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`\n
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- `--json-arg.key1 value1 --json-arg.key2.key3 value2`\n\n"""
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if dataclass_cls is not None:
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def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
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try:
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if hasattr(cls, "from_cli"):
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return cls.from_cli(val)
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return TypeAdapter(cls).validate_json(val)
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except ValidationError as e:
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raise argparse.ArgumentTypeError(repr(e)) from e
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kwargs[name]["type"] = parse_dataclass
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kwargs[name]["help"] += json_tip
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elif contains_type(type_hints, bool):
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# Creates --no-<name> and --<name> flags
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kwargs[name]["action"] = argparse.BooleanOptionalAction
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elif contains_type(type_hints, Literal):
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kwargs[name].update(literal_to_kwargs(type_hints))
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elif contains_type(type_hints, tuple):
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type_hint = get_type(type_hints, tuple)
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types = get_args(type_hint)
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tuple_type = types[0]
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assert all(t is tuple_type for t in types if t is not Ellipsis), (
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"All non-Ellipsis tuple elements must be of the same "
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f"type. Got {types}.")
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kwargs[name]["type"] = tuple_type
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kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
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elif contains_type(type_hints, list):
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type_hint = get_type(type_hints, list)
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types = get_args(type_hint)
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assert len(types) == 1, (
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"List type must have exactly one type. Got "
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f"{type_hint} with types {types}")
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kwargs[name]["type"] = types[0]
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kwargs[name]["nargs"] = "+"
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elif contains_type(type_hints, int):
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kwargs[name]["type"] = int
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# Special case for large integers
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if name in {"max_model_len"}:
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kwargs[name]["type"] = human_readable_int
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elif contains_type(type_hints, float):
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kwargs[name]["type"] = float
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elif (contains_type(type_hints, dict)
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and (contains_type(type_hints, str)
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or any(is_not_builtin(th) for th in type_hints))):
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kwargs[name]["type"] = union_dict_and_str
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elif contains_type(type_hints, dict):
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kwargs[name]["type"] = parse_type(json.loads)
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kwargs[name]["help"] += json_tip
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elif (contains_type(type_hints, str)
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or any(is_not_builtin(th) for th in type_hints)):
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kwargs[name]["type"] = str
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else:
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raise ValueError(
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f"Unsupported type {type_hints} for argument {name}.")
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# If the type hint was a sequence of literals, use the helper function
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# to update the type and choices
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if get_origin(kwargs[name].get("type")) is Literal:
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kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]}))
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# If None is in type_hints, make the argument optional.
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# But not if it's a bool, argparse will handle this better.
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if type(None) in type_hints and not contains_type(type_hints, bool):
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kwargs[name]["type"] = optional_type(kwargs[name]["type"])
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if kwargs[name].get("choices"):
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kwargs[name]["choices"].append("None")
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return kwargs
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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 = ModelConfig.model
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served_model_name: Optional[Union[
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str, List[str]]] = ModelConfig.served_model_name
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tokenizer: Optional[str] = ModelConfig.tokenizer
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hf_config_path: Optional[str] = ModelConfig.hf_config_path
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task: TaskOption = ModelConfig.task
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skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
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enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
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tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
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trust_remote_code: bool = ModelConfig.trust_remote_code
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allowed_local_media_path: str = ModelConfig.allowed_local_media_path
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download_dir: Optional[str] = LoadConfig.download_dir
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load_format: str = LoadConfig.load_format
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config_format: str = ModelConfig.config_format
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dtype: ModelDType = ModelConfig.dtype
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kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
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seed: Optional[int] = ModelConfig.seed
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max_model_len: Optional[int] = ModelConfig.max_model_len
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cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
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"cuda_graph_sizes")
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# Note: Specifying a custom executor backend by passing a class
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# is intended for expert use only. The API may change without
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# notice.
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distributed_executor_backend: Optional[Union[
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DistributedExecutorBackend,
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Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
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# number of P/D disaggregation (or other disaggregation) workers
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pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
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tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
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data_parallel_size: int = ParallelConfig.data_parallel_size
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data_parallel_size_local: Optional[int] = None
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data_parallel_address: Optional[str] = None
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data_parallel_rpc_port: Optional[int] = None
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enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
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max_parallel_loading_workers: Optional[
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int] = ParallelConfig.max_parallel_loading_workers
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block_size: Optional[BlockSize] = CacheConfig.block_size
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enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
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prefix_caching_hash_algo: PrefixCachingHashAlgo = \
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CacheConfig.prefix_caching_hash_algo
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disable_sliding_window: bool = ModelConfig.disable_sliding_window
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disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
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use_v2_block_manager: bool = True
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swap_space: float = CacheConfig.swap_space
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cpu_offload_gb: float = CacheConfig.cpu_offload_gb
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gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
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max_num_batched_tokens: Optional[
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int] = SchedulerConfig.max_num_batched_tokens
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max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
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max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
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long_prefill_token_threshold: int = \
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SchedulerConfig.long_prefill_token_threshold
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max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
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max_logprobs: int = ModelConfig.max_logprobs
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disable_log_stats: bool = False
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revision: Optional[str] = ModelConfig.revision
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code_revision: Optional[str] = ModelConfig.code_revision
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rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
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rope_theta: Optional[float] = ModelConfig.rope_theta
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hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
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hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
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tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
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quantization: Optional[QuantizationMethods] = ModelConfig.quantization
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enforce_eager: bool = ModelConfig.enforce_eager
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max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
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disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
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# The following three fields are deprecated and will be removed in a future
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# release. Setting them will have no effect. Please remove them from your
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# configurations.
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tokenizer_pool_size: int = TokenizerPoolConfig.pool_size
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tokenizer_pool_type: str = TokenizerPoolConfig.pool_type
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tokenizer_pool_extra_config: dict = \
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get_field(TokenizerPoolConfig, "extra_config")
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limit_mm_per_prompt: dict[str, int] = \
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get_field(MultiModalConfig, "limit_per_prompt")
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mm_processor_kwargs: Optional[Dict[str, Any]] = \
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MultiModalConfig.mm_processor_kwargs
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disable_mm_preprocessor_cache: bool = \
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MultiModalConfig.disable_mm_preprocessor_cache
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# LoRA fields
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enable_lora: bool = False
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enable_lora_bias: bool = LoRAConfig.bias_enabled
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max_loras: int = LoRAConfig.max_loras
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max_lora_rank: int = LoRAConfig.max_lora_rank
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fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
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max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
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lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
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lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size
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long_lora_scaling_factors: Optional[tuple[float, ...]] = \
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LoRAConfig.long_lora_scaling_factors
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# PromptAdapter fields
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enable_prompt_adapter: bool = False
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max_prompt_adapters: int = PromptAdapterConfig.max_prompt_adapters
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max_prompt_adapter_token: int = \
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PromptAdapterConfig.max_prompt_adapter_token
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device: Device = DeviceConfig.device
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num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
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multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
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ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
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num_gpu_blocks_override: Optional[
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int] = CacheConfig.num_gpu_blocks_override
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num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
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model_loader_extra_config: dict = \
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get_field(LoadConfig, "model_loader_extra_config")
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ignore_patterns: Optional[Union[str,
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List[str]]] = LoadConfig.ignore_patterns
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preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
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scheduler_delay_factor: float = SchedulerConfig.delay_factor
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enable_chunked_prefill: Optional[
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bool] = SchedulerConfig.enable_chunked_prefill
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disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
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guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
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guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
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guided_decoding_disable_any_whitespace: bool = \
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DecodingConfig.disable_any_whitespace
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guided_decoding_disable_additional_properties: bool = \
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DecodingConfig.disable_additional_properties
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logits_processor_pattern: Optional[
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str] = ModelConfig.logits_processor_pattern
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speculative_config: Optional[Dict[str, Any]] = None
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qlora_adapter_name_or_path: Optional[str] = None
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show_hidden_metrics_for_version: Optional[str] = \
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ObservabilityConfig.show_hidden_metrics_for_version
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otlp_traces_endpoint: Optional[str] = \
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ObservabilityConfig.otlp_traces_endpoint
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collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
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ObservabilityConfig.collect_detailed_traces
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disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
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scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
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scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
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override_neuron_config: dict[str, Any] = \
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get_field(ModelConfig, "override_neuron_config")
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override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
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ModelConfig.override_pooler_config
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compilation_config: CompilationConfig = \
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get_field(VllmConfig, "compilation_config")
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worker_cls: str = ParallelConfig.worker_cls
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worker_extension_cls: str = ParallelConfig.worker_extension_cls
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kv_transfer_config: Optional[KVTransferConfig] = None
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kv_events_config: Optional[KVEventsConfig] = None
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generation_config: str = ModelConfig.generation_config
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enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
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override_generation_config: dict[str, Any] = \
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get_field(ModelConfig, "override_generation_config")
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model_impl: str = ModelConfig.model_impl
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calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
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additional_config: dict[str, Any] = \
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get_field(VllmConfig, "additional_config")
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enable_reasoning: Optional[bool] = None # DEPRECATED
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reasoning_parser: str = DecodingConfig.reasoning_backend
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|
|
use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
|
|
pt_load_map_location: str = LoadConfig.pt_load_map_location
|
|
|
|
def __post_init__(self):
|
|
# support `EngineArgs(compilation_config={...})`
|
|
# without having to manually construct a
|
|
# CompilationConfig object
|
|
if isinstance(self.compilation_config, (int, dict)):
|
|
self.compilation_config = CompilationConfig.from_cli(
|
|
str(self.compilation_config))
|
|
if self.qlora_adapter_name_or_path is not None:
|
|
warnings.warn(
|
|
"The `qlora_adapter_name_or_path` is deprecated "
|
|
"and will be removed in v0.10.0. ",
|
|
DeprecationWarning,
|
|
stacklevel=2,
|
|
)
|
|
# Setup plugins
|
|
from vllm.plugins import load_general_plugins
|
|
load_general_plugins()
|
|
|
|
@staticmethod
|
|
def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
|
|
"""Shared CLI arguments for vLLM engine."""
|
|
|
|
# Model arguments
|
|
model_kwargs = get_kwargs(ModelConfig)
|
|
model_group = parser.add_argument_group(
|
|
title="ModelConfig",
|
|
description=ModelConfig.__doc__,
|
|
)
|
|
if 'serve' not in sys.argv[1:] and '--help' not in sys.argv[1:]:
|
|
model_group.add_argument("--model", **model_kwargs["model"])
|
|
model_group.add_argument("--task", **model_kwargs["task"])
|
|
model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
|
|
model_group.add_argument("--tokenizer-mode",
|
|
**model_kwargs["tokenizer_mode"])
|
|
model_group.add_argument("--trust-remote-code",
|
|
**model_kwargs["trust_remote_code"])
|
|
model_group.add_argument("--dtype", **model_kwargs["dtype"])
|
|
model_group.add_argument("--seed", **model_kwargs["seed"])
|
|
model_group.add_argument("--hf-config-path",
|
|
**model_kwargs["hf_config_path"])
|
|
model_group.add_argument("--allowed-local-media-path",
|
|
**model_kwargs["allowed_local_media_path"])
|
|
model_group.add_argument("--revision", **model_kwargs["revision"])
|
|
model_group.add_argument("--code-revision",
|
|
**model_kwargs["code_revision"])
|
|
model_group.add_argument("--rope-scaling",
|
|
**model_kwargs["rope_scaling"])
|
|
model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
|
|
model_group.add_argument("--tokenizer-revision",
|
|
**model_kwargs["tokenizer_revision"])
|
|
model_group.add_argument("--max-model-len",
|
|
**model_kwargs["max_model_len"])
|
|
model_group.add_argument("--quantization", "-q",
|
|
**model_kwargs["quantization"])
|
|
model_group.add_argument("--enforce-eager",
|
|
**model_kwargs["enforce_eager"])
|
|
model_group.add_argument("--max-seq-len-to-capture",
|
|
**model_kwargs["max_seq_len_to_capture"])
|
|
model_group.add_argument("--max-logprobs",
|
|
**model_kwargs["max_logprobs"])
|
|
model_group.add_argument("--disable-sliding-window",
|
|
**model_kwargs["disable_sliding_window"])
|
|
model_group.add_argument("--disable-cascade-attn",
|
|
**model_kwargs["disable_cascade_attn"])
|
|
model_group.add_argument("--skip-tokenizer-init",
|
|
**model_kwargs["skip_tokenizer_init"])
|
|
model_group.add_argument("--enable-prompt-embeds",
|
|
**model_kwargs["enable_prompt_embeds"])
|
|
model_group.add_argument("--served-model-name",
|
|
**model_kwargs["served_model_name"])
|
|
# This one is a special case because it is the
|
|
# opposite of ModelConfig.use_async_output_proc
|
|
model_group.add_argument(
|
|
"--disable-async-output-proc",
|
|
action="store_true",
|
|
default=EngineArgs.disable_async_output_proc,
|
|
help="Disable async output processing. This may result in "
|
|
"lower performance.")
|
|
model_group.add_argument("--config-format",
|
|
choices=[f.value for f in ConfigFormat],
|
|
**model_kwargs["config_format"])
|
|
# This one is a special case because it can bool
|
|
# or str. TODO: Handle this in get_kwargs
|
|
model_group.add_argument("--hf-token",
|
|
type=str,
|
|
nargs="?",
|
|
const=True,
|
|
default=model_kwargs["hf_token"]["default"],
|
|
help=model_kwargs["hf_token"]["help"])
|
|
model_group.add_argument("--hf-overrides",
|
|
**model_kwargs["hf_overrides"])
|
|
model_group.add_argument("--override-neuron-config",
|
|
**model_kwargs["override_neuron_config"])
|
|
model_group.add_argument("--override-pooler-config",
|
|
**model_kwargs["override_pooler_config"])
|
|
model_group.add_argument("--logits-processor-pattern",
|
|
**model_kwargs["logits_processor_pattern"])
|
|
model_group.add_argument("--generation-config",
|
|
**model_kwargs["generation_config"])
|
|
model_group.add_argument("--override-generation-config",
|
|
**model_kwargs["override_generation_config"])
|
|
model_group.add_argument("--enable-sleep-mode",
|
|
**model_kwargs["enable_sleep_mode"])
|
|
model_group.add_argument("--model-impl",
|
|
choices=[f.value for f in ModelImpl],
|
|
**model_kwargs["model_impl"])
|
|
|
|
# Model loading arguments
|
|
load_kwargs = get_kwargs(LoadConfig)
|
|
load_group = parser.add_argument_group(
|
|
title="LoadConfig",
|
|
description=LoadConfig.__doc__,
|
|
)
|
|
load_group.add_argument("--load-format",
|
|
choices=[f.value for f in LoadFormat],
|
|
**load_kwargs["load_format"])
|
|
load_group.add_argument("--download-dir",
|
|
**load_kwargs["download_dir"])
|
|
load_group.add_argument("--model-loader-extra-config",
|
|
**load_kwargs["model_loader_extra_config"])
|
|
load_group.add_argument("--ignore-patterns",
|
|
**load_kwargs["ignore_patterns"])
|
|
load_group.add_argument("--use-tqdm-on-load",
|
|
**load_kwargs["use_tqdm_on_load"])
|
|
load_group.add_argument(
|
|
"--qlora-adapter-name-or-path",
|
|
type=str,
|
|
default=None,
|
|
help="The `--qlora-adapter-name-or-path` has no effect, do not set"
|
|
" it, and it will be removed in v0.10.0.",
|
|
deprecated=True,
|
|
)
|
|
load_group.add_argument('--pt-load-map-location',
|
|
**load_kwargs["pt_load_map_location"])
|
|
|
|
# Guided decoding arguments
|
|
guided_decoding_kwargs = get_kwargs(DecodingConfig)
|
|
guided_decoding_group = parser.add_argument_group(
|
|
title="DecodingConfig",
|
|
description=DecodingConfig.__doc__,
|
|
)
|
|
guided_decoding_group.add_argument("--guided-decoding-backend",
|
|
**guided_decoding_kwargs["backend"])
|
|
guided_decoding_group.add_argument(
|
|
"--guided-decoding-disable-fallback",
|
|
**guided_decoding_kwargs["disable_fallback"])
|
|
guided_decoding_group.add_argument(
|
|
"--guided-decoding-disable-any-whitespace",
|
|
**guided_decoding_kwargs["disable_any_whitespace"])
|
|
guided_decoding_group.add_argument(
|
|
"--guided-decoding-disable-additional-properties",
|
|
**guided_decoding_kwargs["disable_additional_properties"])
|
|
guided_decoding_group.add_argument(
|
|
"--enable-reasoning",
|
|
action=argparse.BooleanOptionalAction,
|
|
deprecated=True,
|
|
help="[DEPRECATED] The `--enable-reasoning` flag is deprecated as "
|
|
"of v0.9.0. Use `--reasoning-parser` to specify the reasoning "
|
|
"parser backend instead. This flag (`--enable-reasoning`) will be "
|
|
"removed in v0.10.0. When `--reasoning-parser` is specified, "
|
|
"reasoning mode is automatically enabled.")
|
|
guided_decoding_group.add_argument(
|
|
"--reasoning-parser",
|
|
# This choices is a special case because it's not static
|
|
choices=list(ReasoningParserManager.reasoning_parsers),
|
|
**guided_decoding_kwargs["reasoning_backend"])
|
|
|
|
# Parallel arguments
|
|
parallel_kwargs = get_kwargs(ParallelConfig)
|
|
parallel_group = parser.add_argument_group(
|
|
title="ParallelConfig",
|
|
description=ParallelConfig.__doc__,
|
|
)
|
|
parallel_group.add_argument(
|
|
"--distributed-executor-backend",
|
|
**parallel_kwargs["distributed_executor_backend"])
|
|
parallel_group.add_argument(
|
|
"--pipeline-parallel-size", "-pp",
|
|
**parallel_kwargs["pipeline_parallel_size"])
|
|
parallel_group.add_argument("--tensor-parallel-size", "-tp",
|
|
**parallel_kwargs["tensor_parallel_size"])
|
|
parallel_group.add_argument("--data-parallel-size", "-dp",
|
|
**parallel_kwargs["data_parallel_size"])
|
|
parallel_group.add_argument('--data-parallel-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(
|
|
"--enable-expert-parallel",
|
|
**parallel_kwargs["enable_expert_parallel"])
|
|
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"])
|
|
|
|
# KV cache arguments
|
|
cache_kwargs = get_kwargs(CacheConfig)
|
|
cache_group = parser.add_argument_group(
|
|
title="CacheConfig",
|
|
description=CacheConfig.__doc__,
|
|
)
|
|
cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
|
|
cache_group.add_argument("--gpu-memory-utilization",
|
|
**cache_kwargs["gpu_memory_utilization"])
|
|
cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
|
|
cache_group.add_argument("--kv-cache-dtype",
|
|
**cache_kwargs["cache_dtype"])
|
|
cache_group.add_argument("--num-gpu-blocks-override",
|
|
**cache_kwargs["num_gpu_blocks_override"])
|
|
cache_group.add_argument("--enable-prefix-caching",
|
|
**cache_kwargs["enable_prefix_caching"])
|
|
cache_group.add_argument("--prefix-caching-hash-algo",
|
|
**cache_kwargs["prefix_caching_hash_algo"])
|
|
cache_group.add_argument("--cpu-offload-gb",
|
|
**cache_kwargs["cpu_offload_gb"])
|
|
cache_group.add_argument("--calculate-kv-scales",
|
|
**cache_kwargs["calculate_kv_scales"])
|
|
|
|
# Tokenizer arguments
|
|
tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
|
|
tokenizer_group = parser.add_argument_group(
|
|
title="TokenizerPoolConfig",
|
|
description=TokenizerPoolConfig.__doc__,
|
|
)
|
|
tokenizer_group.add_argument("--tokenizer-pool-size",
|
|
**tokenizer_kwargs["pool_size"])
|
|
tokenizer_group.add_argument("--tokenizer-pool-type",
|
|
**tokenizer_kwargs["pool_type"])
|
|
tokenizer_group.add_argument("--tokenizer-pool-extra-config",
|
|
**tokenizer_kwargs["extra_config"])
|
|
|
|
# Multimodal related configs
|
|
multimodal_kwargs = get_kwargs(MultiModalConfig)
|
|
multimodal_group = parser.add_argument_group(
|
|
title="MultiModalConfig",
|
|
description=MultiModalConfig.__doc__,
|
|
)
|
|
multimodal_group.add_argument("--limit-mm-per-prompt",
|
|
**multimodal_kwargs["limit_per_prompt"])
|
|
multimodal_group.add_argument(
|
|
"--mm-processor-kwargs",
|
|
**multimodal_kwargs["mm_processor_kwargs"])
|
|
multimodal_group.add_argument(
|
|
"--disable-mm-preprocessor-cache",
|
|
**multimodal_kwargs["disable_mm_preprocessor_cache"])
|
|
|
|
# LoRA related configs
|
|
lora_kwargs = get_kwargs(LoRAConfig)
|
|
lora_group = parser.add_argument_group(
|
|
title="LoRAConfig",
|
|
description=LoRAConfig.__doc__,
|
|
)
|
|
lora_group.add_argument(
|
|
"--enable-lora",
|
|
action=argparse.BooleanOptionalAction,
|
|
help="If True, enable handling of LoRA adapters.")
|
|
lora_group.add_argument("--enable-lora-bias",
|
|
**lora_kwargs["bias_enabled"])
|
|
lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
|
|
lora_group.add_argument("--max-lora-rank",
|
|
**lora_kwargs["max_lora_rank"])
|
|
lora_group.add_argument("--lora-extra-vocab-size",
|
|
**lora_kwargs["lora_extra_vocab_size"])
|
|
lora_group.add_argument(
|
|
"--lora-dtype",
|
|
**lora_kwargs["lora_dtype"],
|
|
)
|
|
lora_group.add_argument("--long-lora-scaling-factors",
|
|
**lora_kwargs["long_lora_scaling_factors"])
|
|
lora_group.add_argument("--max-cpu-loras",
|
|
**lora_kwargs["max_cpu_loras"])
|
|
lora_group.add_argument("--fully-sharded-loras",
|
|
**lora_kwargs["fully_sharded_loras"])
|
|
|
|
# PromptAdapter related configs
|
|
prompt_adapter_kwargs = get_kwargs(PromptAdapterConfig)
|
|
prompt_adapter_group = parser.add_argument_group(
|
|
title="PromptAdapterConfig",
|
|
description=PromptAdapterConfig.__doc__,
|
|
)
|
|
prompt_adapter_group.add_argument(
|
|
"--enable-prompt-adapter",
|
|
action=argparse.BooleanOptionalAction,
|
|
help="If True, enable handling of PromptAdapters.")
|
|
prompt_adapter_group.add_argument(
|
|
"--max-prompt-adapters",
|
|
**prompt_adapter_kwargs["max_prompt_adapters"])
|
|
prompt_adapter_group.add_argument(
|
|
"--max-prompt-adapter-token",
|
|
**prompt_adapter_kwargs["max_prompt_adapter_token"])
|
|
|
|
# Device arguments
|
|
device_kwargs = get_kwargs(DeviceConfig)
|
|
device_group = parser.add_argument_group(
|
|
title="DeviceConfig",
|
|
description=DeviceConfig.__doc__,
|
|
)
|
|
device_group.add_argument("--device",
|
|
**device_kwargs["device"],
|
|
deprecated=True)
|
|
|
|
# Speculative arguments
|
|
speculative_group = parser.add_argument_group(
|
|
title="SpeculativeConfig",
|
|
description=SpeculativeConfig.__doc__,
|
|
)
|
|
speculative_group.add_argument(
|
|
"--speculative-config",
|
|
type=json.loads,
|
|
default=None,
|
|
help="The configurations for speculative decoding. Should be a "
|
|
"JSON string.")
|
|
|
|
# Observability arguments
|
|
observability_kwargs = get_kwargs(ObservabilityConfig)
|
|
observability_group = parser.add_argument_group(
|
|
title="ObservabilityConfig",
|
|
description=ObservabilityConfig.__doc__,
|
|
)
|
|
observability_group.add_argument(
|
|
"--show-hidden-metrics-for-version",
|
|
**observability_kwargs["show_hidden_metrics_for_version"])
|
|
observability_group.add_argument(
|
|
"--otlp-traces-endpoint",
|
|
**observability_kwargs["otlp_traces_endpoint"])
|
|
# TODO: generalise this special case
|
|
choices = observability_kwargs["collect_detailed_traces"]["choices"]
|
|
metavar = f"{{{','.join(choices)}}}"
|
|
observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
|
|
observability_kwargs["collect_detailed_traces"]["choices"] += [
|
|
",".join(p)
|
|
for p in permutations(get_args(DetailedTraceModules), r=2)
|
|
]
|
|
observability_group.add_argument(
|
|
"--collect-detailed-traces",
|
|
**observability_kwargs["collect_detailed_traces"])
|
|
|
|
# Scheduler arguments
|
|
scheduler_kwargs = get_kwargs(SchedulerConfig)
|
|
scheduler_group = parser.add_argument_group(
|
|
title="SchedulerConfig",
|
|
description=SchedulerConfig.__doc__,
|
|
)
|
|
scheduler_group.add_argument(
|
|
"--max-num-batched-tokens",
|
|
**scheduler_kwargs["max_num_batched_tokens"])
|
|
scheduler_group.add_argument("--max-num-seqs",
|
|
**scheduler_kwargs["max_num_seqs"])
|
|
scheduler_group.add_argument(
|
|
"--max-num-partial-prefills",
|
|
**scheduler_kwargs["max_num_partial_prefills"])
|
|
scheduler_group.add_argument(
|
|
"--max-long-partial-prefills",
|
|
**scheduler_kwargs["max_long_partial_prefills"])
|
|
scheduler_group.add_argument('--cuda-graph-sizes',
|
|
**scheduler_kwargs["cuda_graph_sizes"])
|
|
scheduler_group.add_argument(
|
|
"--long-prefill-token-threshold",
|
|
**scheduler_kwargs["long_prefill_token_threshold"])
|
|
scheduler_group.add_argument("--num-lookahead-slots",
|
|
**scheduler_kwargs["num_lookahead_slots"])
|
|
scheduler_group.add_argument("--scheduler-delay-factor",
|
|
**scheduler_kwargs["delay_factor"])
|
|
scheduler_group.add_argument("--preemption-mode",
|
|
**scheduler_kwargs["preemption_mode"])
|
|
scheduler_group.add_argument("--num-scheduler-steps",
|
|
**scheduler_kwargs["num_scheduler_steps"])
|
|
scheduler_group.add_argument(
|
|
"--multi-step-stream-outputs",
|
|
**scheduler_kwargs["multi_step_stream_outputs"])
|
|
scheduler_group.add_argument("--scheduling-policy",
|
|
**scheduler_kwargs["policy"])
|
|
scheduler_group.add_argument(
|
|
"--enable-chunked-prefill",
|
|
**scheduler_kwargs["enable_chunked_prefill"])
|
|
scheduler_group.add_argument(
|
|
"--disable-chunked-mm-input",
|
|
**scheduler_kwargs["disable_chunked_mm_input"])
|
|
scheduler_group.add_argument("--scheduler-cls",
|
|
**scheduler_kwargs["scheduler_cls"])
|
|
|
|
# vLLM arguments
|
|
vllm_kwargs = get_kwargs(VllmConfig)
|
|
vllm_group = parser.add_argument_group(
|
|
title="VllmConfig",
|
|
description=VllmConfig.__doc__,
|
|
)
|
|
vllm_group.add_argument("--kv-transfer-config",
|
|
**vllm_kwargs["kv_transfer_config"])
|
|
vllm_group.add_argument('--kv-events-config',
|
|
**vllm_kwargs["kv_events_config"])
|
|
vllm_group.add_argument("--compilation-config", "-O",
|
|
**vllm_kwargs["compilation_config"])
|
|
vllm_group.add_argument("--additional-config",
|
|
**vllm_kwargs["additional_config"])
|
|
|
|
# Other arguments
|
|
parser.add_argument('--use-v2-block-manager',
|
|
action='store_true',
|
|
default=True,
|
|
deprecated=True,
|
|
help='[DEPRECATED] block manager v1 has been '
|
|
'removed and SelfAttnBlockSpaceManager (i.e. '
|
|
'block manager v2) is now the default. '
|
|
'Setting this flag to True or False'
|
|
' has no effect on vLLM behavior.')
|
|
parser.add_argument('--disable-log-stats',
|
|
action='store_true',
|
|
help='Disable logging statistics.')
|
|
|
|
return parser
|
|
|
|
@classmethod
|
|
def from_cli_args(cls, args: argparse.Namespace):
|
|
# Get the list of attributes of this dataclass.
|
|
attrs = [attr.name for attr in dataclasses.fields(cls)]
|
|
# Set the attributes from the parsed arguments.
|
|
engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
|
|
return engine_args
|
|
|
|
def create_model_config(self) -> ModelConfig:
|
|
# gguf file needs a specific model loader and doesn't use hf_repo
|
|
if check_gguf_file(self.model):
|
|
self.quantization = self.load_format = "gguf"
|
|
|
|
# NOTE: This is to allow model loading from S3 in CI
|
|
if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3
|
|
and self.model in MODELS_ON_S3
|
|
and self.load_format == LoadFormat.AUTO): # noqa: E501
|
|
self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
|
|
self.load_format = LoadFormat.RUNAI_STREAMER
|
|
|
|
return ModelConfig(
|
|
model=self.model,
|
|
hf_config_path=self.hf_config_path,
|
|
task=self.task,
|
|
tokenizer=self.tokenizer,
|
|
tokenizer_mode=self.tokenizer_mode,
|
|
trust_remote_code=self.trust_remote_code,
|
|
allowed_local_media_path=self.allowed_local_media_path,
|
|
dtype=self.dtype,
|
|
seed=self.seed,
|
|
revision=self.revision,
|
|
code_revision=self.code_revision,
|
|
rope_scaling=self.rope_scaling,
|
|
rope_theta=self.rope_theta,
|
|
hf_token=self.hf_token,
|
|
hf_overrides=self.hf_overrides,
|
|
tokenizer_revision=self.tokenizer_revision,
|
|
max_model_len=self.max_model_len,
|
|
quantization=self.quantization,
|
|
enforce_eager=self.enforce_eager,
|
|
max_seq_len_to_capture=self.max_seq_len_to_capture,
|
|
max_logprobs=self.max_logprobs,
|
|
disable_sliding_window=self.disable_sliding_window,
|
|
disable_cascade_attn=self.disable_cascade_attn,
|
|
skip_tokenizer_init=self.skip_tokenizer_init,
|
|
enable_prompt_embeds=self.enable_prompt_embeds,
|
|
served_model_name=self.served_model_name,
|
|
limit_mm_per_prompt=self.limit_mm_per_prompt,
|
|
use_async_output_proc=not self.disable_async_output_proc,
|
|
config_format=self.config_format,
|
|
mm_processor_kwargs=self.mm_processor_kwargs,
|
|
disable_mm_preprocessor_cache=self.disable_mm_preprocessor_cache,
|
|
override_neuron_config=self.override_neuron_config,
|
|
override_pooler_config=self.override_pooler_config,
|
|
logits_processor_pattern=self.logits_processor_pattern,
|
|
generation_config=self.generation_config,
|
|
override_generation_config=self.override_generation_config,
|
|
enable_sleep_mode=self.enable_sleep_mode,
|
|
model_impl=self.model_impl,
|
|
)
|
|
|
|
def create_load_config(self) -> LoadConfig:
|
|
|
|
if self.quantization == "bitsandbytes":
|
|
self.load_format = "bitsandbytes"
|
|
|
|
return LoadConfig(
|
|
load_format=self.load_format,
|
|
download_dir=self.download_dir,
|
|
model_loader_extra_config=self.model_loader_extra_config,
|
|
ignore_patterns=self.ignore_patterns,
|
|
use_tqdm_on_load=self.use_tqdm_on_load,
|
|
pt_load_map_location=self.pt_load_map_location,
|
|
)
|
|
|
|
def create_speculative_config(
|
|
self,
|
|
target_model_config: ModelConfig,
|
|
target_parallel_config: ParallelConfig,
|
|
enable_chunked_prefill: bool,
|
|
disable_log_stats: bool,
|
|
) -> Optional["SpeculativeConfig"]:
|
|
"""Initializes and returns a SpeculativeConfig object based on
|
|
`speculative_config`.
|
|
|
|
This function utilizes `speculative_config` to create a
|
|
SpeculativeConfig object. The `speculative_config` can either be
|
|
provided as a JSON string input via CLI arguments or directly as a
|
|
dictionary from the engine.
|
|
"""
|
|
if self.speculative_config is None:
|
|
return None
|
|
|
|
# Note(Shangming): These parameters are not obtained from the cli arg
|
|
# '--speculative-config' and must be passed in when creating the engine
|
|
# config.
|
|
self.speculative_config.update({
|
|
"target_model_config": target_model_config,
|
|
"target_parallel_config": target_parallel_config,
|
|
"enable_chunked_prefill": enable_chunked_prefill,
|
|
"disable_log_stats": disable_log_stats,
|
|
})
|
|
speculative_config = SpeculativeConfig.from_dict(
|
|
self.speculative_config)
|
|
|
|
return speculative_config
|
|
|
|
def create_engine_config(
|
|
self,
|
|
usage_context: Optional[UsageContext] = None,
|
|
) -> VllmConfig:
|
|
"""
|
|
Create the VllmConfig.
|
|
|
|
NOTE: for autoselection of V0 vs V1 engine, we need to
|
|
create the ModelConfig first, since ModelConfig's attrs
|
|
(e.g. the model arch) are needed to make the decision.
|
|
|
|
This function set VLLM_USE_V1=X if VLLM_USE_V1 is
|
|
unspecified by the user.
|
|
|
|
If VLLM_USE_V1 is specified by the user but the VllmConfig
|
|
is incompatible, we raise an error.
|
|
"""
|
|
from vllm.platforms import current_platform
|
|
current_platform.pre_register_and_update()
|
|
|
|
device_config = DeviceConfig(device=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)
|
|
else:
|
|
self._set_default_args_v0(model_config)
|
|
|
|
assert self.enable_chunked_prefill is not None
|
|
|
|
if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
|
|
assert self.enforce_eager, (
|
|
"Cuda graph is not supported with DualChunkFlashAttention. "
|
|
"To run the model in eager mode, set 'enforce_eager=True' "
|
|
"or use '--enforce-eager' in the CLI.")
|
|
assert current_platform.is_cuda(), (
|
|
"DualChunkFlashAttention is only supported on CUDA platform.")
|
|
assert not use_v1, (
|
|
"DualChunkFlashAttention is not supported on V1 engine. "
|
|
"To run the model in V0 engine, try set 'VLLM_USE_V1=0'")
|
|
|
|
cache_config = CacheConfig(
|
|
block_size=self.block_size,
|
|
gpu_memory_utilization=self.gpu_memory_utilization,
|
|
swap_space=self.swap_space,
|
|
cache_dtype=self.kv_cache_dtype,
|
|
is_attention_free=model_config.is_attention_free,
|
|
num_gpu_blocks_override=self.num_gpu_blocks_override,
|
|
sliding_window=model_config.get_sliding_window(),
|
|
enable_prefix_caching=self.enable_prefix_caching,
|
|
prefix_caching_hash_algo=self.prefix_caching_hash_algo,
|
|
cpu_offload_gb=self.cpu_offload_gb,
|
|
calculate_kv_scales=self.calculate_kv_scales,
|
|
)
|
|
|
|
# Get the current placement group if Ray is initialized and
|
|
# we are in a Ray actor. If so, then the placement group will be
|
|
# passed to spawned processes.
|
|
placement_group = None
|
|
if is_in_ray_actor():
|
|
import ray
|
|
|
|
# This call initializes Ray automatically if it is not initialized,
|
|
# but we should not do this here.
|
|
placement_group = ray.util.get_current_placement_group()
|
|
|
|
# Local DP size defaults to global DP size if not set.
|
|
data_parallel_size_local = self.data_parallel_size if (
|
|
self.data_parallel_size_local
|
|
is None) else self.data_parallel_size_local
|
|
|
|
# DP address, used in multi-node case for torch distributed group
|
|
# and ZMQ sockets.
|
|
data_parallel_address = self.data_parallel_address if (
|
|
self.data_parallel_address
|
|
is not None) else ParallelConfig.data_parallel_master_ip
|
|
|
|
# This port is only used when there are remote data parallel engines,
|
|
# otherwise the local IPC transport is used.
|
|
data_parallel_rpc_port = self.data_parallel_rpc_port if (
|
|
self.data_parallel_rpc_port
|
|
is not None) else ParallelConfig.data_parallel_rpc_port
|
|
|
|
parallel_config = ParallelConfig(
|
|
pipeline_parallel_size=self.pipeline_parallel_size,
|
|
tensor_parallel_size=self.tensor_parallel_size,
|
|
data_parallel_size=self.data_parallel_size,
|
|
data_parallel_size_local=data_parallel_size_local,
|
|
data_parallel_master_ip=data_parallel_address,
|
|
data_parallel_rpc_port=data_parallel_rpc_port,
|
|
enable_expert_parallel=self.enable_expert_parallel,
|
|
max_parallel_loading_workers=self.max_parallel_loading_workers,
|
|
disable_custom_all_reduce=self.disable_custom_all_reduce,
|
|
ray_workers_use_nsight=self.ray_workers_use_nsight,
|
|
placement_group=placement_group,
|
|
distributed_executor_backend=self.distributed_executor_backend,
|
|
worker_cls=self.worker_cls,
|
|
worker_extension_cls=self.worker_extension_cls,
|
|
)
|
|
|
|
speculative_config = self.create_speculative_config(
|
|
target_model_config=model_config,
|
|
target_parallel_config=parallel_config,
|
|
enable_chunked_prefill=self.enable_chunked_prefill,
|
|
disable_log_stats=self.disable_log_stats,
|
|
)
|
|
|
|
# Reminder: Please update docs/features/compatibility_matrix.md
|
|
# If the feature combo become valid
|
|
if self.num_scheduler_steps > 1:
|
|
if speculative_config is not None:
|
|
raise ValueError("Speculative decoding is not supported with "
|
|
"multi-step (--num-scheduler-steps > 1)")
|
|
if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
|
|
raise ValueError("Multi-Step Chunked-Prefill is not supported "
|
|
"for pipeline-parallel-size > 1")
|
|
from vllm.platforms import current_platform
|
|
if current_platform.is_cpu():
|
|
logger.warning("Multi-Step (--num-scheduler-steps > 1) is "
|
|
"currently not supported for CPUs and has been "
|
|
"disabled.")
|
|
self.num_scheduler_steps = 1
|
|
|
|
# make sure num_lookahead_slots is set the higher value depending on
|
|
# if we are using speculative decoding or multi-step
|
|
num_lookahead_slots = max(self.num_lookahead_slots,
|
|
self.num_scheduler_steps - 1)
|
|
num_lookahead_slots = num_lookahead_slots \
|
|
if speculative_config is None \
|
|
else speculative_config.num_lookahead_slots
|
|
|
|
scheduler_config = SchedulerConfig(
|
|
runner_type=model_config.runner_type,
|
|
max_num_batched_tokens=self.max_num_batched_tokens,
|
|
max_num_seqs=self.max_num_seqs,
|
|
max_model_len=model_config.max_model_len,
|
|
cuda_graph_sizes=self.cuda_graph_sizes,
|
|
num_lookahead_slots=num_lookahead_slots,
|
|
delay_factor=self.scheduler_delay_factor,
|
|
enable_chunked_prefill=self.enable_chunked_prefill,
|
|
disable_chunked_mm_input=self.disable_chunked_mm_input,
|
|
is_multimodal_model=model_config.is_multimodal_model,
|
|
preemption_mode=self.preemption_mode,
|
|
num_scheduler_steps=self.num_scheduler_steps,
|
|
multi_step_stream_outputs=self.multi_step_stream_outputs,
|
|
send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
|
|
and parallel_config.use_ray),
|
|
policy=self.scheduling_policy,
|
|
scheduler_cls=self.scheduler_cls,
|
|
max_num_partial_prefills=self.max_num_partial_prefills,
|
|
max_long_partial_prefills=self.max_long_partial_prefills,
|
|
long_prefill_token_threshold=self.long_prefill_token_threshold,
|
|
)
|
|
|
|
lora_config = LoRAConfig(
|
|
bias_enabled=self.enable_lora_bias,
|
|
max_lora_rank=self.max_lora_rank,
|
|
max_loras=self.max_loras,
|
|
fully_sharded_loras=self.fully_sharded_loras,
|
|
lora_extra_vocab_size=self.lora_extra_vocab_size,
|
|
long_lora_scaling_factors=self.long_lora_scaling_factors,
|
|
lora_dtype=self.lora_dtype,
|
|
max_cpu_loras=self.max_cpu_loras if self.max_cpu_loras
|
|
and self.max_cpu_loras > 0 else None) if self.enable_lora else None
|
|
|
|
# bitsandbytes pre-quantized model need a specific model loader
|
|
if model_config.quantization == "bitsandbytes":
|
|
self.quantization = self.load_format = "bitsandbytes"
|
|
|
|
load_config = self.create_load_config()
|
|
|
|
prompt_adapter_config = PromptAdapterConfig(
|
|
max_prompt_adapters=self.max_prompt_adapters,
|
|
max_prompt_adapter_token=self.max_prompt_adapter_token) \
|
|
if self.enable_prompt_adapter else None
|
|
|
|
decoding_config = DecodingConfig(
|
|
backend=self.guided_decoding_backend,
|
|
disable_fallback=self.guided_decoding_disable_fallback,
|
|
disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
|
|
disable_additional_properties=\
|
|
self.guided_decoding_disable_additional_properties,
|
|
reasoning_backend=self.reasoning_parser
|
|
)
|
|
|
|
observability_config = ObservabilityConfig(
|
|
show_hidden_metrics_for_version=self.
|
|
show_hidden_metrics_for_version,
|
|
otlp_traces_endpoint=self.otlp_traces_endpoint,
|
|
collect_detailed_traces=self.collect_detailed_traces,
|
|
)
|
|
|
|
config = VllmConfig(
|
|
model_config=model_config,
|
|
cache_config=cache_config,
|
|
parallel_config=parallel_config,
|
|
scheduler_config=scheduler_config,
|
|
device_config=device_config,
|
|
lora_config=lora_config,
|
|
speculative_config=speculative_config,
|
|
load_config=load_config,
|
|
decoding_config=decoding_config,
|
|
observability_config=observability_config,
|
|
prompt_adapter_config=prompt_adapter_config,
|
|
compilation_config=self.compilation_config,
|
|
kv_transfer_config=self.kv_transfer_config,
|
|
kv_events_config=self.kv_events_config,
|
|
additional_config=self.additional_config,
|
|
)
|
|
|
|
return config
|
|
|
|
def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
|
|
"""Oracle for whether to use V0 or V1 Engine by default."""
|
|
|
|
#############################################################
|
|
# Unsupported Feature Flags on V1.
|
|
|
|
if self.load_format == LoadFormat.SHARDED_STATE.value:
|
|
_raise_or_fallback(
|
|
feature_name=f"--load_format {self.load_format}",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
if (self.logits_processor_pattern
|
|
!= EngineArgs.logits_processor_pattern):
|
|
_raise_or_fallback(feature_name="--logits-processor-pattern",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
if self.preemption_mode != SchedulerConfig.preemption_mode:
|
|
_raise_or_fallback(feature_name="--preemption-mode",
|
|
recommend_to_remove=True)
|
|
return False
|
|
|
|
if (self.disable_async_output_proc
|
|
!= EngineArgs.disable_async_output_proc):
|
|
_raise_or_fallback(feature_name="--disable-async-output-proc",
|
|
recommend_to_remove=True)
|
|
return False
|
|
|
|
if self.scheduling_policy != SchedulerConfig.policy:
|
|
_raise_or_fallback(feature_name="--scheduling-policy",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
if self.num_scheduler_steps != SchedulerConfig.num_scheduler_steps:
|
|
_raise_or_fallback(feature_name="--num-scheduler-steps",
|
|
recommend_to_remove=True)
|
|
return False
|
|
|
|
if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
|
|
_raise_or_fallback(feature_name="--scheduler-delay-factor",
|
|
recommend_to_remove=True)
|
|
return False
|
|
|
|
if self.guided_decoding_backend not in get_args(
|
|
GuidedDecodingBackendV1):
|
|
_raise_or_fallback(
|
|
feature_name=
|
|
f"--guided-decoding-backend={self.guided_decoding_backend}",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# Need at least Ampere for now (FA support required).
|
|
# Skip this check if we are running on a non-GPU platform,
|
|
# or if the device capability is not available
|
|
# (e.g. in a Ray actor without GPUs).
|
|
from vllm.platforms import current_platform
|
|
if (current_platform.is_cuda()
|
|
and current_platform.get_device_capability()
|
|
and current_platform.get_device_capability().major < 8):
|
|
_raise_or_fallback(feature_name="Compute Capability < 8.0",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No Fp8 KV cache so far.
|
|
if self.kv_cache_dtype != "auto":
|
|
fp8_attention = self.kv_cache_dtype.startswith("fp8")
|
|
will_use_fa = (
|
|
current_platform.is_cuda()
|
|
and not envs.is_set("VLLM_ATTENTION_BACKEND")
|
|
) or envs.VLLM_ATTENTION_BACKEND == "FLASH_ATTN_VLLM_V1"
|
|
supported = False
|
|
if current_platform.is_rocm():
|
|
supported = True
|
|
elif fp8_attention and will_use_fa:
|
|
from vllm.attention.utils.fa_utils import (
|
|
flash_attn_supports_fp8)
|
|
supported = flash_attn_supports_fp8()
|
|
if not supported:
|
|
_raise_or_fallback(feature_name="--kv-cache-dtype",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No Prompt Adapter so far.
|
|
if self.enable_prompt_adapter:
|
|
_raise_or_fallback(feature_name="--enable-prompt-adapter",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No text embedding inputs so far.
|
|
if self.enable_prompt_embeds:
|
|
_raise_or_fallback(feature_name="--enable-prompt-embeds",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# Only Fp16 and Bf16 dtypes since we only support FA.
|
|
V1_SUPPORTED_DTYPES = [torch.bfloat16, torch.float16]
|
|
if model_config.dtype not in V1_SUPPORTED_DTYPES:
|
|
_raise_or_fallback(feature_name=f"--dtype {model_config.dtype}",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No Embedding Models so far.
|
|
if model_config.task not in ["generate"]:
|
|
_raise_or_fallback(feature_name=f"--task {model_config.task}",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No Mamba or Encoder-Decoder so far.
|
|
if not model_config.is_v1_compatible:
|
|
_raise_or_fallback(feature_name=model_config.architectures,
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No Concurrent Partial Prefills so far.
|
|
if (self.max_num_partial_prefills
|
|
!= SchedulerConfig.max_num_partial_prefills
|
|
or self.max_long_partial_prefills
|
|
!= SchedulerConfig.max_long_partial_prefills):
|
|
_raise_or_fallback(feature_name="Concurrent Partial Prefill",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No OTLP observability so far.
|
|
if (self.otlp_traces_endpoint or self.collect_detailed_traces):
|
|
_raise_or_fallback(feature_name="--otlp-traces-endpoint",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# V1 supports N-gram, Medusa, and Eagle speculative decoding.
|
|
is_ngram_enabled = False
|
|
is_eagle_enabled = False
|
|
is_medusa_enabled = False
|
|
if self.speculative_config is not None:
|
|
# This is supported but experimental (handled below).
|
|
speculative_method = self.speculative_config.get("method")
|
|
if speculative_method:
|
|
if speculative_method in ("ngram", "[ngram]"):
|
|
is_ngram_enabled = True
|
|
elif speculative_method == "medusa":
|
|
is_medusa_enabled = True
|
|
elif speculative_method in ("eagle", "eagle3", "deepseek_mtp"):
|
|
is_eagle_enabled = True
|
|
else:
|
|
speculative_model = self.speculative_config.get("model")
|
|
if speculative_model in ("ngram", "[ngram]"):
|
|
is_ngram_enabled = True
|
|
if not (is_ngram_enabled or is_eagle_enabled or is_medusa_enabled):
|
|
# Other speculative decoding methods are not supported yet.
|
|
_raise_or_fallback(feature_name="Speculative Decoding",
|
|
recommend_to_remove=False)
|
|
return False
|
|
|
|
# No XFormers so far.
|
|
V1_BACKENDS = [
|
|
"FLASH_ATTN_VLLM_V1",
|
|
"FLASH_ATTN",
|
|
"PALLAS",
|
|
"PALLAS_VLLM_V1",
|
|
"TRITON_ATTN_VLLM_V1",
|
|
"TRITON_MLA",
|
|
"FLASHMLA",
|
|
"FLASHINFER",
|
|
"FLASHINFER_VLLM_V1",
|
|
"ROCM_AITER_MLA",
|
|
]
|
|
if (envs.is_set("VLLM_ATTENTION_BACKEND")
|
|
and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):
|
|
name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
|
|
_raise_or_fallback(feature_name=name, recommend_to_remove=True)
|
|
return False
|
|
|
|
# Platforms must decide if they can support v1 for this model
|
|
if not current_platform.supports_v1(model_config=model_config):
|
|
_raise_or_fallback(
|
|
feature_name=f"device type={current_platform.device_type}",
|
|
recommend_to_remove=False)
|
|
return False
|
|
#############################################################
|
|
# Experimental Features - allow users to opt in.
|
|
|
|
# Signal Handlers requires running in main thread.
|
|
if (threading.current_thread() != threading.main_thread()
|
|
and _warn_or_fallback("Engine in background thread")):
|
|
return False
|
|
|
|
if (self.pipeline_parallel_size > 1
|
|
and self.distributed_executor_backend
|
|
not in ("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
|
|
|
|
# Non-[CUDA, TPU] may be supported on V1, but off by default for now.
|
|
v0_hardware = not any(
|
|
(current_platform.is_cuda(), current_platform.is_tpu()))
|
|
if v0_hardware and _warn_or_fallback( # noqa: SIM103
|
|
current_platform.device_name):
|
|
return False
|
|
#############################################################
|
|
|
|
return True
|
|
|
|
def _set_default_args_v0(self, model_config: ModelConfig) -> None:
|
|
"""Set Default Arguments for V0 Engine."""
|
|
|
|
max_model_len = model_config.max_model_len
|
|
use_long_context = max_model_len > 32768
|
|
if self.enable_chunked_prefill is None:
|
|
# Chunked prefill not supported for Multimodal or MLA in V0.
|
|
if model_config.is_multimodal_model or model_config.use_mla:
|
|
self.enable_chunked_prefill = False
|
|
|
|
# Enable chunked prefill by default for long context (> 32K)
|
|
# models to avoid OOM errors in initial memory profiling phase.
|
|
elif use_long_context:
|
|
from vllm.platforms import current_platform
|
|
is_gpu = current_platform.is_cuda()
|
|
use_sliding_window = (model_config.get_sliding_window()
|
|
is not None)
|
|
use_spec_decode = self.speculative_config is not None
|
|
|
|
if (is_gpu and not use_sliding_window and not use_spec_decode
|
|
and not self.enable_lora
|
|
and not self.enable_prompt_adapter
|
|
and model_config.runner_type != "pooling"):
|
|
self.enable_chunked_prefill = True
|
|
logger.warning(
|
|
"Chunked prefill is enabled by default for models "
|
|
"with max_model_len > 32K. Chunked prefill might "
|
|
"not work with some features or models. If you "
|
|
"encounter any issues, please disable by launching "
|
|
"with --enable-chunked-prefill=False.")
|
|
|
|
if self.enable_chunked_prefill is None:
|
|
self.enable_chunked_prefill = False
|
|
|
|
if not self.enable_chunked_prefill and use_long_context:
|
|
logger.warning(
|
|
"The model has a long context length (%s). This may cause"
|
|
"OOM during the initial memory profiling phase, or result "
|
|
"in low performance due to small KV cache size. Consider "
|
|
"setting --max-model-len to a smaller value.", max_model_len)
|
|
elif (self.enable_chunked_prefill
|
|
and model_config.runner_type == "pooling"):
|
|
msg = "Chunked prefill is not supported for pooling models"
|
|
raise ValueError(msg)
|
|
|
|
# if using prefix caching, we must set a hash algo
|
|
if self.enable_prefix_caching:
|
|
# Disable prefix caching for multimodal models for VLLM_V0.
|
|
if model_config.is_multimodal_model:
|
|
logger.warning(
|
|
"--enable-prefix-caching is not supported for multimodal "
|
|
"models in V0 and has been disabled.")
|
|
self.enable_prefix_caching = False
|
|
|
|
# VLLM_V0 only supports builtin hash algo for prefix caching.
|
|
if self.prefix_caching_hash_algo == "sha256":
|
|
raise ValueError(
|
|
"sha256 is not supported for prefix caching in V0 engine. "
|
|
"Please use 'builtin'.")
|
|
|
|
# Set max_num_seqs to 256 for VLLM_V0.
|
|
if self.max_num_seqs is None:
|
|
self.max_num_seqs = 256
|
|
|
|
def _set_default_args_v1(self, usage_context: UsageContext) -> None:
|
|
"""Set Default Arguments for V1 Engine."""
|
|
|
|
# V1 always uses chunked prefills.
|
|
self.enable_chunked_prefill = True
|
|
|
|
# V1 enables prefix caching by default.
|
|
if self.enable_prefix_caching is None:
|
|
self.enable_prefix_caching = True
|
|
|
|
# V1 should use the new scheduler by default.
|
|
# Swap it only if this arg is set to the original V0 default
|
|
if self.scheduler_cls == EngineArgs.scheduler_cls:
|
|
self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
|
|
|
|
# When no user override, set the default values based on the usage
|
|
# context.
|
|
# Use different default values for different hardware.
|
|
|
|
# Try to query the device name on the current platform. If it fails,
|
|
# it may be because the platform that imports vLLM is not the same
|
|
# as the platform that vLLM is running on (e.g. the case of scaling
|
|
# vLLM with Ray) and has no GPUs. In this case we use the default
|
|
# values for non-H100/H200 GPUs.
|
|
from vllm.platforms import current_platform
|
|
try:
|
|
device_memory = current_platform.get_device_total_memory()
|
|
device_name = current_platform.get_device_name().lower()
|
|
except Exception:
|
|
# This is only used to set default_max_num_batched_tokens
|
|
device_memory = 0
|
|
|
|
# NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
|
|
# throughput, see PR #17885 for more details.
|
|
# So here we do an extra device name check to prevent such regression.
|
|
if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
|
|
# For GPUs like H100 and MI300x, use larger default values.
|
|
default_max_num_batched_tokens = {
|
|
UsageContext.LLM_CLASS: 16384,
|
|
UsageContext.OPENAI_API_SERVER: 8192,
|
|
}
|
|
default_max_num_seqs = 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 = 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,
|
|
}
|
|
}
|
|
|
|
use_context_value = usage_context.value if usage_context else None
|
|
if (self.max_num_batched_tokens is None
|
|
and usage_context in default_max_num_batched_tokens):
|
|
if current_platform.is_tpu():
|
|
chip_name = current_platform.get_device_name()
|
|
if chip_name in default_max_num_batched_tokens_tpu[
|
|
usage_context]:
|
|
self.max_num_batched_tokens = \
|
|
default_max_num_batched_tokens_tpu[
|
|
usage_context][chip_name]
|
|
else:
|
|
self.max_num_batched_tokens = \
|
|
default_max_num_batched_tokens[usage_context]
|
|
else:
|
|
self.max_num_batched_tokens = default_max_num_batched_tokens[
|
|
usage_context]
|
|
logger.debug(
|
|
"Setting max_num_batched_tokens to %d for %s usage context.",
|
|
self.max_num_batched_tokens, use_context_value)
|
|
|
|
if self.max_num_seqs is None:
|
|
self.max_num_seqs = default_max_num_seqs
|
|
|
|
logger.debug("Setting max_num_seqs to %d for %s usage context.",
|
|
self.max_num_seqs, use_context_value)
|
|
|
|
|
|
@dataclass
|
|
class AsyncEngineArgs(EngineArgs):
|
|
"""Arguments for asynchronous vLLM engine."""
|
|
disable_log_requests: bool = False
|
|
|
|
@staticmethod
|
|
def add_cli_args(parser: FlexibleArgumentParser,
|
|
async_args_only: bool = False) -> FlexibleArgumentParser:
|
|
# Initialize plugin to update the parser, for example, The plugin may
|
|
# adding a new kind of quantization method to --quantization argument or
|
|
# a new device to --device argument.
|
|
load_general_plugins()
|
|
if not async_args_only:
|
|
parser = EngineArgs.add_cli_args(parser)
|
|
parser.add_argument('--disable-log-requests',
|
|
action='store_true',
|
|
help='Disable logging requests.')
|
|
from vllm.platforms import current_platform
|
|
current_platform.pre_register_and_update(parser)
|
|
return parser
|
|
|
|
|
|
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)
|
|
|
|
|
|
# These functions are used by sphinx to build the documentation
|
|
def _engine_args_parser():
|
|
return EngineArgs.add_cli_args(FlexibleArgumentParser())
|
|
|
|
|
|
def _async_engine_args_parser():
|
|
return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
|
|
async_args_only=True)
|