vllm.config.utils ¶

@dataclass_transform(field_specifiers=(PydanticField,))
def config(
    cls: type[ConfigT] | None = None,
    *,
    config: ConfigDict | None = None,
    **kwargs: Any,
) -> type[ConfigT] | Callable[[type[ConfigT]], type[ConfigT]]:
    """Decorator to create a pydantic dataclass with default config. The default config
    for the dataclass forbids extra fields.

    All config classes in vLLM should use this decorator.

    Args:
        cls: The class to decorate
        config: The pydantic ConfigDict to use. If provided, it will be merged with
            the default config.
        **kwargs: Additional arguments to pass to pydantic.dataclass."""
    # Extra fields are forbidden by default
    merged_config = ConfigDict(extra="forbid")
    if config is not None:
        merged_config.update(config)

    def decorator(cls):
        return dataclass(cls, config=merged_config, **kwargs)

    # Called with arguments: @config(config=...)
    if cls is None:
        return decorator
    # Called without arguments: @config
    return decorator(cls)

def get_attr_docs(cls: type[Any]) -> dict[str, str]:
    """
    Get any docstrings placed after attribute assignments in a class body.

    https://davidism.com/mit-license/
    """

    cls_node = ast.parse(textwrap.dedent(inspect.getsource(cls))).body[0]

    if not isinstance(cls_node, ast.ClassDef):
        raise TypeError("Given object was not a class.")

    out = {}

    # Consider each pair of nodes.
    for a, b in pairwise(cls_node.body):
        # Must be an assignment then a constant string.
        if (
            not isinstance(a, (ast.Assign, ast.AnnAssign))
            or not isinstance(b, ast.Expr)
            or not isinstance(b.value, ast.Constant)
            or not isinstance(b.value.value, str)
        ):
            continue

        doc = inspect.cleandoc(b.value.value)

        # An assignment can have multiple targets (a = b = v), but an
        # annotated assignment only has one target.
        targets = a.targets if isinstance(a, ast.Assign) else [a.target]

        for target in targets:
            # Must be assigning to a plain name.
            if not isinstance(target, ast.Name):
                continue

            out[target.id] = doc

    return out

def get_field(cls: ConfigType, name: str) -> Field:
    """Get the default factory field of a dataclass by name. Used for getting
    default factory fields in `EngineArgs`."""
    if not is_dataclass(cls):
        raise TypeError("The given class is not a dataclass.")
    try:
        named_field = next(f for f in fields(cls) if f.name == name)
    except StopIteration as e:
        raise ValueError(f"Field '{name}' not found in {cls.__name__}.") from e

    # The arguments to copy to the new field
    default = named_field.default
    default_factory = named_field.default_factory
    init = named_field.init

    # Handle pydantic.Field
    if isinstance(default, FieldInfo):
        if default.init is not None:
            init = default.init
        if default.default_factory is not None:
            default_factory = cast(Callable[[], Any], default.default_factory)
            default = MISSING
        else:
            default = default.default

    if default is MISSING and default_factory is MISSING:
        logger.warning_once(
            "%s.%s has no default or default factory.", cls.__name__, name
        )
    return field(default=default, default_factory=default_factory, init=init)

def get_hash_factors(config: ConfigT, ignored_factors: set[str]) -> dict[str, object]:
    """Gets the factors used for hashing a config class.
    - Includes all dataclass fields not in `ignored_factors`.
    - Errors on non-normalizable values.
    """
    factors: dict[str, object] = {}
    for dc_field in fields(config):
        factor = dc_field.name
        if factor in ignored_factors:
            continue
        value = getattr(config, factor, None)
        try:
            factors[factor] = normalize_value(value)
        except TypeError as e:
            raise TypeError(
                f"get_hash_factors: unsupported type for key '{factor}' "
                f"({type(value).__name__})"
            ) from e
    return factors

def getattr_iter(
    object: object,
    names: Sequence[str],
    default: Any | None = None,
    default_factory: Callable[[], Any] | None = None,
    warn: bool = False,
) -> Any:
    """
    A helper function that retrieves an attribute from an object which may
    have multiple possible names. This is useful when fetching attributes from
    arbitrary `transformers.PretrainedConfig` instances.

    In the case where the first name in `names` is the preferred name, and
    any other names are deprecated aliases, setting `warn=True` will log a
    warning when a deprecated name is used.
    """
    for i, name in enumerate(names):
        if hasattr(object, name):
            if warn and i > 0:
                logger.warning_once(
                    "%s contains a deprecated attribute name '%s'. "
                    "Please use the preferred attribute name '%s' instead.",
                    type(object).__name__,
                    name,
                    names[0],
                )
            return getattr(object, name)
    return default_factory() if default_factory is not None else default

def handle_deprecated(
    config: ConfigT,
    old_name: str,
    new_name_or_names: str | list[str],
    removal_version: str,
) -> None:
    old_val = getattr(config, old_name)
    if old_val is None:
        return

    if isinstance(new_name_or_names, str):
        new_names = [new_name_or_names]
    else:
        new_names = new_name_or_names

    msg = (
        f"{old_name} is deprecated and will be removed in {removal_version}. "
        f"Use {', '.join(new_names)} instead."
    )
    logger.warning(msg)

    for new_name in new_names:
        setattr(config, new_name, old_val)

def hash_factors(items: dict[str, object]) -> str:
    """Return a SHA-256 hex digest of the canonical items structure."""
    return hashlib.sha256(json.dumps(items, sort_keys=True).encode()).hexdigest()

def is_init_field(cls: ConfigType, name: str) -> bool:
    return get_field(cls, name).init

def normalize_value(x):
    """Return a stable, JSON-serializable canonical form for hashing.
    Order: primitives, special types (Enum, callable, torch.dtype, Path), then
    generic containers (Mapping/Set/Sequence) with recursion.
    """
    # Fast path
    if x is None or isinstance(x, (bool, int, float, str)):
        return x

    # Enums: tag with FQN to avoid primitive collisions.
    # Ex: Enum(1) vs int(1) -> ("module.QualName", value).
    if isinstance(x, enum.Enum):
        enum_type = f"{x.__class__.__module__}.{x.__class__.__qualname__}"
        return (enum_type, normalize_value(x.value))

    # Classes (types) are accepted and canonicalized by their fully-qualified
    # name (module.qualname) for a stable identifier.
    # Instances are only accepted if they expose uuid(); otherwise they are
    # rejected to avoid under-hashing object state.

    # Callables: accept classes only; reject funcs/lambdas/methods.
    # Used by LogitsProcessor types and ModelConfig.hf_overrides.
    if isinstance(x, type):
        module = getattr(x, "__module__", "")
        qual = getattr(x, "__qualname__", getattr(x, "__name__", ""))
        return ".".join([p for p in (module, qual) if p]) or repr(x)

    # Prefer stable uuid identifiers for objects that provide them, even if
    # they are callable instances (e.g., InductorPass wrappers).
    if hasattr(x, "uuid") and callable(getattr(x, "uuid", None)):
        return x.uuid()

    if callable(x):
        raise TypeError("normalize_value: function or callable instance unsupported")

    # Torch dtype: stringify (torch.float64 -> "torch.float64").
    # We rely on the string form here; dtype-bearing fields that need additional
    # disambiguation should encode that at the config layer.
    if isinstance(x, torch.dtype):
        return str(x)

    # Bytes
    if isinstance(x, (bytes, bytearray)):
        return x.hex()

    # Paths (canonicalize)
    if isinstance(x, pathlib.Path):
        try:
            return str(x.expanduser().resolve())
        except Exception:
            return str(x)

    # Dataclasses: represent as (FQN, sorted(field,value) tuple) for stability.
    if is_dataclass(x):
        type_fqn = f"{x.__class__.__module__}.{x.__class__.__qualname__}"
        items = tuple(
            (f.name, normalize_value(getattr(x, f.name)))
            for f in sorted(fields(x), key=lambda f: f.name)
        )
        return (type_fqn, items)

    # Containers (generic)
    if isinstance(x, Mapping):
        return tuple(sorted((str(k), normalize_value(v)) for k, v in x.items()))
    if isinstance(x, Set):
        return tuple(sorted(repr(normalize_value(v)) for v in x))
    if isinstance(x, Sequence) and not isinstance(x, (str, bytes, bytearray)):
        return tuple(normalize_value(v) for v in x)

    # PretrainedConfig
    if hasattr(x, "to_json_string") and callable(x.to_json_string):
        return x.to_json_string()

    # Unsupported type: e.g., modules, generators, open files, or objects
    # without a stable JSON/UUID representation. Hard-error to avoid
    # under-hashing.
    # If you hit this, either reshape your config to use supported primitives
    # and containers, or extend normalize_value to provide a stable encoding
    # (e.g., via uuid() or to_json_string()) for this type.
    raise TypeError(
        f"normalize_value: unsupported type '{type(x).__name__}'. "
        "Ensure config values use supported primitives/containers or add a "
        "stable representation for this type."
    )

def replace(dataclass_instance: ConfigT, /, **kwargs) -> ConfigT:
    """Like [`dataclasses.replace`](https://docs.python.org/3/library/dataclasses.html#dataclasses.replace),
    but compatible with Pydantic dataclasses which use `pydantic.fields.Field` instead
    of `dataclasses.field`"""
    cls = type(dataclass_instance)
    dataclass_dict = dataclass_instance.__dict__
    dataclass_dict = {k: v for k, v in dataclass_dict.items() if is_init_field(cls, k)}
    dataclass_dict.update(kwargs)
    return cls(**dataclass_dict)

def update_config(config: ConfigT, overrides: dict[str, Any]) -> ConfigT:
    processed_overrides = {}
    for field_name, value in overrides.items():
        assert hasattr(config, field_name), (
            f"{type(config)} has no field `{field_name}`"
        )
        current_value = getattr(config, field_name)
        if is_dataclass(current_value) and not is_dataclass(value):
            assert isinstance(value, dict), (
                f"Overrides to {type(config)}.{field_name} must be a dict"
                f"  or {type(current_value)}, but got {type(value)}"
            )
            value = update_config(
                current_value,  # type: ignore[type-var]
                value,
            )
        processed_overrides[field_name] = value
    return replace(config, **processed_overrides)