mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 21:49:24 +08:00
[pytree] align function signature between C++ and Python pytree (#112482)
Change the argument name in C++ and Python pytree APIs. Also add a test to ensure the function signatures are the same in the two implementations. - #112485 Pull Request resolved: https://github.com/pytorch/pytorch/pull/112482 Approved by: https://github.com/zou3519
This commit is contained in:
Xuehai Pan
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
7715b47f44
commit
4893a2814f
@ -21,21 +21,21 @@ SERIALIZED_DATACLASS_TO_PYTHON_DATACLASS: Dict[str, Type[Any]] = {}
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def register_dataclass_as_pytree_node(
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typ: Any,
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cls: Any,
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flatten_fn: Optional[FlattenFunc] = None,
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unflatten_fn: Optional[UnflattenFunc] = None,
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*,
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serialized_type_name: Optional[str] = None,
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to_dumpable_context: Optional[ToDumpableContextFn] = None,
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from_dumpable_context: Optional[FromDumpableContextFn] = None,
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serialized_type_name: Optional[str] = None,
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return_none_fields: bool = False,
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) -> None:
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assert dataclasses.is_dataclass(
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typ
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), f"Only dataclasses can be registered with this function: {typ}"
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cls
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), f"Only dataclasses can be registered with this function: {cls}"
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serialized_type = f"{typ.__module__}.{typ.__name__}"
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SERIALIZED_DATACLASS_TO_PYTHON_DATACLASS[serialized_type] = typ
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serialized_type = f"{cls.__module__}.{cls.__name__}"
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SERIALIZED_DATACLASS_TO_PYTHON_DATACLASS[serialized_type] = cls
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def default_flatten_fn(obj: Any) -> Tuple[List[Any], Context]:
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flattened = []
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@ -48,7 +48,7 @@ def register_dataclass_as_pytree_node(
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flat_names.append(name)
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else:
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none_names.append(name)
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return flattened, (typ, flat_names, none_names)
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return flattened, (cls, flat_names, none_names)
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def default_unflatten_fn(values: Iterable[Any], context: Context) -> Any:
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typ, flat_names, none_names = context
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@ -69,7 +69,7 @@ def register_dataclass_as_pytree_node(
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if (to_dumpable_context is None) ^ (from_dumpable_context is None):
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raise ValueError(
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f"Both to_dumpable_context and from_dumpable_context for {typ} must "
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f"Both to_dumpable_context and from_dumpable_context for {cls} must "
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"be None or registered."
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)
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@ -85,7 +85,7 @@ def register_dataclass_as_pytree_node(
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)
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_register_pytree_node(
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typ,
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cls,
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flatten_fn,
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unflatten_fn,
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serialized_type_name=serialized_type_name,
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@ -570,12 +570,12 @@ def load(
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)
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def register_dataclass(typ: Any) -> None:
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def register_dataclass(cls: Any) -> None:
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"""
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Registers a dataclass as a valid input/output type for :func:`torch.export.export`.
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Args:
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typ: the dataclass type to register
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cls: the dataclass type to register
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Example::
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@ -601,4 +601,4 @@ def register_dataclass(typ: Any) -> None:
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from torch._export.utils import register_dataclass_as_pytree_node
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return register_dataclass_as_pytree_node(typ)
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return register_dataclass_as_pytree_node(cls)
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@ -12,12 +12,12 @@ SUPPORTED_NODES: Dict[Type[Any], FlattenFuncSpec] = {}
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SUPPORTED_NODES_EXACT_MATCH: Dict[Type[Any], Optional[FlattenFuncExactMatchSpec]] = {}
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def register_pytree_flatten_spec(
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typ: Any,
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cls: Any,
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flatten_fn_spec: FlattenFuncSpec,
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flatten_fn_exact_match_spec: Optional[FlattenFuncExactMatchSpec] = None
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) -> None:
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SUPPORTED_NODES[typ] = flatten_fn_spec
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SUPPORTED_NODES_EXACT_MATCH[typ] = flatten_fn_exact_match_spec
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SUPPORTED_NODES[cls] = flatten_fn_spec
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SUPPORTED_NODES_EXACT_MATCH[cls] = flatten_fn_exact_match_spec
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def tree_flatten_spec(pytree: PyTree, spec: TreeSpec, exact_structural_match=False) -> List[Any]:
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if isinstance(spec, LeafSpec):
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@ -58,6 +58,7 @@ __all__ = [
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T = TypeVar("T")
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S = TypeVar("S")
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U = TypeVar("U")
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R = TypeVar("R")
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@ -79,11 +80,11 @@ def _reverse_args(func: UnflattenFunc) -> OpTreeUnflattenFunc:
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def register_pytree_node(
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cls: Type[Any],
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flatten_func: FlattenFunc,
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unflatten_func: UnflattenFunc,
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namespace: str = "torch",
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flatten_fn: FlattenFunc,
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unflatten_fn: UnflattenFunc,
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*,
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serialized_type_name: Optional[str] = None,
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namespace: str = "torch",
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) -> None:
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"""Extend the set of types that are considered internal nodes in pytrees.
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@ -99,20 +100,18 @@ def register_pytree_node(
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Args:
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cls (type): A Python type to treat as an internal pytree node.
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flatten_fn (callable): A function to be used during flattening, taking an instance of ``cls``
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and returning a triple or optionally a pair, with (1) an iterable for the children to be
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flattened recursively, and (2) some hashable auxiliary data to be stored in the treespec
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and to be passed to the ``unflatten_func``, and (3) (optional) an iterable for the tree
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path entries to the corresponding children. If the entries are not provided or given by
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:data:`None`, then `range(len(children))` will be used.
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unflatten_fn (callable): A function taking two arguments: the auxiliary data that was returned
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by ``flatten_func`` and stored in the treespec, and the unflattened children. The function
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should return an instance of ``cls``.
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namespace (str, optional): A non-empty string that uniquely identifies the namespace of the
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type registry. This is used to isolate the registry from other modules that might register
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a different custom behavior for the same type. (default: :const:`"torch"`)
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flatten_fn (callable): A function to be used during flattening, taking an instance of
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``cls`` and returning a pair, with (1) an iterable for the children to be flattened
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recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be
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passed to the ``unflatten_fn``.
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unflatten_fn (callable): A function taking two arguments: the auxiliary data that was
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returned by ``flatten_fn`` and stored in the treespec, and the unflattened children.
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The function should return an instance of ``cls``.
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serialized_type_name (str, optional): A keyword argument used to specify the fully
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qualified name used when serializing the tree spec.
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namespace (str, optional): A non-empty string that uniquely identifies the namespace of the
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type registry. This is used to isolate the registry from other modules that might
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register a different custom behavior for the same type. (default: :const:`"torch"`)
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Example::
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@ -198,15 +197,15 @@ def register_pytree_node(
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_register_pytree_node(
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cls,
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flatten_func,
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unflatten_func,
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flatten_fn,
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unflatten_fn,
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serialized_type_name=serialized_type_name,
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)
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optree.register_pytree_node(
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cls,
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flatten_func,
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_reverse_args(unflatten_func),
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flatten_fn,
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_reverse_args(unflatten_fn),
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namespace=namespace,
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)
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@ -219,7 +218,7 @@ def tree_flatten(
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*,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> Tuple[List[Any], PyTreeSpec]:
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) -> Tuple[List[Any], TreeSpec]:
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"""Flatten a pytree.
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See also :func:`tree_unflatten`.
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@ -269,7 +268,7 @@ def tree_flatten(
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)
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def tree_unflatten(leaves: Iterable[Any], treespec: PyTreeSpec) -> PyTree:
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def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
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"""Reconstruct a pytree from the treespec and the leaves.
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The inverse of :func:`tree_flatten`.
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@ -282,16 +281,16 @@ def tree_unflatten(leaves: Iterable[Any], treespec: PyTreeSpec) -> PyTree:
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Args:
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leaves (iterable): The list of leaves to use for reconstruction. The list must match the
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number of leaves of the treespec.
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treespec (PyTreeSpec): The treespec to reconstruct.
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treespec (TreeSpec): The treespec to reconstruct.
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Returns:
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The reconstructed pytree, containing the ``leaves`` placed in the structure described by
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``treespec``.
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"""
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if not isinstance(treespec, PyTreeSpec):
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if not isinstance(treespec, TreeSpec):
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raise TypeError(
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f"tree_unflatten(values, spec): Expected `spec` to be instance of "
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f"PyTreeSpec but got item of type {type(treespec)}."
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f"TreeSpec but got item of type {type(treespec)}."
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)
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return optree.tree_unflatten(treespec, leaves) # type: ignore[arg-type]
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@ -337,7 +336,7 @@ def tree_structure(
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*,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> PyTreeSpec:
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) -> TreeSpec:
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"""Get the treespec for a pytree.
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See also :func:`tree_flatten`.
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@ -464,9 +463,11 @@ def tree_map_(
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Type2 = Tuple[Type[T], Type[S]]
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Type3 = Tuple[Type[T], Type[S], Type[U]]
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TypeAny = Union[Type[Any], Tuple[Type[Any], ...]]
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Fn2 = Callable[[Union[T, S]], R]
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Fn3 = Callable[[Union[T, S, U]], R]
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Fn = Callable[[T], R]
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FnAny = Callable[[Any], R]
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@ -480,6 +481,11 @@ def map_only(__type_or_types: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]:
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...
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@overload
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def map_only(__type_or_types: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]:
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...
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@overload
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def map_only(__type_or_types: Type[T]) -> MapOnlyFn[Fn[T, Any]]:
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...
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@ -547,6 +553,18 @@ def tree_map_only(
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...
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@overload
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def tree_map_only(
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__type_or_types: Type3[T, S, U],
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func: Fn3[T, S, U, Any],
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tree: PyTree,
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*rests: PyTree,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> PyTree:
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...
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def tree_map_only(
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__type_or_types: TypeAny,
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func: FnAny[Any],
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@ -588,6 +606,18 @@ def tree_map_only_(
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...
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@overload
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def tree_map_only_(
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__type_or_types: Type3[T, S, U],
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func: Fn3[T, S, U, Any],
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tree: PyTree,
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*rests: PyTree,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> PyTree:
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...
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def tree_map_only_(
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__type_or_types: TypeAny,
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func: FnAny[Any],
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@ -651,6 +681,18 @@ def tree_all_only(
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...
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@overload
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def tree_all_only(
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__type_or_types: Type3[T, S, U],
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pred: Fn3[T, S, U, bool],
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tree: PyTree,
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*,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> bool:
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...
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def tree_all_only(
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__type_or_types: TypeAny,
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pred: FnAny[bool],
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@ -687,6 +729,18 @@ def tree_any_only(
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...
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@overload
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def tree_any_only(
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__type_or_types: Type3[T, S, U],
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pred: Fn3[T, S, U, bool],
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tree: PyTree,
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*,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> bool:
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...
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def tree_any_only(
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__type_or_types: TypeAny,
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pred: FnAny[bool],
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@ -764,12 +818,12 @@ def broadcast_prefix(
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# _broadcast_to_and_flatten to check this.
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def _broadcast_to_and_flatten(
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tree: PyTree,
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treespec: PyTreeSpec,
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treespec: TreeSpec,
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*,
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none_is_leaf: bool = True,
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namespace: str = "torch",
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) -> Optional[List[Any]]:
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assert isinstance(treespec, PyTreeSpec)
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assert isinstance(treespec, TreeSpec)
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full_tree = tree_unflatten([0] * treespec.num_leaves, treespec)
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try:
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return broadcast_prefix(
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@ -782,12 +836,12 @@ def _broadcast_to_and_flatten(
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return None
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def treespec_dumps(treespec: PyTreeSpec) -> str:
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def treespec_dumps(treespec: TreeSpec) -> str:
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"""Serialize a treespec to a JSON string."""
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if not isinstance(treespec, PyTreeSpec):
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if not isinstance(treespec, TreeSpec):
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raise TypeError(
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f"treespec_dumps(spec): Expected `spec` to be instance of "
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f"PyTreeSpec but got item of type {type(treespec)}."
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f"TreeSpec but got item of type {type(treespec)}."
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)
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from ._pytree import (
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tree_structure as _tree_structure,
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@ -798,7 +852,7 @@ def treespec_dumps(treespec: PyTreeSpec) -> str:
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return _treespec_dumps(orig_treespec)
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def treespec_loads(serialized: str) -> PyTreeSpec:
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def treespec_loads(serialized: str) -> TreeSpec:
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"""Deserialize a treespec from a JSON string."""
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from ._pytree import (
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tree_unflatten as _tree_unflatten,
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@ -816,7 +870,7 @@ class _DummyLeaf:
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return "*"
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def treespec_pprint(treespec: PyTreeSpec) -> str:
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def treespec_pprint(treespec: TreeSpec) -> str:
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dummy_tree = tree_unflatten(
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[_DummyLeaf() for _ in range(treespec.num_leaves)],
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treespec,
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@ -824,14 +878,11 @@ def treespec_pprint(treespec: PyTreeSpec) -> str:
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return repr(dummy_tree)
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class PyTreeLeafSpecMeta(type(PyTreeSpec)): # type: ignore[misc]
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class LeafSpecMeta(type(TreeSpec)): # type: ignore[misc]
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def __instancecheck__(self, instance: object) -> bool:
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return isinstance(instance, PyTreeSpec) and instance.is_leaf()
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return isinstance(instance, TreeSpec) and instance.is_leaf()
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class PyTreeLeafSpec(PyTreeSpec, metaclass=PyTreeLeafSpecMeta):
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def __new__(cls, none_is_leaf: bool = True) -> "PyTreeLeafSpec":
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class LeafSpec(TreeSpec, metaclass=LeafSpecMeta):
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def __new__(cls, none_is_leaf: bool = True) -> "LeafSpec":
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return optree.treespec_leaf(none_is_leaf=none_is_leaf) # type: ignore[return-value]
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LeafSpec = PyTreeLeafSpec
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@ -7,7 +7,7 @@ Python values. Furthermore, a pytree should not contain reference cycles.
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pytrees are useful for working with nested collections of Tensors. For example,
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one can use `tree_map` to map a function over all Tensors inside some nested
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collection of Tensors and `tree_unflatten` to get a flat list of all Tensors
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collection of Tensors and `tree_leaves` to get a flat list of all Tensors
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inside some nested collection. pytrees are helpful for implementing nested
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collection support for PyTorch APIs.
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@ -121,11 +121,11 @@ SERIALIZED_TYPE_TO_PYTHON_TYPE: Dict[str, Type[Any]] = {}
|
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|
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|
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def _register_pytree_node(
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typ: Any,
|
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cls: Any,
|
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flatten_fn: FlattenFunc,
|
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unflatten_fn: UnflattenFunc,
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to_str_fn: Optional[ToStrFunc] = None,
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maybe_from_str_fn: Optional[MaybeFromStrFunc] = None,
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to_str_fn: Optional[ToStrFunc] = None, # deprecated
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maybe_from_str_fn: Optional[MaybeFromStrFunc] = None, # deprecated
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*,
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serialized_type_name: Optional[str] = None,
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to_dumpable_context: Optional[ToDumpableContextFn] = None,
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@ -133,12 +133,12 @@ def _register_pytree_node(
|
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) -> None:
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"""
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Args:
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typ: the type to register
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cls: the type to register
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flatten_fn: A callable that takes a pytree and returns a flattened
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representation of the pytree and additional context to represent the
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flattened pytree.
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unflatten_fn: A callable that takes a flattened version of the pytree,
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additional context, and returns an unflattedn pytree.
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additional context, and returns an unflattened pytree.
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serialized_type_name: A keyword argument used to specify the fully qualified
|
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name used when serializing the tree spec.
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to_dumpable_context: An optional keyword argument to custom specify how
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@ -157,26 +157,29 @@ def _register_pytree_node(
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)
|
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|
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node_def = NodeDef(
|
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typ,
|
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cls,
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flatten_fn,
|
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unflatten_fn,
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)
|
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SUPPORTED_NODES[typ] = node_def
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SUPPORTED_NODES[cls] = node_def
|
||||
|
||||
if (to_dumpable_context is None) ^ (from_dumpable_context is None):
|
||||
raise ValueError(
|
||||
f"Both to_dumpable_context and from_dumpable_context for {typ} must "
|
||||
f"Both to_dumpable_context and from_dumpable_context for {cls} must "
|
||||
"be None or registered."
|
||||
)
|
||||
|
||||
if serialized_type_name is None:
|
||||
serialized_type_name = f"{typ.__module__}.{typ.__name__}"
|
||||
serialized_type_name = f"{cls.__module__}.{cls.__name__}"
|
||||
|
||||
serialize_node_def = _SerializeNodeDef(
|
||||
typ, serialized_type_name, to_dumpable_context, from_dumpable_context
|
||||
cls,
|
||||
serialized_type_name,
|
||||
to_dumpable_context,
|
||||
from_dumpable_context,
|
||||
)
|
||||
SUPPORTED_SERIALIZED_TYPES[typ] = serialize_node_def
|
||||
SERIALIZED_TYPE_TO_PYTHON_TYPE[serialized_type_name] = typ
|
||||
SUPPORTED_SERIALIZED_TYPES[cls] = serialize_node_def
|
||||
SERIALIZED_TYPE_TO_PYTHON_TYPE[serialized_type_name] = cls
|
||||
|
||||
|
||||
register_pytree_node = _register_pytree_node
|
||||
@ -275,8 +278,8 @@ _register_pytree_node(
|
||||
|
||||
|
||||
# h/t https://stackoverflow.com/questions/2166818/how-to-check-if-an-object-is-an-instance-of-a-namedtuple
|
||||
def _is_namedtuple_instance(pytree: Any) -> bool:
|
||||
typ = type(pytree)
|
||||
def _is_namedtuple_instance(tree: Any) -> bool:
|
||||
typ = type(tree)
|
||||
bases = typ.__bases__
|
||||
if len(bases) != 1 or bases[0] != tuple:
|
||||
return False
|
||||
@ -286,15 +289,15 @@ def _is_namedtuple_instance(pytree: Any) -> bool:
|
||||
return all(type(entry) == str for entry in fields)
|
||||
|
||||
|
||||
def _get_node_type(pytree: Any) -> Any:
|
||||
if _is_namedtuple_instance(pytree):
|
||||
def _get_node_type(tree: Any) -> Any:
|
||||
if _is_namedtuple_instance(tree):
|
||||
return namedtuple
|
||||
return type(pytree)
|
||||
return type(tree)
|
||||
|
||||
|
||||
# A leaf is defined as anything that is not a Node.
|
||||
def _is_leaf(pytree: PyTree) -> bool:
|
||||
return _get_node_type(pytree) not in SUPPORTED_NODES
|
||||
def _is_leaf(tree: PyTree) -> bool:
|
||||
return _get_node_type(tree) not in SUPPORTED_NODES
|
||||
|
||||
|
||||
# A TreeSpec represents the structure of a pytree. It holds:
|
||||
@ -345,109 +348,107 @@ class LeafSpec(TreeSpec):
|
||||
_LEAF_SPEC = LeafSpec()
|
||||
|
||||
|
||||
def _tree_flatten_helper(pytree: PyTree, out_leaves: List[Any]) -> TreeSpec:
|
||||
if _is_leaf(pytree):
|
||||
out_leaves.append(pytree)
|
||||
def _tree_flatten_helper(tree: PyTree, leaves: List[Any]) -> TreeSpec:
|
||||
if _is_leaf(tree):
|
||||
leaves.append(tree)
|
||||
return _LEAF_SPEC
|
||||
|
||||
node_type = _get_node_type(pytree)
|
||||
node_type = _get_node_type(tree)
|
||||
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
|
||||
child_pytrees, context = flatten_fn(pytree)
|
||||
child_pytrees, context = flatten_fn(tree)
|
||||
|
||||
# Recursively flatten the children
|
||||
children_specs = [
|
||||
_tree_flatten_helper(child, out_leaves) for child in child_pytrees
|
||||
]
|
||||
children_specs = [_tree_flatten_helper(child, leaves) for child in child_pytrees]
|
||||
|
||||
return TreeSpec(node_type, context, children_specs)
|
||||
|
||||
|
||||
def tree_flatten(pytree: PyTree) -> Tuple[List[Any], TreeSpec]:
|
||||
def tree_flatten(tree: PyTree) -> Tuple[List[Any], TreeSpec]:
|
||||
"""Flattens a pytree into a list of values and a TreeSpec that can be used
|
||||
to reconstruct the pytree.
|
||||
"""
|
||||
leaves: List[Any] = []
|
||||
spec = _tree_flatten_helper(pytree, leaves)
|
||||
spec = _tree_flatten_helper(tree, leaves)
|
||||
return leaves, spec
|
||||
|
||||
|
||||
def _tree_leaves_helper(pytree: PyTree, out_leaves: List[Any]) -> None:
|
||||
if _is_leaf(pytree):
|
||||
out_leaves.append(pytree)
|
||||
return
|
||||
|
||||
node_type = _get_node_type(pytree)
|
||||
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
|
||||
child_pytrees, _ = flatten_fn(pytree)
|
||||
|
||||
# Recursively flatten the children
|
||||
for child in child_pytrees:
|
||||
_tree_leaves_helper(child, out_leaves)
|
||||
|
||||
|
||||
def tree_leaves(pytree: PyTree) -> List[Any]:
|
||||
"""Get a list of leaves of a pytree."""
|
||||
leaves: List[Any] = []
|
||||
_tree_leaves_helper(pytree, leaves)
|
||||
return leaves
|
||||
|
||||
|
||||
def tree_structure(pytree: PyTree) -> TreeSpec:
|
||||
"""Get the TreeSpec for a pytree."""
|
||||
return tree_flatten(pytree)[1]
|
||||
|
||||
|
||||
def tree_unflatten(values: Iterable[Any], spec: TreeSpec) -> PyTree:
|
||||
def tree_unflatten(leaves: Iterable[Any], treespec: TreeSpec) -> PyTree:
|
||||
"""Given a list of values and a TreeSpec, builds a pytree.
|
||||
This is the inverse operation of `tree_flatten`.
|
||||
"""
|
||||
if not isinstance(spec, TreeSpec):
|
||||
if not isinstance(treespec, TreeSpec):
|
||||
raise TypeError(
|
||||
f"tree_unflatten(values, spec): Expected `spec` to be instance of "
|
||||
f"TreeSpec but got item of type {type(spec)}.",
|
||||
f"tree_unflatten(leaves, treespec): Expected `treespec` to be "
|
||||
f"instance of TreeSpec but got item of type {type(treespec)}.",
|
||||
)
|
||||
if not isinstance(values, (list, tuple)):
|
||||
values = list(values)
|
||||
if len(values) != spec.num_leaves:
|
||||
if not isinstance(leaves, (list, tuple)):
|
||||
leaves = list(leaves)
|
||||
if len(leaves) != treespec.num_leaves:
|
||||
raise ValueError(
|
||||
f"tree_unflatten(values, spec): `values` has length {len(values)} "
|
||||
f"but the spec refers to a pytree that holds {spec.num_leaves} "
|
||||
f"items ({spec}).",
|
||||
f"tree_unflatten(leaves, treespec): `leaves` has length {len(leaves)} "
|
||||
f"but the spec refers to a pytree that holds {treespec.num_leaves} "
|
||||
f"items ({treespec}).",
|
||||
)
|
||||
if isinstance(spec, LeafSpec):
|
||||
return values[0]
|
||||
if isinstance(treespec, LeafSpec):
|
||||
return leaves[0]
|
||||
|
||||
unflatten_fn = SUPPORTED_NODES[spec.type].unflatten_fn
|
||||
unflatten_fn = SUPPORTED_NODES[treespec.type].unflatten_fn
|
||||
|
||||
# Recursively unflatten the children
|
||||
start = 0
|
||||
end = 0
|
||||
child_pytrees = []
|
||||
for child_spec in spec.children_specs:
|
||||
for child_spec in treespec.children_specs:
|
||||
end += child_spec.num_leaves
|
||||
child_pytrees.append(tree_unflatten(values[start:end], child_spec))
|
||||
child_pytrees.append(tree_unflatten(leaves[start:end], child_spec))
|
||||
start = end
|
||||
|
||||
return unflatten_fn(child_pytrees, spec.context)
|
||||
return unflatten_fn(child_pytrees, treespec.context)
|
||||
|
||||
|
||||
def tree_map(fn: Any, pytree: PyTree) -> PyTree:
|
||||
flat_args, spec = tree_flatten(pytree)
|
||||
return tree_unflatten([fn(i) for i in flat_args], spec)
|
||||
def _tree_leaves_helper(tree: PyTree, leaves: List[Any]) -> None:
|
||||
if _is_leaf(tree):
|
||||
leaves.append(tree)
|
||||
return
|
||||
|
||||
node_type = _get_node_type(tree)
|
||||
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
|
||||
child_pytrees, _ = flatten_fn(tree)
|
||||
|
||||
# Recursively flatten the children
|
||||
for child in child_pytrees:
|
||||
_tree_leaves_helper(child, leaves)
|
||||
|
||||
|
||||
def tree_map_(fn: Any, pytree: PyTree) -> PyTree:
|
||||
flat_args = tree_leaves(pytree)
|
||||
deque(map(fn, flat_args), maxlen=0) # consume and exhaust the iterable
|
||||
return pytree
|
||||
def tree_leaves(tree: PyTree) -> List[Any]:
|
||||
"""Get a list of leaves of a pytree."""
|
||||
leaves: List[Any] = []
|
||||
_tree_leaves_helper(tree, leaves)
|
||||
return leaves
|
||||
|
||||
|
||||
def tree_structure(tree: PyTree) -> TreeSpec:
|
||||
"""Get the TreeSpec for a pytree."""
|
||||
return tree_flatten(tree)[1]
|
||||
|
||||
|
||||
def tree_map(func: Any, tree: PyTree) -> PyTree:
|
||||
flat_args, spec = tree_flatten(tree)
|
||||
return tree_unflatten([func(i) for i in flat_args], spec)
|
||||
|
||||
|
||||
def tree_map_(func: Any, tree: PyTree) -> PyTree:
|
||||
flat_args = tree_leaves(tree)
|
||||
deque(map(func, flat_args), maxlen=0) # consume and exhaust the iterable
|
||||
return tree
|
||||
|
||||
|
||||
Type2 = Tuple[Type[T], Type[S]]
|
||||
Type3 = Tuple[Type[T], Type[S], Type[U]]
|
||||
TypeAny = Union[Type[Any], Tuple[Type[Any], ...]]
|
||||
|
||||
Fn3 = Callable[[Union[T, S, U]], R]
|
||||
Fn2 = Callable[[Union[T, S]], R]
|
||||
Fn3 = Callable[[Union[T, S, U]], R]
|
||||
Fn = Callable[[T], R]
|
||||
FnAny = Callable[[Any], R]
|
||||
|
||||
@ -457,22 +458,27 @@ MapOnlyFn = Callable[[T], Callable[[Any], Any]]
|
||||
# These specializations help with type inference on the lambda passed to this
|
||||
# function
|
||||
@overload
|
||||
def map_only(ty: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]:
|
||||
def map_only(__type_or_types: Type2[T, S]) -> MapOnlyFn[Fn2[T, S, Any]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def map_only(ty: Type[T]) -> MapOnlyFn[Fn[T, Any]]:
|
||||
def map_only(__type_or_types: Type3[T, S, U]) -> MapOnlyFn[Fn3[T, S, U, Any]]:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def map_only(__type_or_types: Type[T]) -> MapOnlyFn[Fn[T, Any]]:
|
||||
...
|
||||
|
||||
|
||||
# This specialization is needed for the implementations below that call
|
||||
@overload
|
||||
def map_only(ty: TypeAny) -> MapOnlyFn[FnAny[Any]]:
|
||||
def map_only(__type_or_types: TypeAny) -> MapOnlyFn[FnAny[Any]]:
|
||||
...
|
||||
|
||||
|
||||
def map_only(ty: TypeAny) -> MapOnlyFn[FnAny[Any]]:
|
||||
def map_only(__type_or_types: TypeAny) -> MapOnlyFn[FnAny[Any]]:
|
||||
"""
|
||||
Suppose you are writing a tree_map over tensors, leaving everything
|
||||
else unchanged. Ordinarily you would have to write:
|
||||
@ -492,99 +498,168 @@ def map_only(ty: TypeAny) -> MapOnlyFn[FnAny[Any]]:
|
||||
You can also directly use 'tree_map_only'
|
||||
"""
|
||||
|
||||
def deco(f: Callable[[T], Any]) -> Callable[[Any], Any]:
|
||||
def inner(x: T) -> Any:
|
||||
if isinstance(x, ty):
|
||||
return f(x)
|
||||
else:
|
||||
def wrapper(func: Callable[[T], Any]) -> Callable[[Any], Any]:
|
||||
# @functools.wraps(func) # torch dynamo doesn't support this yet
|
||||
def wrapped(x: T) -> Any:
|
||||
if isinstance(x, __type_or_types):
|
||||
return func(x)
|
||||
return x
|
||||
|
||||
return inner
|
||||
return wrapped
|
||||
|
||||
return deco
|
||||
return wrapper
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only(ty: Type[T], fn: Fn[T, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only(
|
||||
__type_or_types: Type[T],
|
||||
func: Fn[T, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only(ty: Type2[T, S], fn: Fn2[T, S, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only(
|
||||
__type_or_types: Type2[T, S],
|
||||
func: Fn2[T, S, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only(ty: Type3[T, S, U], fn: Fn3[T, S, U, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only(
|
||||
__type_or_types: Type3[T, S, U],
|
||||
func: Fn3[T, S, U, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
def tree_map_only(ty: TypeAny, fn: FnAny[Any], pytree: PyTree) -> PyTree:
|
||||
return tree_map(map_only(ty)(fn), pytree)
|
||||
def tree_map_only(
|
||||
__type_or_types: TypeAny,
|
||||
func: FnAny[Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
return tree_map(map_only(__type_or_types)(func), tree)
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only_(ty: Type[T], fn: Fn[T, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only_(
|
||||
__type_or_types: Type[T],
|
||||
func: Fn[T, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only_(ty: Type2[T, S], fn: Fn2[T, S, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only_(
|
||||
__type_or_types: Type2[T, S],
|
||||
func: Fn2[T, S, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_map_only_(ty: Type3[T, S, U], fn: Fn3[T, S, U, Any], pytree: PyTree) -> PyTree:
|
||||
def tree_map_only_(
|
||||
__type_or_types: Type3[T, S, U],
|
||||
func: Fn3[T, S, U, Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
...
|
||||
|
||||
|
||||
def tree_map_only_(ty: TypeAny, fn: FnAny[Any], pytree: PyTree) -> PyTree:
|
||||
return tree_map_(map_only(ty)(fn), pytree)
|
||||
def tree_map_only_(
|
||||
__type_or_types: TypeAny,
|
||||
func: FnAny[Any],
|
||||
tree: PyTree,
|
||||
) -> PyTree:
|
||||
return tree_map_(map_only(__type_or_types)(func), tree)
|
||||
|
||||
|
||||
def tree_all(pred: Callable[[Any], bool], pytree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(pytree)
|
||||
def tree_all(pred: Callable[[Any], bool], tree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(tree)
|
||||
return all(map(pred, flat_args))
|
||||
|
||||
|
||||
def tree_any(pred: Callable[[Any], bool], pytree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(pytree)
|
||||
def tree_any(pred: Callable[[Any], bool], tree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(tree)
|
||||
return any(map(pred, flat_args))
|
||||
|
||||
|
||||
@overload
|
||||
def tree_all_only(ty: Type[T], pred: Fn[T, bool], pytree: PyTree) -> bool:
|
||||
def tree_all_only(
|
||||
__type_or_types: Type[T],
|
||||
pred: Fn[T, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_all_only(ty: Type2[T, S], pred: Fn2[T, S, bool], pytree: PyTree) -> bool:
|
||||
def tree_all_only(
|
||||
__type_or_types: Type2[T, S],
|
||||
pred: Fn2[T, S, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_all_only(ty: Type3[T, S, U], pred: Fn3[T, S, U, bool], pytree: PyTree) -> bool:
|
||||
def tree_all_only(
|
||||
__type_or_types: Type3[T, S, U],
|
||||
pred: Fn3[T, S, U, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
def tree_all_only(ty: TypeAny, pred: FnAny[bool], pytree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(pytree)
|
||||
return all(pred(x) for x in flat_args if isinstance(x, ty))
|
||||
def tree_all_only(
|
||||
__type_or_types: TypeAny,
|
||||
pred: FnAny[bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
flat_args = tree_leaves(tree)
|
||||
return all(pred(x) for x in flat_args if isinstance(x, __type_or_types))
|
||||
|
||||
|
||||
@overload
|
||||
def tree_any_only(ty: Type[T], pred: Fn[T, bool], pytree: PyTree) -> bool:
|
||||
def tree_any_only(
|
||||
__type_or_types: Type[T],
|
||||
pred: Fn[T, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
@overload
|
||||
def tree_any_only(ty: Type2[T, S], pred: Fn2[T, S, bool], pytree: PyTree) -> bool:
|
||||
def tree_any_only(
|
||||
__type_or_types: Type2[T, S],
|
||||
pred: Fn2[T, S, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
def tree_any_only(ty: TypeAny, pred: FnAny[bool], pytree: PyTree) -> bool:
|
||||
flat_args = tree_leaves(pytree)
|
||||
return any(pred(x) for x in flat_args if isinstance(x, ty))
|
||||
@overload
|
||||
def tree_any_only(
|
||||
__type_or_types: Type3[T, S, U],
|
||||
pred: Fn3[T, S, U, bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
...
|
||||
|
||||
|
||||
def tree_any_only(
|
||||
__type_or_types: TypeAny,
|
||||
pred: FnAny[bool],
|
||||
tree: PyTree,
|
||||
) -> bool:
|
||||
flat_args = tree_leaves(tree)
|
||||
return any(pred(x) for x in flat_args if isinstance(x, __type_or_types))
|
||||
|
||||
|
||||
# Broadcasts a pytree to the provided TreeSpec and returns the flattened
|
||||
@ -595,27 +670,27 @@ def tree_any_only(ty: TypeAny, pred: FnAny[bool], pytree: PyTree) -> bool:
|
||||
# a user can pass in vmap(fn, in_dims)(*inputs). `in_dims` should be
|
||||
# broadcastable to the tree structure of `inputs` and we use
|
||||
# _broadcast_to_and_flatten to check this.
|
||||
def _broadcast_to_and_flatten(pytree: PyTree, spec: TreeSpec) -> Optional[List[Any]]:
|
||||
assert isinstance(spec, TreeSpec)
|
||||
def _broadcast_to_and_flatten(tree: PyTree, treespec: TreeSpec) -> Optional[List[Any]]:
|
||||
assert isinstance(treespec, TreeSpec)
|
||||
|
||||
if _is_leaf(pytree):
|
||||
return [pytree] * spec.num_leaves
|
||||
if isinstance(spec, LeafSpec):
|
||||
if _is_leaf(tree):
|
||||
return [tree] * treespec.num_leaves
|
||||
if isinstance(treespec, LeafSpec):
|
||||
return None
|
||||
node_type = _get_node_type(pytree)
|
||||
if node_type != spec.type:
|
||||
node_type = _get_node_type(tree)
|
||||
if node_type != treespec.type:
|
||||
return None
|
||||
|
||||
flatten_fn = SUPPORTED_NODES[node_type].flatten_fn
|
||||
child_pytrees, ctx = flatten_fn(pytree)
|
||||
child_pytrees, ctx = flatten_fn(tree)
|
||||
|
||||
# Check if the Node is different from the spec
|
||||
if len(child_pytrees) != len(spec.children_specs) or ctx != spec.context:
|
||||
if len(child_pytrees) != len(treespec.children_specs) or ctx != treespec.context:
|
||||
return None
|
||||
|
||||
# Recursively flatten the children
|
||||
result: List[Any] = []
|
||||
for child, child_spec in zip(child_pytrees, spec.children_specs):
|
||||
for child, child_spec in zip(child_pytrees, treespec.children_specs):
|
||||
flat = _broadcast_to_and_flatten(child, child_spec)
|
||||
if flat is not None:
|
||||
result += flat
|
||||
@ -648,23 +723,28 @@ class _ProtocolFn(NamedTuple):
|
||||
_SUPPORTED_PROTOCOLS: Dict[int, _ProtocolFn] = {}
|
||||
|
||||
|
||||
def _treespec_to_json(spec: TreeSpec) -> _TreeSpecSchema:
|
||||
if isinstance(spec, LeafSpec):
|
||||
def _treespec_to_json(treespec: TreeSpec) -> _TreeSpecSchema:
|
||||
if isinstance(treespec, LeafSpec):
|
||||
return _TreeSpecSchema(None, None, [])
|
||||
|
||||
serialize_node_def = SUPPORTED_SERIALIZED_TYPES[spec.type]
|
||||
if treespec.type not in SUPPORTED_SERIALIZED_TYPES:
|
||||
raise NotImplementedError(
|
||||
f"Serializing {treespec.type} in pytree is not registered."
|
||||
)
|
||||
|
||||
serialize_node_def = SUPPORTED_SERIALIZED_TYPES[treespec.type]
|
||||
|
||||
serialized_type_name = serialize_node_def.serialized_type_name
|
||||
|
||||
if serialized_type_name == NO_SERIALIZED_TYPE_NAME_FOUND:
|
||||
raise NotImplementedError(
|
||||
f"No registered serialization name for {spec.type} found. "
|
||||
f"No registered serialization name for {treespec.type} found. "
|
||||
"Please update your _register_pytree_node call with a `serialized_type_name` kwarg."
|
||||
)
|
||||
|
||||
if serialize_node_def.to_dumpable_context is None:
|
||||
try:
|
||||
serialized_context = json.dumps(spec.context)
|
||||
serialized_context = json.dumps(treespec.context)
|
||||
except TypeError as e:
|
||||
raise TypeError(
|
||||
"Unable to serialize context. "
|
||||
@ -672,9 +752,9 @@ def _treespec_to_json(spec: TreeSpec) -> _TreeSpecSchema:
|
||||
"custom serializer using _register_pytree_node."
|
||||
) from e
|
||||
else:
|
||||
serialized_context = serialize_node_def.to_dumpable_context(spec.context)
|
||||
serialized_context = serialize_node_def.to_dumpable_context(treespec.context)
|
||||
|
||||
child_schemas = [_treespec_to_json(child) for child in spec.children_specs]
|
||||
child_schemas = [_treespec_to_json(child) for child in treespec.children_specs]
|
||||
|
||||
return _TreeSpecSchema(serialized_type_name, serialized_context, child_schemas)
|
||||
|
||||
@ -764,9 +844,9 @@ def treespec_pprint(treespec: TreeSpec) -> str:
|
||||
|
||||
|
||||
# TODO(angelayi): remove this function after OSS/internal stabilize
|
||||
def pytree_to_str(spec: TreeSpec) -> str:
|
||||
def pytree_to_str(treespec: TreeSpec) -> str:
|
||||
warnings.warn("pytree_to_str is deprecated. Please use treespec_dumps")
|
||||
return treespec_dumps(spec)
|
||||
return treespec_dumps(treespec)
|
||||
|
||||
|
||||
# TODO(angelayi): remove this function after OSS/internal stabilize
|
||||
|
||||
Reference in New Issue
Block a user