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- from typing import Callable, Any, Tuple, List, Dict, Type, NamedTuple
- from torch.utils._pytree import PyTree, TreeSpec, LeafSpec
- from collections import namedtuple
- FlattenFuncSpec = Callable[[PyTree, TreeSpec], List]
- SUPPORTED_NODES: Dict[Type[Any], Any] = {}
- def register_pytree_flatten_spec(typ: Any, flatten_fn_spec: FlattenFuncSpec) -> None:
- SUPPORTED_NODES[typ] = flatten_fn_spec
- def tree_flatten_spec(pytree: PyTree, spec: TreeSpec) -> List[Any]:
- if isinstance(spec, LeafSpec):
- return [pytree]
- if spec.type not in SUPPORTED_NODES:
- raise RuntimeError(
- f"{type(pytree)} does not have a flatten_fn_spec associated with it. Please register one with"
- "torch.fx._pytree.register_pytree_flatten_spec. If you have serialized your model, make"
- "sure that any custom pytrees have been registered before loading it.")
- flatten_fn_spec = SUPPORTED_NODES[spec.type]
- child_pytrees = flatten_fn_spec(pytree, spec)
- result = []
- for child, child_spec in zip(child_pytrees, spec.children_specs):
- flat = tree_flatten_spec(child, child_spec)
- result += flat
- return result
- def _dict_flatten_spec(d: Dict[Any, Any], spec: TreeSpec) -> List[Any]:
- return [d[k] for k in spec.context]
- def _list_flatten_spec(d: List[Any], spec: TreeSpec) -> List[Any]:
- return [d[i] for i in range(len(spec.children_specs))]
- def _tuple_flatten_spec(d: Tuple[Any], spec: TreeSpec) -> List[Any]:
- return [d[i] for i in range(len(spec.children_specs))]
- def _namedtuple_flatten_spec(d: NamedTuple, spec: TreeSpec) -> List[Any]:
- return [d[i] for i in range(len(spec.children_specs))]
- register_pytree_flatten_spec(dict, _dict_flatten_spec)
- register_pytree_flatten_spec(list, _list_flatten_spec)
- register_pytree_flatten_spec(tuple, _tuple_flatten_spec)
- register_pytree_flatten_spec(namedtuple, _tuple_flatten_spec)
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