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- # mypy: allow-untyped-defs
- import importlib
- import torch
- lib = torch.library.Library("export", "FRAGMENT") # noqa: TOR901
- lib.define(
- "access_subclass_inner_tensor(Tensor src_subclass_tensor, str attr) -> Tensor"
- )
- @torch.library.impl(lib, "access_subclass_inner_tensor", "Autograd")
- # When running under torch.inference_mode(), we seem to skip AUtograd key
- # so we should desugar this op as soon as we start tracing to post-dispatch.
- @torch.library.impl(lib, "access_subclass_inner_tensor", "Python")
- def _access_subclass_inner_tensor(
- src_subclass_tensor: torch.Tensor, attr: str
- ) -> torch.Tensor:
- from torch.utils._python_dispatch import is_traceable_wrapper_subclass
- assert is_traceable_wrapper_subclass(src_subclass_tensor)
- val = getattr(src_subclass_tensor, attr, None)
- if val is None or not isinstance(val, torch.Tensor):
- raise RuntimeError(
- f"Attribute {attr} is not a tensor or doesn't exist in {src_subclass_tensor}"
- )
- return val
- def _call_custom_autograd_function_in_pre_dispatch(function_cls_name, *args, **kwargs):
- """
- Import a custom autograd function by string name and call it. This is pretty bad
- because:
- 1) There is no schema
- Ideally we should automatically wrap custom autograd functions with a custom op, but
- that is too much work because we need to schematize custom autograd functions. For now,
- we just hackily put it in the IR.
- """
- # Parse module and class name
- module_name, class_name = function_cls_name.rsplit(".", 1)
- # Import the module and get the class
- module = importlib.import_module(module_name)
- function_cls = getattr(module, class_name)
- assert hasattr(function_cls, "apply")
- return function_cls.apply(*args, **kwargs)
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