Drop dynamo-specific type hints on Tensor in favor of type-ignores (#…

…113720)

Summary:
Per [this][1] discussion, plus some offline discussion. The summary:
albanD considers the core PyTorch types like Tensor to be extremely
brittle, and does not think the risk of adding these typed attributes to
be worth it.

eellison mentioned that we could use `WeakTensorKeyDictionary` instead.
However, based on the sparse usage of these bonus attributes, I think
that would be overkill. So I've opted to go with a few more type-ignore
comments instead.

[1]: pytorch/pytorch#113610 (comment)

X-link: pytorch/pytorch#113720
Approved by: https://github.com/ezyang, https://github.com/albanD, https://github.com/eellison
ghstack dependencies: #113534, #113610

Reviewed By: huydhn

Differential Revision: D51383035

Pulled By: int3

fbshipit-source-id: 31f2dc3e56986728c5a7cb69df59dceac6e6551b

userbenchmark/dynamo/dynamobench/_dynamo/utils.py

@@ -636,7 +636,7 @@ def torch_clone(x):
  if x.is_leaf and x.grad is not None:
  y.grad = clone_input(x.grad, dtype=dtype)
  if hasattr(x, "_dynamo_dynamic_indices"):
- y._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy()
+ y._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() # type: ignore[attr-defined]
  return y
 
  with torch.no_grad():
@@ -669,7 +669,7 @@ def torch_clone(x):
  # performing the operation.
  return torch_clone(x)
  if hasattr(x, "_dynamo_dynamic_indices"):
- result._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy()
+ result._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() # type: ignore[attr-defined]
  return result