Add some support for detecting false aliasing in AOTAutograd (#106461)
This is a partial fix for https://github.com/pytorch/pytorch/issues/106457. In the examples with the shampoo optimizer that i ran, they were enough to remove the parameter aliasing in shampoo.
I added some new logic for detecting if two inputs have overlapping memory in specific cases: if they're both 2D tensors with stride 1. In that case (the case for shampoo), I try to compute a bunch of contiguous intervals on the two tensors, and check if any of the intervals overlap. In theory this is slow, since if our two tensors are e.g. of size (256, N), we'll need to create 256 intervals to check for overlap on. This seems... probably fine, since I think we do more egregious things in the compile stack to cause slowness. Open to suggestions though!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106461
Approved by: https://github.com/albanD
ghstack dependencies: #106460
