Add A ValueRange Analysis Pass to convert int64 indexing to int32 (#91028)
Builds up sympy expressions computing the lower and upper bound of ranges, and then finds `op.to_dtype(x, torch.int64)` nodes whose dominated values can all be computed in a lower precision. I haven't gotten all the way to work with dynamic shapes but it should be a fairly small change. There's still additional work to get torchinductor to work with large tensors (see https://github.com/pytorch/torchdynamo/issues/1819) because we would need to add explicit dtype annotations to int64 which we're not doing right now.
Fix for https://github.com/pytorch/torchdynamo/issues/1293.
Performance Before OpBench aten.upsample_bilinear2d.vec float32:
(25th %, 50th %, 75th %)
Before
[0.7521964636710751, 0.8645357996607477, 2.8746003906598494]
After:
[0.9511363478204263, 1.0295566597806718, 3.2662165264101755]
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91028
Approved by: https://github.com/jansel
