Specialize optional tensor inputs to graphs in the JIT (#18360)
Summary:
This specializes optional tensor inputs to either a DimensionedTensorType or, when None is passed,
UndefinedTensor (aka AutogradZeroTensorType).
This works because we already have different specs and thus separate plans for the two cases.
It enhances the shape analysis - because now unwrapped optional tensors will have DimensionedTensorType with appropriate shape and required grad etc.
Also, when combined with "if-pruning" (which I understand #18259 works towards), we actually get much nicer concrete graphs, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18360
Differential Revision: D14590577
Pulled By: soumith
fbshipit-source-id: cac204a506d1d38b15703cbcc67a6b75fd4979f4
