Reduce broadcasted inputs in derivative code (#14485)

Reduce broadcasted inputs in derivative code (#14485)

Summary:
Previously symbolic AD formulas assumed that no broadcasting happened,
and would return gradients of incorrect shapes (possibly leading to
silent errors later).

Fixes a few bugs (known and unknown):
- #11736
- ArgumentSpec didn't compute the input types correctly [(it didn't advance the offset for non-tensor args)](https://github.com/pytorch/pytorch/pull/14485/files#diff-4fd3157a056596aefb8cdf41022a208bR153)
- Symbolic AD could suffer from use after free (dangling pointers in grad map), because [`EliminateDeadCode` could have removed nodes](https://github.com/pytorch/pytorch/pull/14485/files#diff-25d33ad1ed6855684dec79d927ca6142L781) that referenced gradients of certain values.
- Undefined behavior in `aten::size`

During my tests I've also found a few new problems, and I have opened issues for them:
- FusionGroup seems to think that cat nodes broadcast their inputs (#14483)
- `prim::ConstantChunk` derivative formula doesn't handle undefined inputs (#14484)

This patch unfortunately deoptimizes some of our code (Fusion doesn't happen past chunk nodes, and outputs more tensors only because we have to get their size). I know how to fix those issues, but wanted to fix this terrible bug quickly.

cc zou3519 zdevito ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14485

Differential Revision: D13280899

Pulled By: soumith

fbshipit-source-id: 80cc5ec9331be80e1bb9ddfe85b81c2b997e0b0c