This replaces the ad-hoc use of `i64` throughout the Torch layer, and
helps to keep it crystal clear the distinction between `!torch.int`
(which is modeling the Python `int` type) and the various types that
serve as dtypes of tensors, which are a totally different type universe.
Changes:
- `!torch.int` type and C bindings.
- Change `torch.constant.int` parser to not need the `: i64` at the end.
- `m_TorchConstantInt` matcher to aid with matching constants.
- BackendTypeConversion changes for `!torch.int` -> `i64` type
conversion.
- Refactor finalizing patterns in FinalizingBackendTypeConversionPass
(they were getting very repetitive).
- Mechanical rewriting of `!torch.int` to `i64` in all the tests, and
`AnyTorchIntType` to `Torch_IntType` in the `.td` files.
- Add `torch.constant.none` op to construct it (naming is chosen to be
analogous to Torch's representation of a prim::Constant with
NoneType, rather than using the "singleton" terminology of Basicpy).
These tests pass on the reference backend.
- Add aten.linear op + shape xfer function + ATen->Linalg lowering.
- Note: this needs to be more automated, and needs to cover more cases.
- Current not implemented caveats:
- size-1 broadcasting for bias vector (either static-size-1 or ? case)
- higher-rank aten.linear ops (not produced by torch.nn.Linear though)
- type promotion (still don't even know the exact rules here)
- Add folder for torch.derefine op. Now the inliner can clean it up as
it inlines. (call boundaries are a main place we need to insert
torch.derefine) This is brittle -- the other important case is control
flow which will need to be handled via an extension to
RefineTypes.cpp (as will more robust call handling). River has an
in-flight patch to update it to the new dataflow framework so I didn't
want to do anything intrusive here.
- Also adjust torch.derefine syntax to use the keyword `to` instead of
`->`, as most type-only, cast-like ops do.
Sean Silva