This leads to much more succinct types in many cases:
```
!torch.list<!torch.int>
!torch.list<int>
!torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>>
!torch.tuple<list<int>, list<int>>
!torch.optional<!torch.list<!torch.int>>
!torch.optional<list<int>>
!torch.list<list<list<tensor>>>
!torch.list<!torch.list<!torch.list<!torch.tensor>>>
```
I would like to take this further and allow omitting the `!torch.`
prefix in all cases, but that's harder -- for example, we currently use
`FuncOp` for functions, and so I don't think we can customize the
printing there. It seems like it will be a longer road to getting that
level of customization.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.
This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.
Recommended review order:
1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
`canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.
Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).
Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
Prior to this commit, importing a `prim::Constant` node with list type would result in an error since it was not supported. `ivalue_importer::importIValue` was modified to return the MlirValue corresponding to the root so its parent operation could be extracted.
- Move `run_pipeline_with_repro_report` to a more common place, and use it
consistently
- Attach a `torch.debug_module_name` to the enclosing `builtin.module`
op to allow for self-contained error reporting (not needing to pass
the names around.
- Remove redundant error reporting in linalg_on_tensors_backend.py and
tosa_backend.py (their respective backend abstract base classes now
take care of the error reports themselves)
- Save off original value of sys.stderr, rather than always resetting to
`sys.__stderr__`. This is just more hygienic, and allows nesting if
desired.
Sean Silva
Henry Tu