This only exposes the ability to round-trip a textual pipeline at the
moment.
To exercise it, we also bind the libTransforms in a new Python extension. This
does not include any interesting bindings, but it includes all the
mechanism to add separate native extensions and load them dynamically.
As such passes in libTransforms are only registered after `import
mlir.transforms`.
To support this global registration, the TableGen backend is also
extended to bind to the C API the group registration for passes.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D90819
* All functions that return an Operation now return an OpView.
* All functions that accept an Operation now accept an _OperationBase, which both Operation and OpView extend and can resolve to the backing Operation.
* Moves user-facing instance methods from Operation -> _OperationBase so that both can have the same API.
* Concretely, this means that if there are custom op classes defined (i.e. in Python), any iteration or creation will return the appropriate instance (i.e. if you get/create an std.addf, you will get an instance of the mlir.dialects.std.AddFOp class, getting full access to any custom API it exposes).
* Refactors all __eq__ methods after realizing the proper way to do this for _OperationBase.
Differential Revision: https://reviews.llvm.org/D90584
* Still rough edges that need more sugar but the bones are there. Notes left in the test case for things that can be improved.
* Does not actually yield custom OpViews yet for traversing. Will rework that in a followup.
Differential Revision: https://reviews.llvm.org/D89932
Summary:
* Native '_mlir' extension module.
* Python mlir/__init__.py trampoline module.
* Lit test that checks a message.
* Uses some cmake configurations that have worked for me in the past but likely needs further elaboration.
Subscribers: mgorny, mehdi_amini, rriddle, jpienaar, shauheen, antiagainst, nicolasvasilache, arpith-jacob, mgester, lucyrfox, aartbik, liufengdb, stephenneuendorffer, Joonsoo, grosul1, Kayjukh, jurahul, msifontes
Tags: #mlir
Differential Revision: https://reviews.llvm.org/D83279
