Based on the PyType and PyConcreteType classes, this patch implements the bindings of Index Type, Floating Point Type and None Type subclasses.
These three subclasses share the same binding strategy:
- The function pointer `isaFunction` points to `mlirTypeIsA***`.
- The `mlir***TypeGet` C API is bound with the `***Type` constructor in the python side.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86466
* Generic mlir.ir.Attribute class.
* First standard attribute (mlir.ir.StringAttr), following the same pattern as generic vs standard types.
* NamedAttribute class.
Differential Revision: https://reviews.llvm.org/D86250
Provide C API for MLIR standard attributes. Since standard attributes live
under lib/IR in core MLIR, place the C APIs in the IR library as well (standard
ops will go in a separate library).
Affine map and integer set attributes are only exposed as placeholder types
with IsA support due to the lack of C APIs for the corresponding types.
Integer and floating point attribute APIs expecting APInt and APFloat are not
exposed pending decision on how to support APInt and APFloat.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D86143
* The binding for Type is trivial and should be non-controversial.
* The way that I define the IntegerType should serve as a pattern for what I want to do next.
* I propose defining the rest of the standard types in this fashion and then generalizing for dialect types as necessary.
* Essentially, creating/accessing a concrete Type (vs interacting with the string form) is done by "casting" to the concrete type (i.e. IntegerType can be constructed with a Type and will throw if the cast is illegal).
* This deviates from some of our previous discussions about global objects but I think produces a usable API and we should go this way.
Differential Revision: https://reviews.llvm.org/D86179
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
zhanghb97