bfb1ba7526
depends on D150839
This diff uses `MlirTypeID` to register `TypeCaster`s (i.e., `[](PyType pyType) -> DerivedTy { return pyType; }`) for all concrete types (i.e., `PyConcrete<...>`) that are then queried for (by `MlirTypeID`) and called in `struct type_caster<MlirType>::cast`. The result is that anywhere an `MlirType mlirType` is returned from a python binding, that `mlirType` is automatically cast to the correct concrete type. For example:
```
c0 = arith.ConstantOp(f32, 0.0)
# CHECK: F32Type(f32)
print(repr(c0.result.type))
unranked_tensor_type = UnrankedTensorType.get(f32)
unranked_tensor = tensor.FromElementsOp(unranked_tensor_type, [c0]).result
# CHECK: UnrankedTensorType
print(type(unranked_tensor.type).__name__)
# CHECK: UnrankedTensorType(tensor<*xf32>)
print(repr(unranked_tensor.type))
```
This functionality immediately extends to typed attributes (i.e., `attr.type`).
The diff also implements similar functionality for `mlir_type_subclass`es but in a slightly different way - for such types (which have no cpp corresponding `class` or `struct`) the user must provide a type caster in python (similar to how `AttrBuilder` works) or in cpp as a `py::cpp_function`.
Reviewed By: ftynse
Differential Revision: https://reviews.llvm.org/D150927
81 lines
3.2 KiB
C++
81 lines
3.2 KiB
C++
//===- PythonTestModule.cpp - Python extension for the PythonTest dialect -===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "PythonTestCAPI.h"
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#include "mlir-c/BuiltinAttributes.h"
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#include "mlir-c/BuiltinTypes.h"
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#include "mlir-c/IR.h"
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#include "mlir/Bindings/Python/PybindAdaptors.h"
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namespace py = pybind11;
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using namespace mlir::python::adaptors;
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using namespace pybind11::literals;
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static bool mlirTypeIsARankedIntegerTensor(MlirType t) {
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return mlirTypeIsARankedTensor(t) &&
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mlirTypeIsAInteger(mlirShapedTypeGetElementType(t));
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}
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PYBIND11_MODULE(_mlirPythonTest, m) {
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m.def(
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"register_python_test_dialect",
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[](MlirContext context, bool load) {
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MlirDialectHandle pythonTestDialect =
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mlirGetDialectHandle__python_test__();
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mlirDialectHandleRegisterDialect(pythonTestDialect, context);
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if (load) {
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mlirDialectHandleLoadDialect(pythonTestDialect, context);
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}
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},
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py::arg("context"), py::arg("load") = true);
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mlir_attribute_subclass(m, "TestAttr",
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mlirAttributeIsAPythonTestTestAttribute)
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.def_classmethod(
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"get",
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[](py::object cls, MlirContext ctx) {
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return cls(mlirPythonTestTestAttributeGet(ctx));
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},
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py::arg("cls"), py::arg("context") = py::none());
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mlir_type_subclass(m, "TestType", mlirTypeIsAPythonTestTestType,
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mlirPythonTestTestTypeGetTypeID)
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.def_classmethod(
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"get",
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[](py::object cls, MlirContext ctx) {
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return cls(mlirPythonTestTestTypeGet(ctx));
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},
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py::arg("cls"), py::arg("context") = py::none());
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auto cls =
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mlir_type_subclass(m, "TestIntegerRankedTensorType",
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mlirTypeIsARankedIntegerTensor,
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py::module::import(MAKE_MLIR_PYTHON_QUALNAME("ir"))
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.attr("RankedTensorType"))
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.def_classmethod(
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"get",
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[](const py::object &cls, std::vector<int64_t> shape,
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unsigned width, MlirContext ctx) {
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MlirAttribute encoding = mlirAttributeGetNull();
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return cls(mlirRankedTensorTypeGet(
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shape.size(), shape.data(), mlirIntegerTypeGet(ctx, width),
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encoding));
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},
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"cls"_a, "shape"_a, "width"_a, "context"_a = py::none());
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assert(py::hasattr(cls.get_class(), "static_typeid") &&
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"TestIntegerRankedTensorType has no static_typeid");
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MlirTypeID mlirTypeID = mlirRankedTensorTypeGetTypeID();
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py::module::import(MAKE_MLIR_PYTHON_QUALNAME("ir"))
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.attr(MLIR_PYTHON_CAPI_TYPE_CASTER_REGISTER_ATTR)(
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mlirTypeID, pybind11::cpp_function([cls](const py::object &mlirType) {
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return cls.get_class()(mlirType);
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}),
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/*replace=*/true);
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mlir_value_subclass(m, "TestTensorValue",
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mlirTypeIsAPythonTestTestTensorValue)
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.def("is_null", [](MlirValue &self) { return mlirValueIsNull(self); });
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}
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