| 1 | //===- PythonTestModuleNanobind.cpp - PythonTest dialect extension --------===// |
| 2 | // |
| 3 | // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| 4 | // See https://llvm.org/LICENSE.txt for license information. |
| 5 | // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| 6 | // |
| 7 | //===----------------------------------------------------------------------===// |
| 8 | // This is the nanobind edition of the PythonTest dialect module. |
| 9 | //===----------------------------------------------------------------------===// |
| 10 | |
| 11 | #include "PythonTestCAPI.h" |
| 12 | #include "mlir-c/BuiltinAttributes.h" |
| 13 | #include "mlir-c/BuiltinTypes.h" |
| 14 | #include "mlir-c/Diagnostics.h" |
| 15 | #include "mlir-c/IR.h" |
| 16 | #include "mlir/Bindings/Python/Diagnostics.h" |
| 17 | #include "mlir/Bindings/Python/Nanobind.h" |
| 18 | #include "mlir/Bindings/Python/NanobindAdaptors.h" |
| 19 | #include "nanobind/nanobind.h" |
| 20 | |
| 21 | namespace nb = nanobind; |
| 22 | using namespace mlir::python::nanobind_adaptors; |
| 23 | |
| 24 | static bool mlirTypeIsARankedIntegerTensor(MlirType t) { |
| 25 | return mlirTypeIsARankedTensor(type: t) && |
| 26 | mlirTypeIsAInteger(type: mlirShapedTypeGetElementType(type: t)); |
| 27 | } |
| 28 | |
| 29 | NB_MODULE(_mlirPythonTestNanobind, m) { |
| 30 | m.def( |
| 31 | "register_python_test_dialect" , |
| 32 | [](MlirContext context, bool load) { |
| 33 | MlirDialectHandle pythonTestDialect = |
| 34 | mlirGetDialectHandle__python_test__(); |
| 35 | mlirDialectHandleRegisterDialect(pythonTestDialect, context); |
| 36 | if (load) { |
| 37 | mlirDialectHandleLoadDialect(pythonTestDialect, context); |
| 38 | } |
| 39 | }, |
| 40 | nb::arg("context" ), nb::arg("load" ) = true); |
| 41 | |
| 42 | m.def( |
| 43 | "register_dialect" , |
| 44 | [](MlirDialectRegistry registry) { |
| 45 | MlirDialectHandle pythonTestDialect = |
| 46 | mlirGetDialectHandle__python_test__(); |
| 47 | mlirDialectHandleInsertDialect(pythonTestDialect, registry); |
| 48 | }, |
| 49 | nb::arg("registry" )); |
| 50 | |
| 51 | m.def("test_diagnostics_with_errors_and_notes" , [](MlirContext ctx) { |
| 52 | mlir::python::CollectDiagnosticsToStringScope handler(ctx); |
| 53 | |
| 54 | mlirPythonTestEmitDiagnosticWithNote(ctx); |
| 55 | throw nb::value_error(handler.takeMessage().c_str()); |
| 56 | }); |
| 57 | |
| 58 | mlir_attribute_subclass(m, "TestAttr" , |
| 59 | mlirAttributeIsAPythonTestTestAttribute, |
| 60 | mlirPythonTestTestAttributeGetTypeID) |
| 61 | .def_classmethod( |
| 62 | "get" , |
| 63 | [](const nb::object &cls, MlirContext ctx) { |
| 64 | return cls(mlirPythonTestTestAttributeGet(ctx)); |
| 65 | }, |
| 66 | nb::arg("cls" ), nb::arg("context" ).none() = nb::none()); |
| 67 | |
| 68 | mlir_type_subclass(m, "TestType" , mlirTypeIsAPythonTestTestType, |
| 69 | mlirPythonTestTestTypeGetTypeID) |
| 70 | .def_classmethod( |
| 71 | "get" , |
| 72 | [](const nb::object &cls, MlirContext ctx) { |
| 73 | return cls(mlirPythonTestTestTypeGet(ctx)); |
| 74 | }, |
| 75 | nb::arg("cls" ), nb::arg("context" ).none() = nb::none()); |
| 76 | |
| 77 | auto typeCls = |
| 78 | mlir_type_subclass(m, "TestIntegerRankedTensorType" , |
| 79 | mlirTypeIsARankedIntegerTensor, |
| 80 | nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir" )) |
| 81 | .attr("RankedTensorType" )) |
| 82 | .def_classmethod( |
| 83 | "get" , |
| 84 | [](const nb::object &cls, std::vector<int64_t> shape, |
| 85 | unsigned width, MlirContext ctx) { |
| 86 | MlirAttribute encoding = mlirAttributeGetNull(); |
| 87 | return cls(mlirRankedTensorTypeGet( |
| 88 | shape.size(), shape.data(), mlirIntegerTypeGet(ctx, width), |
| 89 | encoding)); |
| 90 | }, |
| 91 | nb::arg("cls" ), nb::arg("shape" ), nb::arg("width" ), |
| 92 | nb::arg("context" ).none() = nb::none()); |
| 93 | |
| 94 | assert(nb::hasattr(typeCls.get_class(), "static_typeid" ) && |
| 95 | "TestIntegerRankedTensorType has no static_typeid" ); |
| 96 | |
| 97 | MlirTypeID mlirRankedTensorTypeID = mlirRankedTensorTypeGetTypeID(); |
| 98 | |
| 99 | nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir" )) |
| 100 | .attr(MLIR_PYTHON_CAPI_TYPE_CASTER_REGISTER_ATTR)( |
| 101 | mlirRankedTensorTypeID, nb::arg("replace" ) = true)( |
| 102 | nanobind::cpp_function([typeCls](const nb::object &mlirType) { |
| 103 | return typeCls.get_class()(mlirType); |
| 104 | })); |
| 105 | |
| 106 | auto valueCls = mlir_value_subclass(m, "TestTensorValue" , |
| 107 | mlirTypeIsAPythonTestTestTensorValue) |
| 108 | .def("is_null" , [](MlirValue &self) { |
| 109 | return mlirValueIsNull(self); |
| 110 | }); |
| 111 | |
| 112 | nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir" )) |
| 113 | .attr(MLIR_PYTHON_CAPI_VALUE_CASTER_REGISTER_ATTR)( |
| 114 | mlirRankedTensorTypeID)( |
| 115 | nanobind::cpp_function([valueCls](const nb::object &valueObj) { |
| 116 | nb::object capsule = mlirApiObjectToCapsule(valueObj); |
| 117 | MlirValue v = mlirPythonCapsuleToValue(capsule.ptr()); |
| 118 | MlirType t = mlirValueGetType(v); |
| 119 | // This is hyper-specific in order to exercise/test registering a |
| 120 | // value caster from cpp (but only for a single test case; see |
| 121 | // testTensorValue python_test.py). |
| 122 | if (mlirShapedTypeHasStaticShape(t) && |
| 123 | mlirShapedTypeGetDimSize(t, 0) == 1 && |
| 124 | mlirShapedTypeGetDimSize(t, 1) == 2 && |
| 125 | mlirShapedTypeGetDimSize(t, 2) == 3) |
| 126 | return valueCls.get_class()(valueObj); |
| 127 | return valueObj; |
| 128 | })); |
| 129 | } |
| 130 | |