b15ccd436a
Updated the Python diagnostics handler to emit notes (in addition to errors) into the output stream so that users have more context as to where in the IR the error is occurring.
130 lines
5.2 KiB
C++
130 lines
5.2 KiB
C++
//===- PythonTestModuleNanobind.cpp - PythonTest dialect extension --------===//
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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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// This is the nanobind edition of the PythonTest dialect module.
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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/Diagnostics.h"
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#include "mlir-c/IR.h"
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#include "mlir/Bindings/Python/Diagnostics.h"
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#include "mlir/Bindings/Python/Nanobind.h"
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#include "mlir/Bindings/Python/NanobindAdaptors.h"
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#include "nanobind/nanobind.h"
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namespace nb = nanobind;
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using namespace mlir::python::nanobind_adaptors;
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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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NB_MODULE(_mlirPythonTestNanobind, 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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nb::arg("context"), nb::arg("load") = true);
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m.def(
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"register_dialect",
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[](MlirDialectRegistry registry) {
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MlirDialectHandle pythonTestDialect =
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mlirGetDialectHandle__python_test__();
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mlirDialectHandleInsertDialect(pythonTestDialect, registry);
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},
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nb::arg("registry"));
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m.def("test_diagnostics_with_errors_and_notes", [](MlirContext ctx) {
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mlir::python::CollectDiagnosticsToStringScope handler(ctx);
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mlirPythonTestEmitDiagnosticWithNote(ctx);
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throw nb::value_error(handler.takeMessage().c_str());
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});
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mlir_attribute_subclass(m, "TestAttr",
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mlirAttributeIsAPythonTestTestAttribute,
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mlirPythonTestTestAttributeGetTypeID)
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.def_classmethod(
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"get",
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[](const nb::object &cls, MlirContext ctx) {
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return cls(mlirPythonTestTestAttributeGet(ctx));
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},
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nb::arg("cls"), nb::arg("context").none() = nb::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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[](const nb::object &cls, MlirContext ctx) {
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return cls(mlirPythonTestTestTypeGet(ctx));
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},
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nb::arg("cls"), nb::arg("context").none() = nb::none());
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auto typeCls =
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mlir_type_subclass(m, "TestIntegerRankedTensorType",
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mlirTypeIsARankedIntegerTensor,
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nb::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 nb::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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nb::arg("cls"), nb::arg("shape"), nb::arg("width"),
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nb::arg("context").none() = nb::none());
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assert(nb::hasattr(typeCls.get_class(), "static_typeid") &&
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"TestIntegerRankedTensorType has no static_typeid");
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MlirTypeID mlirRankedTensorTypeID = mlirRankedTensorTypeGetTypeID();
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nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir"))
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.attr(MLIR_PYTHON_CAPI_TYPE_CASTER_REGISTER_ATTR)(
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mlirRankedTensorTypeID, nb::arg("replace") = true)(
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nanobind::cpp_function([typeCls](const nb::object &mlirType) {
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return typeCls.get_class()(mlirType);
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}));
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auto valueCls = mlir_value_subclass(m, "TestTensorValue",
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mlirTypeIsAPythonTestTestTensorValue)
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.def("is_null", [](MlirValue &self) {
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return mlirValueIsNull(self);
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});
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nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir"))
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.attr(MLIR_PYTHON_CAPI_VALUE_CASTER_REGISTER_ATTR)(
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mlirRankedTensorTypeID)(
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nanobind::cpp_function([valueCls](const nb::object &valueObj) {
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nb::object capsule = mlirApiObjectToCapsule(valueObj);
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MlirValue v = mlirPythonCapsuleToValue(capsule.ptr());
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MlirType t = mlirValueGetType(v);
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// This is hyper-specific in order to exercise/test registering a
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// value caster from cpp (but only for a single test case; see
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// testTensorValue python_test.py).
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if (mlirShapedTypeHasStaticShape(t) &&
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mlirShapedTypeGetDimSize(t, 0) == 1 &&
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mlirShapedTypeGetDimSize(t, 1) == 2 &&
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mlirShapedTypeGetDimSize(t, 2) == 3)
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return valueCls.get_class()(valueObj);
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return valueObj;
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}));
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}
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