1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
|
//===- PythonTestModuleNanobind.cpp - PythonTest dialect extension --------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
// This is the nanobind edition of the PythonTest dialect module.
//===----------------------------------------------------------------------===//
#include "PythonTestCAPI.h"
#include "mlir-c/BuiltinAttributes.h"
#include "mlir-c/BuiltinTypes.h"
#include "mlir-c/Diagnostics.h"
#include "mlir-c/IR.h"
#include "mlir/Bindings/Python/Diagnostics.h"
#include "mlir/Bindings/Python/Nanobind.h"
#include "mlir/Bindings/Python/NanobindAdaptors.h"
#include "nanobind/nanobind.h"
namespace nb = nanobind;
using namespace mlir::python::nanobind_adaptors;
static bool mlirTypeIsARankedIntegerTensor(MlirType t) {
return mlirTypeIsARankedTensor(t) &&
mlirTypeIsAInteger(mlirShapedTypeGetElementType(t));
}
NB_MODULE(_mlirPythonTestNanobind, m) {
m.def(
"register_python_test_dialect",
[](MlirContext context, bool load) {
MlirDialectHandle pythonTestDialect =
mlirGetDialectHandle__python_test__();
mlirDialectHandleRegisterDialect(pythonTestDialect, context);
if (load) {
mlirDialectHandleLoadDialect(pythonTestDialect, context);
}
},
nb::arg("context"), nb::arg("load") = true,
// clang-format off
nb::sig("def register_python_test_dialect(context: " MAKE_MLIR_PYTHON_QUALNAME("ir.Context") ", load: bool = True) -> None"));
// clang-format on
m.def(
"register_dialect",
[](MlirDialectRegistry registry) {
MlirDialectHandle pythonTestDialect =
mlirGetDialectHandle__python_test__();
mlirDialectHandleInsertDialect(pythonTestDialect, registry);
},
nb::arg("registry"),
// clang-format off
nb::sig("def register_dialect(registry: " MAKE_MLIR_PYTHON_QUALNAME("ir.DialectRegistry") ") -> None"));
// clang-format on
m.def(
"test_diagnostics_with_errors_and_notes",
[](MlirContext ctx) {
mlir::python::CollectDiagnosticsToStringScope handler(ctx);
mlirPythonTestEmitDiagnosticWithNote(ctx);
throw nb::value_error(handler.takeMessage().c_str());
},
// clang-format off
nb::sig("def test_diagnostics_with_errors_and_notes(arg: " MAKE_MLIR_PYTHON_QUALNAME("ir.Context") ", /) -> None"));
// clang-format on
mlir_attribute_subclass(m, "TestAttr",
mlirAttributeIsAPythonTestTestAttribute,
mlirPythonTestTestAttributeGetTypeID)
.def_classmethod(
"get",
[](const nb::object &cls, MlirContext ctx) {
return cls(mlirPythonTestTestAttributeGet(ctx));
},
// clang-format off
nb::sig("def get(cls: object, context: " MAKE_MLIR_PYTHON_QUALNAME("ir.Context") " | None = None) -> object"),
// clang-format on
nb::arg("cls"), nb::arg("context").none() = nb::none());
mlir_type_subclass(m, "TestType", mlirTypeIsAPythonTestTestType,
mlirPythonTestTestTypeGetTypeID)
.def_classmethod(
"get",
[](const nb::object &cls, MlirContext ctx) {
return cls(mlirPythonTestTestTypeGet(ctx));
},
// clang-format off
nb::sig("def get(cls: object, context: " MAKE_MLIR_PYTHON_QUALNAME("ir.Context") " | None = None) -> object"),
// clang-format on
nb::arg("cls"), nb::arg("context").none() = nb::none());
auto typeCls =
mlir_type_subclass(m, "TestIntegerRankedTensorType",
mlirTypeIsARankedIntegerTensor,
nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir"))
.attr("RankedTensorType"))
.def_classmethod(
"get",
[](const nb::object &cls, std::vector<int64_t> shape,
unsigned width, MlirContext ctx) {
MlirAttribute encoding = mlirAttributeGetNull();
return cls(mlirRankedTensorTypeGet(
shape.size(), shape.data(), mlirIntegerTypeGet(ctx, width),
encoding));
},
// clang-format off
nb::sig("def get(cls: object, shape: collections.abc.Sequence[int], width: int, context: " MAKE_MLIR_PYTHON_QUALNAME("ir.Context") " | None = None) -> object"),
// clang-format on
nb::arg("cls"), nb::arg("shape"), nb::arg("width"),
nb::arg("context").none() = nb::none());
assert(nb::hasattr(typeCls.get_class(), "static_typeid") &&
"TestIntegerRankedTensorType has no static_typeid");
MlirTypeID mlirRankedTensorTypeID = mlirRankedTensorTypeGetTypeID();
nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir"))
.attr(MLIR_PYTHON_CAPI_TYPE_CASTER_REGISTER_ATTR)(
mlirRankedTensorTypeID, nb::arg("replace") = true)(
nanobind::cpp_function([typeCls](const nb::object &mlirType) {
return typeCls.get_class()(mlirType);
}));
auto valueCls = mlir_value_subclass(m, "TestTensorValue",
mlirTypeIsAPythonTestTestTensorValue)
.def("is_null", [](MlirValue &self) {
return mlirValueIsNull(self);
});
nb::module_::import_(MAKE_MLIR_PYTHON_QUALNAME("ir"))
.attr(MLIR_PYTHON_CAPI_VALUE_CASTER_REGISTER_ATTR)(
mlirRankedTensorTypeID)(
nanobind::cpp_function([valueCls](const nb::object &valueObj) {
std::optional<nb::object> capsule =
mlirApiObjectToCapsule(valueObj);
assert(capsule.has_value() && "capsule is not null");
MlirValue v = mlirPythonCapsuleToValue(capsule.value().ptr());
MlirType t = mlirValueGetType(v);
// This is hyper-specific in order to exercise/test registering a
// value caster from cpp (but only for a single test case; see
// testTensorValue python_test.py).
if (mlirShapedTypeHasStaticShape(t) &&
mlirShapedTypeGetDimSize(t, 0) == 1 &&
mlirShapedTypeGetDimSize(t, 1) == 2 &&
mlirShapedTypeGetDimSize(t, 2) == 3)
return valueCls.get_class()(valueObj);
return valueObj;
}));
}
|
