5cd4274772
This is a companion to #118583, although it can be landed independently because since #117922 dialects do not have to use the same Python binding framework as the Python core code. This PR ports all of the in-tree dialect and pass extensions to nanobind, with the exception of those that remain for testing pybind11 support. This PR also: * removes CollectDiagnosticsToStringScope from NanobindAdaptors.h. This was overlooked in a previous PR and it is duplicated in Diagnostics.h. --------- Co-authored-by: Jacques Pienaar <jpienaar@google.com>
91 lines
3.8 KiB
C++
91 lines
3.8 KiB
C++
//===- DialectGPU.cpp - Pybind module for the GPU passes ------------------===//
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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 "mlir-c/Dialect/GPU.h"
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#include "mlir-c/IR.h"
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#include "mlir-c/Support.h"
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#include "mlir/Bindings/Python/NanobindAdaptors.h"
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#include "mlir/Bindings/Python/Nanobind.h"
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namespace nb = nanobind;
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using namespace nanobind::literals;
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using namespace mlir;
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using namespace mlir::python;
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using namespace mlir::python::nanobind_adaptors;
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// -----------------------------------------------------------------------------
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// Module initialization.
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// -----------------------------------------------------------------------------
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NB_MODULE(_mlirDialectsGPU, m) {
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m.doc() = "MLIR GPU Dialect";
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//===-------------------------------------------------------------------===//
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// AsyncTokenType
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//===-------------------------------------------------------------------===//
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auto mlirGPUAsyncTokenType =
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mlir_type_subclass(m, "AsyncTokenType", mlirTypeIsAGPUAsyncTokenType);
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mlirGPUAsyncTokenType.def_classmethod(
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"get",
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[](nb::object cls, MlirContext ctx) {
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return cls(mlirGPUAsyncTokenTypeGet(ctx));
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},
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"Gets an instance of AsyncTokenType in the same context", nb::arg("cls"),
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nb::arg("ctx").none() = nb::none());
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//===-------------------------------------------------------------------===//
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// ObjectAttr
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//===-------------------------------------------------------------------===//
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mlir_attribute_subclass(m, "ObjectAttr", mlirAttributeIsAGPUObjectAttr)
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.def_classmethod(
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"get",
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[](nb::object cls, MlirAttribute target, uint32_t format,
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nb::bytes object, std::optional<MlirAttribute> mlirObjectProps,
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std::optional<MlirAttribute> mlirKernelsAttr) {
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MlirStringRef objectStrRef = mlirStringRefCreate(
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static_cast<char *>(const_cast<void *>(object.data())),
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object.size());
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return cls(mlirGPUObjectAttrGetWithKernels(
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mlirAttributeGetContext(target), target, format, objectStrRef,
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mlirObjectProps.has_value() ? *mlirObjectProps
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: MlirAttribute{nullptr},
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mlirKernelsAttr.has_value() ? *mlirKernelsAttr
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: MlirAttribute{nullptr}));
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},
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"cls"_a, "target"_a, "format"_a, "object"_a,
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"properties"_a.none() = nb::none(), "kernels"_a.none() = nb::none(),
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"Gets a gpu.object from parameters.")
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.def_property_readonly(
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"target",
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[](MlirAttribute self) { return mlirGPUObjectAttrGetTarget(self); })
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.def_property_readonly(
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"format",
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[](MlirAttribute self) { return mlirGPUObjectAttrGetFormat(self); })
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.def_property_readonly(
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"object",
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[](MlirAttribute self) {
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MlirStringRef stringRef = mlirGPUObjectAttrGetObject(self);
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return nb::bytes(stringRef.data, stringRef.length);
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})
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.def_property_readonly("properties",
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[](MlirAttribute self) -> nb::object {
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if (mlirGPUObjectAttrHasProperties(self))
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return nb::cast(
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mlirGPUObjectAttrGetProperties(self));
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return nb::none();
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})
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.def_property_readonly("kernels", [](MlirAttribute self) -> nb::object {
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if (mlirGPUObjectAttrHasKernels(self))
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return nb::cast(mlirGPUObjectAttrGetKernels(self));
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return nb::none();
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});
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}
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