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//===- DialectSparseTensor.cpp - 'sparse_tensor' dialect submodule --------===//
//
// 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
//
//===----------------------------------------------------------------------===//
#include "mlir-c/AffineMap.h"
#include "mlir-c/Dialect/SparseTensor.h"
#include "mlir-c/IR.h"
#include "mlir/Bindings/Python/PybindAdaptors.h"
#include <optional>
#include <pybind11/cast.h>
#include <pybind11/detail/common.h>
#include <pybind11/pybind11.h>
#include <pybind11/pytypes.h>
#include <vector>
namespace py = pybind11;
using namespace llvm;
using namespace mlir;
using namespace mlir::python::adaptors;
static void populateDialectSparseTensorSubmodule(const py::module &m) {
py::enum_<MlirSparseTensorLevelFormat>(m, "LevelFormat", py::module_local())
.value("dense", MLIR_SPARSE_TENSOR_LEVEL_DENSE)
.value("n_out_of_m", MLIR_SPARSE_TENSOR_LEVEL_N_OUT_OF_M)
.value("compressed", MLIR_SPARSE_TENSOR_LEVEL_COMPRESSED)
.value("singleton", MLIR_SPARSE_TENSOR_LEVEL_SINGLETON)
.value("loose_compressed", MLIR_SPARSE_TENSOR_LEVEL_LOOSE_COMPRESSED);
py::enum_<MlirSparseTensorLevelPropertyNondefault>(m, "LevelProperty",
py::module_local())
.value("non_ordered", MLIR_SPARSE_PROPERTY_NON_ORDERED)
.value("non_unique", MLIR_SPARSE_PROPERTY_NON_UNIQUE);
mlir_attribute_subclass(m, "EncodingAttr",
mlirAttributeIsASparseTensorEncodingAttr)
.def_classmethod(
"get",
[](py::object cls, std::vector<MlirSparseTensorLevelType> lvlTypes,
std::optional<MlirAffineMap> dimToLvl,
std::optional<MlirAffineMap> lvlToDim, int posWidth, int crdWidth,
std::optional<MlirAttribute> explicitVal,
std::optional<MlirAttribute> implicitVal, MlirContext context) {
return cls(mlirSparseTensorEncodingAttrGet(
context, lvlTypes.size(), lvlTypes.data(),
dimToLvl ? *dimToLvl : MlirAffineMap{nullptr},
lvlToDim ? *lvlToDim : MlirAffineMap{nullptr}, posWidth,
crdWidth, explicitVal ? *explicitVal : MlirAttribute{nullptr},
implicitVal ? *implicitVal : MlirAttribute{nullptr}));
},
py::arg("cls"), py::arg("lvl_types"), py::arg("dim_to_lvl"),
py::arg("lvl_to_dim"), py::arg("pos_width"), py::arg("crd_width"),
py::arg("explicit_val") = py::none(),
py::arg("implicit_val") = py::none(), py::arg("context") = py::none(),
"Gets a sparse_tensor.encoding from parameters.")
.def_classmethod(
"build_level_type",
[](py::object cls, MlirSparseTensorLevelFormat lvlFmt,
const std::vector<MlirSparseTensorLevelPropertyNondefault>
&properties,
unsigned n, unsigned m) {
return mlirSparseTensorEncodingAttrBuildLvlType(
lvlFmt, properties.data(), properties.size(), n, m);
},
py::arg("cls"), py::arg("lvl_fmt"),
py::arg("properties") =
std::vector<MlirSparseTensorLevelPropertyNondefault>(),
py::arg("n") = 0, py::arg("m") = 0,
"Builds a sparse_tensor.encoding.level_type from parameters.")
.def_property_readonly(
"lvl_types",
[](MlirAttribute self) {
const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
std::vector<MlirSparseTensorLevelType> ret;
ret.reserve(lvlRank);
for (int l = 0; l < lvlRank; ++l)
ret.push_back(mlirSparseTensorEncodingAttrGetLvlType(self, l));
return ret;
})
.def_property_readonly(
"dim_to_lvl",
[](MlirAttribute self) -> std::optional<MlirAffineMap> {
MlirAffineMap ret = mlirSparseTensorEncodingAttrGetDimToLvl(self);
if (mlirAffineMapIsNull(ret))
return {};
return ret;
})
.def_property_readonly(
"lvl_to_dim",
[](MlirAttribute self) -> std::optional<MlirAffineMap> {
MlirAffineMap ret = mlirSparseTensorEncodingAttrGetLvlToDim(self);
if (mlirAffineMapIsNull(ret))
return {};
return ret;
})
.def_property_readonly("pos_width",
mlirSparseTensorEncodingAttrGetPosWidth)
.def_property_readonly("crd_width",
mlirSparseTensorEncodingAttrGetCrdWidth)
.def_property_readonly(
"explicit_val",
[](MlirAttribute self) -> std::optional<MlirAttribute> {
MlirAttribute ret =
mlirSparseTensorEncodingAttrGetExplicitVal(self);
if (mlirAttributeIsNull(ret))
return {};
return ret;
})
.def_property_readonly(
"implicit_val",
[](MlirAttribute self) -> std::optional<MlirAttribute> {
MlirAttribute ret =
mlirSparseTensorEncodingAttrGetImplicitVal(self);
if (mlirAttributeIsNull(ret))
return {};
return ret;
})
.def_property_readonly(
"structured_n",
[](MlirAttribute self) -> unsigned {
const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
return mlirSparseTensorEncodingAttrGetStructuredN(
mlirSparseTensorEncodingAttrGetLvlType(self, lvlRank - 1));
})
.def_property_readonly(
"structured_m",
[](MlirAttribute self) -> unsigned {
const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
return mlirSparseTensorEncodingAttrGetStructuredM(
mlirSparseTensorEncodingAttrGetLvlType(self, lvlRank - 1));
})
.def_property_readonly("lvl_formats_enum", [](MlirAttribute self) {
const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
std::vector<MlirSparseTensorLevelFormat> ret;
ret.reserve(lvlRank);
for (int l = 0; l < lvlRank; l++)
ret.push_back(mlirSparseTensorEncodingAttrGetLvlFmt(self, l));
return ret;
});
}
PYBIND11_MODULE(_mlirDialectsSparseTensor, m) {
m.doc() = "MLIR SparseTensor dialect.";
populateDialectSparseTensorSubmodule(m);
}
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