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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/Dialect/SparseTensor.h"
#include "mlir-c/IR.h"
#include "mlir/Bindings/Python/PybindAdaptors.h"
#include <optional>
namespace py = pybind11;
using namespace llvm;
using namespace mlir;
using namespace mlir::python::adaptors;
static void populateDialectSparseTensorSubmodule(const py::module &m) {
py::enum_<MlirSparseTensorDimLevelType>(m, "DimLevelType", py::module_local())
.value("dense", MLIR_SPARSE_TENSOR_DIM_LEVEL_DENSE)
.value("compressed24", MLIR_SPARSE_TENSOR_DIM_LEVEL_TWO_OUT_OF_FOUR)
.value("compressed", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED)
.value("compressed-nu", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NU)
.value("compressed-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NO)
.value("compressed-nu-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NU_NO)
.value("singleton", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON)
.value("singleton-nu", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NU)
.value("singleton-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NO)
.value("singleton-nu-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NU_NO)
.value("compressed-hi", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI)
.value("compressed-hi-nu",
MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NU)
.value("compressed-hi-no",
MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NO)
.value("compressed-hi-nu-no",
MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NU_NO);
mlir_attribute_subclass(m, "EncodingAttr",
mlirAttributeIsASparseTensorEncodingAttr)
.def_classmethod(
"get",
[](py::object cls, std::vector<MlirSparseTensorDimLevelType> lvlTypes,
std::optional<MlirAffineMap> dimToLvl, int posWidth, int crdWidth,
MlirContext context) {
return cls(mlirSparseTensorEncodingAttrGet(
context, lvlTypes.size(), lvlTypes.data(),
dimToLvl ? *dimToLvl : MlirAffineMap{nullptr}, posWidth,
crdWidth));
},
py::arg("cls"), py::arg("lvl_types"), py::arg("dim_to_lvl"),
py::arg("pos_width"), py::arg("crd_width"),
py::arg("context") = py::none(),
"Gets a sparse_tensor.encoding from parameters.")
.def_property_readonly(
"lvl_types",
[](MlirAttribute self) {
const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
std::vector<MlirSparseTensorDimLevelType> 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("pos_width",
mlirSparseTensorEncodingAttrGetPosWidth)
.def_property_readonly("crd_width",
mlirSparseTensorEncodingAttrGetCrdWidth);
}
PYBIND11_MODULE(_mlirDialectsSparseTensor, m) {
m.doc() = "MLIR SparseTensor dialect.";
populateDialectSparseTensorSubmodule(m);
}
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