76647fce13
This is a major step along the way towards the new STEA design. While a great deal of this patch is simple renaming, there are several significant changes as well. I've done my best to ensure that this patch retains the previous behavior and error-conditions, even though those are at odds with the eventual intended semantics of the `dimToLvl` mapping. Since the majority of the compiler does not yet support non-permutations, I've also added explicit assertions in places that previously had implicitly assumed it was dealing with permutations. Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D151505
82 lines
3.6 KiB
C++
82 lines
3.6 KiB
C++
//===- DialectSparseTensor.cpp - 'sparse_tensor' dialect submodule --------===//
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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/SparseTensor.h"
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#include "mlir-c/IR.h"
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#include "mlir/Bindings/Python/PybindAdaptors.h"
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#include <optional>
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namespace py = pybind11;
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using namespace llvm;
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using namespace mlir;
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using namespace mlir::python::adaptors;
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static void populateDialectSparseTensorSubmodule(const py::module &m) {
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py::enum_<MlirSparseTensorDimLevelType>(m, "DimLevelType", py::module_local())
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.value("dense", MLIR_SPARSE_TENSOR_DIM_LEVEL_DENSE)
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.value("compressed", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED)
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.value("compressed-nu", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NU)
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.value("compressed-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NO)
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.value("compressed-nu-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_NU_NO)
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.value("singleton", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON)
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.value("singleton-nu", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NU)
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.value("singleton-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NO)
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.value("singleton-nu-no", MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NU_NO)
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.value("compressed-hi", MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI)
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.value("compressed-hi-nu",
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MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NU)
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.value("compressed-hi-no",
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MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NO)
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.value("compressed-hi-nu-no",
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MLIR_SPARSE_TENSOR_DIM_LEVEL_COMPRESSED_WITH_HI_NU_NO);
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mlir_attribute_subclass(m, "EncodingAttr",
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mlirAttributeIsASparseTensorEncodingAttr)
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.def_classmethod(
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"get",
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[](py::object cls, std::vector<MlirSparseTensorDimLevelType> lvlTypes,
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std::optional<MlirAffineMap> dimToLvl, int posWidth, int crdWidth,
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MlirContext context) {
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return cls(mlirSparseTensorEncodingAttrGet(
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context, lvlTypes.size(), lvlTypes.data(),
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dimToLvl ? *dimToLvl : MlirAffineMap{nullptr}, posWidth,
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crdWidth));
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},
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py::arg("cls"), py::arg("lvl_types"), py::arg("dim_to_lvl"),
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py::arg("pos_width"), py::arg("crd_width"),
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py::arg("context") = py::none(),
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"Gets a sparse_tensor.encoding from parameters.")
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.def_property_readonly(
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"lvl_types",
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[](MlirAttribute self) {
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const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
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std::vector<MlirSparseTensorDimLevelType> ret;
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ret.reserve(lvlRank);
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for (int l = 0; l < lvlRank; ++l)
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ret.push_back(mlirSparseTensorEncodingAttrGetLvlType(self, l));
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return ret;
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})
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.def_property_readonly(
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"dim_to_lvl",
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[](MlirAttribute self) -> std::optional<MlirAffineMap> {
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MlirAffineMap ret = mlirSparseTensorEncodingAttrGetDimToLvl(self);
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if (mlirAffineMapIsNull(ret))
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return {};
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return ret;
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})
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.def_property_readonly("pos_width",
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mlirSparseTensorEncodingAttrGetPosWidth)
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.def_property_readonly("crd_width",
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mlirSparseTensorEncodingAttrGetCrdWidth);
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}
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PYBIND11_MODULE(_mlirDialectsSparseTensor, m) {
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m.doc() = "MLIR SparseTensor dialect.";
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populateDialectSparseTensorSubmodule(m);
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}
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