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[mlir][sparse] minor edits in runtime lib Cpp files (#68165)

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Aart Bik 2023-10-03 16:28:54 -07:00 committed by GitHub
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commit 427f120f60
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4 changed files with 10 additions and 44 deletions
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6
mlir/include/mlir/ExecutionEngine/SparseTensor/File.h
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@ -1,4 +1,4 @@
//===- File.h - Parsing sparse tensors from files ---------------*- C++ -*-===//
//===- File.h - Reading/writing sparse tensors from/to files ----*- C++ -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
@ -6,8 +6,8 @@
//
//===----------------------------------------------------------------------===//
//
// This file implements reading and writing files in one of the following
// external formats:
// This file implements reading and writing sparse tensor files in one of the
// following external formats:
//
// (1) Matrix Market Exchange (MME): *.mtx
// https://math.nist.gov/MatrixMarket/formats.html

17
mlir/lib/ExecutionEngine/SparseTensor/File.cpp
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@ -1,4 +1,4 @@
//===- File.cpp - Parsing sparse tensors from files -----------------------===//
//===- File.cpp - Reading/writing sparse tensors from/to files ------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
@ -6,20 +6,7 @@
//
//===----------------------------------------------------------------------===//
//
// This file implements parsing and printing of files in one of the
// following external formats:
//
// (1) Matrix Market Exchange (MME): *.mtx
// https://math.nist.gov/MatrixMarket/formats.html
//
// (2) Formidable Repository of Open Sparse Tensors and Tools (FROSTT): *.tns
// http://frostt.io/tensors/file-formats.html
//
// This file is part of the lightweight runtime support library for sparse
// tensor manipulations. The functionality of the support library is meant
// to simplify benchmarking, testing, and debugging MLIR code operating on
// sparse tensors. However, the provided functionality is **not** part of
// core MLIR itself.
// This file implements reading and writing sparse tensor files.
//
//===----------------------------------------------------------------------===//

7
mlir/lib/ExecutionEngine/SparseTensor/NNZ.cpp
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@ -8,19 +8,12 @@
//
// This file contains method definitions for `SparseTensorNNZ`.
//
// This file is part of the lightweight runtime support library for sparse
// tensor manipulations. The functionality of the support library is meant
// to simplify benchmarking, testing, and debugging MLIR code operating on
// sparse tensors. However, the provided functionality is **not** part of
// core MLIR itself.
//
//===----------------------------------------------------------------------===//
#include "mlir/ExecutionEngine/SparseTensor/Storage.h"
using namespace mlir::sparse_tensor;
//===----------------------------------------------------------------------===//
SparseTensorNNZ::SparseTensorNNZ(const std::vector<uint64_t> &lvlSizes,
const std::vector<DimLevelType> &lvlTypes)
: lvlSizes(lvlSizes), lvlTypes(lvlTypes), nnz(getLvlRank()) {

24
mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
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@ -9,14 +9,7 @@
// This file contains method definitions for `SparseTensorStorageBase`.
// In particular we want to ensure that the default implementations of
// the "partial method specialization" trick aren't inline (since there's
// no benefit). Though this also helps ensure that we avoid weak-vtables:
// <https://llvm.org/docs/CodingStandards.html#provide-a-virtual-method-anchor-for-classes-in-headers>
//
// This file is part of the lightweight runtime support library for sparse
// tensor manipulations. The functionality of the support library is meant
// to simplify benchmarking, testing, and debugging MLIR code operating on
// sparse tensors. However, the provided functionality is **not** part of
// core MLIR itself.
// no benefit).
//
//===----------------------------------------------------------------------===//
@ -32,10 +25,6 @@ SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
lvlSizes(lvlSizes, lvlSizes + lvlRank),
lvlTypes(lvlTypes, lvlTypes + lvlRank),
lvl2dim(lvl2dim, lvl2dim + lvlRank) {
// TODO: If we do get any nullptrs, I'm pretty sure these assertions
// will run too late (i.e., after copying things into vectors above).
// But since those fields are const I'm not sure there's any clean way
// to assert things before copying...
assert(dimSizes && "Got nullptr for dimension sizes");
assert(lvlSizes && "Got nullptr for level sizes");
assert(lvlTypes && "Got nullptr for level types");
@ -44,18 +33,15 @@ SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
assert(dimRank > 0 && "Trivial shape is unsupported");
for (uint64_t d = 0; d < dimRank; ++d)
assert(dimSizes[d] > 0 && "Dimension size zero has trivial storage");
// Validate level-indexed parameters.
// Validate lvl-indexed parameters.
assert(lvlRank > 0 && "Trivial shape is unsupported");
for (uint64_t l = 0; l < lvlRank; ++l) {
assert(lvlSizes[l] > 0 && "Level size zero has trivial storage");
const auto dlt = lvlTypes[l]; // Avoid redundant bounds checking.
// We use `MLIR_SPARSETENSOR_FATAL` here instead of `assert` so that
// when this ctor is successful then all the methods can rely on the
// fact that each level-type satisfies one of these options (even
// when `NDEBUG` is true), thereby reducing the need to re-assert things.
if (!(isDenseDLT(dlt) || isCompressedDLT(dlt) || isSingletonDLT(dlt)))
const auto dlt = lvlTypes[l];
if (!(isDenseDLT(dlt) || isCompressedDLT(dlt) || isSingletonDLT(dlt))) {
MLIR_SPARSETENSOR_FATAL("unsupported level type: %d\n",
static_cast<uint8_t>(dlt));
}
}
}