[mlir][sparse] bug fix on all-dense lex insertion #73987
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Fixes a bug that appended values after insertion completed. Also slight optimization by avoiding all-Dense computation for every lexInsert call
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@llvm/pr-subscribers-mlir @llvm/pr-subscribers-mlir-execution-engine Author: Aart Bik (aartbik) ChangesFixes a bug that appended values after insertion completed. Also slight optimization by avoiding all-Dense computation for every lexInsert call Full diff: https://github.com/llvm/llvm-project/pull/73987.diff 2 Files Affected:
diff --git a/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h b/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
index 19c49e6c487dff7..01c5f2382ffe69c 100644
--- a/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
+++ b/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
@@ -186,6 +186,7 @@ class SparseTensorStorageBase {
protected:
const MapRef map; // non-owning pointers into dim2lvl/lvl2dim vectors
+ const bool allDense;
};
/// A memory-resident sparse tensor using a storage scheme based on
@@ -293,8 +294,6 @@ class SparseTensorStorage final : public SparseTensorStorageBase {
/// Partially specialize lexicographical insertions based on template types.
void lexInsert(const uint64_t *lvlCoords, V val) final {
assert(lvlCoords);
- bool allDense = std::all_of(getLvlTypes().begin(), getLvlTypes().end(),
- [](LevelType lt) { return isDenseLT(lt); });
if (allDense) {
uint64_t lvlRank = getLvlRank();
uint64_t valIdx = 0;
@@ -363,10 +362,12 @@ class SparseTensorStorage final : public SparseTensorStorageBase {
/// Finalizes lexicographic insertions.
void endLexInsert() final {
- if (values.empty())
- finalizeSegment(0);
- else
- endPath(0);
+ if (!allDense) {
+ if (values.empty())
+ finalizeSegment(0);
+ else
+ endPath(0);
+ }
}
/// Allocates a new COO object and initializes it with the contents.
@@ -705,7 +706,6 @@ SparseTensorStorage<P, C, V>::SparseTensorStorage(
// we reserve position/coordinate space based on all previous dense
// levels, which works well up to first sparse level; but we should
// really use nnz and dense/sparse distribution.
- bool allDense = true;
uint64_t sz = 1;
for (uint64_t l = 0; l < lvlRank; l++) {
if (isCompressedLvl(l)) {
@@ -713,23 +713,19 @@ SparseTensorStorage<P, C, V>::SparseTensorStorage(
positions[l].push_back(0);
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (isLooseCompressedLvl(l)) {
positions[l].reserve(2 * sz + 1); // last one unused
positions[l].push_back(0);
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (isSingletonLvl(l)) {
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (is2OutOf4Lvl(l)) {
- assert(allDense && l == lvlRank - 1 && "unexpected 2:4 usage");
+ assert(l == lvlRank - 1 && "unexpected 2:4 usage");
sz = detail::checkedMul(sz, lvlSizes[l]) / 2;
coordinates[l].reserve(sz);
values.reserve(sz);
- allDense = false;
} else { // Dense level.
assert(isDenseLvl(l));
sz = detail::checkedMul(sz, lvlSizes[l]);
diff --git a/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp b/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
index 7f8f76f8ec18901..0c7b3a228a65cf7 100644
--- a/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
+++ b/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
@@ -17,6 +17,13 @@
using namespace mlir::sparse_tensor;
+static inline bool isAllDense(uint64_t lvlRank, const LevelType *lvlTypes) {
+ for (uint64_t l = 0; l < lvlRank; l++)
+ if (!isDenseLT(lvlTypes[l]))
+ return false;
+ return true;
+}
+
SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
const uint64_t *lvlSizes, const LevelType *lvlTypes,
@@ -26,15 +33,16 @@ SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
lvlTypes(lvlTypes, lvlTypes + lvlRank),
dim2lvlVec(dim2lvl, dim2lvl + lvlRank),
lvl2dimVec(lvl2dim, lvl2dim + dimRank),
- map(dimRank, lvlRank, dim2lvlVec.data(), lvl2dimVec.data()) {
+ map(dimRank, lvlRank, dim2lvlVec.data(), lvl2dimVec.data()),
+ allDense(isAllDense(lvlRank, lvlTypes)) {
assert(dimSizes && lvlSizes && lvlTypes && dim2lvl && lvl2dim);
// Validate dim-indexed parameters.
assert(dimRank > 0 && "Trivial shape is unsupported");
- for (uint64_t d = 0; d < dimRank; ++d)
+ for (uint64_t d = 0; d < dimRank; d++)
assert(dimSizes[d] > 0 && "Dimension size zero has trivial storage");
// Validate lvl-indexed parameters.
assert(lvlRank > 0 && "Trivial shape is unsupported");
- for (uint64_t l = 0; l < lvlRank; ++l) {
+ for (uint64_t l = 0; l < lvlRank; l++) {
assert(lvlSizes[l] > 0 && "Level size zero has trivial storage");
assert(isDenseLvl(l) || isCompressedLvl(l) || isLooseCompressedLvl(l) ||
isSingletonLvl(l) || is2OutOf4Lvl(l));
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@llvm/pr-subscribers-mlir-sparse Author: Aart Bik (aartbik) ChangesFixes a bug that appended values after insertion completed. Also slight optimization by avoiding all-Dense computation for every lexInsert call Full diff: https://github.com/llvm/llvm-project/pull/73987.diff 2 Files Affected:
diff --git a/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h b/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
index 19c49e6c487dff7..01c5f2382ffe69c 100644
--- a/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
+++ b/mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h
@@ -186,6 +186,7 @@ class SparseTensorStorageBase {
protected:
const MapRef map; // non-owning pointers into dim2lvl/lvl2dim vectors
+ const bool allDense;
};
/// A memory-resident sparse tensor using a storage scheme based on
@@ -293,8 +294,6 @@ class SparseTensorStorage final : public SparseTensorStorageBase {
/// Partially specialize lexicographical insertions based on template types.
void lexInsert(const uint64_t *lvlCoords, V val) final {
assert(lvlCoords);
- bool allDense = std::all_of(getLvlTypes().begin(), getLvlTypes().end(),
- [](LevelType lt) { return isDenseLT(lt); });
if (allDense) {
uint64_t lvlRank = getLvlRank();
uint64_t valIdx = 0;
@@ -363,10 +362,12 @@ class SparseTensorStorage final : public SparseTensorStorageBase {
/// Finalizes lexicographic insertions.
void endLexInsert() final {
- if (values.empty())
- finalizeSegment(0);
- else
- endPath(0);
+ if (!allDense) {
+ if (values.empty())
+ finalizeSegment(0);
+ else
+ endPath(0);
+ }
}
/// Allocates a new COO object and initializes it with the contents.
@@ -705,7 +706,6 @@ SparseTensorStorage<P, C, V>::SparseTensorStorage(
// we reserve position/coordinate space based on all previous dense
// levels, which works well up to first sparse level; but we should
// really use nnz and dense/sparse distribution.
- bool allDense = true;
uint64_t sz = 1;
for (uint64_t l = 0; l < lvlRank; l++) {
if (isCompressedLvl(l)) {
@@ -713,23 +713,19 @@ SparseTensorStorage<P, C, V>::SparseTensorStorage(
positions[l].push_back(0);
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (isLooseCompressedLvl(l)) {
positions[l].reserve(2 * sz + 1); // last one unused
positions[l].push_back(0);
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (isSingletonLvl(l)) {
coordinates[l].reserve(sz);
sz = 1;
- allDense = false;
} else if (is2OutOf4Lvl(l)) {
- assert(allDense && l == lvlRank - 1 && "unexpected 2:4 usage");
+ assert(l == lvlRank - 1 && "unexpected 2:4 usage");
sz = detail::checkedMul(sz, lvlSizes[l]) / 2;
coordinates[l].reserve(sz);
values.reserve(sz);
- allDense = false;
} else { // Dense level.
assert(isDenseLvl(l));
sz = detail::checkedMul(sz, lvlSizes[l]);
diff --git a/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp b/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
index 7f8f76f8ec18901..0c7b3a228a65cf7 100644
--- a/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
+++ b/mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp
@@ -17,6 +17,13 @@
using namespace mlir::sparse_tensor;
+static inline bool isAllDense(uint64_t lvlRank, const LevelType *lvlTypes) {
+ for (uint64_t l = 0; l < lvlRank; l++)
+ if (!isDenseLT(lvlTypes[l]))
+ return false;
+ return true;
+}
+
SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
const uint64_t *lvlSizes, const LevelType *lvlTypes,
@@ -26,15 +33,16 @@ SparseTensorStorageBase::SparseTensorStorageBase( // NOLINT
lvlTypes(lvlTypes, lvlTypes + lvlRank),
dim2lvlVec(dim2lvl, dim2lvl + lvlRank),
lvl2dimVec(lvl2dim, lvl2dim + dimRank),
- map(dimRank, lvlRank, dim2lvlVec.data(), lvl2dimVec.data()) {
+ map(dimRank, lvlRank, dim2lvlVec.data(), lvl2dimVec.data()),
+ allDense(isAllDense(lvlRank, lvlTypes)) {
assert(dimSizes && lvlSizes && lvlTypes && dim2lvl && lvl2dim);
// Validate dim-indexed parameters.
assert(dimRank > 0 && "Trivial shape is unsupported");
- for (uint64_t d = 0; d < dimRank; ++d)
+ for (uint64_t d = 0; d < dimRank; d++)
assert(dimSizes[d] > 0 && "Dimension size zero has trivial storage");
// Validate lvl-indexed parameters.
assert(lvlRank > 0 && "Trivial shape is unsupported");
- for (uint64_t l = 0; l < lvlRank; ++l) {
+ for (uint64_t l = 0; l < lvlRank; l++) {
assert(lvlSizes[l] > 0 && "Level size zero has trivial storage");
assert(isDenseLvl(l) || isCompressedLvl(l) || isLooseCompressedLvl(l) ||
isSingletonLvl(l) || is2OutOf4Lvl(l));
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PeimingLiu
approved these changes
Nov 30, 2023
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Fixes a bug that appended values after insertion completed. Also slight optimization by avoiding all-Dense computation for every lexInsert call