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[mlir][sparse] Renaming the STEA field dimLevelType to lvlTypes

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This commit is part of the migration of towards the new STEA syntax/design.  In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
  * `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
  * `Merger::{getDimLevelType => getLvlType}` (for consistency)
  * `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
  * the STEA parser and printer
  * the C and Python bindings
  * PyTACO

However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D150330
This commit is contained in:
wren romano
2023-05-17 13:09:53 -07:00
parent 4dc205f016
commit a0615d020a
172 changed files with 1229 additions and 1240 deletions
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4
mlir/include/mlir-c/Dialect/SparseTensor.h
View File

@@ -52,7 +52,7 @@ mlirAttributeIsASparseTensorEncodingAttr(MlirAttribute attr);
/// Creates a `sparse_tensor.encoding` attribute with the given parameters.
MLIR_CAPI_EXPORTED MlirAttribute mlirSparseTensorEncodingAttrGet(
MlirContext ctx, intptr_t lvlRank,
enum MlirSparseTensorDimLevelType const *dimLevelTypes,
enum MlirSparseTensorDimLevelType const *lvlTypes,
MlirAffineMap dimOrdering, MlirAffineMap higherOrdering, int posWidth,
int crdWidth);
@@ -62,7 +62,7 @@ mlirSparseTensorEncodingGetLvlRank(MlirAttribute attr);
/// Returns a specified level-type of the `sparse_tensor.encoding` attribute.
MLIR_CAPI_EXPORTED enum MlirSparseTensorDimLevelType
mlirSparseTensorEncodingAttrGetDimLevelType(MlirAttribute attr, intptr_t lvl);
mlirSparseTensorEncodingAttrGetLvlType(MlirAttribute attr, intptr_t lvl);
/// Returns the dimension-ordering of the `sparse_tensor.encoding` attribute.
MLIR_CAPI_EXPORTED MlirAffineMap

28
mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h
View File

@@ -300,7 +300,7 @@ constexpr std::optional<LevelFormat> getLevelFormat(DimLevelType dlt) {
/// TODO: factor out a new LevelProperties type so we can add new properties
/// without changing this function's signature
constexpr std::optional<DimLevelType>
getDimLevelType(LevelFormat lf, bool ordered, bool unique) {
buildLevelType(LevelFormat lf, bool ordered, bool unique) {
auto dlt = static_cast<DimLevelType>(static_cast<uint8_t>(lf) |
(ordered ? 0 : 2) | (unique ? 0 : 1));
return isValidDLT(dlt) ? std::optional(dlt) : std::nullopt;
@@ -321,27 +321,27 @@ static_assert(
"getLevelFormat conversion is broken");
static_assert(
(getDimLevelType(LevelFormat::Dense, false, true) == std::nullopt &&
getDimLevelType(LevelFormat::Dense, true, false) == std::nullopt &&
getDimLevelType(LevelFormat::Dense, false, false) == std::nullopt &&
*getDimLevelType(LevelFormat::Dense, true, true) == DimLevelType::Dense &&
*getDimLevelType(LevelFormat::Compressed, true, true) ==
(buildLevelType(LevelFormat::Dense, false, true) == std::nullopt &&
buildLevelType(LevelFormat::Dense, true, false) == std::nullopt &&
buildLevelType(LevelFormat::Dense, false, false) == std::nullopt &&
*buildLevelType(LevelFormat::Dense, true, true) == DimLevelType::Dense &&
*buildLevelType(LevelFormat::Compressed, true, true) ==
DimLevelType::Compressed &&
*getDimLevelType(LevelFormat::Compressed, true, false) ==
*buildLevelType(LevelFormat::Compressed, true, false) ==
DimLevelType::CompressedNu &&
*getDimLevelType(LevelFormat::Compressed, false, true) ==
*buildLevelType(LevelFormat::Compressed, false, true) ==
DimLevelType::CompressedNo &&
*getDimLevelType(LevelFormat::Compressed, false, false) ==
*buildLevelType(LevelFormat::Compressed, false, false) ==
DimLevelType::CompressedNuNo &&
*getDimLevelType(LevelFormat::Singleton, true, true) ==
*buildLevelType(LevelFormat::Singleton, true, true) ==
DimLevelType::Singleton &&
*getDimLevelType(LevelFormat::Singleton, true, false) ==
*buildLevelType(LevelFormat::Singleton, true, false) ==
DimLevelType::SingletonNu &&
*getDimLevelType(LevelFormat::Singleton, false, true) ==
*buildLevelType(LevelFormat::Singleton, false, true) ==
DimLevelType::SingletonNo &&
*getDimLevelType(LevelFormat::Singleton, false, false) ==
*buildLevelType(LevelFormat::Singleton, false, false) ==
DimLevelType::SingletonNuNo),
"getDimLevelType conversion is broken");
"buildLevelType conversion is broken");
// Ensure the above predicates work as intended.
static_assert((isValidDLT(DimLevelType::Undef) &&

6
mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td
View File

@@ -266,7 +266,7 @@ def SparseTensorEncodingAttr : SparseTensor_Attr<"SparseTensorEncoding",
ArrayRefParameter<
"::mlir::sparse_tensor::DimLevelType",
"level-types"
>: $dimLevelType,
>: $lvlTypes,
// A permutation from (higher-ordering)-coordinates to level-coordinates.
"AffineMap":$dimOrdering,
// A mapping from dimension-coordinates to (higher-ordering)-coordinates.
@@ -283,12 +283,12 @@ def SparseTensorEncodingAttr : SparseTensor_Attr<"SparseTensorEncoding",
);
let builders = [
AttrBuilder<(ins "ArrayRef<::mlir::sparse_tensor::DimLevelType>":$dimLevelType,
AttrBuilder<(ins "ArrayRef<::mlir::sparse_tensor::DimLevelType>":$lvlTypes,
"AffineMap":$dimOrdering,
"AffineMap":$higherOrdering,
"unsigned":$posWidth,
"unsigned":$crdWidth), [{
return $_get($_ctxt, dimLevelType,
return $_get($_ctxt, lvlTypes,
dimOrdering,
higherOrdering,
posWidth,

8
mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h
View File

@@ -377,14 +377,14 @@ public:
bool hasSparseIdxReduction(const BitVector &bits) const;
/// Gets the level-type of the `t`th tensor on `i`th loop.
DimLevelType getDimLevelType(TensorId t, LoopId i) const {
DimLevelType getLvlType(TensorId t, LoopId i) const {
assert(isValidTensorId(t) && isValidLoopId(i));
return lvlTypes[t][i];
}
/// Gets the level-type of the TensorLoopId.
DimLevelType getDimLevelType(TensorLoopId b) const {
return getDimLevelType(tensor(b), loop(b));
DimLevelType getLvlType(TensorLoopId b) const {
return getLvlType(tensor(b), loop(b));
}
/// Gets the loop identifier for the `lvl`th level of the `t`th tensor.
@@ -434,7 +434,7 @@ public:
for (const TensorLoopId b : bits.set_bits()) {
const TensorId t = tensor(b);
const auto optLvl = getLvl(b);
const auto lvlTp = getDimLevelType(b);
const auto lvlTp = getLvlType(b);
if (isLvlWithNonTrivialIdxExp(b)) {
// This must be an undefined level.
assert(!optLvl.has_value());

12
mlir/lib/Bindings/Python/DialectSparseTensor.cpp
View File

@@ -39,30 +39,28 @@ static void populateDialectSparseTensorSubmodule(const py::module &m) {
mlirAttributeIsASparseTensorEncodingAttr)
.def_classmethod(
"get",
[](py::object cls,
std::vector<MlirSparseTensorDimLevelType> dimLevelTypes,
[](py::object cls, std::vector<MlirSparseTensorDimLevelType> lvlTypes,
std::optional<MlirAffineMap> dimOrdering,
std::optional<MlirAffineMap> higherOrdering, int posWidth,
int crdWidth, MlirContext context) {
return cls(mlirSparseTensorEncodingAttrGet(
context, dimLevelTypes.size(), dimLevelTypes.data(),
context, lvlTypes.size(), lvlTypes.data(),
dimOrdering ? *dimOrdering : MlirAffineMap{nullptr},
higherOrdering ? *higherOrdering : MlirAffineMap{nullptr},
posWidth, crdWidth));
},
py::arg("cls"), py::arg("dim_level_types"), py::arg("dim_ordering"),
py::arg("cls"), py::arg("lvl_types"), py::arg("dim_ordering"),
py::arg("higher_ordering"), py::arg("pos_width"),
py::arg("crd_width"), py::arg("context") = py::none(),
"Gets a sparse_tensor.encoding from parameters.")
.def_property_readonly(
"dim_level_types",
"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(
mlirSparseTensorEncodingAttrGetDimLevelType(self, l));
ret.push_back(mlirSparseTensorEncodingAttrGetLvlType(self, l));
return ret;
})
.def_property_readonly(

17
mlir/lib/CAPI/Dialect/SparseTensor.cpp
View File

@@ -47,16 +47,15 @@ bool mlirAttributeIsASparseTensorEncodingAttr(MlirAttribute attr) {
MlirAttribute mlirSparseTensorEncodingAttrGet(
MlirContext ctx, intptr_t lvlRank,
MlirSparseTensorDimLevelType const *dimLevelTypes,
MlirAffineMap dimOrdering, MlirAffineMap higherOrdering, int posWidth,
int crdWidth) {
SmallVector<DimLevelType> cppDimLevelTypes;
cppDimLevelTypes.reserve(lvlRank);
MlirSparseTensorDimLevelType const *lvlTypes, MlirAffineMap dimOrdering,
MlirAffineMap higherOrdering, int posWidth, int crdWidth) {
SmallVector<DimLevelType> cppLvlTypes;
cppLvlTypes.reserve(lvlRank);
for (intptr_t l = 0; l < lvlRank; ++l)
cppDimLevelTypes.push_back(static_cast<DimLevelType>(dimLevelTypes[l]));
cppLvlTypes.push_back(static_cast<DimLevelType>(lvlTypes[l]));
return wrap(SparseTensorEncodingAttr::get(
unwrap(ctx), cppDimLevelTypes, unwrap(dimOrdering),
unwrap(higherOrdering), posWidth, crdWidth));
unwrap(ctx), cppLvlTypes, unwrap(dimOrdering), unwrap(higherOrdering),
posWidth, crdWidth));
}
MlirAffineMap mlirSparseTensorEncodingAttrGetDimOrdering(MlirAttribute attr) {
@@ -73,7 +72,7 @@ intptr_t mlirSparseTensorEncodingGetLvlRank(MlirAttribute attr) {
}
MlirSparseTensorDimLevelType
mlirSparseTensorEncodingAttrGetDimLevelType(MlirAttribute attr, intptr_t lvl) {
mlirSparseTensorEncodingAttrGetLvlType(MlirAttribute attr, intptr_t lvl) {
return static_cast<MlirSparseTensorDimLevelType>(
cast<SparseTensorEncodingAttr>(unwrap(attr)).getLvlType(lvl));
}

48
mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
View File

@@ -130,23 +130,22 @@ Type SparseTensorEncodingAttr::getCrdType() const {
}
SparseTensorEncodingAttr SparseTensorEncodingAttr::withoutOrdering() const {
return SparseTensorEncodingAttr::get(getContext(), getDimLevelType(),
AffineMap(), AffineMap(), getPosWidth(),
return SparseTensorEncodingAttr::get(getContext(), getLvlTypes(), AffineMap(),
AffineMap(), getPosWidth(),
getCrdWidth());
}
SparseTensorEncodingAttr SparseTensorEncodingAttr::withoutBitWidths() const {
return SparseTensorEncodingAttr::get(getContext(), getDimLevelType(),
getDimOrdering(), getHigherOrdering(), 0,
0);
return SparseTensorEncodingAttr::get(
getContext(), getLvlTypes(), getDimOrdering(), getHigherOrdering(), 0, 0);
}
bool SparseTensorEncodingAttr::isAllDense() const {
return !getImpl() || llvm::all_of(getDimLevelType(), isDenseDLT);
return !getImpl() || llvm::all_of(getLvlTypes(), isDenseDLT);
}
bool SparseTensorEncodingAttr::isAllOrdered() const {
return !getImpl() || llvm::all_of(getDimLevelType(), isOrderedDLT);
return !getImpl() || llvm::all_of(getLvlTypes(), isOrderedDLT);
}
bool SparseTensorEncodingAttr::hasIdDimOrdering() const {
@@ -155,14 +154,14 @@ bool SparseTensorEncodingAttr::hasIdDimOrdering() const {
Level SparseTensorEncodingAttr::getLvlRank() const {
assert(getImpl() && "Uninitialized SparseTensorEncodingAttr");
return getDimLevelType().size();
return getLvlTypes().size();
}
DimLevelType SparseTensorEncodingAttr::getLvlType(Level l) const {
if (!getImpl())
return DimLevelType::Dense;
assert(l < getLvlRank() && "Level is out of bounds");
return getDimLevelType()[l];
return getLvlTypes()[l];
}
std::optional<uint64_t>
@@ -243,9 +242,8 @@ Attribute SparseTensorEncodingAttr::parse(AsmParser &parser, Type type) {
StringRef attrName;
// Exactly 6 keys.
SmallVector<StringRef, 6> keys = {"dimLevelType", "dimOrdering",
"higherOrdering", "posWidth",
"crdWidth", "slice"};
SmallVector<StringRef, 6> keys = {"lvlTypes", "dimOrdering", "higherOrdering",
"posWidth", "crdWidth", "slice"};
while (succeeded(parser.parseOptionalKeyword(&attrName))) {
if (!llvm::is_contained(keys, attrName)) {
parser.emitError(parser.getNameLoc(), "unexpected key: ") << attrName;
@@ -258,7 +256,7 @@ Attribute SparseTensorEncodingAttr::parse(AsmParser &parser, Type type) {
// cost of the `is_contained` check above. Should instead use some
// "find" function that returns the index into `keys` so that we can
// dispatch on that instead.
if (attrName == "dimLevelType") {
if (attrName == "lvlTypes") {
Attribute attr;
RETURN_ON_FAIL(parser.parseAttribute(attr));
auto arrayAttr = llvm::dyn_cast<ArrayAttr>(attr);
@@ -336,8 +334,8 @@ Attribute SparseTensorEncodingAttr::parse(AsmParser &parser, Type type) {
void SparseTensorEncodingAttr::print(AsmPrinter &printer) const {
// Print the struct-like storage in dictionary fashion.
printer << "<{ dimLevelType = [ ";
llvm::interleaveComma(getDimLevelType(), printer, [&](DimLevelType dlt) {
printer << "<{ lvlTypes = [ ";
llvm::interleaveComma(getLvlTypes(), printer, [&](DimLevelType dlt) {
printer << "\"" << toMLIRString(dlt) << "\"";
});
printer << " ]";
@@ -366,7 +364,7 @@ void SparseTensorEncodingAttr::print(AsmPrinter &printer) const {
LogicalResult SparseTensorEncodingAttr::verify(
function_ref<InFlightDiagnostic()> emitError,
ArrayRef<DimLevelType> dimLevelType, AffineMap dimOrdering,
ArrayRef<DimLevelType> lvlTypes, AffineMap dimOrdering,
AffineMap higherOrdering, unsigned posWidth, unsigned crdWidth,
ArrayRef<SparseTensorDimSliceAttr> dimSlices) {
if (!acceptBitWidth(posWidth))
@@ -378,7 +376,7 @@ LogicalResult SparseTensorEncodingAttr::verify(
// the `getLvlRank` method is the length of the level-types array,
// since it must always be provided and have full rank; therefore we
// use that same source-of-truth here.
const Level lvlRank = dimLevelType.size();
const Level lvlRank = lvlTypes.size();
if (lvlRank == 0)
return emitError() << "expected a non-empty array for level types";
if (dimOrdering) {
@@ -415,9 +413,9 @@ LogicalResult SparseTensorEncodingAttr::verifyEncoding(
function_ref<InFlightDiagnostic()> emitError) const {
// Check structural integrity. In particular, this ensures that the
// level-rank is coherent across all the fields.
RETURN_FAILURE_IF_FAILED(verify(emitError, getDimLevelType(),
getDimOrdering(), getHigherOrdering(),
getPosWidth(), getCrdWidth(), getDimSlices()))
RETURN_FAILURE_IF_FAILED(verify(emitError, getLvlTypes(), getDimOrdering(),
getHigherOrdering(), getPosWidth(),
getCrdWidth(), getDimSlices()))
// Check integrity with tensor type specifics. In particular, we
// need only check that the dimension-rank of the tensor agrees with
// the dimension-rank of the encoding.
@@ -496,14 +494,14 @@ RankedTensorType sparse_tensor::getCOOFromTypeWithOrdering(RankedTensorType rtt,
// An unordered and non-unique compressed level at beginning.
// If this is also the last level, then it is unique.
lvlTypes.push_back(
*getDimLevelType(LevelFormat::Compressed, ordered, lvlRank == 1));
*buildLevelType(LevelFormat::Compressed, ordered, lvlRank == 1));
if (lvlRank > 1) {
// TODO: it is actually ordered at the level for ordered input.
// Followed by unordered non-unique n-2 singleton levels.
std::fill_n(std::back_inserter(lvlTypes), lvlRank - 2,
*getDimLevelType(LevelFormat::Singleton, ordered, false));
*buildLevelType(LevelFormat::Singleton, ordered, false));
// Ends by a unique singleton level unless the lvlRank is 1.
lvlTypes.push_back(*getDimLevelType(LevelFormat::Singleton, ordered, true));
lvlTypes.push_back(*buildLevelType(LevelFormat::Singleton, ordered, true));
}
// TODO: Maybe pick the bitwidth based on input/output tensors (probably the
@@ -580,8 +578,8 @@ Level mlir::sparse_tensor::toStoredDim(RankedTensorType type, Dimension d) {
static SparseTensorEncodingAttr
getNormalizedEncodingForSpecifier(SparseTensorEncodingAttr enc) {
SmallVector<DimLevelType> dlts;
for (auto dlt : enc.getDimLevelType())
dlts.push_back(*getDimLevelType(*getLevelFormat(dlt), true, true));
for (auto dlt : enc.getLvlTypes())
dlts.push_back(*buildLevelType(*getLevelFormat(dlt), true, true));
return SparseTensorEncodingAttr::get(
enc.getContext(), dlts,

6
mlir/lib/Dialect/SparseTensor/Transforms/CodegenEnv.h
View File

@@ -79,11 +79,9 @@ public:
const LatPoint &lat(LatPointId l) const { return latticeMerger.lat(l); }
ArrayRef<LatPointId> set(LatSetId s) const { return latticeMerger.set(s); }
DimLevelType dlt(TensorId t, LoopId i) const {
return latticeMerger.getDimLevelType(t, i);
}
DimLevelType dlt(TensorLoopId b) const {
return latticeMerger.getDimLevelType(b);
return latticeMerger.getLvlType(t, i);
}
DimLevelType dlt(TensorLoopId b) const { return latticeMerger.getLvlType(b); }
//
// LoopEmitter delegates.

2
mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp
View File

@@ -288,7 +288,7 @@ void LoopEmitter::initialize(ValueRange ts, StringAttr loopTag, bool hasOutput,
if (stt.hasEncoding() && !(isOutputTensor(tid) && isSparseOut)) {
const auto enc = stt.getEncoding();
isSparseSlices[tid] = enc.isSlice();
for (auto lvlTp : enc.getDimLevelType())
for (auto lvlTp : enc.getLvlTypes())
lvlTypes[tid].push_back(lvlTp);
} else {
lvlTypes[tid].assign(lvlRank, DimLevelType::Dense);

2
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
View File

@@ -1159,7 +1159,7 @@ public:
// TODO: We should check these in ExtractSliceOp::verify.
if (!srcEnc || !dstEnc || !dstEnc.isSlice())
return failure();
assert(srcEnc.getDimLevelType() == dstEnc.getDimLevelType());
assert(srcEnc.getLvlTypes() == dstEnc.getLvlTypes());
assert(srcEnc.getDimOrdering() == dstEnc.getDimOrdering());
assert(srcEnc.getHigherOrdering() == dstEnc.getHigherOrdering());
assert(srcEnc.getPosWidth() == dstEnc.getPosWidth());

10
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
View File

@@ -205,7 +205,7 @@ static Value genLvlTypesBuffer(OpBuilder &builder, Location loc,
SparseTensorType stt) {
SmallVector<Value> lvlTypes;
lvlTypes.reserve(stt.getLvlRank());
for (const auto dlt : stt.getEncoding().getDimLevelType())
for (const auto dlt : stt.getEncoding().getLvlTypes())
lvlTypes.push_back(constantDimLevelTypeEncoding(builder, loc, dlt));
return allocaBuffer(builder, loc, lvlTypes);
}
@@ -565,7 +565,7 @@ static void genSparseCOOIterationLoop(
rewriter.setInsertionPointToStart(after);
const bool hasDenseDim =
llvm::any_of(stt.getEncoding().getDimLevelType(), isDenseDLT);
llvm::any_of(stt.getEncoding().getLvlTypes(), isDenseDLT);
if (hasDenseDim) {
Value elemV = rewriter.create<memref::LoadOp>(loc, elemPtr);
Value isZero = genIsNonzero(rewriter, loc, elemV);
@@ -880,11 +880,11 @@ public:
break;
case SparseToSparseConversionStrategy::kDirect:
useDirectConversion = true;
assert(canUseDirectConversion(dstEnc.getDimLevelType()) &&
assert(canUseDirectConversion(dstEnc.getLvlTypes()) &&
"Unsupported target for direct sparse-to-sparse conversion");
break;
case SparseToSparseConversionStrategy::kAuto:
useDirectConversion = canUseDirectConversion(dstEnc.getDimLevelType());
useDirectConversion = canUseDirectConversion(dstEnc.getLvlTypes());
break;
}
if (useDirectConversion) {
@@ -896,7 +896,7 @@ public:
// method calls can share most parameters, while still providing
// the correct sparsity information to either of them.
const auto mixedEnc = SparseTensorEncodingAttr::get(
op->getContext(), dstEnc.getDimLevelType(), dstEnc.getDimOrdering(),
op->getContext(), dstEnc.getLvlTypes(), dstEnc.getDimOrdering(),
dstEnc.getHigherOrdering(), srcEnc.getPosWidth(),
srcEnc.getCrdWidth());
// TODO: This is the only place where `kToCOO` (or `kToIterator`)

3
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp
View File

@@ -44,8 +44,7 @@ static bool isZeroValue(Value val) {
// Helper to detect a sparse tensor type operand.
static bool isSparseTensor(OpOperand *op) {
auto enc = getSparseTensorEncoding(op->get().getType());
return enc &&
llvm::is_contained(enc.getDimLevelType(), DimLevelType::Compressed);
return enc && llvm::is_contained(enc.getLvlTypes(), DimLevelType::Compressed);
}
// Helper method to find zero/uninitialized allocation.

2
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorStorageLayout.cpp
View File

@@ -134,7 +134,7 @@ void sparse_tensor::foreachFieldInSparseTensor(
if (!(callback(fidx, kind, dim, dlt))) \
return;
const auto lvlTypes = enc.getDimLevelType();
const auto lvlTypes = enc.getLvlTypes();
const Level lvlRank = enc.getLvlRank();
const Level cooStart = getCOOStart(enc);
const Level end = cooStart == lvlRank ? cooStart : cooStart + 1;

11
mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
View File

@@ -232,7 +232,7 @@ static bool findAffine(Merger &merger, TensorId tid, Level lvl, AffineExpr a,
switch (a.getKind()) {
case AffineExprKind::DimId: {
const LoopId idx = merger.makeLoopId(a.cast<AffineDimExpr>().getPosition());
if (!isUndefDLT(merger.getDimLevelType(tid, idx)))
if (!isUndefDLT(merger.getLvlType(tid, idx)))
return false; // used more than once
if (setLvlFormat)
@@ -243,7 +243,7 @@ static bool findAffine(Merger &merger, TensorId tid, Level lvl, AffineExpr a,
case AffineExprKind::Mul:
case AffineExprKind::Constant: {
if (!isDenseDLT(dlt) && setLvlFormat) {
assert(isUndefDLT(merger.getDimLevelType(tid, filterLdx)));
assert(isUndefDLT(merger.getLvlType(tid, filterLdx)));
// Use a filter loop for sparse affine expression.
merger.setLevelAndType(tid, filterLdx, lvl, dlt);
++filterLdx;
@@ -287,7 +287,7 @@ static bool findDepIdxSet(Merger &merger, TensorId tensor, Level lvl,
switch (a.getKind()) {
case AffineExprKind::DimId: {
const LoopId ldx = merger.makeLoopId(a.cast<AffineDimExpr>().getPosition());
if (!isUndefDLT(merger.getDimLevelType(tensor, ldx)))
if (!isUndefDLT(merger.getLvlType(tensor, ldx)))
return false; // used more than once, e.g., A[i][i]
// TODO: Generalizes the following two cases. A[i] (with trivial index
@@ -624,8 +624,7 @@ static void addFilterLoopBasedConstraints(CodegenEnv &env, OpOperand &t,
// Filter loops should be constructed after all the dependent loops,
// i.e., d0 + d1 < filter_loop(d0 + d1)
if (tldx && env.merger().isFilterLoop(*tldx)) {
assert(!ta.isa<AffineDimExpr>() &&
!isDenseDLT(enc.getDimLevelType()[lvl]));
assert(!ta.isa<AffineDimExpr>() && !isDenseDLT(enc.getLvlTypes()[lvl]));
addAffineOrderings(adjM, inDegree, ta, AffineExpr(), std::nullopt, tldx);
// Now that the ordering of affine expression is captured by filter
// loop idx, we only need to ensure the affine ordering against filter
@@ -1922,7 +1921,7 @@ private:
//
auto srcTp = getRankedTensorType(tval);
auto dstEnc = SparseTensorEncodingAttr::get(
getContext(), srcEnc.getDimLevelType(),
getContext(), srcEnc.getLvlTypes(),
permute(env, env.op().getMatchingIndexingMap(t)), // new order
srcEnc.getHigherOrdering(), srcEnc.getPosWidth(),
srcEnc.getCrdWidth());

6
mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp
View File

@@ -405,7 +405,7 @@ BitVector Merger::simplifyCond(LatSetId s0, LatPointId p0) {
// Starts resetting from a dense level, so that the first bit (if kept)
// is not undefined level-type.
for (unsigned b = 0; b < be; b++) {
if (simple[b] && isDenseDLT(getDimLevelType(TensorLoopId{b}))) {
if (simple[b] && isDenseDLT(getLvlType(TensorLoopId{b}))) {
offset = be - b - 1; // relative to the end
break;
}
@@ -417,7 +417,7 @@ BitVector Merger::simplifyCond(LatSetId s0, LatPointId p0) {
b = b == 0 ? be - 1 : b - 1, i++) {
// Slice on dense level has `locate` property as well, and can be optimized.
if (simple[b] && !isSparseLvlWithNonTrivialIdxExp(b)) {
const auto dlt = getDimLevelType(b);
const auto dlt = getLvlType(b);
if (!isCompressedDLT(dlt) && !isSingletonDLT(dlt) && !isCompressedWithHiDLT(dlt)) {
if (reset)
simple.reset(b);
@@ -584,7 +584,7 @@ bool Merger::isSingleCondition(TensorId t, ExprId e) const {
bool Merger::hasAnySparse(const BitVector &bits) const {
for (TensorLoopId b : bits.set_bits()) {
const auto dlt = getDimLevelType(b);
const auto dlt = getLvlType(b);
if (isCompressedDLT(dlt) || isSingletonDLT(dlt) || isCompressedWithHiDLT(dlt))
return true;
}

16
mlir/test/CAPI/sparse_tensor.c
View File

@@ -25,7 +25,7 @@ static int testRoundtripEncoding(MlirContext ctx) {
// clang-format off
const char *originalAsm =
"#sparse_tensor.encoding<{ "
"dimLevelType = [ \"dense\", \"compressed\", \"compressed\"], "
"lvlTypes = [ \"dense\", \"compressed\", \"compressed\"], "
"dimOrdering = affine_map<(d0, d1, d2) -> (d0, d1, d2)>, "
"higherOrdering = affine_map<(d0, d1)[s0] -> (s0, d0, d1)>, "
"posWidth = 32, crdWidth = 64 }>";
@@ -47,12 +47,11 @@ static int testRoundtripEncoding(MlirContext ctx) {
// CHECK: level_type: 8
// CHECK: level_type: 8
int lvlRank = mlirSparseTensorEncodingGetLvlRank(originalAttr);
enum MlirSparseTensorDimLevelType *levelTypes =
enum MlirSparseTensorDimLevelType *lvlTypes =
malloc(sizeof(enum MlirSparseTensorDimLevelType) * lvlRank);
for (int l = 0; l < lvlRank; ++l) {
levelTypes[l] =
mlirSparseTensorEncodingAttrGetDimLevelType(originalAttr, l);
fprintf(stderr, "level_type: %d\n", levelTypes[l]);
lvlTypes[l] = mlirSparseTensorEncodingAttrGetLvlType(originalAttr, l);
fprintf(stderr, "level_type: %d\n", lvlTypes[l]);
}
// CHECK: posWidth: 32
int posWidth = mlirSparseTensorEncodingAttrGetPosWidth(originalAttr);
@@ -61,14 +60,13 @@ static int testRoundtripEncoding(MlirContext ctx) {
int crdWidth = mlirSparseTensorEncodingAttrGetCrdWidth(originalAttr);
fprintf(stderr, "crdWidth: %d\n", crdWidth);
MlirAttribute newAttr =
mlirSparseTensorEncodingAttrGet(ctx, lvlRank, levelTypes, dimOrdering,
higherOrdering, posWidth, crdWidth);
MlirAttribute newAttr = mlirSparseTensorEncodingAttrGet(
ctx, lvlRank, lvlTypes, dimOrdering, higherOrdering, posWidth, crdWidth);
mlirAttributeDump(newAttr); // For debugging filecheck output.
// CHECK: equal: 1
fprintf(stderr, "equal: %d\n", mlirAttributeEqual(originalAttr, newAttr));
free(levelTypes);
free(lvlTypes);
return 0;
}

4
mlir/test/Dialect/Bufferization/invalid.mlir
View File

@@ -58,7 +58,7 @@ func.func @escape_attr_non_bufferizable(%m0: memref<?xf32>) {
// -----
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
func.func @sparse_alloc_direct_return() -> tensor<20x40xf32, #DCSR> {
// expected-error @+1{{sparse tensor allocation should not escape function}}
@@ -68,7 +68,7 @@ func.func @sparse_alloc_direct_return() -> tensor<20x40xf32, #DCSR> {
// -----
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
func.func private @foo(tensor<20x40xf32, #DCSR>) -> ()

2
mlir/test/Dialect/Bufferization/ops.mlir
View File

@@ -2,7 +2,7 @@
// RUN: mlir-opt %s --mlir-print-op-generic | mlir-opt | FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
// CHECK-LABEL: func @test_clone

2
mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir
View File

@@ -854,7 +854,7 @@ func.func @input_stays_same(%arg0 : memref<?x1x?xf32, strided<[?, 1, 1]>>, %arg1
iterator_types = ["parallel", "reduction"]
}
#CSR = #sparse_tensor.encoding<{ dimLevelType = ["dense", "compressed"] }>
#CSR = #sparse_tensor.encoding<{ lvlTypes = ["dense", "compressed"] }>
func.func @sparse_case(%arg0: tensor<8x8xf32, #CSR>, %arg1: tensor<8xf32>) -> tensor<8xf32> {
%0 = tensor.empty() : tensor<8xf32>

2
mlir/test/Dialect/SparseTensor/GPU/gpu_combi.mlir
View File

@@ -3,7 +3,7 @@
// RUN: --sparsification="parallelization-strategy=dense-outer-loop" \
// RUN: --sparse-gpu-codegen | FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
//
// CHECK-LABEL: gpu.module @sparse_kernels

2
mlir/test/Dialect/SparseTensor/GPU/gpu_matmul.mlir
View File

@@ -3,7 +3,7 @@
// RUN: --sparsification="parallelization-strategy=dense-outer-loop" \
// RUN: --sparse-gpu-codegen | FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
//
// Compute matrix matrix C = AB

2
mlir/test/Dialect/SparseTensor/GPU/gpu_matvec.mlir
View File

@@ -3,7 +3,7 @@
// RUN: --sparsification="parallelization-strategy=dense-outer-loop" \
// RUN: --sparse-gpu-codegen | FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
//
// Compute matrix vector y = Ax

32
mlir/test/Dialect/SparseTensor/codegen.mlir
View File

@@ -1,62 +1,62 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize -cse | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ],
lvlTypes = [ "compressed" ],
crdWidth = 64,
posWidth = 32
}>
#Dense2D = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ],
lvlTypes = [ "dense", "dense" ],
crdWidth = 64,
posWidth = 32
}>
#Row = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
crdWidth = 64,
posWidth = 32
}>
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
crdWidth = 64,
posWidth = 32
}>
#UCSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed-no" ]
lvlTypes = [ "dense", "compressed-no" ]
}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i, j) -> (j, i)>
}>
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
crdWidth = 64,
posWidth = 32
}>
#Dense3D = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "dense" ],
lvlTypes = [ "dense", "dense", "dense" ],
dimOrdering = affine_map<(i, j, k) -> (k, i, j)>
}>
#Coo = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton" ]
}>
#CooPNo = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton-no" ],
lvlTypes = [ "compressed-nu", "singleton-no" ],
dimOrdering = affine_map<(i, j) -> (j, i)>
}>
#ccoo = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed-nu", "singleton" ]
lvlTypes = [ "compressed", "compressed-nu", "singleton" ]
}>
// CHECK-LABEL: func @sparse_nop(
@@ -680,7 +680,7 @@ func.func @sparse_convert_element_type(%arg0: tensor<32xf32, #SparseVector>) ->
}
// CHECK-LABEL: func.func @sparse_new_coo(
// CHECK-SAME: %[[A0:.*]]: !llvm.ptr<i8>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>) {
// CHECK-SAME: %[[A0:.*]]: !llvm.ptr<i8>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ lvlTypes = [ "compressed", "singleton" ] }>>) {
// CHECK-DAG: %[[A1:.*]] = arith.constant false
// CHECK-DAG: %[[A2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[A3:.*]] = arith.constant 0 : index
@@ -697,7 +697,7 @@ func.func @sparse_convert_element_type(%arg0: tensor<32xf32, #SparseVector>) ->
// CHECK: %[[A13:.*]] = memref.cast %[[A12]] : memref<2xindex> to memref<?xindex>
// CHECK: %[[A14:.*]] = memref.alloc(%[[A11]]) : memref<?xindex>
// CHECK: %[[A15:.*]] = memref.alloc(%[[A10]]) : memref<?xf32>
// CHECK: %[[A16:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>
// CHECK: %[[A16:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ lvlTypes = [ "compressed", "singleton" ] }>>
// CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.set %[[A16]] lvl_sz at 0 with %[[A8]]
// CHECK: %[[A19:.*]] = sparse_tensor.storage_specifier.get %[[A18]] pos_mem_sz at 0
// CHECK: %[[A21:.*]], %[[A22:.*]] = sparse_tensor.push_back %[[A19]], %[[A13]], %[[A3]]
@@ -725,7 +725,7 @@ func.func @sparse_new_coo(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #Coo> {
}
// CHECK-LABEL: func.func @sparse_new_coo_permute_no(
// CHECK-SAME: %[[A0:.*]]: !llvm.ptr<i8>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>) {
// CHECK-SAME: %[[A0:.*]]: !llvm.ptr<i8>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ lvlTypes = [ "compressed", "singleton" ] }>>) {
// CHECK-DAG: %[[A1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[A2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[A3:.*]] = arith.constant 2 : index
@@ -741,7 +741,7 @@ func.func @sparse_new_coo(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #Coo> {
// CHECK: %[[A12:.*]] = memref.cast %[[A11]] : memref<2xindex> to memref<?xindex>
// CHECK: %[[A13:.*]] = memref.alloc(%[[A10]]) : memref<?xindex>
// CHECK: %[[A14:.*]] = memref.alloc(%[[A9]]) : memref<?xf32>
// CHECK: %[[A15:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>
// CHECK: %[[A15:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ lvlTypes = [ "compressed", "singleton" ] }>>
// CHECK: %[[A17:.*]] = sparse_tensor.storage_specifier.set %[[A15]] lvl_sz at 0 with %[[A8]]
// CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.get %[[A17]] pos_mem_sz at 0
// CHECK: %[[A20:.*]], %[[A21:.*]] = sparse_tensor.push_back %[[A18]], %[[A12]], %[[A2]]

2
mlir/test/Dialect/SparseTensor/codegen_buffer_initialization.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --sparse-tensor-codegen=enable-buffer-initialization=true --canonicalize --cse | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
// CHECK-LABEL: func.func @sparse_alloc_sparse_vector(
// CHECK-SAME: %[[VAL_0:.*]]: index) -> (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier

4
mlir/test/Dialect/SparseTensor/codegen_sparse_alloc.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = ["dense", "compressed"]}>
#COO = #sparse_tensor.encoding<{ dimLevelType = ["compressed-nu", "singleton"]}>
#CSR = #sparse_tensor.encoding<{ lvlTypes = ["dense", "compressed"]}>
#COO = #sparse_tensor.encoding<{ lvlTypes = ["compressed-nu", "singleton"]}>
// CHECK-LABEL: func.func @sparse_alloc_copy_CSR(
// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,

4
mlir/test/Dialect/SparseTensor/codegen_sparse_dealloc.mlir
View File

@@ -6,9 +6,9 @@
// RUN: --sparse-tensor-codegen=create-sparse-deallocs=true \
// RUN: --canonicalize --cse | FileCheck %s -check-prefix=CHECK-DEALLOC
#CSR = #sparse_tensor.encoding<{ dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{ lvlTypes = ["dense", "compressed"]}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"],
lvlTypes = ["dense", "compressed"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

2
mlir/test/Dialect/SparseTensor/codegen_to_llvm.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --sparse-storage-specifier-to-llvm | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = ["compressed"] }>
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = ["compressed"] }>
// CHECK-LABEL: func @sparse_nop(
// CHECK-SAME: %[[A0:.*0]]: memref<?xindex>,

2
mlir/test/Dialect/SparseTensor/constant_index_map.mlir
View File

@@ -5,7 +5,7 @@
#map1 = affine_map<(d0) -> (0, d0)>
#map2 = affine_map<(d0) -> (d0)>
#SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SpVec = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
// CHECK-LABEL: func.func @main(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<1x77xi1>,

12
mlir/test/Dialect/SparseTensor/conversion.mlir
View File

@@ -1,32 +1,32 @@
// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#SparseVector64 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 64,
crdWidth = 64
}>
#SparseVector32 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 32,
crdWidth = 32
}>
#CSR = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"],
lvlTypes = ["dense", "compressed"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed", "compressed"],
lvlTypes = ["dense", "compressed", "compressed"],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>

12
mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
View File

@@ -3,20 +3,20 @@
// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#CSR = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i, j) -> (j, i)>
}>
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed", "compressed"],
lvlTypes = ["dense", "compressed", "compressed"],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>
@@ -113,7 +113,7 @@ func.func @sparse_convert_complex(%arg0: tensor<100xcomplex<f64>>) -> tensor<100
// CHECK: return %[[T]] : !llvm.ptr<i8>
// CHECK-RWT-LABEL: func.func @sparse_convert_2d(
// CHECK-RWT-SAME: %[[T0:.*]]: tensor<2x4xf64>) -> tensor<2x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> {
// CHECK-RWT-SAME: %[[T0:.*]]: tensor<2x4xf64>) -> tensor<2x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> {
// CHECK-RWT: %[[T1:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[T2:.*]] = sparse_tensor.foreach in %[[T0]] init(%[[T1]])
// CHECK-RWT: ^bb0(%[[L0I0:.*]]: index, %[[L0I1:.*]]: index, %[[L0V:.*]]: f64, %[[L0T:.*]]: tensor
@@ -164,7 +164,7 @@ func.func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #CSR> {
// CHECK: call @delSparseTensorCOOF32(%[[C]])
// CHECK: return %[[T]] : !llvm.ptr<i8>
// CHECK-RWT-LABEL: func.func @sparse_constant() -> tensor<8x7xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> {
// CHECK-RWT-LABEL: func.func @sparse_constant() -> tensor<8x7xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> {
// CHECK-RWT: %[[F0:.*]] = arith.constant sparse<{{\[\[}}0, 0], [1, 6]], [1.000000e+00, 5.000000e+00]> : tensor<8x7xf32>
// CHECK-RWT: %[[T0:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[T1:.*]] = sparse_tensor.foreach in %[[F0]] init(%[[T0]])

10
mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
View File

@@ -4,15 +4,15 @@
// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed", "compressed"],
lvlTypes = ["dense", "compressed", "compressed"],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>
@@ -145,7 +145,7 @@ func.func @sparse_convert_1d_dyn(%arg0: tensor<?xi32, #SparseVector>) -> tensor<
// CHECK: return %[[T]] : tensor<2x4xf64>
// CHECK-RWT-LABEL: func.func @sparse_convert_2d(
// CHECK-RWT-SAME: %[[A:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<2x4xf64> {
// CHECK-RWT-SAME: %[[A:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<2x4xf64> {
// CHECK-RWT: %[[F0:.*]] = arith.constant 0.000000e+00 : f64
// CHECK-RWT: %[[B:.*]] = memref.alloc() : memref<2x4xf64>
// CHECK-RWT: linalg.fill ins(%[[F0]] : f64) outs(%[[B]]
@@ -301,7 +301,7 @@ func.func @sparse_convert_2d_dyn1(%arg0: tensor<2x?xf64, #SparseMatrix>) -> tens
// CHECK: return %[[T]] : tensor<?x?xf64>
// CHECK-RWT-LABEL: func.func @sparse_convert_2d_dyn2(
// CHECK-RWT-SAME: %[[A:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<?x?xf64> {
// CHECK-RWT-SAME: %[[A:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<?x?xf64> {
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f64

18
mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
View File

@@ -10,37 +10,37 @@
// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector64 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 64,
crdWidth = 64
}>
#SparseVector32 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 32,
crdWidth = 32
}>
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#SortedCOO2D = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ],
lvlTypes = [ "compressed-nu", "singleton" ],
}>
#SortedCOO3D = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton-nu", "singleton" ]
}>
#TsssPermuted = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed", "compressed" ],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>
#COOSlice = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ],
lvlTypes = [ "compressed-nu", "singleton" ],
slice = [ (2, 2, 1), (12, 13, 1) ]
}>
@@ -115,13 +115,13 @@ func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32
}
#SparseSingleton64 = #sparse_tensor.encoding<{
dimLevelType = ["singleton"],
lvlTypes = ["singleton"],
posWidth = 64,
crdWidth = 64
}>
#SparseSingleton32 = #sparse_tensor.encoding<{
dimLevelType = ["singleton"],
lvlTypes = ["singleton"],
posWidth = 32,
crdWidth = 32
}>

4
mlir/test/Dialect/SparseTensor/convert_sparse2sparse_element.mlir
View File

@@ -1,13 +1,13 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s
#SparseVector64 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 64,
crdWidth = 64
}>
#SparseVector32 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
posWidth = 32,
crdWidth = 32
}>

2
mlir/test/Dialect/SparseTensor/dense.mlir
View File

@@ -7,7 +7,7 @@
// latter class is linearized into one-dimensional buffers that are backed
// by the runtime support library.
#DenseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>
#DenseMatrix = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>
#trait_2d = {
indexing_maps = [

2
mlir/test/Dialect/SparseTensor/fold.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --canonicalize --cse | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_nop_dense2dense_convert(
// CHECK-SAME: %[[A:.*]]: tensor<64xf32>)

90
mlir/test/Dialect/SparseTensor/invalid.mlir
View File

@@ -8,7 +8,7 @@ func.func @invalid_new_dense(%arg0: !llvm.ptr<i8>) -> tensor<32xf32> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @non_static_pack_ret(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
-> tensor<?xf64, #SparseVector> {
@@ -20,7 +20,7 @@ func.func @non_static_pack_ret(%values: tensor<6xf64>, %coordinates: tensor<6x1x
// -----
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"], crdWidth=32}>
#DenseVector = #sparse_tensor.encoding<{lvlTypes = ["dense"], crdWidth=32}>
func.func @invalid_pack_dense(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
-> tensor<100xf64, #DenseVector> {
@@ -32,7 +32,7 @@ func.func @invalid_pack_dense(%values: tensor<6xf64>, %coordinates: tensor<6x1xi
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_pack_data(%values: tensor<6x1xf64>, %coordinates: tensor<6x1xi32>)
-> tensor<100xf64, #SparseVector> {
@@ -44,7 +44,7 @@ func.func @invalid_pack_data(%values: tensor<6x1xf64>, %coordinates: tensor<6x1x
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
-> tensor<100xf32, #SparseVector> {
@@ -56,7 +56,7 @@ func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x1xi3
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_pack_type(%values: tensor<5xf64>, %coordinates: tensor<6x1xi32>)
-> tensor<100xf64, #SparseVector> {
@@ -68,7 +68,7 @@ func.func @invalid_pack_type(%values: tensor<5xf64>, %coordinates: tensor<6x1xi3
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x2xi32>)
-> tensor<100xf64, #SparseVector> {
@@ -80,7 +80,7 @@ func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x2xi3
// -----
#BCOO = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed-hi"], crdWidth=32}>
#BCOO = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed-hi"], crdWidth=32}>
func.func @invalid_pack_batched(%values: tensor<2x6xf64>, %coordinates: tensor<3x6x1xi32>)
-> tensor<2x100xf64, #BCOO> {
@@ -92,7 +92,7 @@ func.func @invalid_pack_batched(%values: tensor<2x6xf64>, %coordinates: tensor<3
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
-> (tensor<6xf64>, tensor<6x1xi32>, i32) {
@@ -104,7 +104,7 @@ func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
-> (tensor<5xf32>, tensor<6x1xi32>, i32) {
@@ -116,7 +116,7 @@ func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
-> (tensor<6xf32>, tensor<6x2xi32>, i32) {
@@ -128,7 +128,7 @@ func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
// -----
#BCOO = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed-hi"], crdWidth=32}>
#BCOO = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed-hi"], crdWidth=32}>
func.func @invalid_unpack_type(%sp: tensor<2x100xf32, #BCOO>)
-> (tensor<2x6xf32>, tensor<3x6x2xi32>, i32) {
@@ -156,7 +156,7 @@ func.func @invalid_positions_unranked(%arg0: tensor<*xf64>) -> memref<?xindex> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], posWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], posWidth=32}>
func.func @mismatch_positions_types(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
// expected-error@+1 {{unexpected type for positions}}
@@ -166,7 +166,7 @@ func.func @mismatch_positions_types(%arg0: tensor<128xf64, #SparseVector>) -> me
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @positions_oob(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
// expected-error@+1 {{requested level is out of bounds}}
@@ -192,7 +192,7 @@ func.func @invalid_indices_unranked(%arg0: tensor<*xf64>) -> memref<?xindex> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @mismatch_indices_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<?xi32> {
// expected-error@+1 {{unexpected type for coordinates}}
@@ -202,7 +202,7 @@ func.func @mismatch_indices_types(%arg0: tensor<?xf64, #SparseVector>) -> memref
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @indices_oob(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
// expected-error@+1 {{requested level is out of bounds}}
@@ -220,7 +220,7 @@ func.func @invalid_values_dense(%arg0: tensor<1024xf32>) -> memref<?xf32> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @indices_buffer_noncoo(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
// expected-error@+1 {{expected sparse tensor with a COO region}}
@@ -238,7 +238,7 @@ func.func @indices_buffer_dense(%arg0: tensor<1024xf32>) -> memref<?xindex> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @mismatch_values_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<?xf32> {
// expected-error@+1 {{unexpected mismatch in element types}}
@@ -249,7 +249,7 @@ func.func @mismatch_values_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
@@ -262,7 +262,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
@@ -274,7 +274,7 @@ func.func @sparse_slice_stride(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
// expected-error@+1 {{redundant level argument for querying value memory size}}
@@ -285,7 +285,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> i64 {
// expected-error@+1 {{requested slice data on non-slice tensor}}
@@ -296,7 +296,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
// expected-error@+1 {{missing level argument}}
@@ -307,7 +307,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
// expected-error@+1 {{requested level is out of bounds}}
@@ -318,7 +318,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#COO = #sparse_tensor.encoding<{dimLevelType = ["compressed-nu", "singleton"]}>
#COO = #sparse_tensor.encoding<{lvlTypes = ["compressed-nu", "singleton"]}>
func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#COO>) -> index {
// expected-error@+1 {{requested position memory size on a singleton level}}
@@ -345,7 +345,7 @@ func.func @sparse_unannotated_insert(%arg0: tensor<128xf64>, %arg1: index, %arg2
// -----
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @sparse_wrong_arity_insert(%arg0: tensor<128x64xf64, #CSR>, %arg1: index, %arg2: f64) {
// expected-error@+1 {{'sparse_tensor.insert' op incorrect number of coordinates}}
@@ -395,7 +395,7 @@ func.func @sparse_unannotated_compression(%arg0: memref<?xf64>,
// -----
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @sparse_wrong_arity_compression(%arg0: memref<?xf64>,
%arg1: memref<?xi1>,
@@ -419,7 +419,7 @@ func.func @sparse_convert_unranked(%arg0: tensor<*xf32>) -> tensor<10xf32> {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_convert_rank_mismatch(%arg0: tensor<10x10xf64, #DCSR>) -> tensor<?xf64> {
// expected-error@+1 {{unexpected conversion mismatch in rank}}
@@ -429,7 +429,7 @@ func.func @sparse_convert_rank_mismatch(%arg0: tensor<10x10xf64, #DCSR>) -> tens
// -----
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @sparse_convert_dim_mismatch(%arg0: tensor<10x?xf32>) -> tensor<10x10xf32, #CSR> {
// expected-error@+1 {{unexpected conversion mismatch in dimension 1}}
@@ -448,7 +448,7 @@ func.func @invalid_out_dense(%arg0: tensor<10xf64>, %arg1: !llvm.ptr<i8>) {
// -----
#CSR = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"],
lvlTypes = ["dense", "compressed"],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
@@ -680,7 +680,7 @@ func.func @invalid_select_wrong_yield(%arg0: f64) -> f64 {
// -----
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @invalid_concat_less_inputs(%arg: tensor<9x4xf64, #DC>) -> tensor<9x4xf64, #DC> {
// expected-error@+1 {{Need at least two tensors to concatenate.}}
%0 = sparse_tensor.concatenate %arg {dimension = 1 : index}
@@ -690,7 +690,7 @@ func.func @invalid_concat_less_inputs(%arg: tensor<9x4xf64, #DC>) -> tensor<9x4x
// -----
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @invalid_concat_dim(%arg0: tensor<2x4xf64, #DC>,
%arg1: tensor<3x4xf64, #DC>,
%arg2: tensor<4x4xf64, #DC>) -> tensor<9x4xf64, #DC> {
@@ -704,9 +704,9 @@ func.func @invalid_concat_dim(%arg0: tensor<2x4xf64, #DC>,
// -----
#C = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DCC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed", "compressed"]}>
#C = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#DCC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed", "compressed"]}>
func.func @invalid_concat_rank_mismatch(%arg0: tensor<2xf64, #C>,
%arg1: tensor<3x4xf64, #DC>,
%arg2: tensor<4x4x4xf64, #DCC>) -> tensor<9x4xf64, #DC> {
@@ -720,7 +720,7 @@ func.func @invalid_concat_rank_mismatch(%arg0: tensor<2xf64, #C>,
// -----
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @invalid_concat_size_mismatch_dyn(%arg0: tensor<?x4xf64, #DC>,
%arg1: tensor<5x4xf64, #DC>,
%arg2: tensor<4x4xf64, #DC>) -> tensor<9x4xf64, #DC> {
@@ -734,7 +734,7 @@ func.func @invalid_concat_size_mismatch_dyn(%arg0: tensor<?x4xf64, #DC>,
// -----
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @invalid_concat_size_mismatch(%arg0: tensor<3x4xf64, #DC>,
%arg1: tensor<5x4xf64, #DC>,
%arg2: tensor<4x4xf64, #DC>) -> tensor<9x4xf64, #DC> {
@@ -748,7 +748,7 @@ func.func @invalid_concat_size_mismatch(%arg0: tensor<3x4xf64, #DC>,
// -----
#DC = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#DC = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @invalid_concat_size_mismatch(%arg0: tensor<2x4xf64, #DC>,
%arg1: tensor<3x3xf64, #DC>,
%arg2: tensor<4x4xf64, #DC>) -> tensor<9x4xf64, #DC> {
@@ -762,7 +762,7 @@ func.func @invalid_concat_size_mismatch(%arg0: tensor<2x4xf64, #DC>,
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// expected-error@+1 {{Unmatched number of arguments in the block}}
sparse_tensor.foreach in %arg0 : tensor<2x4xf64, #DCSR> do {
@@ -773,7 +773,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// expected-error@+1 {{Expecting Index type for argument at index 1}}
sparse_tensor.foreach in %arg0 : tensor<2x4xf64, #DCSR> do {
@@ -784,7 +784,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// expected-error@+1 {{Unmatched element type between input tensor and block argument}}
sparse_tensor.foreach in %arg0 : tensor<2x4xf64, #DCSR> do {
@@ -795,7 +795,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// expected-error@+1 {{Unmatched element type between input tensor and block argument}}
sparse_tensor.foreach in %arg0 : tensor<2x4xf64, #DCSR> do {
@@ -806,7 +806,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>, %arg1: f32) -> () {
// expected-error@+1 {{Mismatch in number of init arguments and results}}
sparse_tensor.foreach in %arg0 init(%arg1) : tensor<2x4xf64, #DCSR>, f32 do {
@@ -817,7 +817,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>, %arg1: f32) -> (
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>, %arg1: f32) -> () {
// expected-error@+1 {{Mismatch in types of init arguments and results}}
%1 = sparse_tensor.foreach in %arg0 init(%arg1) : tensor<2x4xf64, #DCSR>, f32 -> i32 do {
@@ -828,7 +828,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>, %arg1: f32) -> (
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>, %arg1: f32) -> () {
// expected-error@+1 {{Mismatch in types of yield values and results}}
%1 = sparse_tensor.foreach in %arg0 init(%arg1) : tensor<2x4xf64, #DCSR>, f32 -> f32 do {
@@ -892,7 +892,7 @@ func.func @sparse_sort_coo_y_too_small(%arg0: memref<60xindex>, %arg1: memref<10
// -----
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func @sparse_alloc_escapes(%arg0: index) -> tensor<10x?xf64, #CSR> {
// expected-error@+1 {{sparse tensor allocation should not escape function}}

16
mlir/test/Dialect/SparseTensor/invalid_encoding.mlir
View File

@@ -1,27 +1,27 @@
// RUN: mlir-opt %s -split-input-file -verify-diagnostics
// expected-error@+1 {{expected a non-empty array for level types}}
#a = #sparse_tensor.encoding<{dimLevelType = []}>
#a = #sparse_tensor.encoding<{lvlTypes = []}>
func.func private @scalar(%arg0: tensor<f64, #a>) -> ()
// -----
#a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#a = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
func.func private @tensor_dimlevel_size_mismatch(%arg0: tensor<8xi32, #a>) -> () // expected-error {{expected an array of size 1 for dimension level types}}
// -----
#a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"], dimOrdering = affine_map<(i) -> (i)>}> // expected-error {{unexpected mismatch in ordering and dimension level types size}}
#a = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"], dimOrdering = affine_map<(i) -> (i)>}> // expected-error {{unexpected mismatch in ordering and dimension level types size}}
func.func private @tensor_sizes_mismatch(%arg0: tensor<8xi32, #a>) -> ()
// -----
#a = #sparse_tensor.encoding<{dimLevelType = [1]}> // expected-error {{expected a string value in dimension level types}}
#a = #sparse_tensor.encoding<{lvlTypes = [1]}> // expected-error {{expected a string value in dimension level types}}
func.func private @tensor_type_mismatch(%arg0: tensor<8xi32, #a>) -> ()
// -----
#a = #sparse_tensor.encoding<{dimLevelType = ["strange"]}> // expected-error {{unexpected dimension level type: strange}}
#a = #sparse_tensor.encoding<{lvlTypes = ["strange"]}> // expected-error {{unexpected dimension level type: strange}}
func.func private @tensor_value_mismatch(%arg0: tensor<8xi32, #a>) -> ()
// -----
@@ -37,7 +37,7 @@ func.func private @tensor_highorder_mismatch(%arg0: tensor<8xi32, #a>) -> ()
// -----
// expected-error@+1 {{expected a permutation affine map for dimension ordering}}
#a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"], dimOrdering = affine_map<(i,j) -> (i,i)>}>
#a = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"], dimOrdering = affine_map<(i,j) -> (i,i)>}>
func.func private @tensor_no_permutation(%arg0: tensor<16x32xf32, #a>) -> ()
// -----
@@ -67,13 +67,13 @@ func.func private @tensor_invalid_key(%arg0: tensor<16x32xf32, #a>) -> ()
// -----
#a = #sparse_tensor.encoding<{dimLevelType = [ "compressed", "compressed", "dense", "dense" ], dimOrdering = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>, higherOrdering = affine_map<(i, j) -> (j, i)>}> // expected-error {{unexpected higher ordering mapping from 2 to 2}}
#a = #sparse_tensor.encoding<{lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimOrdering = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>, higherOrdering = affine_map<(i, j) -> (j, i)>}> // expected-error {{unexpected higher ordering mapping from 2 to 2}}
func.func private @tensor_invalid_key(%arg0: tensor<10x60xf32, #a>) -> ()
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (-1, ?, 1), (?, 4, 2) ] // expected-error{{expect positive value or ? for slice offset/size/stride}}
}>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)

4
mlir/test/Dialect/SparseTensor/one_shot_bufferize_tensor_copy_insertion.mlir
View File

@@ -2,7 +2,7 @@
// RUN: mlir-opt %s -test-tensor-copy-insertion="bufferize-function-boundaries allow-return-allocs" | FileCheck %s --check-prefix=CHECK-FUNC
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
@@ -41,7 +41,7 @@ func.func @sparse_tensor_convert() -> tensor<20x40xf32> {
return %2 : tensor<20x40xf32>
}
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait = {
indexing_maps = [

10
mlir/test/Dialect/SparseTensor/one_trip.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparsification -cse | FileCheck %s
#Dense = #sparse_tensor.encoding<{
dimLevelType = [ "dense" , "dense" ]
lvlTypes = [ "dense" , "dense" ]
}>
#trait_scale = {
@@ -13,15 +13,15 @@
}
// CHECK-LABEL: func.func @sparse_scale(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>)
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>)
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f32
// CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK: %[[VAL_4:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
// CHECK: %[[VAL_5:.*]] = arith.mulf %[[VAL_4]], %[[VAL_2]] : f32
// CHECK: memref.store %[[VAL_5]], %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
// CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>
// CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>
func.func @sparse_scale(%argx: tensor<1x1xf32, #Dense>) -> tensor<1x1xf32, #Dense> {
%c = arith.constant 2.0 : f32
%0 = linalg.generic #trait_scale

4
mlir/test/Dialect/SparseTensor/post_rewriting.mlir
View File

@@ -1,11 +1,11 @@
// RUN: mlir-opt %s -post-sparsification-rewrite | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
lvlTypes = ["compressed", "compressed"]
}>
// CHECK-LABEL: func.func @expand_dense(

6
mlir/test/Dialect/SparseTensor/pre_rewriting.mlir
View File

@@ -1,15 +1,15 @@
// RUN: mlir-opt %s -pre-sparsification-rewrite | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#SortedCOO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton" ]
}>
#Slice = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ],
lvlTypes = [ "compressed-nu", "singleton" ],
slice = [ (?, 1, 1), (?, 3, 1) ]
}>

4
mlir/test/Dialect/SparseTensor/rejected.mlir
View File

@@ -3,7 +3,7 @@
// The file contains examples that will be rejected by sparse compiler
// (we expect the linalg.generic unchanged).
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -15,7 +15,7 @@
// CHECK-LABEL: func.func @sparse_reduction_subi(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK: %[[VAL_2:.*]] = linalg.generic
// CHECK: ^bb0(%[[VAL_3:.*]]: i32, %[[VAL_4:.*]]: i32):
// CHECK: %[[VAL_5:.*]] = arith.subi %[[VAL_3]], %[[VAL_4]] : i32

20
mlir/test/Dialect/SparseTensor/rewriting_for_codegen.mlir
View File

@@ -2,21 +2,21 @@
// RUN: FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i, j) -> (j, i)>
}>
#COO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton" ]
}>
// CHECK-LABEL: func.func @sparse_new(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[COO]]
// CHECK: bufferization.dealloc_tensor %[[COO]]
// CHECK: return %[[R]]
@@ -26,8 +26,8 @@ func.func @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #CSR> {
}
// CHECK-LABEL: func.func @sparse_new_csc(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[COO]]
// CHECK: bufferization.dealloc_tensor %[[COO]]
// CHECK: return %[[R]]
@@ -37,8 +37,8 @@ func.func @sparse_new_csc(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #CSC> {
}
// CHECK-LABEL: func.func @sparse_new_coo(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> {
// CHECK: %[[COO:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>
// CHECK: return %[[COO]]
func.func @sparse_new_coo(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #COO> {
%0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<?x?xf32, #COO>
@@ -46,7 +46,7 @@ func.func @sparse_new_coo(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #COO> {
}
// CHECK-LABEL: func.func @sparse_out(
// CHECK-SAME: %[[A:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[A:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[B:.*]]: !llvm.ptr<i8>) {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index

72
mlir/test/Dialect/SparseTensor/roundtrip.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s -split-input-file | mlir-opt -split-input-file | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_new(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
@@ -13,7 +13,7 @@ func.func @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<128xf64, #SparseVector> {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
// CHECK-LABEL: func @sparse_pack(
// CHECK-SAME: %[[D:.*]]: tensor<6xf64>,
@@ -29,7 +29,7 @@ func.func @sparse_pack(%data: tensor<6xf64>, %index: tensor<6x1xi32>)
// -----
#BCOO = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed-hi"], crdWidth=32}>
#BCOO = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed-hi"], crdWidth=32}>
// CHECK-LABEL: func @sparse_pack_batched(
// CHECK-SAME: %[[D:.*]]: tensor<2x6xf64>,
// CHECK-SAME: %[[I:.*]]: tensor<2x6x1xi32>)
@@ -44,7 +44,7 @@ func.func @sparse_pack_batched(%values: tensor<2x6xf64>, %coordinates: tensor<2x
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"], crdWidth=32}>
// CHECK-LABEL: func @sparse_unpack(
// CHECK-SAME: %[[T:.*]]: tensor<100xf64, #
@@ -59,7 +59,7 @@ func.func @sparse_unpack(%sp : tensor<100xf64, #SparseVector>)
// -----
#BatchedSparseVector = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed-hi"], crdWidth=32}>
#BatchedSparseVector = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed-hi"], crdWidth=32}>
// CHECK-LABEL: func @sparse_unpack(
// CHECK-SAME: %[[T:.*]]: tensor<2x100xf64, #
@@ -74,7 +74,7 @@ func.func @sparse_unpack(%sp : tensor<2x100xf64, #BatchedSparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_dealloc(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>
@@ -87,7 +87,7 @@ func.func @sparse_dealloc(%arg0: tensor<128xf64, #SparseVector>) {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_convert_1d_to_sparse(
// CHECK-SAME: %[[A:.*]]: tensor<64xf32>)
@@ -100,7 +100,7 @@ func.func @sparse_convert_1d_to_sparse(%arg0: tensor<64xf32>) -> tensor<64xf32,
// -----
#SparseTensor = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>
#SparseTensor = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>
// CHECK-LABEL: func @sparse_convert_3d_from_sparse(
// CHECK-SAME: %[[A:.*]]: tensor<8x8x8xf64, #{{.*}}>)
@@ -113,7 +113,7 @@ func.func @sparse_convert_3d_from_sparse(%arg0: tensor<8x8x8xf64, #SparseTensor>
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_positions(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
@@ -126,7 +126,7 @@ func.func @sparse_positions(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xi
// -----
#COO = #sparse_tensor.encoding<{dimLevelType = ["compressed-nu", "singleton"]}>
#COO = #sparse_tensor.encoding<{lvlTypes = ["compressed-nu", "singleton"]}>
// CHECK-LABEL: func @sparse_indices_buffer(
// CHECK-SAME: %[[A:.*]]: tensor<?x?xf64, #{{.*}}>)
@@ -139,7 +139,7 @@ func.func @sparse_indices_buffer(%arg0: tensor<?x?xf64, #COO>) -> memref<?xindex
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_indices(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
@@ -152,7 +152,7 @@ func.func @sparse_indices(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xind
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_values(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
@@ -166,7 +166,7 @@ func.func @sparse_values(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xf64>
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
@@ -182,7 +182,7 @@ func.func @sparse_slice_offset(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
@@ -197,7 +197,7 @@ func.func @sparse_slice_stride(%arg0: tensor<2x8xf64, #CSR_SLICE>) -> index {
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_metadata_init(
// CHECK: %[[T:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#{{.*}}>
@@ -209,9 +209,9 @@ func.func @sparse_metadata_init() -> !sparse_tensor.storage_specifier<#SparseVec
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#SparseVector_Slice = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
slice = [ (?, ?, ?) ]
}>
@@ -228,7 +228,7 @@ func.func @sparse_metadata_init(%src : !sparse_tensor.storage_specifier<#SparseV
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_get_md(
// CHECK-SAME: %[[A:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>
@@ -243,7 +243,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector_Slice = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
slice = [ (?, ?, ?) ]
}>
@@ -260,7 +260,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector_S
// -----
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
lvlTypes = ["compressed"],
slice = [ (?, ?, ?) ]
}>
@@ -277,7 +277,7 @@ func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>)
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_set_md(
// CHECK-SAME: %[[A:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>,
@@ -293,7 +293,7 @@ func.func @sparse_set_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>,
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_noe(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
@@ -306,7 +306,7 @@ func.func @sparse_noe(%arg0: tensor<128xf64, #SparseVector>) -> index {
// -----
#DenseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","dense"]}>
#DenseMatrix = #sparse_tensor.encoding<{lvlTypes = ["dense","dense"]}>
// CHECK-LABEL: func @sparse_load(
// CHECK-SAME: %[[A:.*]]: tensor<16x32xf64, #{{.*}}>)
@@ -319,7 +319,7 @@ func.func @sparse_load(%arg0: tensor<16x32xf64, #DenseMatrix>) -> tensor<16x32xf
// -----
#DenseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","dense"]}>
#DenseMatrix = #sparse_tensor.encoding<{lvlTypes = ["dense","dense"]}>
// CHECK-LABEL: func @sparse_load_ins(
// CHECK-SAME: %[[A:.*]]: tensor<16x32xf64, #{{.*}}>)
@@ -332,7 +332,7 @@ func.func @sparse_load_ins(%arg0: tensor<16x32xf64, #DenseMatrix>) -> tensor<16x
// -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
// CHECK-LABEL: func @sparse_insert(
// CHECK-SAME: %[[A:.*]]: tensor<128xf64, #sparse_tensor.encoding<{{.*}}>>,
@@ -387,7 +387,7 @@ func.func @sparse_push_back_n(%arg0: index, %arg1: memref<?xf64>, %arg2: f64, %a
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_expansion(
// CHECK-SAME: %[[A:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>>)
@@ -401,7 +401,7 @@ func.func @sparse_expansion(%tensor: tensor<8x8xf64, #SparseMatrix>) -> index {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_compression(
// CHECK-SAME: %[[A0:.*0]]: memref<?xf64>,
@@ -425,7 +425,7 @@ func.func @sparse_compression(%values: memref<?xf64>,
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_out(
// CHECK-SAME: %[[A:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{{.*}}>>,
@@ -439,7 +439,7 @@ func.func @sparse_out(%arg0: tensor<?x?xf64, #SparseMatrix>, %arg1: !llvm.ptr<i8
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_binary(
// CHECK-SAME: %[[A:.*]]: f64, %[[B:.*]]: i64) -> f64 {
@@ -473,7 +473,7 @@ func.func @sparse_binary(%arg0: f64, %arg1: i64) -> f64 {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_unary(
// CHECK-SAME: %[[A:.*]]: f64) -> f64 {
@@ -503,7 +503,7 @@ func.func @sparse_unary(%arg0: f64) -> f64 {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_unary(
// CHECK-SAME: %[[A:.*]]: f64) -> i64 {
@@ -530,7 +530,7 @@ func.func @sparse_unary(%arg0: f64) -> i64 {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_reduce_2d_to_1d(
// CHECK-SAME: %[[A:.*]]: f64, %[[B:.*]]: f64) -> f64 {
@@ -552,7 +552,7 @@ func.func @sparse_reduce_2d_to_1d(%arg0: f64, %arg1: f64) -> f64 {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_select(
// CHECK-SAME: %[[A:.*]]: f64) -> f64 {
@@ -576,7 +576,7 @@ func.func @sparse_select(%arg0: f64) -> f64 {
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @concat_sparse_sparse(
// CHECK-SAME: %[[A0:.*]]: tensor<2x4xf64
@@ -600,7 +600,7 @@ func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #SparseMatrix>,
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_tensor_foreach(
// CHECK-SAME: %[[A0:.*]]: tensor<2x4xf64
@@ -615,7 +615,7 @@ func.func @sparse_tensor_foreach(%arg0: tensor<2x4xf64, #DCSR>) -> () {
// -----
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_tensor_foreach(
// CHECK-SAME: %[[A0:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{{{.*}}}>>,

48
mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir
View File

@@ -1,132 +1,132 @@
// RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
// CHECK-LABEL: func private @sparse_1d_tensor(
// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>)
func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = ["compressed"] }>>)
// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>)
func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = ["compressed"] }>>)
// -----
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>,
posWidth = 64,
crdWidth = 64
}>
// CHECK-LABEL: func private @sparse_csr(
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
// -----
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>,
posWidth = 0,
crdWidth = 0
}>
// CHECK-LABEL: func private @sparse_csc(
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>)
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>)
func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
// -----
#DCSC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>,
posWidth = 0,
crdWidth = 64
}>
// CHECK-LABEL: func private @sparse_dcsc(
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
// -----
#COO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu-no", "singleton-no" ]
lvlTypes = [ "compressed-nu-no", "singleton-no" ]
}>
// CHECK-LABEL: func private @sparse_coo(
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu-no", "singleton-no" ] }>>)
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu-no", "singleton-no" ] }>>)
func.func private @sparse_coo(tensor<?x?xf32, #COO>)
// -----
#BCOO = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed-hi-nu", "singleton" ]
lvlTypes = [ "dense", "compressed-hi-nu", "singleton" ]
}>
// CHECK-LABEL: func private @sparse_bcoo(
// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed-hi-nu", "singleton" ] }>>)
// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed-hi-nu", "singleton" ] }>>)
func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
// -----
#SortedCOO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton" ]
}>
// CHECK-LABEL: func private @sparse_sorted_coo(
// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>)
// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>)
func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
// -----
#BCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "dense", "dense" ],
lvlTypes = [ "compressed", "compressed", "dense", "dense" ],
dimOrdering = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>,
higherOrdering = affine_map<(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)>
}>
// CHECK-LABEL: func private @sparse_bcsr(
// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense", "dense" ], higherOrdering = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], higherOrdering = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
// -----
#ELL = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "compressed" ],
lvlTypes = [ "dense", "dense", "compressed" ],
dimOrdering = affine_map<(ii, i, j) -> (ii, i, j)>,
higherOrdering = affine_map<(i,j)[c] -> (c*4*i, i, j)>
}>
// CHECK-LABEL: func private @sparse_ell(
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], higherOrdering = affine_map<(d0, d1)[s0] -> (d0 * (s0 * 4), d0, d1)> }>>
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ], higherOrdering = affine_map<(d0, d1)[s0] -> (d0 * (s0 * 4), d0, d1)> }>>
func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, 4, 1), (1, 4, 2) ]
}>
// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (1, ?, 1), (?, 4, 2) ]
}>
// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], slice = [ (1, ?, 1), (?, 4, 2) ] }>>
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, ?, 1), (?, 4, 2) ] }>>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)

2
mlir/test/Dialect/SparseTensor/scf_1_N_conversion.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s -sparse-tensor-codegen -cse | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
// CHECK-LABEL: func.func @for(
// CHECK-SAME: %[[VAL_1:.*0]]: memref<?xindex>,

44
mlir/test/Dialect/SparseTensor/sorted_coo.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s
#SortedCOO = #sparse_tensor.encoding<{
dimLevelType = [ "compressed-nu", "singleton" ]
lvlTypes = [ "compressed-nu", "singleton" ]
}>
#trait_scale = {
@@ -37,14 +37,14 @@
//
// CHECK-LABEL: func.func @sparse_scale(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = scf.while (%[[VAL_11:.*]] = %[[VAL_8]]) : (index) -> index {
@@ -75,8 +75,8 @@
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: scf.yield %[[VAL_28:.*]] : index
// CHECK: } attributes {"Emitted from" = "linalg.generic"}
// CHECK: %[[VAL_29:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
// CHECK: return %[[VAL_29]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>
// CHECK: %[[VAL_29:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>
// CHECK: return %[[VAL_29]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>
// CHECK: }
func.func @sparse_scale(%argx: tensor<?x?xf32, #SortedCOO>) -> tensor<?x?xf32, #SortedCOO> {
%c = arith.constant 2.0 : f32
@@ -90,16 +90,16 @@ func.func @sparse_scale(%argx: tensor<?x?xf32, #SortedCOO>) -> tensor<?x?xf32, #
}
// CHECK-LABEL: func.func @matvec(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
@@ -155,21 +155,21 @@ func.func @matvec(%arga: tensor<32x64xf64, #SortedCOO>,
}
// CHECK-LABEL: func.func @mateltmul(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f64
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
// CHECK: linalg.fill ins(%[[VAL_4]] : f64) outs(%[[VAL_15]] : memref<32x64xf64>)
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>

222
mlir/test/Dialect/SparseTensor/sparse_1d.mlir
View File

@@ -1,8 +1,8 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#DV = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait1 = {
indexing_maps = [
@@ -14,13 +14,13 @@
}
// CHECK-LABEL: func @add_d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_8]] : memref<32xf32>)
// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
@@ -43,14 +43,14 @@ func.func @add_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>)
}
// CHECK-LABEL: func @add_d_init(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32) -> tensor<32xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_INITTENSOR:.*]] = tensor.empty() : tensor<32xf32>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_INITTENSOR]] : memref<32xf32>
// CHECK: linalg.fill ins(%[[VAL_3]] : f32) outs(%[[VAL_7]] : memref<32xf32>)
// CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
@@ -74,13 +74,13 @@ func.func @add_d_init(%arga: tensor<32xf32, #DV>, %argb: f32) -> tensor<32xf32>
}
// CHECK-LABEL: func @mul_d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_8]] : memref<32xf32>)
// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
@@ -103,16 +103,16 @@ func.func @mul_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>)
}
// CHECK-LABEL: func @add_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]]
@@ -158,13 +158,13 @@ func.func @add_s(%arga: tensor<32xf32, #SV>, %argb: f32, %argx: tensor<32xf32>)
}
// CHECK-LABEL: func @repeated_add_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]]
// CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -197,14 +197,14 @@ func.func @repeated_add_s(%arga: tensor<32xf32, #SV>, %argx: tensor<32xf32>) ->
}
// CHECK-LABEL: func @mul_s(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: f32,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_9]] : memref<32xf32>)
// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -240,13 +240,13 @@ func.func @mul_s(%arga: tensor<32xf32, #SV>, %argb: f32, %argx: tensor<32xf32>)
}
// CHECK-LABEL: func @add_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_9]] : memref<32xf32>)
@@ -271,13 +271,13 @@ func.func @add_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tens
}
// CHECK-LABEL: func @mul_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_9]] : memref<32xf32>)
@@ -303,16 +303,16 @@ func.func @mul_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tens
// CHECK-LABEL: func @add_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -362,14 +362,14 @@ func.func @add_ds(%arga: tensor<32xf32>, %argb: tensor<32xf32, #SV>, %argx: tens
// CHECK-LABEL: func @mul_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32xf32>)
// CHECK-DAG: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -396,16 +396,16 @@ func.func @mul_ds(%arga: tensor<32xf32>, %argb: tensor<32xf32, #SV>, %argx: tens
}
// CHECK-LABEL: func @add_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
@@ -455,14 +455,14 @@ func.func @add_sd(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32>, %argx: tens
}
// CHECK-LABEL: func @mul_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32xf32>)
@@ -490,17 +490,17 @@ func.func @mul_sd(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32>, %argx: tens
}
// CHECK-LABEL: func @add_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -573,17 +573,17 @@ func.func @add_ss(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32, #SV>, %argx:
}
// CHECK-LABEL: func @mul_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -634,18 +634,18 @@ func.func @mul_ss(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32, #SV>, %argx:
}
// CHECK-LABEL: func @two_way_inv(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: f32,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<16xf32>)
// CHECK-DAG: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -727,18 +727,18 @@ func.func @two_way_inv(%arga: tensor<16xf32, #SV>, %argb: tensor<16xf32, #SV>, %
}
// CHECK-LABEL: func @two_way_inv_alt(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: f32,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<16xf32>)
// CHECK-DAG: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -828,12 +828,12 @@ func.func @two_way_inv_alt(%arga: tensor<16xf32, #SV>,
}
// CHECK-LABEL: func @sum_reduction(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-DAG: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -869,17 +869,17 @@ func.func @sum_reduction(%arga: tensor<?xf32, #SV>, %argx: tensor<f32>) -> tenso
}
// CHECK-LABEL: func @sum_reduction_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
// CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_11]][] : memref<f32>
// CHECK-DAG: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -975,19 +975,19 @@ func.func @sum_reduction_ss(%arga: tensor<16xf32, #SV>,
}
// CHECK-LABEL: func @sum_reduction_inv(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<f32>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f32>
// CHECK-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_13]][] : memref<f32>
// CHECK-DAG: %[[VAL_16:.*]] = memref.load %[[VAL_9]][] : memref<f32>
@@ -1091,21 +1091,21 @@ func.func @sum_reduction_inv(%arga: tensor<16xf32, #SV>,
// CHECK-LABEL: func @four_tensors_op(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_4:.*]]: tensor<?xf64>) -> tensor<?xf64> {
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?xf64>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?xf64>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_16:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor<?xf64>
// CHECK-DAG: %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_4]]
// CHECK-DAG: linalg.fill ins(%{{.*}} : f64) outs(%[[VAL_18]] : memref<?xf64>)
@@ -1268,21 +1268,21 @@ func.func @four_tensors_op(%arga: tensor<?xf64>,
}
// CHECK-LABEL: func @red3s(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<f64>) -> tensor<f64> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f64>
// CHECK-DAG: %[[VAL_17:.*]] = memref.load %[[VAL_15]][] : memref<f64>
// CHECK-DAG: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>

216
mlir/test/Dialect/SparseTensor/sparse_2d.mlir
View File

@@ -1,10 +1,10 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#Tdd = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>
#Tds = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#Tsd = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>
#Tss = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#Tdd = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>
#Tds = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
#Tsd = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>
#Tss = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
#trait2 = {
indexing_maps = [
@@ -17,14 +17,14 @@
}
// CHECK-LABEL: func @add_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32x16xf32>)
@@ -53,14 +53,14 @@ func.func @add_dd(%arga: tensor<32x16xf32, #Tdd>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @mul_dd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32x16xf32>)
@@ -89,7 +89,7 @@ func.func @mul_dd(%arga: tensor<32x16xf32, #Tdd>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @add_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
@@ -97,9 +97,9 @@ func.func @mul_dd(%arga: tensor<32x16xf32, #Tdd>, %argb: tensor<32x16xf32>, %arg
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
@@ -152,15 +152,15 @@ func.func @add_ds(%arga: tensor<32x16xf32, #Tds>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @mul_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_11]] : memref<32x16xf32>)
@@ -191,7 +191,7 @@ func.func @mul_ds(%arga: tensor<32x16xf32, #Tds>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @add_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
@@ -199,9 +199,9 @@ func.func @mul_ds(%arga: tensor<32x16xf32, #Tds>, %argb: tensor<32x16xf32>, %arg
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
@@ -259,15 +259,15 @@ func.func @add_sd(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @mul_sd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_11]] : memref<32x16xf32>)
@@ -299,7 +299,7 @@ func.func @mul_sd(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @add_ss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
@@ -307,11 +307,11 @@ func.func @mul_sd(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32>, %arg
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_15]] : memref<32x16xf32>)
@@ -393,16 +393,16 @@ func.func @add_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @mul_ss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32x16xf32>)
@@ -436,21 +436,21 @@ func.func @mul_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32>, %arg
}
// CHECK-LABEL: func @add_ss_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_16]] : memref<32x16xf32>)
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -600,7 +600,7 @@ func.func @add_ss_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32, #T
}
#BatchedVector = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed-hi" ],
lvlTypes = [ "dense", "compressed-hi" ],
}>
// CHECK-LABEL: func.func @sub_ss_batched(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<2x3xf64, #{{.*}}>>,
@@ -704,21 +704,21 @@ func.func @sub_ss_batched(%0: tensor<2x3xf64, #BatchedVector>, %1: tensor<2x3xf6
}
// CHECK-LABEL: func @mul_ss_ss(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_16]] : memref<32x16xf32>)
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -800,20 +800,20 @@ func.func @mul_ss_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32, #T
}
// CHECK-LABEL: func @add_sd_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_15]] : memref<32x16xf32>)
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
@@ -906,18 +906,18 @@ func.func @add_sd_ds(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32, #T
}
// CHECK-LABEL: func @mul_sd_ds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
// CHECK: linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -962,15 +962,15 @@ func.func @mul_sd_ds(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32, #T
}
// CHECK-LABEL: func @matvec(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
@@ -1013,13 +1013,13 @@ func.func @matvec(%argA: tensor<16x32xf32, #Tds>, %argb: tensor<32xf32>, %argx:
}
// CHECK-LABEL: func @sum_reduction(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (f32) {
@@ -1058,14 +1058,14 @@ func.func @sum_reduction(%arga: tensor<10x20xf32, #Tds>, %argx: tensor<f32>) ->
}
// CHECK-LABEL: func @scale(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf64>) -> tensor<?x?xf64> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf64>
// CHECK: linalg.fill ins(%{{.*}} : f64) outs(%[[VAL_11]] : memref<?x?xf64>)
@@ -1107,17 +1107,17 @@ func.func @scale(%arga: tensor<?x?xf64, #Tds>, %argx: tensor<?x?xf64>) -> tensor
}
// CHECK-LABEL: func.func @sampled_dense_dense(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
@@ -1176,26 +1176,26 @@ func.func @sampled_dense_dense(%args: tensor<?x?xf32, #Tss>,
}
// CHECK-LABEL: func @sum_kernel_with_inv(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?xf32>,
// CHECK-SAME: %[[VAL_4:.*4]]: tensor<f32>,
// CHECK-SAME: %[[VAL_5:.*5]]: tensor<?xf32>) -> tensor<?xf32> {
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?xf32>
// CHECK-DAG: %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref<f32>
// CHECK-DAG: %[[VAL_22:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32,

190
mlir/test/Dialect/SparseTensor/sparse_3d.mlir
View File

@@ -1,16 +1,16 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#Td = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#Td = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#Tddd = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ] }>
#Tdds = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>
#Tdsd = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>
#Tdss = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>
#Tsdd = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>
#Tsds = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>
#Tssd = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>
#Tsss = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>
#Tddd = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense" ] }>
#Tdds = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>
#Tdsd = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>
#Tdss = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>
#Tsdd = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>
#Tsds = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>
#Tssd = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>
#Tsss = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>
#trait3 = {
indexing_maps = [
@@ -23,7 +23,7 @@
}
// CHECK-LABEL: func @add_ddd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -32,7 +32,7 @@
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_11]] : memref<32x16x8xf32>)
@@ -65,7 +65,7 @@ func.func @add_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_ddd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -74,7 +74,7 @@ func.func @add_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_11]] : memref<32x16x8xf32>)
@@ -107,7 +107,7 @@ func.func @mul_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_dds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -117,9 +117,9 @@ func.func @mul_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_15]] : memref<32x16x8xf32>)
@@ -176,7 +176,7 @@ func.func @add_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_dds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -184,9 +184,9 @@ func.func @add_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_13]] : memref<32x16x8xf32>)
@@ -221,7 +221,7 @@ func.func @mul_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_dsd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -231,9 +231,9 @@ func.func @mul_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
@@ -294,7 +294,7 @@ func.func @add_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_dsd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -302,9 +302,9 @@ func.func @add_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_12]] : memref<32x16x8xf32>)
@@ -339,7 +339,7 @@ func.func @mul_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_dss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -349,11 +349,11 @@ func.func @mul_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_17]] : memref<32x16x8xf32>)
@@ -438,18 +438,18 @@ func.func @add_dss(%arga: tensor<32x16x8xf32, #Tdss>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_dss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
@@ -486,7 +486,7 @@ func.func @mul_dss(%arga: tensor<32x16x8xf32, #Tdss>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_sdd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -496,9 +496,9 @@ func.func @mul_dss(%arga: tensor<32x16x8xf32, #Tdss>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
@@ -564,7 +564,7 @@ func.func @add_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_sdd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -572,9 +572,9 @@ func.func @add_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_12]] : memref<32x16x8xf32>)
@@ -610,7 +610,7 @@ func.func @mul_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_sds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -620,11 +620,11 @@ func.func @mul_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_17]] : memref<32x16x8xf32>)
@@ -714,18 +714,18 @@ func.func @add_sds(%arga: tensor<32x16x8xf32, #Tsds>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_sds(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
@@ -763,7 +763,7 @@ func.func @mul_sds(%arga: tensor<32x16x8xf32, #Tsds>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_ssd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -773,11 +773,11 @@ func.func @mul_sds(%arga: tensor<32x16x8xf32, #Tsds>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_16]] : memref<32x16x8xf32>)
@@ -871,18 +871,18 @@ func.func @add_ssd(%arga: tensor<32x16x8xf32, #Tssd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_ssd(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_13]] : memref<32x16x8xf32>)
@@ -920,7 +920,7 @@ func.func @mul_ssd(%arga: tensor<32x16x8xf32, #Tssd>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @add_sss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
@@ -930,13 +930,13 @@ func.func @mul_ssd(%arga: tensor<32x16x8xf32, #Tssd>, %argb: tensor<32x16x8xf32>
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_19]] : memref<32x16x8xf32>)
@@ -1054,19 +1054,19 @@ func.func @add_sss(%arga: tensor<32x16x8xf32, #Tsss>, %argb: tensor<32x16x8xf32>
}
// CHECK-LABEL: func @mul_sss(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_15]] : memref<32x16x8xf32>)
@@ -1118,14 +1118,14 @@ func.func @mul_sss(%arga: tensor<32x16x8xf32, #Tsss>, %argb: tensor<32x16x8xf32>
// CHECK-LABEL: func @kernel_3d(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = tensor.dim %[[VAL_1]], %[[VAL_6]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32>
@@ -1294,7 +1294,7 @@ func.func @sum_reduction_inv(%arga: tensor<?x?x?xf32>,
}
// CHECK-LABEL: func @invariants(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<30xf32>,
// CHECK-SAME: %[[VAL_3:.*]]: tensor<10x20x30xf32>) -> tensor<10x20x30xf32> {
@@ -1304,7 +1304,7 @@ func.func @sum_reduction_inv(%arga: tensor<?x?x?xf32>,
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20xf32>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<30xf32>
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref<10x20x30xf32>

8
mlir/test/Dialect/SparseTensor/sparse_affine.mlir
View File

@@ -1,10 +1,10 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#Row = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>
#EncDenseVec = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#SpVec = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>
#Row = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "dense" ] }>
#EncDenseVec = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#trait1 = {
indexing_maps = [

4
mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s --sparsification --canonicalize --cse | FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#SparseTensor = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
#SparseTensor = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>
#trait = {
indexing_maps = [

6
mlir/test/Dialect/SparseTensor/sparse_concat.mlir
View File

@@ -1,14 +1,14 @@
// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#SparseMatrix_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#SparseMatrix_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ],
lvlTypes = [ "dense", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

12
mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
View File

@@ -1,10 +1,10 @@
// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-convert=false" \
// RUN: | FileCheck %s
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DENSE = #sparse_tensor.encoding<{dimLevelType = ["dense", "dense"]}>
#DCSR = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#DENSE = #sparse_tensor.encoding<{lvlTypes = ["dense", "dense"]}>
#DENSE_P = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
lvlTypes = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
// CHECK-LABEL: @concat_sparse_sparse(
@@ -270,7 +270,7 @@ func.func @concat_sparse_sparse_dense(%arg0: tensor<2x4xf64, #DCSR>,
// CHECK-DAG: %[[TMP_c9:.*]] = arith.constant 9 : index
// CHECK-DAG: %[[TMP_c4:.*]] = arith.constant 4 : index
// CHECK: %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
// CHECK: %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf64>
// CHECK: %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>> to memref<?xf64>
// CHECK: %[[DIM_0:.*]] = memref.alloca() : memref<2xindex>
// CHECK: memref.store %[[TMP_c9]], %[[DIM_0]][%[[TMP_c0]]] : memref<2xindex>
// CHECK: memref.store %[[TMP_c4]], %[[DIM_0]][%[[TMP_c1]]] : memref<2xindex>
@@ -332,7 +332,7 @@ func.func @concat_sparse_sparse_dense(%arg0: tensor<2x4xf64, #DCSR>,
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ] }>>
func.func @concat_sparse_sparse_annotated_dense(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)
@@ -417,7 +417,7 @@ func.func @concat_sparse_sparse_annotated_dense(%arg0: tensor<2x4xf64, #DCSR>,
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
func.func @concat_sparse_sparse_annotated_dense_permute(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)

2
mlir/test/Dialect/SparseTensor/sparse_conv_2d_slice_based.mlir
View File

@@ -4,7 +4,7 @@
#map1 = affine_map<(d0, d1, d2, d3) -> (d2, d3)>
#map2 = affine_map<(d0, d1, d2, d3) -> (d0, d1)>
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
// CHECK-LABEL: func.func @conv2d_all_sparse_CSR(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32, #{{.*}}>,

8
mlir/test/Dialect/SparseTensor/sparse_expand.mlir
View File

@@ -8,21 +8,21 @@
// RUN: FileCheck %s --check-prefix=CHECK-CONVERT
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ]
lvlTypes = [ "dense", "compressed" ]
}>
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#DCSC = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#SV = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ]
lvlTypes = [ "compressed" ]
}>
#rowsum = {

6
mlir/test/Dialect/SparseTensor/sparse_extract_slice.mlir
View File

@@ -1,11 +1,11 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --cse | FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ]
lvlTypes = [ "dense", "compressed" ]
}>
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
slice = [ (0, 4, 1), (0, 8, 1) ]
}>
@@ -13,7 +13,7 @@
// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_1:.*1]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_2:.*2]]: memref<?xf64>,
// CHECK-SAME: %[[VAL_3:.*3]]: !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>)
// CHECK-SAME: %[[VAL_3:.*3]]: !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>)
// CHECK: %[[VAL_4:.*]] = sparse_tensor.storage_specifier.init with %[[VAL_3]]
// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 4 : index

2
mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --linalg-generalize-named-ops --pre-sparsification-rewrite --sparsification --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
// CHECK-LABEL: func.func @fill_zero_after_alloc(
// CHECK-SAME: %[[Arg0:.*]]: !llvm.ptr<i8>,

6
mlir/test/Dialect/SparseTensor/sparse_foreach.mlir
View File

@@ -29,12 +29,12 @@ func.func @sparse_foreach_constant() -> () {
}
#CSR_SLICE = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
slice = [ (0, 4, 1), (2, 4, 1) ]
}>
#CSR_SLICE_DYN = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
slice = [ (?, ?, ?), (?, ?, ?) ]
}>
@@ -141,7 +141,7 @@ func.func @foreach_print_slice(%A: tensor<4x4xf64, #CSR_SLICE>) {
}
#BCOO = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed-hi-nu", "singleton" ],
lvlTypes = [ "dense", "compressed-hi-nu", "singleton" ],
}>
// CHECK-LABEL: func.func @foreach_bcoo(

34
mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait1 = {
indexing_maps = [
@@ -351,13 +351,13 @@ func.func @divbyc(%arga: tensor<32xf64, #SV>,
}
// CHECK-LABEL: func.func @zero_preserving_math(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[T:.*]] = scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] {{.*}} {
@@ -371,11 +371,11 @@ func.func @divbyc(%arga: tensor<32xf64, #SV>,
// CHECK: %[[VAL_17:.*]] = math.log1p %[[VAL_16]] : f64
// CHECK: %[[VAL_18:.*]] = math.sin %[[VAL_17]] : f64
// CHECK: %[[VAL_19:.*]] = math.tanh %[[VAL_18]] : f64
// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_19]] into %{{.*}}[%[[VAL_10]]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_19]] into %{{.*}}[%[[VAL_10]]] : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: scf.yield %[[Y]]
// CHECK: }
// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: return %[[VAL_20]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: return %[[VAL_20]] : tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: }
func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #SV> {
%c32 = arith.constant 32 : index
@@ -398,25 +398,25 @@ func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #S
}
// CHECK-LABEL: func.func @complex_divbyc(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_3:.*]] = complex.constant [0.000000e+00, 1.000000e+00] : complex<f64>
// CHECK: %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xcomplex<f64>>
// CHECK: %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xcomplex<f64>>
// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[T:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_2]] {{.*}} {
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xindex>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<?xcomplex<f64>>
// CHECK: %[[VAL_13:.*]] = complex.div %[[VAL_12]], %[[VAL_3]] : complex<f64>
// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_13]] into %{{.*}}[%[[VAL_11]]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[VAL_13]] into %{{.*}}[%[[VAL_11]]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: scf.yield %[[Y]]
// CHECK: }
// CHECK: %[[VAL_14:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: return %[[VAL_14]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: return %[[VAL_14]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
// CHECK: }
func.func @complex_divbyc(%arg0: tensor<32xcomplex<f64>, #SV>) -> tensor<32xcomplex<f64>, #SV> {
%c = complex.constant [0.0, 1.0] : complex<f64>

4
mlir/test/Dialect/SparseTensor/sparse_index.mlir
View File

@@ -1,11 +1,11 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"]
lvlTypes = ["dense", "dense"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
lvlTypes = ["compressed", "compressed"]
}>
#trait = {

2
mlir/test/Dialect/SparseTensor/sparse_int_ops.mlir
View File

@@ -1,7 +1,7 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait2 = {
indexing_maps = [

110
mlir/test/Dialect/SparseTensor/sparse_kernels.mlir
View File

@@ -2,22 +2,22 @@
// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
// RUN: --sparsification | FileCheck %s
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
// CHECK-LABEL: func.func @matmul1(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -53,7 +53,7 @@ func.func @matmul1(%a: tensor<10x20xf32, #DCSR>,
// CHECK-LABEL: func.func @matmul_sparse_rhs(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
@@ -102,40 +102,40 @@ func.func @matmul_sparse_rhs(%a: tensor<10x20xf32>,
// Computes C = A x B with all matrices sparse (SpMSpM) in DCSR.
//
// CHECK-LABEL: func.func @matmul2(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] iter_args(%[[VAL_21:.*]] = %[[VAL_6]]) -> (tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] iter_args(%[[VAL_21:.*]] = %[[VAL_6]]) -> (tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_20]], %[[VAL_3]] : index
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_28]]] : memref<?xindex>
// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_32:.*]]:4 = scf.while (%[[VAL_33:.*]] = %[[VAL_27]], %[[VAL_34:.*]] = %[[VAL_30]], %[[VAL_35:.*]] = %[[VAL_26]], %[[VAL_36:.*]] = %[[VAL_21]]) : (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_32:.*]]:4 = scf.while (%[[VAL_33:.*]] = %[[VAL_27]], %[[VAL_34:.*]] = %[[VAL_30]], %[[VAL_35:.*]] = %[[VAL_26]], %[[VAL_36:.*]] = %[[VAL_21]]) : (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> (index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_37:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_29]] : index
// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_31]] : index
// CHECK: %[[VAL_39:.*]] = arith.andi %[[VAL_37]], %[[VAL_38]] : i1
// CHECK: scf.condition(%[[VAL_39]]) %[[VAL_33]], %[[VAL_34]], %[[VAL_35]], %[[VAL_36]] : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.condition(%[[VAL_39]]) %[[VAL_33]], %[[VAL_34]], %[[VAL_35]], %[[VAL_36]] : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index, %[[VAL_42:.*]]: index, %[[VAL_43:.*]]: tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>):
// CHECK: ^bb0(%[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index, %[[VAL_42:.*]]: index, %[[VAL_43:.*]]: tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>):
// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_40]]] : memref<?xindex>
// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_41]]] : memref<?xindex>
// CHECK: %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index
@@ -143,7 +143,7 @@ func.func @matmul_sparse_rhs(%a: tensor<10x20xf32>,
// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
// CHECK: %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
// CHECK: %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1
// CHECK: %[[VAL_51:.*]]:2 = scf.if %[[VAL_50]] -> (index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_51:.*]]:2 = scf.if %[[VAL_50]] -> (index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_40]]] : memref<?xf64>
// CHECK: %[[VAL_53:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_41]]] : memref<?xindex>
// CHECK: %[[VAL_54:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index
@@ -167,9 +167,9 @@ func.func @matmul_sparse_rhs(%a: tensor<10x20xf32>,
// CHECK: memref.store %[[VAL_63]], %[[VAL_23]]{{\[}}%[[VAL_59]]] : memref<?xf64>
// CHECK: scf.yield %[[VAL_68:.*]] : index
// CHECK: }
// CHECK: scf.yield %[[VAL_69:.*]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_69:.*]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_42]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_42]], %[[VAL_43]] : index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_70:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
// CHECK: %[[VAL_71:.*]] = arith.addi %[[VAL_40]], %[[VAL_3]] : index
@@ -177,13 +177,13 @@ func.func @matmul_sparse_rhs(%a: tensor<10x20xf32>,
// CHECK: %[[VAL_73:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
// CHECK: %[[VAL_74:.*]] = arith.addi %[[VAL_41]], %[[VAL_3]] : index
// CHECK: %[[VAL_75:.*]] = arith.select %[[VAL_73]], %[[VAL_74]], %[[VAL_41]] : index
// CHECK: scf.yield %[[VAL_72]], %[[VAL_75]], %[[VAL_76:.*]]#0, %[[VAL_76]]#1 : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_72]], %[[VAL_75]], %[[VAL_76:.*]]#0, %[[VAL_76]]#1 : index, index, index, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_77:.*]] = sparse_tensor.compress %[[VAL_23]], %[[VAL_24]], %[[VAL_25]], %[[VAL_78:.*]]#2 into %[[VAL_78]]#3{{\[}}%[[VAL_22]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_77]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_77:.*]] = sparse_tensor.compress %[[VAL_23]], %[[VAL_24]], %[[VAL_25]], %[[VAL_78:.*]]#2 into %[[VAL_78]]#3{{\[}}%[[VAL_22]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_77]] : tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_79:.*]] = sparse_tensor.load %[[VAL_80:.*]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_79]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_79:.*]] = sparse_tensor.load %[[VAL_80:.*]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_79]] : tensor<4x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @matmul2(%A: tensor<4x8xf64, #DCSR>,
%B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {
@@ -197,17 +197,17 @@ func.func @matmul2(%A: tensor<4x8xf64, #DCSR>,
// CHECK-LABEL: func.func @conv2d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 6 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32>
// CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -247,18 +247,18 @@ func.func @conv2d(%input: tensor<8x8xi32>,
// CHECK-LABEL: func.func @quantized_matmul(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 5 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : i64
// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi8>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xi8>
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64>
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -297,17 +297,17 @@ func.func @quantized_matmul(%input1: tensor<5x3xi8>,
}
// CHECK-LABEL: func.func @sparse_dot(
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_11]][] : memref<f32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>

2
mlir/test/Dialect/SparseTensor/sparse_lower.mlir
View File

@@ -8,7 +8,7 @@
// RUN: --tensor-bufferize --finalizing-bufferize | \
// RUN: FileCheck %s --check-prefix=CHECK-LIR
#CSR = #sparse_tensor.encoding<{dimLevelType = [ "dense", "compressed" ]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = [ "dense", "compressed" ]}>
#trait_matvec = {
indexing_maps = [

10
mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir
View File

@@ -9,7 +9,7 @@
// RUN: FileCheck %s --check-prefix=CHECK-LIR
#CSC = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
@@ -24,15 +24,15 @@
}
// CHECK-HIR-LABEL: func @matvec(
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>,
// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>,
// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
// CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
// CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index
// CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
// CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
// CHECK-HIR-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
// CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {

2
mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir
View File

@@ -8,7 +8,7 @@
// RUN: --tensor-bufferize --finalizing-bufferize | \
// RUN: FileCheck %s --check-prefix=CHECK-LIR
#CSR = #sparse_tensor.encoding<{dimLevelType = [ "dense", "compressed" ]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = [ "dense", "compressed" ]}>
#trait_matvec = {
indexing_maps = [

2
mlir/test/Dialect/SparseTensor/sparse_matmul_codegen.mlir
View File

@@ -5,7 +5,7 @@
// RUN: --canonicalize --cse | FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>

14
mlir/test/Dialect/SparseTensor/sparse_nd.mlir
View File

@@ -5,7 +5,7 @@
// but an acyclic iteration graph using sparse constraints only.
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "dense", "compressed",
lvlTypes = [ "dense", "dense", "dense", "compressed",
"compressed", "dense", "dense", "dense" ]
}>
@@ -22,7 +22,7 @@
// CHECK-LABEL: func @mul(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> {
// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 10 : index
@@ -34,11 +34,11 @@
// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_12:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32>
// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] {

202
mlir/test/Dialect/SparseTensor/sparse_out.mlir
View File

@@ -1,17 +1,17 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#SparseTensor = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed", "compressed" ]
lvlTypes = [ "compressed", "compressed", "compressed" ]
}>
#trait_scale_inpl = {
@@ -23,13 +23,13 @@
}
// CHECK-LABEL: func.func @sparse_simply_dynamic1(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] {
@@ -42,8 +42,8 @@
// CHECK: memref.store %[[VAL_15]], %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xf32>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
%c = arith.constant 2.0 : f32
@@ -57,12 +57,12 @@ func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x
}
// CHECK-LABEL: func.func @sparse_simply_dynamic2(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_6:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] {
@@ -76,8 +76,8 @@ func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x
// CHECK: memref.store %[[VAL_15]], %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_simply_dynamic2(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
%0 = linalg.generic #trait_scale_inpl
@@ -99,30 +99,30 @@ func.func @sparse_simply_dynamic2(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x
}
// CHECK-LABEL: func.func @sparse_truly_dynamic(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 10 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32
// CHECK-DAG: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_5]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK-DAG: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_5]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xindex>
// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_10]], %[[VAL_3]] : index
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xf32>
// CHECK: %[[VAL_20:.*]] = arith.mulf %[[VAL_19]], %[[VAL_4]] : f32
// CHECK: %[[VAL_21:.*]] = sparse_tensor.insert %[[VAL_20]] into %[[VAL_17]]{{\[}}%[[VAL_10]], %[[VAL_18]]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_21]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_21:.*]] = sparse_tensor.insert %[[VAL_20]] into %[[VAL_17]]{{\[}}%[[VAL_10]], %[[VAL_18]]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_21]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: scf.yield %[[VAL_22:.*]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_22:.*]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_24:.*]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_23]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_24:.*]] hasInserts : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_23]] : tensor<10x20xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20xf32, #DCSR> {
%s = arith.constant 2.0 : f32
@@ -148,41 +148,41 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
}
// CHECK-LABEL: func.func @sumred(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>) -> tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>) -> tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[VAL_FALSE:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_TRUE:.*]] = arith.constant true
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>
// CHECK: %[[VAL_7:.*]] = bufferization.alloc_tensor(%[[VAL_5]], %[[VAL_6]]) : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_19:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_20:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_21:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>>
// CHECK: %[[VAL_7:.*]] = bufferization.alloc_tensor(%[[VAL_5]], %[[VAL_6]]) : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_19:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_20:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_21:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]], %[[VAL_29:.*]] = %[[VAL_7]]) : (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]], %[[VAL_29:.*]] = %[[VAL_7]]) : (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_23]] : index
// CHECK: %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index
// CHECK: %[[VAL_32:.*]] = arith.andi %[[VAL_30]], %[[VAL_31]] : i1
// CHECK: scf.condition(%[[VAL_32]]) %[[VAL_27]], %[[VAL_28]], %[[VAL_29]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.condition(%[[VAL_32]]) %[[VAL_27]], %[[VAL_28]], %[[VAL_29]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_33:.*]]: index, %[[VAL_34:.*]]: index, %[[VAL_35:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>):
// CHECK: ^bb0(%[[VAL_33:.*]]: index, %[[VAL_34:.*]]: index, %[[VAL_35:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>):
// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_33]]] : memref<?xindex>
// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref<?xindex>
// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_36]] : index
@@ -190,20 +190,20 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index
// CHECK: %[[VAL_41:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index
// CHECK: %[[VAL_42:.*]] = arith.andi %[[VAL_40]], %[[VAL_41]] : i1
// CHECK: %[[VAL_43:.*]] = scf.if %[[VAL_42]] -> (tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_43:.*]] = scf.if %[[VAL_42]] -> (tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_33]]] : memref<?xindex>
// CHECK: %[[VAL_45:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index
// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_45]]] : memref<?xindex>
// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_34]]] : memref<?xindex>
// CHECK: %[[VAL_48:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index
// CHECK: %[[VAL_49:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_48]]] : memref<?xindex>
// CHECK: %[[VAL_50:.*]]:3 = scf.while (%[[VAL_51:.*]] = %[[VAL_44]], %[[VAL_52:.*]] = %[[VAL_47]], %[[VAL_53:.*]] = %[[VAL_35]]) : (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_50:.*]]:3 = scf.while (%[[VAL_51:.*]] = %[[VAL_44]], %[[VAL_52:.*]] = %[[VAL_47]], %[[VAL_53:.*]] = %[[VAL_35]]) : (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_54:.*]] = arith.cmpi ult, %[[VAL_51]], %[[VAL_46]] : index
// CHECK: %[[VAL_55:.*]] = arith.cmpi ult, %[[VAL_52]], %[[VAL_49]] : index
// CHECK: %[[VAL_56:.*]] = arith.andi %[[VAL_54]], %[[VAL_55]] : i1
// CHECK: scf.condition(%[[VAL_56]]) %[[VAL_51]], %[[VAL_52]], %[[VAL_53]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.condition(%[[VAL_56]]) %[[VAL_51]], %[[VAL_52]], %[[VAL_53]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_57:.*]]: index, %[[VAL_58:.*]]: index, %[[VAL_59:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>):
// CHECK: ^bb0(%[[VAL_57:.*]]: index, %[[VAL_58:.*]]: index, %[[VAL_59:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>):
// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_57]]] : memref<?xindex>
// CHECK: %[[VAL_61:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_58]]] : memref<?xindex>
// CHECK: %[[VAL_62:.*]] = arith.cmpi ult, %[[VAL_61]], %[[VAL_60]] : index
@@ -211,20 +211,20 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_64:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index
// CHECK: %[[VAL_65:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index
// CHECK: %[[VAL_66:.*]] = arith.andi %[[VAL_64]], %[[VAL_65]] : i1
// CHECK: %[[VAL_67:.*]] = scf.if %[[VAL_66]] -> (tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_67:.*]] = scf.if %[[VAL_66]] -> (tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_68:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_57]]] : memref<?xindex>
// CHECK: %[[VAL_69:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index
// CHECK: %[[VAL_70:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_69]]] : memref<?xindex>
// CHECK: %[[VAL_71:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_58]]] : memref<?xindex>
// CHECK: %[[VAL_72:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index
// CHECK: %[[VAL_73:.*]] = memref.load %[[VAL_19]]{{\[}}%[[VAL_72]]] : memref<?xindex>
// CHECK: %[[VAL_74:.*]]:5 = scf.while (%[[VAL_75:.*]] = %[[VAL_68]], %[[VAL_76:.*]] = %[[VAL_71]], %[[VAL_77:.*]] = %[[VAL_4]], %[[VAL_200:.*]] = %[[VAL_FALSE]], %[[VAL_78:.*]] = %[[VAL_59]]) : (index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_74:.*]]:5 = scf.while (%[[VAL_75:.*]] = %[[VAL_68]], %[[VAL_76:.*]] = %[[VAL_71]], %[[VAL_77:.*]] = %[[VAL_4]], %[[VAL_200:.*]] = %[[VAL_FALSE]], %[[VAL_78:.*]] = %[[VAL_59]]) : (index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> (index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_79:.*]] = arith.cmpi ult, %[[VAL_75]], %[[VAL_70]] : index
// CHECK: %[[VAL_80:.*]] = arith.cmpi ult, %[[VAL_76]], %[[VAL_73]] : index
// CHECK: %[[VAL_81:.*]] = arith.andi %[[VAL_79]], %[[VAL_80]] : i1
// CHECK: scf.condition(%[[VAL_81]]) %[[VAL_75]], %[[VAL_76]], %[[VAL_77]], %[[VAL_200]], %[[VAL_78]] : index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.condition(%[[VAL_81]]) %[[VAL_75]], %[[VAL_76]], %[[VAL_77]], %[[VAL_200]], %[[VAL_78]] : index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_82:.*]]: index, %[[VAL_83:.*]]: index, %[[VAL_84:.*]]: i32, %[[VAL_201:.*]]: i1, %[[VAL_85:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>):
// CHECK: ^bb0(%[[VAL_82:.*]]: index, %[[VAL_83:.*]]: index, %[[VAL_84:.*]]: i32, %[[VAL_201:.*]]: i1, %[[VAL_85:.*]]: tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>):
// CHECK: %[[VAL_86:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_82]]] : memref<?xindex>
// CHECK: %[[VAL_87:.*]] = memref.load %[[VAL_20]]{{\[}}%[[VAL_83]]] : memref<?xindex>
// CHECK: %[[VAL_88:.*]] = arith.cmpi ult, %[[VAL_87]], %[[VAL_86]] : index
@@ -232,14 +232,14 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_90:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index
// CHECK: %[[VAL_91:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index
// CHECK: %[[VAL_92:.*]] = arith.andi %[[VAL_90]], %[[VAL_91]] : i1
// CHECK: %[[VAL_93:.*]]:3 = scf.if %[[VAL_92]] -> (i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_93:.*]]:3 = scf.if %[[VAL_92]] -> (i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_94:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_82]]] : memref<?xi32>
// CHECK: %[[VAL_95:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_83]]] : memref<?xi32>
// CHECK: %[[VAL_96:.*]] = arith.muli %[[VAL_94]], %[[VAL_95]] : i32
// CHECK: %[[VAL_97:.*]] = arith.addi %[[VAL_84]], %[[VAL_96]] : i32
// CHECK: scf.yield %[[VAL_97]], %[[VAL_TRUE]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_97]], %[[VAL_TRUE]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_84]], %[[VAL_201]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_84]], %[[VAL_201]], %[[VAL_85]] : i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_98:.*]] = arith.cmpi eq, %[[VAL_86]], %[[VAL_89]] : index
// CHECK: %[[VAL_99:.*]] = arith.addi %[[VAL_82]], %[[VAL_3]] : index
@@ -247,17 +247,17 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_101:.*]] = arith.cmpi eq, %[[VAL_87]], %[[VAL_89]] : index
// CHECK: %[[VAL_102:.*]] = arith.addi %[[VAL_83]], %[[VAL_3]] : index
// CHECK: %[[VAL_103:.*]] = arith.select %[[VAL_101]], %[[VAL_102]], %[[VAL_83]] : index
// CHECK: scf.yield %[[VAL_100]], %[[VAL_103]], %[[VAL_104:.*]]#0, %[[VAL_104]]#1, %[[VAL_104]]#2 : index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_100]], %[[VAL_103]], %[[VAL_104:.*]]#0, %[[VAL_104]]#1, %[[VAL_104]]#2 : index, index, i32, i1, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_202:.*]] = scf.if %[[VAL_74]]#3 -> (tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_105:.*]] = sparse_tensor.insert %[[VAL_74]]#2 into %[[VAL_74]]#4{{\[}}%[[VAL_39]], %[[VAL_63]]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_105]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_202:.*]] = scf.if %[[VAL_74]]#3 -> (tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_105:.*]] = sparse_tensor.insert %[[VAL_74]]#2 into %[[VAL_74]]#4{{\[}}%[[VAL_39]], %[[VAL_63]]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_105]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_74]]#4 : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_74]]#4 : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: scf.yield %[[VAL_202]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_202]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_59]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_59]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_107:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_63]] : index
// CHECK: %[[VAL_108:.*]] = arith.addi %[[VAL_57]], %[[VAL_3]] : index
@@ -265,11 +265,11 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_110:.*]] = arith.cmpi eq, %[[VAL_61]], %[[VAL_63]] : index
// CHECK: %[[VAL_111:.*]] = arith.addi %[[VAL_58]], %[[VAL_3]] : index
// CHECK: %[[VAL_112:.*]] = arith.select %[[VAL_110]], %[[VAL_111]], %[[VAL_58]] : index
// CHECK: scf.yield %[[VAL_109]], %[[VAL_112]], %[[VAL_113:.*]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_109]], %[[VAL_112]], %[[VAL_113:.*]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: scf.yield %[[VAL_114:.*]]#2 : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_114:.*]]#2 : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_35]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_35]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_115:.*]] = arith.cmpi eq, %[[VAL_36]], %[[VAL_39]] : index
// CHECK: %[[VAL_116:.*]] = arith.addi %[[VAL_33]], %[[VAL_3]] : index
@@ -277,10 +277,10 @@ func.func @sparse_truly_dynamic(%arga: tensor<10x20xf32, #CSR>) -> tensor<10x20x
// CHECK: %[[VAL_118:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_39]] : index
// CHECK: %[[VAL_119:.*]] = arith.addi %[[VAL_34]], %[[VAL_3]] : index
// CHECK: %[[VAL_120:.*]] = arith.select %[[VAL_118]], %[[VAL_119]], %[[VAL_34]] : index
// CHECK: scf.yield %[[VAL_117]], %[[VAL_120]], %[[VAL_121:.*]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_117]], %[[VAL_120]], %[[VAL_121:.*]] : index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_122:.*]] = sparse_tensor.load %[[VAL_123:.*]]#2 hasInserts : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_122]] : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_122:.*]] = sparse_tensor.load %[[VAL_123:.*]]#2 hasInserts : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_122]] : tensor<?x?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
%argb: tensor<?x?x?xi32, #SparseTensor>) -> tensor<?x?xi32, #DCSR> {
@@ -312,42 +312,42 @@ func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
}
// CHECK-LABEL: func.func @matmat(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_6]], %[[VAL_7]]) : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_6]], %[[VAL_7]]) : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_8]]) -> (tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_8]]) -> (tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex>
// CHECK: %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf32>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_22]], %[[VAL_3]] : index
// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_30]]] : memref<?xindex>
// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_34:.*]]:4 = scf.while (%[[VAL_35:.*]] = %[[VAL_29]], %[[VAL_36:.*]] = %[[VAL_32]], %[[VAL_37:.*]] = %[[VAL_28]], %[[VAL_38:.*]] = %[[VAL_23]]) : (index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_34:.*]]:4 = scf.while (%[[VAL_35:.*]] = %[[VAL_29]], %[[VAL_36:.*]] = %[[VAL_32]], %[[VAL_37:.*]] = %[[VAL_28]], %[[VAL_38:.*]] = %[[VAL_23]]) : (index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> (index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_35]], %[[VAL_31]] : index
// CHECK: %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_36]], %[[VAL_33]] : index
// CHECK: %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1
// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_35]], %[[VAL_36]], %[[VAL_37]], %[[VAL_38]] : index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_35]], %[[VAL_36]], %[[VAL_37]], %[[VAL_38]] : index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index, %[[VAL_44:.*]]: index, %[[VAL_45:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>):
// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index, %[[VAL_44:.*]]: index, %[[VAL_45:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>):
// CHECK: %[[VAL_46:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_42]]] : memref<?xindex>
// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_43]]] : memref<?xindex>
// CHECK: %[[VAL_48:.*]] = arith.cmpi ult, %[[VAL_47]], %[[VAL_46]] : index
@@ -355,7 +355,7 @@ func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
// CHECK: %[[VAL_50:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index
// CHECK: %[[VAL_51:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index
// CHECK: %[[VAL_52:.*]] = arith.andi %[[VAL_50]], %[[VAL_51]] : i1
// CHECK: %[[VAL_53:.*]]:2 = scf.if %[[VAL_52]] -> (index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_53:.*]]:2 = scf.if %[[VAL_52]] -> (index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_54:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_42]]] : memref<?xf32>
// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_43]]] : memref<?xindex>
// CHECK: %[[VAL_56:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index
@@ -379,9 +379,9 @@ func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
// CHECK: memref.store %[[VAL_65]], %[[VAL_25]]{{\[}}%[[VAL_61]]] : memref<?xf32>
// CHECK: scf.yield %[[VAL_70:.*]] : index
// CHECK: }
// CHECK: scf.yield %[[VAL_71:.*]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_71:.*]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_44]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_44]], %[[VAL_45]] : index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_72:.*]] = arith.cmpi eq, %[[VAL_46]], %[[VAL_49]] : index
// CHECK: %[[VAL_73:.*]] = arith.addi %[[VAL_42]], %[[VAL_3]] : index
@@ -389,13 +389,13 @@ func.func @sumred(%arga: tensor<?x?x?xi32, #SparseTensor>,
// CHECK: %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_49]] : index
// CHECK: %[[VAL_76:.*]] = arith.addi %[[VAL_43]], %[[VAL_3]] : index
// CHECK: %[[VAL_77:.*]] = arith.select %[[VAL_75]], %[[VAL_76]], %[[VAL_43]] : index
// CHECK: scf.yield %[[VAL_74]], %[[VAL_77]], %[[VAL_78:.*]]#0, %[[VAL_78]]#1 : index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_74]], %[[VAL_77]], %[[VAL_78:.*]]#0, %[[VAL_78]]#1 : index, index, index, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_79:.*]] = sparse_tensor.compress %[[VAL_25]], %[[VAL_26]], %[[VAL_27]], %[[VAL_80:.*]]#2 into %[[VAL_80]]#3{{\[}}%[[VAL_24]]] : memref<?xf32>, memref<?xi1>, memref<?xindex>, tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_79]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_79:.*]] = sparse_tensor.compress %[[VAL_25]], %[[VAL_26]], %[[VAL_27]], %[[VAL_80:.*]]#2 into %[[VAL_80]]#3{{\[}}%[[VAL_24]]] : memref<?xf32>, memref<?xi1>, memref<?xindex>, tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_79]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_81:.*]] = sparse_tensor.load %[[VAL_82:.*]] hasInserts : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_81]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_81:.*]] = sparse_tensor.load %[[VAL_82:.*]] hasInserts : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_81]] : tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @matmat(%arga: tensor<?x?xf32, #DCSR>,
%argb: tensor<?x?xf32, #DCSR>) -> tensor<?x?xf32, #DCSR> {

2
mlir/test/Dialect/SparseTensor/sparse_outbuf.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait = {
indexing_maps = [

2
mlir/test/Dialect/SparseTensor/sparse_pack.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s --canonicalize --post-sparsification-rewrite="enable-runtime-library=false" --sparse-tensor-codegen -cse | FileCheck %s
#COO = #sparse_tensor.encoding<{
dimLevelType = ["compressed-nu", "singleton"],
lvlTypes = ["compressed-nu", "singleton"],
crdWidth=32
}>

6
mlir/test/Dialect/SparseTensor/sparse_parallel.mlir
View File

@@ -10,15 +10,15 @@
// RUN: FileCheck %s --check-prefix=CHECK-PAR4
#DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ]
lvlTypes = [ "dense", "dense" ]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ]
lvlTypes = [ "compressed", "compressed" ]
}>
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ]
lvlTypes = [ "dense", "compressed" ]
}>
#trait_dd = {

4
mlir/test/Dialect/SparseTensor/sparse_parallel_reduce.mlir
View File

@@ -2,7 +2,7 @@
// RUN: FileCheck %s
#CSR = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ]
lvlTypes = [ "dense", "compressed" ]
}>
#trait_matvec = {
@@ -15,7 +15,7 @@
doc = "x(i) += A(i,j) * b(j)"
}
// CHECK-LABEL: func.func @matvec(
// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[TMP_arg1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[TMP_arg2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[TMP_c16:.*]] = arith.constant 16 : index

2
mlir/test/Dialect/SparseTensor/sparse_perm.mlir
View File

@@ -2,7 +2,7 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#X = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "dense" ],
lvlTypes = [ "dense", "dense", "dense" ],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>

2
mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir
View File

@@ -4,7 +4,7 @@
// RUN: FileCheck %s --check-prefix=CHECK-MIR
#X = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "dense" ],
lvlTypes = [ "dense", "dense", "dense" ],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>

12
mlir/test/Dialect/SparseTensor/sparse_reshape.mlir
View File

@@ -3,8 +3,8 @@
// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-convert=false" \
// RUN: --cse --canonicalize | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SparseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#SparseMatrix = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
//
// roundtrip:
@@ -62,7 +62,7 @@
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT-NOT: sparse_tensor.convert
// CHECK-RWT: return %[[NT1]] : tensor<10x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-RWT: return %[[NT1]] : tensor<10x10xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
//
func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] :
@@ -135,7 +135,7 @@ func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10x
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT-NOT: sparse_tensor.convert
// CHECK-RWT: return %[[NT1]] : tensor<100xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK-RWT: return %[[NT1]] : tensor<100xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
//
func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<100xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] :
@@ -210,7 +210,7 @@ func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<10
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT-NOT: sparse_tensor.convert
// CHECK-RWT: return %[[NT1]] : tensor<?x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-RWT: return %[[NT1]] : tensor<?x10xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
//
func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?x10xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] :
@@ -292,7 +292,7 @@ func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<
// CHECK-RWT: }
// CHECK-RWT: %[[NT1:.*]] = sparse_tensor.load %[[RET]] hasInserts
// CHECK-RWT-NOT: sparse_tensor.convert
// CHECK-RWT: return %[[NT1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
// CHECK-RWT: return %[[NT1]] : tensor<?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>
//
func.func @dynamic_sparse_collapse(%arg0: tensor<10x?xf64, #SparseMatrix>) -> tensor<?xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] :

6
mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir
View File

@@ -1,12 +1,12 @@
// RUN: mlir-opt %s --linalg-generalize-named-ops --sparsification --cse --canonicalize | FileCheck %s
#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_2D = #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_3D = #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
// CHECK-LABEL: func.func @sparse_reshape_fused(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x6xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<6x2x3xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>>) -> tensor<?x?x?xf32> {
// CHECK-SAME: %[[VAL_1:.*]]: tensor<6x2x3xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>>) -> tensor<?x?x?xf32> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 5 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index

2
mlir/test/Dialect/SparseTensor/sparse_scalars.mlir
View File

@@ -1,7 +1,7 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SparseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#SparseMatrix = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
// A contrived example that demonstrates the many different ways
// in which scalar values can be involved in a sparse kernel

32
mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --test-tensor-copy-insertion --pre-sparsification-rewrite --sparsification --cse | FileCheck %s
#SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#SM = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
#trait_matmul = {
indexing_maps = [
@@ -57,7 +57,7 @@ func.func @fold_yield_direct_zero() -> tensor<32xf64> {
}
// CHECK-LABEL: func.func @sampled_dd_unfused(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
@@ -123,9 +123,9 @@ func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
}
// CHECK-LABEL: func.func @sparse_sampled_dd_unfused(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
@@ -133,19 +133,19 @@ func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_8:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
// CHECK-DAG: %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) {bufferization.escape = [false]} : tensor<8x8xf64>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] iter_args(%[[VAL_22:.*]] = %[[VAL_10]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] iter_args(%[[VAL_22:.*]] = %[[VAL_10]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_21]]] : memref<?xindex>
// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_28:.*]] = scf.for %[[VAL_29:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_30:.*]] = %[[VAL_27]]) -> (index) {
// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]], %[[VAL_29]]] : memref<8x8xf64>
// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_21]]] : memref<?xindex>
@@ -174,11 +174,11 @@ func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
// CHECK: }
// CHECK: scf.yield %[[VAL_50:.*]] : index
// CHECK: }
// CHECK: %[[VAL_51:.*]] = sparse_tensor.compress %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_52:.*]] into %[[VAL_22]]{{\[}}%[[VAL_23]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_51:.*]] = sparse_tensor.compress %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_52:.*]] into %[[VAL_22]]{{\[}}%[[VAL_23]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_53:.*]] = sparse_tensor.load %[[VAL_54:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_53]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_53:.*]] = sparse_tensor.load %[[VAL_54:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_53]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,

30
mlir/test/Dialect/SparseTensor/sparse_sddmm_org.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s --pre-sparsification-rewrite --sparsification --cse | FileCheck %s
#SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
#SM = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
#trait_matmul = {
indexing_maps = [
@@ -21,27 +21,27 @@
}
// CHECK-LABEL: func.func @sparse_sampled_dd_unfused(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant false
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_8]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_8]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
// CHECK: %[[VAL_26:.*]] = scf.for %[[VAL_27:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_28:.*]] = %[[VAL_25]]) -> (index) {
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64>
// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_19]]] : memref<?xindex>
@@ -70,11 +70,11 @@
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: scf.yield %[[VAL_48:.*]] : index
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: %[[VAL_49:.*]] = sparse_tensor.compress %[[VAL_22]], %[[VAL_23]], %[[VAL_24]], %[[VAL_50:.*]] into %[[VAL_20]]{{\[}}%[[VAL_21]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_49]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_49:.*]] = sparse_tensor.compress %[[VAL_22]], %[[VAL_23]], %[[VAL_24]], %[[VAL_50:.*]] into %[[VAL_20]]{{\[}}%[[VAL_21]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_49]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: } {"Emitted from" = "linalg.generic"}
// CHECK: %[[VAL_51:.*]] = sparse_tensor.load %[[VAL_52:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_51:.*]] = sparse_tensor.load %[[VAL_52:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: return %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,
%arga: tensor<8x8xf64>,

4
mlir/test/Dialect/SparseTensor/sparse_storage.mlir
View File

@@ -1,13 +1,13 @@
// RUN: mlir-opt %s -sparsification= | FileCheck %s
#SparseVector64 = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ],
lvlTypes = [ "compressed" ],
posWidth = 64,
crdWidth = 64
}>
#SparseVector32 = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ],
lvlTypes = [ "compressed" ],
posWidth = 32,
crdWidth = 32
}>

34
mlir/test/Dialect/SparseTensor/sparse_transpose.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparsification | FileCheck %s
#DCSR = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ]
lvlTypes = [ "compressed", "compressed" ]
}>
#transpose_trait = {
@@ -16,34 +16,34 @@
// TODO: improve auto-conversion followed by yield
// CHECK-LABEL: func.func @sparse_transpose_auto(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
// CHECK-DAG: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_3]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_3]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>
// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_2]] iter_args(%[[VAL_21:.*]] = %[[VAL_14]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_2]] iter_args(%[[VAL_21:.*]] = %[[VAL_14]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) {
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xf64>
// CHECK: %[[VAL_24:.*]] = sparse_tensor.insert %[[VAL_23]] into %[[VAL_21]]{{\[}}%[[VAL_15]], %[[VAL_22]]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_24]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_24:.*]] = sparse_tensor.insert %[[VAL_23]] into %[[VAL_21]]{{\[}}%[[VAL_15]], %[[VAL_22]]] : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_24]] : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: scf.yield %[[VAL_25:.*]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: scf.yield %[[VAL_25:.*]] : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
// CHECK: %[[VAL_26:.*]] = sparse_tensor.load %[[VAL_27:.*]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: bufferization.dealloc_tensor %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK: return %[[VAL_26]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
// CHECK: %[[VAL_26:.*]] = sparse_tensor.load %[[VAL_27:.*]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: bufferization.dealloc_tensor %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
// CHECK: return %[[VAL_26]] : tensor<4x3xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>
// CHECK: }
func.func @sparse_transpose_auto(%arga: tensor<3x4xf64, #DCSR>)
-> tensor<4x3xf64, #DCSR> {

10
mlir/test/Dialect/SparseTensor/sparse_vector.mlir
View File

@@ -7,7 +7,7 @@
// RUN: mlir-opt %s -sparsification -cse -sparse-vectorization="vl=4 enable-vla-vectorization=true" -cse -split-input-file | \
// RUN: FileCheck %s --check-prefix=CHECK-VEC4-SVE
#DenseVector = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#DenseVector = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#trait_scale_d = {
indexing_maps = [
@@ -86,7 +86,7 @@ func.func @scale_d(%arga: tensor<1024xf32, #DenseVector>, %b: f32, %argx: tensor
// -----
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ],
lvlTypes = [ "compressed" ],
posWidth = 32,
crdWidth = 32
}>
@@ -209,7 +209,7 @@ func.func @mul_s(%arga: tensor<1024xf32, #SparseVector>,
// -----
#DenseVector = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#DenseVector = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#trait_reduction_d = {
indexing_maps = [
@@ -309,7 +309,7 @@ func.func @reduction_d(%arga: tensor<1024xf32, #DenseVector>,
// -----
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
posWidth = 32,
crdWidth = 32
}>
@@ -448,7 +448,7 @@ func.func @mul_ds(%arga: tensor<512x1024xf32, #SparseMatrix>,
// -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["dense","compressed"]}>
#trait_affine = {
indexing_maps = [

18
mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparsification -cse -sparse-vectorization="vl=8" -cse | \
// RUN: FileCheck %s
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}>
#SparseMatrix = #sparse_tensor.encoding<{lvlTypes = ["dense","compressed"]}>
#trait = {
indexing_maps = [
@@ -18,19 +18,19 @@
//
// CHECK-LABEL: func.func @sparse_matrix_sum(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<f64>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-SAME: %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 64 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref<f64>
// CHECK: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f64) {

6
mlir/test/Dialect/SparseTensor/sparse_vector_concat.mlir
View File

@@ -1,16 +1,16 @@
// RUN: mlir-opt %s --sparse-compiler="enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
#MAT_D_C = #sparse_tensor.encoding<{
dimLevelType = ["dense", "compressed"]
lvlTypes = ["dense", "compressed"]
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

18
mlir/test/Dialect/SparseTensor/sparse_vector_index.mlir
View File

@@ -4,7 +4,7 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
lvlTypes = ["compressed"]
}>
#trait_1d = {
@@ -17,7 +17,7 @@
}
// CHECK-LABEL: func.func @sparse_index_1d_conj(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<8xi64> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<8xi64> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<0> : vector<8xi64>
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xindex>
@@ -25,9 +25,9 @@
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_7:.*]] = tensor.empty() : tensor<8xi64>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi64>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi64>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_7]] : memref<8xi64>
// CHECK: linalg.fill ins(%[[VAL_4]] : i64) outs(%[[VAL_11]] : memref<8xi64>)
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
@@ -59,7 +59,7 @@ func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8
}
// CHECK-LABEL: func.func @sparse_index_1d_disj(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<8xi64> {
// CHECK-SAME: %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<8xi64> {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 8 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex>
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i64
@@ -67,9 +67,9 @@ func.func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>) -> tensor<8
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true
// CHECK-DAG: %[[VAL_7:.*]] = tensor.empty() : tensor<8xi64>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi64>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi64>
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_7]] : memref<8xi64>
// CHECK: linalg.fill ins(%[[VAL_3]] : i64) outs(%[[VAL_11]] : memref<8xi64>)
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>

2
mlir/test/Dialect/SparseTensor/sparse_vector_mv.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparse-compiler="vl=8" | FileCheck %s
#Dense = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ]
lvlTypes = [ "dense", "dense" ]
}>
#matvec = {

2
mlir/test/Dialect/SparseTensor/sparse_vector_ops.mlir
View File

@@ -1,7 +1,7 @@
// RUN: mlir-opt %s -sparsification -cse -sparse-vectorization="vl=8" -cse | \
// RUN: FileCheck %s
#DenseVector = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#DenseVector = #sparse_tensor.encoding<{ lvlTypes = [ "dense" ] }>
#trait = {
indexing_maps = [

2
mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir
View File

@@ -2,7 +2,7 @@
// RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = [ "compressed" ],
lvlTypes = [ "compressed" ],
posWidth = 32,
crdWidth = 32
}>

2
mlir/test/Dialect/SparseTensor/specifier_to_llvm.mlir
View File

@@ -1,6 +1,6 @@
// RUN: mlir-opt %s -sparse-storage-specifier-to-llvm --cse --canonicalize | FileCheck %s
#CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#CSR = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
// CHECK-LABEL: func.func @sparse_metadata_init() -> !llvm.struct<(array<2 x i64>, array<3 x i64>)> {
// CHECK: %[[VAL_0:.*]] = arith.constant 0 : i64

162
mlir/test/Dialect/SparseTensor/vectorize_reduction.mlir
View File

@@ -28,15 +28,15 @@
//
// CHECK-ON-LABEL: func.func @sparse_product_reduction_dense_sparse(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f64>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<1.000000e+00> : vector<8xf64>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-ON-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-ON: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f64>
// CHECK-ON: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_7]] step %[[VAL_6]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f64) {
@@ -62,12 +62,12 @@
//
// CHECK-OFF-LABEL: func.func @sparse_product_reduction_dense_sparse(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f64>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>) -> tensor<f64> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-OFF: %[[VAL_4:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>> to memref<?xf64>
// CHECK-OFF: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_7]][] : memref<f64>
// CHECK-OFF: %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_4]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_8]]) -> (f64) {
@@ -86,7 +86,7 @@
// CHECK-OFF: return %[[VAL_22]] : tensor<f64>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["dense","compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["dense","compressed"]}>
#trait = {
indexing_maps = [
@@ -115,15 +115,15 @@ func.func @sparse_product_reduction_dense_sparse(%argx: tensor<f64>,
//
// CHECK-ON-LABEL: func.func @sparse_product_reduction_sparse_sparse(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f64>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<1.000000e+00> : vector<8xf64>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-ON: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f64>
// CHECK-ON: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
@@ -151,12 +151,12 @@ func.func @sparse_product_reduction_dense_sparse(%argx: tensor<f64>,
//
// CHECK-OFF-LABEL: func.func @sparse_product_reduction_sparse_sparse(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f64>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>> to memref<?xf64>
// CHECK-OFF: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_7]][] : memref<f64>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -176,7 +176,7 @@ func.func @sparse_product_reduction_dense_sparse(%argx: tensor<f64>,
// CHECK-OFF: %[[VAL_24:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<f64>
// CHECK-OFF: return %[[VAL_24]] : tensor<f64>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed","compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed","compressed"]}>
#trait = {
indexing_maps = [
@@ -211,13 +211,13 @@ func.func @sparse_product_reduction_sparse_sparse(%argx: tensor<f64>,
// constant type for the pass-through value.
// CHECK-ON-LABEL: func.func @sparse_reduction_ori(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi13>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -239,11 +239,11 @@ func.func @sparse_product_reduction_sparse_sparse(%argx: tensor<f64>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_ori(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi13>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -257,7 +257,7 @@ func.func @sparse_product_reduction_sparse_sparse(%argx: tensor<f64>,
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13>
// CHECK-OFF: return %[[VAL_16]] : tensor<i13>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -289,13 +289,13 @@ func.func @sparse_reduction_ori(%argx: tensor<i13>,
// CHECK-ON-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi13>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -317,11 +317,11 @@ func.func @sparse_reduction_ori(%argx: tensor<i13>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i13> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi13>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -335,7 +335,7 @@ func.func @sparse_reduction_ori(%argx: tensor<i13>,
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13>
// CHECK-OFF: return %[[VAL_16]] : tensor<i13>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -364,13 +364,13 @@ func.func @sparse_reduction_ori_accumulator_on_rhs(%argx: tensor<i13>,
//
// CHECK-ON-LABEL: func.func @sparse_reduction_subi(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
@@ -392,11 +392,11 @@ func.func @sparse_reduction_ori_accumulator_on_rhs(%argx: tensor<i13>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_subi(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -410,7 +410,7 @@ func.func @sparse_reduction_ori_accumulator_on_rhs(%argx: tensor<i13>,
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32>
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -441,13 +441,13 @@ func.func @sparse_reduction_subi(%argx: tensor<i32>,
// Check that we vectorize xor.
// CHECK-ON-LABEL: func.func @sparse_reduction_xor(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -469,11 +469,11 @@ func.func @sparse_reduction_subi(%argx: tensor<i32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_xor(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -488,7 +488,7 @@ func.func @sparse_reduction_subi(%argx: tensor<i32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -515,13 +515,13 @@ func.func @sparse_reduction_xor(%argx: tensor<i32>,
// Check that we vectorize and.
// CHECK-ON-LABEL: func.func @sparse_reduction_and(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -543,11 +543,11 @@ func.func @sparse_reduction_xor(%argx: tensor<i32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_and(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -562,7 +562,7 @@ func.func @sparse_reduction_xor(%argx: tensor<i32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -589,14 +589,14 @@ func.func @sparse_reduction_and(%argx: tensor<i32>,
// Check that we vectorize muli.
// CHECK-ON-LABEL: func.func @sparse_reduction_muli(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<1> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_9]][] : memref<i32>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -618,11 +618,11 @@ func.func @sparse_reduction_and(%argx: tensor<i32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_muli(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -637,7 +637,7 @@ func.func @sparse_reduction_and(%argx: tensor<i32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -664,13 +664,13 @@ func.func @sparse_reduction_muli(%argx: tensor<i32>,
// Check that we vectorize addi.
// CHECK-ON-LABEL: func.func @sparse_reduction_addi(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -692,11 +692,11 @@ func.func @sparse_reduction_muli(%argx: tensor<i32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_addi(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xi32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -711,7 +711,7 @@ func.func @sparse_reduction_muli(%argx: tensor<i32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -738,13 +738,13 @@ func.func @sparse_reduction_addi(%argx: tensor<i32>,
// Check that we vectorize subf.
// CHECK-ON-LABEL: func.func @sparse_reduction_subf(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -766,11 +766,11 @@ func.func @sparse_reduction_addi(%argx: tensor<i32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_subf(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -785,7 +785,7 @@ func.func @sparse_reduction_addi(%argx: tensor<i32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<f32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [
@@ -812,13 +812,13 @@ func.func @sparse_reduction_subf(%argx: tensor<f32>,
// Check that we vectorize addf.
// CHECK-ON-LABEL: func.func @sparse_reduction_addf(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-ON: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-ON: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-ON: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
@@ -840,11 +840,11 @@ func.func @sparse_reduction_subf(%argx: tensor<f32>,
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_addf(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<f32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
// CHECK-OFF: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xindex>
// CHECK-OFF: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>> to memref<?xf32>
// CHECK-OFF: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
@@ -859,7 +859,7 @@ func.func @sparse_reduction_subf(%argx: tensor<f32>,
// CHECK-OFF: return %[[VAL_16]] : tensor<f32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait = {
indexing_maps = [

14
mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0.mlir
View File

@@ -26,26 +26,26 @@
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_C_C = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{lvlTypes = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
lvlTypes = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

14
mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0_permute.mlir
View File

@@ -26,26 +26,26 @@
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_C_C = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{lvlTypes = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
lvlTypes = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

14
mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1.mlir
View File

@@ -16,26 +16,26 @@
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true vl=4 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_C_C = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{lvlTypes = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
lvlTypes = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

14
mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1_permute.mlir
View File

@@ -26,26 +26,26 @@
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_C_C = #sparse_tensor.encoding<{lvlTypes = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{lvlTypes = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{lvlTypes = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
lvlTypes = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
lvlTypes = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
lvlTypes = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>

4
mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output.mlir
View File

@@ -31,12 +31,12 @@
!Filename = !llvm.ptr<i8>
#DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ],
lvlTypes = [ "dense", "dense" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
lvlTypes = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>

4
mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output_bf16.mlir
View File

@@ -17,8 +17,8 @@
// UNSUPPORTED: target=aarch64{{.*}}
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{lvlTypes = ["dense"]}>
#trait_vec_op = {
indexing_maps = [

4
mlir/test/Integration/Dialect/SparseTensor/CPU/dense_output_f16.mlir
View File

@@ -26,8 +26,8 @@
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"]}>
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#DenseVector = #sparse_tensor.encoding<{lvlTypes = ["dense"]}>
#trait_vec_op = {
indexing_maps = [

4
mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir
View File

@@ -16,8 +16,8 @@
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_2D = #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_3D = #sparse_tensor.encoding<{ lvlTypes = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
module {
func.func private @printMemref3dF32(%ptr : tensor<?x?x?xf32>) attributes { llvm.emit_c_interface }

2
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_abs.mlir
View File

@@ -26,7 +26,7 @@
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SparseVector = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait_op = {
indexing_maps = [

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