[Mlir-commits] [mlir] [mlir][sparse] replace specialized buffer setup with util code (PR #68461)
Aart Bik
llvmlistbot at llvm.org
Fri Oct 6 18:27:27 PDT 2023
https://github.com/aartbik created https://github.com/llvm/llvm-project/pull/68461
This completely centralizes all set up related to dim2lvl and lvl2dim for the runtime library (and even parts of direct IR codegen) into one place! And all comptatible with the MapRef data structure that should be used in all remaining clients of dim2lvl and lvl2dim.
NOTE: the convert_x2y.mlir tests were becoming too overloaded
so I decided to bring them back to the basics; if e.g.
more coverage of the foreach is required, they should
go into isolated smalle tests
>From 8af9c333bba802bcee0ef90cfec87a93b0da3eed Mon Sep 17 00:00:00 2001
From: Aart Bik <ajcbik at google.com>
Date: Fri, 6 Oct 2023 18:21:14 -0700
Subject: [PATCH] [mlir][sparse] replace specialized buffer setup with util
code
This completely centralizes all set up related to dim2lvl and lvl2dim
for the runtime library (and even parts of direct IR codegen) into one
place! And all comptatible with the MapRef data structure that should
be used in all remaining clients of dim2lvl and lvl2dim.
NOTE: the convert_x2y.mlir tests were becoming too overloaded
so I decided to bring them back to the basics; if e.g.
more coverage of the foreach is required, they should
go into isolated smalle tests
---
.../ExecutionEngine/SparseTensorRuntime.h | 5 +-
.../Transforms/SparseTensorConversion.cpp | 93 +--
.../ExecutionEngine/SparseTensorRuntime.cpp | 12 +-
.../test/Dialect/SparseTensor/conversion.mlir | 12 +-
.../SparseTensor/convert_dense2sparse.mlir | 479 ++++++++------
.../SparseTensor/convert_sparse2dense.mlir | 615 +++++++++---------
.../SparseTensor/convert_sparse2sparse.mlir | 267 ++++----
.../SparseTensor/sparse_fill_zero.mlir | 136 ++--
8 files changed, 820 insertions(+), 799 deletions(-)
diff --git a/mlir/include/mlir/ExecutionEngine/SparseTensorRuntime.h b/mlir/include/mlir/ExecutionEngine/SparseTensorRuntime.h
index f25df11d15fdad1..e723a354345849d 100644
--- a/mlir/include/mlir/ExecutionEngine/SparseTensorRuntime.h
+++ b/mlir/include/mlir/ExecutionEngine/SparseTensorRuntime.h
@@ -57,8 +57,8 @@ MLIR_CRUNNERUTILS_EXPORT void *_mlir_ciface_newSparseTensor( // NOLINT
StridedMemRefType<index_type, 1> *dimSizesRef,
StridedMemRefType<index_type, 1> *lvlSizesRef,
StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
- StridedMemRefType<index_type, 1> *lvl2dimRef,
- StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+ StridedMemRefType<index_type, 1> *dim2lvlRef,
+ StridedMemRefType<index_type, 1> *lvl2dimRef, OverheadType posTp,
OverheadType crdTp, PrimaryType valTp, Action action, void *ptr);
/// Tensor-storage method to obtain direct access to the values array.
@@ -85,6 +85,7 @@ MLIR_SPARSETENSOR_FOREVERY_O(DECL_SPARSECOORDINATES)
#undef DECL_SPARSECOORDINATES
/// Coordinate-scheme method for adding a new element.
+/// TODO: remove dim2lvl
#define DECL_ADDELT(VNAME, V) \
MLIR_CRUNNERUTILS_EXPORT void *_mlir_ciface_addElt##VNAME( \
void *lvlCOO, StridedMemRefType<V, 0> *vref, \
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
index eb0c5160e8d6193..bb92029ff1e924d 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp
@@ -187,25 +187,38 @@ static Value genLvlPtrsBuffers(OpBuilder &builder, Location loc,
/// This class abstracts over the API of `_mlir_ciface_newSparseTensor`:
/// the "swiss army knife" method of the sparse runtime support library
-/// for materializing sparse tensors into the computation. This abstraction
-/// reduces the need to make modifications to client code whenever that
-/// API changes.
+/// for materializing sparse tensors into the computation. This abstraction
+/// reduces the need for modifications when the API changes.
class NewCallParams final {
public:
- /// Allocates the `ValueRange` for the `func::CallOp` parameters,
- /// but does not initialize them.
+ /// Allocates the `ValueRange` for the `func::CallOp` parameters.
NewCallParams(OpBuilder &builder, Location loc)
: builder(builder), loc(loc), pTp(getOpaquePointerType(builder)) {}
/// Initializes all static parameters (i.e., those which indicate
/// type-level information such as the encoding and sizes), generating
/// MLIR buffers as needed, and returning `this` for method chaining.
- /// This method does not set the action and pointer arguments, since
- /// those are handled by `genNewCall` instead.
- NewCallParams &genBuffers(SparseTensorType stt, ValueRange dimSizes);
+ NewCallParams &genBuffers(SparseTensorType stt,
+ ArrayRef<Value> dimSizesValues) {
+ const Dimension dimRank = stt.getDimRank();
+ assert(dimSizesValues.size() == static_cast<size_t>(dimRank));
+ // Sparsity annotations.
+ params[kParamLvlTypes] = genLvlTypesBuffer(builder, loc, stt);
+ // Construct dimSizes, lvlSizes, dim2lvl, and lvl2dim buffers.
+ params[kParamDimSizes] = allocaBuffer(builder, loc, dimSizesValues);
+ params[kParamLvlSizes] = genReaderBuffers(
+ builder, loc, stt, dimSizesValues, params[kParamDimSizes],
+ params[kParamDim2Lvl], params[kParamLvl2Dim]);
+ // Secondary and primary types encoding.
+ setTemplateTypes(stt);
+ // Finally, make note that initialization is complete.
+ assert(isInitialized() && "Initialization failed");
+ // And return `this` for method chaining.
+ return *this;
+ }
/// (Re)sets the C++ template type parameters, and returns `this`
- /// for method chaining. This is already done as part of `genBuffers`,
+ /// for method chaining. This is already done as part of `genBuffers`,
/// but is factored out so that it can also be called independently
/// whenever subsequent `genNewCall` calls want to reuse the same
/// buffers but different type parameters.
@@ -236,7 +249,7 @@ class NewCallParams final {
// this one-off getter, and to avoid potential mixups)?
Value getDimToLvl() const {
assert(isInitialized() && "Must initialize before getDimToLvl");
- return params[kParamDimToLvl];
+ return params[kParamDim2Lvl];
}
/// Generates a function call, with the current static parameters
@@ -257,8 +270,8 @@ class NewCallParams final {
static constexpr unsigned kParamDimSizes = 0;
static constexpr unsigned kParamLvlSizes = 1;
static constexpr unsigned kParamLvlTypes = 2;
- static constexpr unsigned kParamLvlToDim = 3;
- static constexpr unsigned kParamDimToLvl = 4;
+ static constexpr unsigned kParamDim2Lvl = 3;
+ static constexpr unsigned kParamLvl2Dim = 4;
static constexpr unsigned kParamPosTp = 5;
static constexpr unsigned kParamCrdTp = 6;
static constexpr unsigned kParamValTp = 7;
@@ -271,62 +284,6 @@ class NewCallParams final {
Value params[kNumParams];
};
-// TODO: see the note at `_mlir_ciface_newSparseTensor` about how
-// the meaning of the various arguments (e.g., "sizes" vs "shapes")
-// is inconsistent between the different actions.
-NewCallParams &NewCallParams::genBuffers(SparseTensorType stt,
- ValueRange dimSizes) {
- const Level lvlRank = stt.getLvlRank();
- const Dimension dimRank = stt.getDimRank();
- // Sparsity annotations.
- params[kParamLvlTypes] = genLvlTypesBuffer(builder, loc, stt);
- // Dimension-sizes array of the enveloping tensor. Useful for either
- // verification of external data, or for construction of internal data.
- assert(dimSizes.size() == static_cast<size_t>(dimRank) &&
- "Dimension-rank mismatch");
- params[kParamDimSizes] = allocaBuffer(builder, loc, dimSizes);
- // The level-sizes array must be passed as well, since for arbitrary
- // dimToLvl mappings it cannot be trivially reconstructed at runtime.
- // For now however, since we're still assuming permutations, we will
- // initialize this parameter alongside the `dimToLvl` and `lvlToDim`
- // parameters below. We preinitialize `lvlSizes` for code symmetry.
- SmallVector<Value> lvlSizes(lvlRank);
- // The dimension-to-level mapping and its inverse. We must preinitialize
- // `dimToLvl` so that the true branch below can perform random-access
- // `operator[]` assignment. We preinitialize `lvlToDim` for code symmetry.
- SmallVector<Value> dimToLvl(dimRank);
- SmallVector<Value> lvlToDim(lvlRank);
- if (!stt.isIdentity()) {
- const auto dimToLvlMap = stt.getDimToLvl();
- assert(dimToLvlMap.isPermutation());
- for (Level l = 0; l < lvlRank; l++) {
- // The `d`th source variable occurs in the `l`th result position.
- const Dimension d = dimToLvlMap.getDimPosition(l);
- dimToLvl[d] = constantIndex(builder, loc, l);
- lvlToDim[l] = constantIndex(builder, loc, d);
- lvlSizes[l] = dimSizes[d];
- }
- } else {
- // The `SparseTensorType` ctor already ensures `dimRank == lvlRank`
- // when `isIdentity`; so no need to re-assert it here.
- for (Level l = 0; l < lvlRank; l++) {
- dimToLvl[l] = lvlToDim[l] = constantIndex(builder, loc, l);
- lvlSizes[l] = dimSizes[l];
- }
- }
- params[kParamLvlSizes] = allocaBuffer(builder, loc, lvlSizes);
- params[kParamLvlToDim] = allocaBuffer(builder, loc, lvlToDim);
- params[kParamDimToLvl] = stt.isIdentity()
- ? params[kParamLvlToDim]
- : allocaBuffer(builder, loc, dimToLvl);
- // Secondary and primary types encoding.
- setTemplateTypes(stt);
- // Finally, make note that initialization is complete.
- assert(isInitialized() && "Initialization failed");
- // And return `this` for method chaining.
- return *this;
-}
-
/// Generates a call to obtain the values array.
static Value genValuesCall(OpBuilder &builder, Location loc, ShapedType tp,
ValueRange ptr) {
diff --git a/mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp b/mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp
index 5b910716c0f9e59..05da8cd79190ed0 100644
--- a/mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp
+++ b/mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp
@@ -231,8 +231,8 @@ void *_mlir_ciface_newSparseTensor( // NOLINT
StridedMemRefType<index_type, 1> *dimSizesRef,
StridedMemRefType<index_type, 1> *lvlSizesRef,
StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
- StridedMemRefType<index_type, 1> *lvl2dimRef,
- StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+ StridedMemRefType<index_type, 1> *dim2lvlRef,
+ StridedMemRefType<index_type, 1> *lvl2dimRef, OverheadType posTp,
OverheadType crdTp, PrimaryType valTp, Action action, void *ptr) {
ASSERT_NO_STRIDE(dimSizesRef);
ASSERT_NO_STRIDE(lvlSizesRef);
@@ -250,6 +250,9 @@ void *_mlir_ciface_newSparseTensor( // NOLINT
const index_type *dim2lvl = MEMREF_GET_PAYLOAD(dim2lvlRef);
const index_type *lvl2dim = MEMREF_GET_PAYLOAD(lvl2dimRef);
+ // Prepare map.
+ // TODO: start using MapRef map(dimRank, lvlRank, dim2lvl, lvl2dim) below
+
// Rewrite kIndex to kU64, to avoid introducing a bunch of new cases.
// This is safe because of the static_assert above.
if (posTp == OverheadType::kIndex)
@@ -400,6 +403,7 @@ MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSECOORDINATES)
#undef IMPL_GETOVERHEAD
// TODO: use MapRef here for translation of coordinates
+// TOOD: remove dim2lvl
#define IMPL_ADDELT(VNAME, V) \
void *_mlir_ciface_addElt##VNAME( \
void *lvlCOO, StridedMemRefType<V, 0> *vref, \
@@ -540,13 +544,13 @@ void *_mlir_ciface_newSparseTensorFromReader(
SparseTensorReader &reader = *static_cast<SparseTensorReader *>(p);
ASSERT_NO_STRIDE(lvlSizesRef);
ASSERT_NO_STRIDE(lvlTypesRef);
- ASSERT_NO_STRIDE(lvl2dimRef);
ASSERT_NO_STRIDE(dim2lvlRef);
+ ASSERT_NO_STRIDE(lvl2dimRef);
const uint64_t dimRank = reader.getRank();
const uint64_t lvlRank = MEMREF_GET_USIZE(lvlSizesRef);
ASSERT_USIZE_EQ(lvlTypesRef, lvlRank);
- ASSERT_USIZE_EQ(lvl2dimRef, lvlRank);
ASSERT_USIZE_EQ(dim2lvlRef, dimRank);
+ ASSERT_USIZE_EQ(lvl2dimRef, lvlRank);
(void)dimRank;
const index_type *lvlSizes = MEMREF_GET_PAYLOAD(lvlSizesRef);
const DimLevelType *lvlTypes = MEMREF_GET_PAYLOAD(lvlTypesRef);
diff --git a/mlir/test/Dialect/SparseTensor/conversion.mlir b/mlir/test/Dialect/SparseTensor/conversion.mlir
index 138736e26c1dfdd..29093a055ab2e04 100644
--- a/mlir/test/Dialect/SparseTensor/conversion.mlir
+++ b/mlir/test/Dialect/SparseTensor/conversion.mlir
@@ -136,18 +136,16 @@ func.func @sparse_new3d(%arg0: !llvm.ptr<i8>) -> tensor<?x?x?xf32, #SparseTensor
// CHECK-DAG: %[[Empty:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[DimSizes0:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes0:.*]] = memref.alloca() : memref<2xindex>
// CHECK-DAG: %[[LvlTypes0:.*]] = memref.alloca() : memref<2xi8>
+// CHECK-DAG: %[[Sizes0:.*]] = memref.alloca() : memref<2xindex>
// CHECK-DAG: %[[Iota0:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.cast %[[DimSizes0]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.cast %[[LvlSizes0]] : memref<2xindex> to memref<?xindex>
// CHECK-DAG: %[[LvlTypes:.*]] = memref.cast %[[LvlTypes0]] : memref<2xi8> to memref<?xi8>
+// CHECK-DAG: %[[Sizes:.*]] = memref.cast %[[Sizes0]] : memref<2xindex> to memref<?xindex>
// CHECK-DAG: %[[Iota:.*]] = memref.cast %[[Iota0]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I]], %[[DimSizes0]][%[[C0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[J]], %[[DimSizes0]][%[[C1]]] : memref<2xindex>
+// CHECK-DAG: memref.store %[[I]], %[[Sizes0]][%[[C0]]] : memref<2xindex>
+// CHECK-DAG: memref.store %[[J]], %[[Sizes0]][%[[C1]]] : memref<2xindex>
// CHECK: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizes]], %[[LvlSizes]], %[[LvlTypes]], %[[Iota]], %[[Iota]], %{{.*}}, %{{.*}}, %{{.*}}, %[[Empty]], %[[NP]])
+// CHECK: %[[T:.*]] = call @newSparseTensor(%[[Sizes]], %[[Sizes]], %[[LvlTypes]], %[[Iota]], %[[Iota]], %{{.*}}, %{{.*}}, %{{.*}}, %[[Empty]], %[[NP]])
// CHECK: return %[[T]] : !llvm.ptr<i8>
func.func @sparse_init(%arg0: index, %arg1: index) -> tensor<?x?xf64, #CSR> {
%0 = tensor.empty(%arg0, %arg1) : tensor<?x?xf64, #CSR>
diff --git a/mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir b/mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
index b11da60cd48308b..1a69c80f7ecadfd 100644
--- a/mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
+++ b/mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir
@@ -1,6 +1,4 @@
// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
-// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-foreach=false" \
-// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector = #sparse_tensor.encoding<{
map = (d0) -> (d0 : compressed)
@@ -18,161 +16,187 @@
map = (d0, d1, d2) -> (d2 : dense, d0 : compressed, d1 : compressed)
}>
-// CHECK-LABEL: func @sparse_convert_1d(
-// CHECK-SAME: %[[A:.*]]: tensor<?xi32>) -> !llvm.ptr<i8> {
-// CHECK-DAG: %[[EmptyCOO:.*]] = arith.constant 4 : i32
-// CHECK-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : i32
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[U:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?xi32>
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[EmptyCOO]], %[[NP]])
-// CHECK: %[[M:.*]] = memref.alloca() : memref<1xindex>
-// CHECK: %[[T:.*]] = memref.cast %[[M]] : memref<1xindex> to memref<?xindex>
-// CHECK: %[[BUF:.*]] = memref.alloca() : memref<i32>
-// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[U]] step %[[C1]] {
-// CHECK: %[[E:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<?xi32>
-// CHECK: %[[N:.*]] = arith.cmpi ne, %[[E]], %[[I0]] : i32
-// CHECK: scf.if %[[N]] {
-// CHECK: memref.store %[[I]], %[[M]][%[[C0]]] : memref<1xindex>
-// CHECK: memref.store %[[E]], %[[BUF]][] : memref<i32>
-// CHECK: call @addEltI32(%[[C]], %[[BUF]], %[[T]], %[[IotaP]])
-// CHECK: }
-// CHECK: }
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK: call @delSparseTensorCOOI32(%[[C]])
-// CHECK: return %[[T]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_convert_1d(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<?xi32>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_0]], %[[VAL_7]] : tensor<?xi32>
+// CHECK: %[[VAL_9:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_10:.*]] = memref.cast %[[VAL_9]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<1xi8>
+// CHECK: %[[VAL_11:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = memref.cast %[[VAL_11]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_11]]{{\[}}%[[VAL_7]]] : memref<1xindex>
+// CHECK: %[[VAL_13:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_14:.*]] = memref.cast %[[VAL_13]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<1xindex>
+// CHECK: %[[VAL_15:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_12]], %[[VAL_12]], %[[VAL_10]], %[[VAL_14]], %[[VAL_14]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_15]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_18:.*]] = memref.cast %[[VAL_17]] : memref<1xindex> to memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.alloca() : memref<i32>
+// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_5]] {
+// CHECK: %[[VAL_21:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_20]]] : tensor<?xi32>
+// CHECK: %[[VAL_22:.*]] = arith.cmpi ne, %[[VAL_21]], %[[VAL_4]] : i32
+// CHECK: scf.if %[[VAL_22]] {
+// CHECK: memref.store %[[VAL_20]], %[[VAL_17]]{{\[}}%[[VAL_7]]] : memref<1xindex>
+// CHECK: memref.store %[[VAL_21]], %[[VAL_19]][] : memref<i32>
+// CHECK: %[[VAL_23:.*]] = func.call @addEltI32(%[[VAL_16]], %[[VAL_19]], %[[VAL_18]], %[[VAL_14]]) : (!llvm.ptr<i8>, memref<i32>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: }
+// CHECK: %[[VAL_24:.*]] = call @newSparseTensor(%[[VAL_12]], %[[VAL_12]], %[[VAL_10]], %[[VAL_14]], %[[VAL_14]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_1]], %[[VAL_16]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOI32(%[[VAL_16]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_24]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_1d(%arg0: tensor<?xi32>) -> tensor<?xi32, #SparseVector> {
%0 = sparse_tensor.convert %arg0 : tensor<?xi32> to tensor<?xi32, #SparseVector>
return %0 : tensor<?xi32, #SparseVector>
}
-// CHECK-LABEL: func @sparse_convert_complex(
-// CHECK-SAME: %[[A:.*]]: tensor<100xcomplex<f64>>) -> !llvm.ptr<i8> {
-// CHECK-DAG: %[[CC:.*]] = complex.constant [0.000000e+00, 0.000000e+00] : complex<f64>
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[C100:.*]] = arith.constant 100 : index
-// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[C100]] step %[[C1]] {
-// CHECK: %[[E:.*]] = tensor.extract %[[A]][%[[I]]] : tensor<100xcomplex<f64>>
-// CHECK: %[[N:.*]] = complex.neq %[[E]], %[[CC]] : complex<f64>
-// CHECK: scf.if %[[N]] {
-// CHECK: memref.store %[[I]], %{{.*}}[%[[C0]]] : memref<1xindex>
-// CHECK: call @addEltC64
-// CHECK: }
-// CHECK: }
-// CHECK: %[[T:.*]] = call @newSparseTensor
-// CHECK: call @delSparseTensorCOOC64
-// CHECK: return %[[T]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_convert_complex(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<100xcomplex<f64>>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = complex.constant [0.000000e+00, 0.000000e+00] : complex<f64>
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 9 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 100 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_10]]{{\[}}%[[VAL_6]]] : memref<1xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_12]]{{\[}}%[[VAL_6]]] : memref<1xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_6]]] : memref<1xindex>
+// CHECK: %[[VAL_16:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_3]], %[[VAL_16]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_18:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_19:.*]] = memref.cast %[[VAL_18]] : memref<1xindex> to memref<?xindex>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<complex<f64>>
+// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_9]] {
+// CHECK: %[[VAL_22:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_21]]] : tensor<100xcomplex<f64>>
+// CHECK: %[[VAL_23:.*]] = complex.neq %[[VAL_22]], %[[VAL_2]] : complex<f64>
+// CHECK: scf.if %[[VAL_23]] {
+// CHECK: memref.store %[[VAL_21]], %[[VAL_18]]{{\[}}%[[VAL_6]]] : memref<1xindex>
+// CHECK: memref.store %[[VAL_22]], %[[VAL_20]][] : memref<complex<f64>>
+// CHECK: %[[VAL_24:.*]] = func.call @addEltC64(%[[VAL_17]], %[[VAL_20]], %[[VAL_19]], %[[VAL_15]]) : (!llvm.ptr<i8>, memref<complex<f64>>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: }
+// CHECK: %[[VAL_25:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_1]], %[[VAL_17]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOC64(%[[VAL_17]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_25]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_complex(%arg0: tensor<100xcomplex<f64>>) -> tensor<100xcomplex<f64>, #SparseVector> {
%0 = sparse_tensor.convert %arg0 : tensor<100xcomplex<f64>> to tensor<100xcomplex<f64>, #SparseVector>
return %0 : tensor<100xcomplex<f64>, #SparseVector>
}
-// CHECK-LABEL: func @sparse_convert_2d(
-// CHECK-SAME: %[[A:.*]]: tensor<2x4xf64>) -> !llvm.ptr<i8>
-// CHECK-DAG: %[[EmptyCOO:.*]] = arith.constant 4 : i32
-// CHECK-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[EmptyCOO]], %[[NP]])
-// CHECK: %[[M:.*]] = memref.alloca() : memref<2xindex>
-// CHECK: %[[T:.*]] = memref.cast %[[M]] : memref<2xindex> to memref<?xindex>
-// CHECK: %[[BUF:.*]] = memref.alloca() : memref<f64>
-// CHECK: scf.for %[[I:.*]] = %[[C0]] to %{{.*}} step %[[C1]] {
-// CHECK: scf.for %[[J:.*]] = %[[C0]] to %{{.*}} step %[[C1]] {
-// CHECK: %[[E:.*]] = tensor.extract %[[A]][%[[I]], %[[J]]] : tensor<2x4xf64>
-// CHECK: memref.store %[[I]], %[[M]][%[[C0]]] : memref<2xindex>
-// CHECK: memref.store %[[J]], %[[M]][%[[C1]]] : memref<2xindex>
-// CHECK: memref.store %[[E]], %[[BUF]][] : memref<f64>
-// CHECK: call @addEltF64(%[[C]], %[[BUF]], %[[T]], %[[IotaP]])
-// CHECK: }
-// CHECK: }
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK: call @delSparseTensorCOOF64(%[[C]])
-// 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<{{{.*}}}>> {
-// 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
-// CHECK-RWT: %[[CMP:.*]] = arith.cmpf une, %[[L0V]]
-// CHECK-RWT: %[[IFR:.*]] = scf.if %[[CMP]]
-// CHECK-RWT: %[[L0T2:.*]] = sparse_tensor.insert %[[L0V]] into %[[L0T]]{{\[}}%[[L0I0]], %[[L0I1]]]
-// CHECK-RWT: scf.yield %[[L0T2]]
-// CHECK-RWT: } else {
-// CHECK-RWT: scf.yield %[[L0T]]
-// CHECK-RWT: }
-// CHECK-RWT: sparse_tensor.yield %[[IFR]]
-// CHECK-RWT: }
-// CHECK-RWT: %[[R:.*]] = sparse_tensor.load %[[T2]] hasInserts
-// CHECK-RWT: return %[[R]]
-// CHECK-RWT: }
+// CHECK-LABEL: func.func @sparse_convert_2d(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<2x4xf64>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 4 : index
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 8 : i8
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_12]]{{\[}}%[[VAL_6]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_11]], %[[VAL_12]]{{\[}}%[[VAL_8]]] : memref<2xi8>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_14]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_14]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_16]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_16]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_19:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_15]], %[[VAL_13]], %[[VAL_17]], %[[VAL_17]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_3]], %[[VAL_18]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_22:.*]] = memref.alloca() : memref<f64>
+// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_8]] {
+// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_6]] to %[[VAL_9]] step %[[VAL_8]] {
+// CHECK: %[[VAL_25:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_23]], %[[VAL_24]]] : tensor<2x4xf64>
+// CHECK: %[[VAL_26:.*]] = arith.cmpf une, %[[VAL_25]], %[[VAL_2]] : f64
+// CHECK: scf.if %[[VAL_26]] {
+// CHECK: memref.store %[[VAL_23]], %[[VAL_20]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_24]], %[[VAL_20]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_25]], %[[VAL_22]][] : memref<f64>
+// CHECK: %[[VAL_27:.*]] = func.call @addEltF64(%[[VAL_19]], %[[VAL_22]], %[[VAL_21]], %[[VAL_17]]) : (!llvm.ptr<i8>, memref<f64>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: }
+// CHECK: }
+// CHECK: %[[VAL_28:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_15]], %[[VAL_13]], %[[VAL_17]], %[[VAL_17]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_1]], %[[VAL_19]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOF64(%[[VAL_19]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_28]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_2d(%arg0: tensor<2x4xf64>) -> tensor<2x4xf64, #CSR> {
%0 = sparse_tensor.convert %arg0 : tensor<2x4xf64> to tensor<2x4xf64, #CSR>
return %0 : tensor<2x4xf64, #CSR>
}
-// CHECK-LABEL: func @sparse_constant() -> !llvm.ptr<i8> {
-// CHECK-DAG: %[[EmptyCOO:.*]] = arith.constant 4 : i32
-// CHECK-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[EmptyCOO]], %[[NP]])
-// CHECK: %[[M:.*]] = memref.alloca() : memref<2xindex>
-// CHECK: %[[N:.*]] = memref.cast %[[M]] : memref<2xindex> to memref<?xindex>
-// CHECK: %[[BUF:.*]] = memref.alloca() : memref<f32>
-// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[C2]] step %[[C1]] {
-// CHECK-DAG: memref.store %{{.*}}, %[[M]][%[[C0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %{{.*}}, %[[M]][%[[C1]]] : memref<2xindex>
-// CHECK-DAG: %[[V:.*]] = tensor.extract %{{.*}}[%[[I]]] : tensor<2xf32>
-// CHECK: memref.store %[[V]], %[[BUF]][] : memref<f32>
-// CHECK: call @addEltF32(%{{.*}}, %[[BUF]], %[[N]], %[[IotaP]])
-// CHECK: }
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK: call @delSparseTensorCOOF32(%[[C]])
-// CHECK: return %[[T]] : !llvm.ptr<i8>
-
-// CHECK-RWT-LABEL: func.func @sparse_constant() -> tensor<8x7xf32, #sparse_tensor.encoding<{{{.*}}}>> {
-// 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]])
-// CHECK-RWT: ^bb0(%[[L0I0:.*]]: index, %[[L0I1:.*]]: index, %[[L0V:.*]]: f32, %[[L0T:.*]]: tensor
-// CHECK-RWT: %[[L0T2:.*]] = sparse_tensor.insert %[[L0V]] into %[[L0T]]{{\[}}%[[L0I0]], %[[L0I1]]]
-// CHECK-RWT: sparse_tensor.yield %[[L0T2]]
-// CHECK-RWT: }
-// CHECK-RWT: %[[R:.*]] = sparse_tensor.load %[[T1]] hasInserts
-// CHECK-RWT: return %[[R]]
-// CHECK-RWT: }
+// CHECK-LABEL: func.func @sparse_constant() -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_0:.*]] = arith.constant dense<[1.000000e+00, 5.000000e+00]> : tensor<2xf32>
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<{{\[\[}}0, 0], [1, 6]]> : tensor<2x2xi64>
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 7 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 2 : index
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_12]]{{\[}}%[[VAL_7]]] : memref<2xi8>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_14]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_16]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_16]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_19:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_15]], %[[VAL_13]], %[[VAL_17]], %[[VAL_17]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_18]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_22:.*]] = memref.alloca() : memref<f32>
+// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_5]] to %[[VAL_11]] step %[[VAL_7]] {
+// CHECK: %[[VAL_24:.*]] = tensor.extract %[[VAL_1]]{{\[}}%[[VAL_23]], %[[VAL_5]]] : tensor<2x2xi64>
+// CHECK: %[[VAL_25:.*]] = arith.index_cast %[[VAL_24]] : i64 to index
+// CHECK: %[[VAL_26:.*]] = tensor.extract %[[VAL_1]]{{\[}}%[[VAL_23]], %[[VAL_7]]] : tensor<2x2xi64>
+// CHECK: %[[VAL_27:.*]] = arith.index_cast %[[VAL_26]] : i64 to index
+// CHECK: %[[VAL_28:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_23]]] : tensor<2xf32>
+// CHECK: memref.store %[[VAL_25]], %[[VAL_20]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_27]], %[[VAL_20]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_28]], %[[VAL_22]][] : memref<f32>
+// CHECK: %[[VAL_29:.*]] = func.call @addEltF32(%[[VAL_19]], %[[VAL_22]], %[[VAL_21]], %[[VAL_17]]) : (!llvm.ptr<i8>, memref<f32>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: %[[VAL_30:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_15]], %[[VAL_13]], %[[VAL_17]], %[[VAL_17]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_3]], %[[VAL_19]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOF32(%[[VAL_19]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_30]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_constant() -> tensor<8x7xf32, #CSR>{
// Initialize a tensor.
%0 = arith.constant sparse<[[0, 0], [1, 6]], [1.0, 5.0]> : tensor<8x7xf32>
@@ -181,17 +205,59 @@ func.func @sparse_constant() -> tensor<8x7xf32, #CSR>{
return %1 : tensor<8x7xf32, #CSR>
}
-// CHECK-RWT-LABEL: func.func @sparse_constant_csc() -> tensor<8x7xf32,
-// CHECK-RWT: %[[VAL_0:.*]] = arith.constant sparse<{{\[\[}}0, 0], [1, 6]], [1.000000e+00, 5.000000e+00]> : tensor<8x7xf32>
-// CHECK-RWT: %[[VAL_1:.*]] = bufferization.alloc_tensor() :
-// CHECK-RWT: %[[VAL_2:.*]] = sparse_tensor.foreach in %[[VAL_0]] init(%[[VAL_1]]) {order = #map} : tensor<8x7xf32>,
-// CHECK-RWT: ^bb0(%[[VAL_3:.*]]: index, %[[VAL_4:.*]]: index, %[[VAL_5:.*]]: f32, %[[VAL_6:.*]]: tensor
-// CHECK-RWT: %[[VAL_7:.*]] = sparse_tensor.insert %[[VAL_5]] into %[[VAL_6]]{{\[}}%[[VAL_4]], %[[VAL_3]]] :
-// CHECK-RWT: sparse_tensor.yield %[[VAL_7]] :
-// CHECK-RWT: }
-// CHECK-RWT: %[[VAL_8:.*]] = sparse_tensor.load %[[VAL_9:.*]] hasInserts :
-// CHECK-RWT: return %[[VAL_8]] :
-// CHECK-RWT: }
+// CHECK-LABEL: func.func @sparse_constant_csc() -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_0:.*]] = arith.constant dense<[1.000000e+00, 5.000000e+00]> : tensor<2xf32>
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<{{\[\[}}0, 0], [1, 6]]> : tensor<2x2xi64>
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 7 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 2 : index
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_12]]{{\[}}%[[VAL_7]]] : memref<2xi8>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_14]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_16]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_16]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_19:.*]] = memref.cast %[[VAL_18]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_18]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_18]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_20]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_20]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_22:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_23:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_21]], %[[VAL_13]], %[[VAL_17]], %[[VAL_19]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_22]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_24:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_25:.*]] = memref.cast %[[VAL_24]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_26:.*]] = memref.alloca() : memref<f32>
+// CHECK: scf.for %[[VAL_27:.*]] = %[[VAL_5]] to %[[VAL_11]] step %[[VAL_7]] {
+// CHECK: %[[VAL_28:.*]] = tensor.extract %[[VAL_1]]{{\[}}%[[VAL_27]], %[[VAL_5]]] : tensor<2x2xi64>
+// CHECK: %[[VAL_29:.*]] = arith.index_cast %[[VAL_28]] : i64 to index
+// CHECK: %[[VAL_30:.*]] = tensor.extract %[[VAL_1]]{{\[}}%[[VAL_27]], %[[VAL_7]]] : tensor<2x2xi64>
+// CHECK: %[[VAL_31:.*]] = arith.index_cast %[[VAL_30]] : i64 to index
+// CHECK: %[[VAL_32:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_27]]] : tensor<2xf32>
+// CHECK: memref.store %[[VAL_29]], %[[VAL_24]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_31]], %[[VAL_24]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_32]], %[[VAL_26]][] : memref<f32>
+// CHECK: %[[VAL_33:.*]] = func.call @addEltF32(%[[VAL_23]], %[[VAL_26]], %[[VAL_25]], %[[VAL_17]]) : (!llvm.ptr<i8>, memref<f32>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: %[[VAL_34:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_21]], %[[VAL_13]], %[[VAL_17]], %[[VAL_19]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_3]], %[[VAL_23]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOF32(%[[VAL_23]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_34]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_constant_csc() -> tensor<8x7xf32, #CSC>{
// Initialize a tensor.
%0 = arith.constant sparse<[[0, 0], [1, 6]], [1.0, 5.0]> : tensor<8x7xf32>
@@ -200,46 +266,73 @@ func.func @sparse_constant_csc() -> tensor<8x7xf32, #CSC>{
return %1 : tensor<8x7xf32, #CSC>
}
-// CHECK-LABEL: func @sparse_convert_3d(
-// CHECK-SAME: %[[A:.*]]: tensor<?x?x?xf64>) -> !llvm.ptr<i8>
-// CHECK-DAG: %[[EmptyCOO:.*]] = arith.constant 4 : i32
-// CHECK-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[U1:.*]] = tensor.dim %[[A]], %[[C0]] : tensor<?x?x?xf64>
-// CHECK-DAG: %[[U2:.*]] = tensor.dim %[[A]], %[[C1]] : tensor<?x?x?xf64>
-// CHECK-DAG: %[[U3:.*]] = tensor.dim %[[A]], %[[C2]] : tensor<?x?x?xf64>
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<3xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[Lvl2Dim:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[Dim2Lvl:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<3xi8> to memref<?xi8>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: %[[Lvl2DimP:.*]] = memref.cast %[[Lvl2Dim]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: %[[Dim2LvlP:.*]] = memref.cast %[[Dim2Lvl]] : memref<3xindex> to memref<?xindex>
-// CHECK: %[[NP:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[Lvl2DimP]], %[[Dim2LvlP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[EmptyCOO]], %[[NP]])
-// CHECK: %[[M:.*]] = memref.alloca() : memref<3xindex>
-// CHECK: %[[N:.*]] = memref.cast %[[M]] : memref<3xindex> to memref<?xindex>
-// CHECK: %[[BUF:.*]] = memref.alloca() : memref<f64>
-// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[U1]] step %[[C1]] {
-// CHECK: scf.for %[[J:.*]] = %[[C0]] to %[[U2]] step %[[C1]] {
-// CHECK: scf.for %[[K:.*]] = %[[C0]] to %[[U3]] step %[[C1]] {
-// CHECK: %[[E:.*]] = tensor.extract %[[A]][%[[I]], %[[J]], %[[K]]] : tensor<?x?x?xf64>
-// CHECK: memref.store %[[I]], %[[M]][%[[C0]]] : memref<3xindex>
-// CHECK: memref.store %[[J]], %[[M]][%[[C1]]] : memref<3xindex>
-// CHECK: memref.store %[[K]], %[[M]][%[[C2]]] : memref<3xindex>
-// CHECK: memref.store %[[E]], %[[BUF]][] : memref<f64>
-// CHECK: call @addEltF64(%[[C]], %[[BUF]], %[[N]], %[[Dim2LvlP]])
-// CHECK: }
-// CHECK: }
-// CHECK: }
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[Lvl2DimP]], %[[Dim2LvlP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK: call @delSparseTensorCOOF64(%[[C]])
-// CHECK: return %[[T]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_convert_3d(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf64>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 4 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_11:.*]] = tensor.dim %[[VAL_0]], %[[VAL_10]] : tensor<?x?x?xf64>
+// CHECK: %[[VAL_12:.*]] = tensor.dim %[[VAL_0]], %[[VAL_9]] : tensor<?x?x?xf64>
+// CHECK: %[[VAL_13:.*]] = tensor.dim %[[VAL_0]], %[[VAL_8]] : tensor<?x?x?xf64>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<3xi8>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<3xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_14]]{{\[}}%[[VAL_10]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_9]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_8]]] : memref<3xi8>
+// CHECK: %[[VAL_16:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_11]], %[[VAL_16]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_12]], %[[VAL_16]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_13]], %[[VAL_16]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: %[[VAL_21:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_22:.*]] = memref.cast %[[VAL_21]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_21]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_21]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_21]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: %[[VAL_23:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_24:.*]] = memref.cast %[[VAL_23]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_23]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_23]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_23]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: %[[VAL_25:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_26:.*]] = memref.cast %[[VAL_25]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_18]], %[[VAL_25]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_19]], %[[VAL_25]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_20]], %[[VAL_25]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: %[[VAL_27:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_28:.*]] = call @newSparseTensor(%[[VAL_17]], %[[VAL_26]], %[[VAL_15]], %[[VAL_22]], %[[VAL_24]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_3]], %[[VAL_27]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_29:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_30:.*]] = memref.cast %[[VAL_29]] : memref<3xindex> to memref<?xindex>
+// CHECK: %[[VAL_31:.*]] = memref.alloca() : memref<f64>
+// CHECK: scf.for %[[VAL_32:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_9]] {
+// CHECK: scf.for %[[VAL_33:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_9]] {
+// CHECK: scf.for %[[VAL_34:.*]] = %[[VAL_10]] to %[[VAL_13]] step %[[VAL_9]] {
+// CHECK: %[[VAL_35:.*]] = tensor.extract %[[VAL_0]]{{\[}}%[[VAL_32]], %[[VAL_33]], %[[VAL_34]]] : tensor<?x?x?xf64>
+// CHECK: %[[VAL_36:.*]] = arith.cmpf une, %[[VAL_35]], %[[VAL_2]] : f64
+// CHECK: scf.if %[[VAL_36]] {
+// CHECK: memref.store %[[VAL_32]], %[[VAL_29]]{{\[}}%[[VAL_10]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_33]], %[[VAL_29]]{{\[}}%[[VAL_9]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_34]], %[[VAL_29]]{{\[}}%[[VAL_8]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_35]], %[[VAL_31]][] : memref<f64>
+// CHECK: %[[VAL_37:.*]] = func.call @addEltF64(%[[VAL_28]], %[[VAL_31]], %[[VAL_30]], %[[VAL_22]]) : (!llvm.ptr<i8>, memref<f64>, memref<?xindex>, memref<?xindex>) -> !llvm.ptr<i8>
+// CHECK: }
+// CHECK: }
+// CHECK: }
+// CHECK: }
+// CHECK: %[[VAL_38:.*]] = call @newSparseTensor(%[[VAL_17]], %[[VAL_26]], %[[VAL_15]], %[[VAL_22]], %[[VAL_24]], %[[VAL_5]], %[[VAL_5]], %[[VAL_4]], %[[VAL_1]], %[[VAL_28]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOF64(%[[VAL_28]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_38]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_3d(%arg0: tensor<?x?x?xf64>) -> tensor<?x?x?xf64, #SparseTensor> {
%0 = sparse_tensor.convert %arg0 : tensor<?x?x?xf64> to tensor<?x?x?xf64, #SparseTensor>
return %0 : tensor<?x?x?xf64, #SparseTensor>
diff --git a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
index 17394aa46783aee..ffc0f57a231103e 100644
--- a/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
+++ b/mlir/test/Dialect/SparseTensor/convert_sparse2dense.mlir
@@ -1,8 +1,5 @@
// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
-// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-foreach=false" \
-// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
-
#SparseVector = #sparse_tensor.encoding<{
map = (d0) -> (d0 : compressed)
}>
@@ -15,358 +12,326 @@
map = (d0, d1, d2) -> (d2 : dense, d0 : compressed, d1 : compressed)
}>
-// CHECK-LABEL: func @sparse_convert_1d(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<13xi32>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I13:.*]] = arith.constant 13 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : i32
-// CHECK-DAG: %[[C6:.*]] = arith.constant 6 : i32
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<1xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I13]], %[[DimSizes]][%[[I0]]] : memref<1xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<1xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %[[C0]], %[[C0]], %[[C6]], %[[C6]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<i32>
-// CHECK-DAG: %[[M:.*]] = memref.alloc() : memref<13xi32>
-// CHECK-DAG: linalg.fill ins(%[[C0]] : i32) outs(%[[M]] : memref<13xi32>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextI32(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<i32>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<1xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<i32>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]]] : memref<13xi32>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<13xi32>
-// CHECK: return %[[T]] : tensor<13xi32>
+// CHECK-LABEL: func.func @sparse_convert_1d(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<13xi32> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 13 : index
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 4 : i8
+// CHECK: %[[VAL_6:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<1xi8>
+// CHECK: %[[VAL_8:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<1xindex>
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_3]], %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = call @newSparseTensor(%[[VAL_9]], %[[VAL_9]], %[[VAL_7]], %[[VAL_11]], %[[VAL_11]], %[[VAL_2]], %[[VAL_2]], %[[VAL_1]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_13:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_14:.*]] = memref.cast %[[VAL_13]] : memref<1xindex> to memref<?xindex>
+// CHECK: %[[VAL_15:.*]] = memref.alloca() : memref<i32>
+// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<13xi32>
+// CHECK: linalg.fill ins(%[[VAL_2]] : i32) outs(%[[VAL_16]] : memref<13xi32>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_17:.*]] = func.call @getNextI32(%[[VAL_12]], %[[VAL_14]], %[[VAL_15]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<i32>) -> i1
+// CHECK: scf.condition(%[[VAL_17]])
+// CHECK: } do {
+// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_3]]] : memref<1xindex>
+// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_15]][] : memref<i32>
+// CHECK: memref.store %[[VAL_19]], %[[VAL_16]]{{\[}}%[[VAL_18]]] : memref<13xi32>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorI32(%[[VAL_12]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_16]] : memref<13xi32>
+// CHECK: return %[[VAL_20]] : tensor<13xi32>
+// CHECK: }
func.func @sparse_convert_1d(%arg0: tensor<13xi32, #SparseVector>) -> tensor<13xi32> {
%0 = sparse_tensor.convert %arg0 : tensor<13xi32, #SparseVector> to tensor<13xi32>
return %0 : tensor<13xi32>
}
-// CHECK-LABEL: func @sparse_convert_1d_dyn(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<?xi32>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : i32
-// CHECK-DAG: %[[C6:.*]] = arith.constant 6 : i32
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<1xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[SizeI0:.*]] = call @sparseDimSize(%[[Arg]], %[[I0]]) : (!llvm.ptr<i8>, index) -> index
-// CHECK-DAG: memref.store %[[SizeI0]], %[[DimSizes]][%[[I0]]] : memref<1xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<1xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %[[C0]], %[[C0]], %[[C6]], %[[C6]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<i32>
-// CHECK-DAG: %[[M:.*]] = memref.alloc(%[[SizeI0]]) : memref<?xi32>
-// CHECK-DAG: linalg.fill ins(%[[C0]] : i32) outs(%[[M]] : memref<?xi32>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextI32(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<i32>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<1xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<i32>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]]] : memref<?xi32>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<?xi32>
-// CHECK: return %[[T]] : tensor<?xi32>
+// CHECK-LABEL: func.func @sparse_convert_1d_dyn(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<?xi32> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_6:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_3]], %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<1xi8>
+// CHECK: %[[VAL_8:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = call @newSparseTensor(%[[VAL_9]], %[[VAL_9]], %[[VAL_7]], %[[VAL_11]], %[[VAL_11]], %[[VAL_2]], %[[VAL_2]], %[[VAL_1]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_13:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_14:.*]] = memref.cast %[[VAL_13]] : memref<1xindex> to memref<?xindex>
+// CHECK: %[[VAL_15:.*]] = memref.alloca() : memref<i32>
+// CHECK: %[[VAL_16:.*]] = memref.alloc(%[[VAL_5]]) : memref<?xi32>
+// CHECK: linalg.fill ins(%[[VAL_2]] : i32) outs(%[[VAL_16]] : memref<?xi32>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_17:.*]] = func.call @getNextI32(%[[VAL_12]], %[[VAL_14]], %[[VAL_15]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<i32>) -> i1
+// CHECK: scf.condition(%[[VAL_17]])
+// CHECK: } do {
+// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_15]][] : memref<i32>
+// CHECK: memref.store %[[VAL_19]], %[[VAL_16]]{{\[}}%[[VAL_18]]] : memref<?xi32>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorI32(%[[VAL_12]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_16]] : memref<?xi32>
+// CHECK: return %[[VAL_20]] : tensor<?xi32>
+// CHECK: }
func.func @sparse_convert_1d_dyn(%arg0: tensor<?xi32, #SparseVector>) -> tensor<?xi32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xi32, #SparseVector> to tensor<?xi32>
return %0 : tensor<?xi32>
}
-// CHECK-LABEL: func @sparse_convert_2d(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<2x4xf64>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[I2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[I4:.*]] = arith.constant 4 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[ActionToIter:.*]] = arith.constant 6 : i32
-// CHECK-DAG: %[[E0:.*]] = arith.constant 0.000000e+00 : f64
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<2xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I1]]] : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I2]], %[[DimSizes]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I4]], %[[DimSizes]][%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ActionToIter]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<f64>
-// CHECK-DAG: %[[M:.*]] = memref.alloc() : memref<2x4xf64>
-// CHECK-DAG: linalg.fill ins(%[[E0]] : f64) outs(%[[M]] : memref<2x4xf64>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextF64(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<2xindex>
-// CHECK: %[[Iv1:.*]] = memref.load %[[IndS]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<f64>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]], %[[Iv1]]] : memref<2x4xf64>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<2x4xf64>
-// CHECK: return %[[T]] : tensor<2x4xf64>
-
-// CHECK-RWT-LABEL: func.func @sparse_convert_2d(
-// CHECK-RWT-SAME: %[[A:.*]]: tensor<2x4xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> 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]]
-// CHECK-RWT: sparse_tensor.foreach in %[[A]]
-// CHECK-RWT: ^bb0(%[[FI0:.*]]: index, %[[FI1:.*]]: index, %[[FV:.*]]: f64):
-// CHECK-RWT: memref.store %[[FV]], %[[B]]{{\[}}%[[FI0]], %[[FI1]]]
-// CHECK-RWT: }
-// CHECK-RWT: %[[T:.*]] = bufferization.to_tensor %[[B]]
-// CHECK-RWT: return %[[T]] : tensor<2x4xf64>
+// CHECK-LABEL: func.func @sparse_convert_2d(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<2x4xf64> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 4 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 4 : i8
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_10]]{{\[}}%[[VAL_6]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_12]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = memref.cast %[[VAL_17]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.alloca() : memref<f64>
+// CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<2x4xf64>
+// CHECK: linalg.fill ins(%[[VAL_1]] : f64) outs(%[[VAL_20]] : memref<2x4xf64>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_21:.*]] = func.call @getNextF64(%[[VAL_16]], %[[VAL_18]], %[[VAL_19]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
+// CHECK: scf.condition(%[[VAL_21]])
+// CHECK: } do {
+// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_19]][] : memref<f64>
+// CHECK: memref.store %[[VAL_24]], %[[VAL_20]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<2x4xf64>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorF64(%[[VAL_16]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_25:.*]] = bufferization.to_tensor %[[VAL_20]] : memref<2x4xf64>
+// CHECK: return %[[VAL_25]] : tensor<2x4xf64>
+// CHECK: }
func.func @sparse_convert_2d(%arg0: tensor<2x4xf64, #SparseMatrix>) -> tensor<2x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x4xf64, #SparseMatrix> to tensor<2x4xf64>
return %0 : tensor<2x4xf64>
}
-// CHECK-LABEL: func @sparse_convert_2d_dyn0(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<?x4xf64>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[I4:.*]] = arith.constant 4 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[ActionToIter:.*]] = arith.constant 6 : i32
-// CHECK-DAG: %[[E0:.*]] = arith.constant 0.000000e+00 : f64
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<2xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I1]]] : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[SizeI0:.*]] = call @sparseDimSize(%[[Arg]], %[[I0]]) : (!llvm.ptr<i8>, index) -> index
-// CHECK-DAG: memref.store %[[SizeI0]], %[[DimSizes]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I4]], %[[DimSizes]][%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ActionToIter]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<f64>
-// CHECK-DAG: %[[M:.*]] = memref.alloc(%[[SizeI0]]) : memref<?x4xf64>
-// CHECK-DAG: linalg.fill ins(%[[E0]] : f64) outs(%[[M]] : memref<?x4xf64>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextF64(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<2xindex>
-// CHECK: %[[Iv1:.*]] = memref.load %[[IndS]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<f64>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]], %[[Iv1]]] : memref<?x4xf64>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<?x4xf64>
-// CHECK: return %[[T]] : tensor<?x4xf64>
+// CHECK-LABEL: func.func @sparse_convert_2d_dyn0(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<?x4xf64> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 4 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_9:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_8]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_12]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_14]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = memref.cast %[[VAL_17]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.alloca() : memref<f64>
+// CHECK: %[[VAL_20:.*]] = memref.alloc(%[[VAL_9]]) : memref<?x4xf64>
+// CHECK: linalg.fill ins(%[[VAL_1]] : f64) outs(%[[VAL_20]] : memref<?x4xf64>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_21:.*]] = func.call @getNextF64(%[[VAL_16]], %[[VAL_18]], %[[VAL_19]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
+// CHECK: scf.condition(%[[VAL_21]])
+// CHECK: } do {
+// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_19]][] : memref<f64>
+// CHECK: memref.store %[[VAL_24]], %[[VAL_20]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<?x4xf64>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorF64(%[[VAL_16]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_25:.*]] = bufferization.to_tensor %[[VAL_20]] : memref<?x4xf64>
+// CHECK: return %[[VAL_25]] : tensor<?x4xf64>
+// CHECK: }
func.func @sparse_convert_2d_dyn0(%arg0: tensor<?x4xf64, #SparseMatrix>) -> tensor<?x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<?x4xf64, #SparseMatrix> to tensor<?x4xf64>
return %0 : tensor<?x4xf64>
}
-// CHECK-LABEL: func @sparse_convert_2d_dyn1(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<2x?xf64>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[I2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[ActionToIter:.*]] = arith.constant 6 : i32
-// CHECK-DAG: %[[E0:.*]] = arith.constant 0.000000e+00 : f64
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<2xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I1]]] : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[SizeI1:.*]] = call @sparseDimSize(%[[Arg]], %[[I1]]) : (!llvm.ptr<i8>, index) -> index
-// CHECK-DAG: memref.store %[[I2]], %[[DimSizes]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[SizeI1]], %[[DimSizes]][%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ActionToIter]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<f64>
-// CHECK-DAG: %[[M:.*]] = memref.alloc(%[[SizeI1]]) : memref<2x?xf64>
-// CHECK-DAG: linalg.fill ins(%[[E0]] : f64) outs(%[[M]] : memref<2x?xf64>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextF64(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<2xindex>
-// CHECK: %[[Iv1:.*]] = memref.load %[[IndS]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<f64>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]], %[[Iv1]]] : memref<2x?xf64>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<2x?xf64>
-// CHECK: return %[[T]] : tensor<2x?xf64>
+// CHECK-LABEL: func.func @sparse_convert_2d_dyn1(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<2x?xf64> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_9:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_8]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<2xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_12]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_14]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = memref.cast %[[VAL_17]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.alloca() : memref<f64>
+// CHECK: %[[VAL_20:.*]] = memref.alloc(%[[VAL_9]]) : memref<2x?xf64>
+// CHECK: linalg.fill ins(%[[VAL_1]] : f64) outs(%[[VAL_20]] : memref<2x?xf64>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_21:.*]] = func.call @getNextF64(%[[VAL_16]], %[[VAL_18]], %[[VAL_19]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
+// CHECK: scf.condition(%[[VAL_21]])
+// CHECK: } do {
+// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_8]]] : memref<2xindex>
+// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_19]][] : memref<f64>
+// CHECK: memref.store %[[VAL_24]], %[[VAL_20]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<2x?xf64>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorF64(%[[VAL_16]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_25:.*]] = bufferization.to_tensor %[[VAL_20]] : memref<2x?xf64>
+// CHECK: return %[[VAL_25]] : tensor<2x?xf64>
+// CHECK: }
func.func @sparse_convert_2d_dyn1(%arg0: tensor<2x?xf64, #SparseMatrix>) -> tensor<2x?xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x?xf64, #SparseMatrix> to tensor<2x?xf64>
return %0 : tensor<2x?xf64>
}
-// CHECK-LABEL: func @sparse_convert_2d_dyn2(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf64>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[ActionToIter:.*]] = arith.constant 6 : i32
-// CHECK-DAG: %[[E0:.*]] = arith.constant 0.000000e+00 : f64
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<2xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I1]]] : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[SizeI0:.*]] = call @sparseDimSize(%[[Arg]], %[[I0]]) : (!llvm.ptr<i8>, index) -> index
-// CHECK-DAG: %[[SizeI1:.*]] = call @sparseDimSize(%[[Arg]], %[[I1]]) : (!llvm.ptr<i8>, index) -> index
-// CHECK-DAG: memref.store %[[SizeI0]], %[[DimSizes]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[SizeI1]], %[[DimSizes]][%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ActionToIter]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<f64>
-// CHECK-DAG: %[[M:.*]] = memref.alloc(%[[SizeI0]], %[[SizeI1]]) : memref<?x?xf64>
-// CHECK-DAG: linalg.fill ins(%[[E0]] : f64) outs(%[[M]] : memref<?x?xf64>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextF64(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<2xindex>
-// CHECK: %[[Iv1:.*]] = memref.load %[[IndS]][%[[I1]]] : memref<2xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<f64>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]], %[[Iv1]]] : memref<?x?xf64>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<?x?xf64>
-// 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<{{{.*}}}>>) -> 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
-// CHECK-RWT: %[[D0:.*]] = tensor.dim %[[A]], %[[C0]]
-// CHECK-RWT: %[[D1:.*]] = tensor.dim %[[A]], %[[C1]]
-// CHECK-RWT: %[[B:.*]] = memref.alloc(%[[D0]], %[[D1]])
-// CHECK-RWT: linalg.fill ins(%[[F0]] : f64) outs(%[[B]]
-// CHECK-RWT: sparse_tensor.foreach in %[[A]]
-// CHECK-RWT: ^bb0(%[[FI0:.*]]: index, %[[FI1:.*]]: index, %[[FV:.*]]: f64):
-// CHECK-RWT: memref.store %[[FV]], %[[B]]{{\[}}%[[FI0]], %[[FI1]]]
-// CHECK-RWT: }
-// CHECK-RWT: %[[T:.*]] = bufferization.to_tensor %[[B]]
-// CHECK-RWT: return %[[T]] : tensor<?x?xf64>
+// CHECK-LABEL: func.func @sparse_convert_2d_dyn2(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<?x?xf64> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 4 : i8
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_8:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_7]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_9:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_10]]{{\[}}%[[VAL_7]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_10]]{{\[}}%[[VAL_6]]] : memref<2xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_12]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_12]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_14]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_14]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_17:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = memref.cast %[[VAL_17]] : memref<2xindex> to memref<?xindex>
+// CHECK: %[[VAL_19:.*]] = memref.alloca() : memref<f64>
+// CHECK: %[[VAL_20:.*]] = memref.alloc(%[[VAL_8]], %[[VAL_9]]) : memref<?x?xf64>
+// CHECK: linalg.fill ins(%[[VAL_1]] : f64) outs(%[[VAL_20]] : memref<?x?xf64>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_21:.*]] = func.call @getNextF64(%[[VAL_16]], %[[VAL_18]], %[[VAL_19]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
+// CHECK: scf.condition(%[[VAL_21]])
+// CHECK: } do {
+// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_7]]] : memref<2xindex>
+// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_19]][] : memref<f64>
+// CHECK: memref.store %[[VAL_24]], %[[VAL_20]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<?x?xf64>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorF64(%[[VAL_16]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_25:.*]] = bufferization.to_tensor %[[VAL_20]] : memref<?x?xf64>
+// CHECK: return %[[VAL_25]] : tensor<?x?xf64>
+// CHECK: }
func.func @sparse_convert_2d_dyn2(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x?xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<?x?xf64, #SparseMatrix> to tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
-// CHECK-LABEL: func @sparse_convert_3d(
-// CHECK-SAME: %[[Arg:.*]]: !llvm.ptr<i8>) -> tensor<2x3x4xf64>
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[I2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[I3:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[I4:.*]] = arith.constant 4 : index
-// CHECK-DAG: %[[DenseDLT:.*]] = arith.constant 4 : i8
-// CHECK-DAG: %[[ActionToIter:.*]] = arith.constant 6 : i32
-// CHECK-DAG: %[[E0:.*]] = arith.constant 0.000000e+00 : f64
-//
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<3xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<3xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I0]]] : memref<3xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I1]]] : memref<3xi8>
-// CHECK-DAG: memref.store %[[DenseDLT]], %[[LvlTypes]][%[[I2]]] : memref<3xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I2]], %[[DimSizes]][%[[I0]]] : memref<3xindex>
-// CHECK-DAG: memref.store %[[I3]], %[[DimSizes]][%[[I1]]] : memref<3xindex>
-// CHECK-DAG: memref.store %[[I4]], %[[DimSizes]][%[[I2]]] : memref<3xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]][%[[I0]]] : memref<3xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]][%[[I1]]] : memref<3xindex>
-// CHECK-DAG: memref.store %[[I2]], %[[Iota]][%[[I2]]] : memref<3xindex>
-// CHECK: %[[Iter:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ActionToIter]], %[[Arg]])
-//
-// CHECK-DAG: %[[IndS:.*]] = memref.alloca() : memref<3xindex>
-// CHECK-DAG: %[[IndD:.*]] = memref.cast %[[IndS]] : memref<3xindex> to memref<?xindex>
-// CHECK-DAG: %[[ElemBuffer:.*]] = memref.alloca() : memref<f64>
-// CHECK-DAG: %[[M:.*]] = memref.alloc() : memref<2x3x4xf64>
-// CHECK-DAG: linalg.fill ins(%[[E0]] : f64) outs(%[[M]] : memref<2x3x4xf64>)
-// CHECK: scf.while : () -> () {
-// CHECK: %[[Cond:.*]] = func.call @getNextF64(%[[Iter]], %[[IndD]], %[[ElemBuffer]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
-// CHECK: scf.condition(%[[Cond]])
-// CHECK: } do {
-// CHECK: %[[Iv0:.*]] = memref.load %[[IndS]][%[[I0]]] : memref<3xindex>
-// CHECK: %[[Iv1:.*]] = memref.load %[[IndS]][%[[I1]]] : memref<3xindex>
-// CHECK: %[[Iv2:.*]] = memref.load %[[IndS]][%[[I2]]] : memref<3xindex>
-// CHECK: %[[ElemVal:.*]] = memref.load %[[ElemBuffer]][] : memref<f64>
-// CHECK: memref.store %[[ElemVal]], %[[M]][%[[Iv0]], %[[Iv1]], %[[Iv2]]] : memref<2x3x4xf64>
-// CHECK: scf.yield
-// CHECK: }
-// CHECK: %[[T:.*]] = bufferization.to_tensor %[[M]] : memref<2x3x4xf64>
-// CHECK: return %[[T]] : tensor<2x3x4xf64>
+// CHECK-LABEL: func.func @sparse_convert_3d(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> tensor<2x3x4xf64> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 3 : index
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 4 : index
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 4 : i8
+// CHECK: %[[VAL_11:.*]] = memref.alloca() : memref<3xi8>
+// CHECK: %[[VAL_12:.*]] = memref.cast %[[VAL_11]] : memref<3xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_11]]{{\[}}%[[VAL_7]]] : memref<3xi8>
+// CHECK: %[[VAL_13:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_14:.*]] = memref.cast %[[VAL_13]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_13]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: %[[VAL_15:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_16:.*]] = memref.cast %[[VAL_15]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_15]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_15]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_15]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: %[[VAL_17:.*]] = call @newSparseTensor(%[[VAL_14]], %[[VAL_14]], %[[VAL_12]], %[[VAL_16]], %[[VAL_16]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_2]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_18:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_19:.*]] = memref.cast %[[VAL_18]] : memref<3xindex> to memref<?xindex>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<f64>
+// CHECK: %[[VAL_21:.*]] = memref.alloc() : memref<2x3x4xf64>
+// CHECK: linalg.fill ins(%[[VAL_1]] : f64) outs(%[[VAL_21]] : memref<2x3x4xf64>)
+// CHECK: scf.while : () -> () {
+// CHECK: %[[VAL_22:.*]] = func.call @getNextF64(%[[VAL_17]], %[[VAL_19]], %[[VAL_20]]) : (!llvm.ptr<i8>, memref<?xindex>, memref<f64>) -> i1
+// CHECK: scf.condition(%[[VAL_22]])
+// CHECK: } do {
+// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_20]][] : memref<f64>
+// CHECK: memref.store %[[VAL_26]], %[[VAL_21]]{{\[}}%[[VAL_23]], %[[VAL_24]], %[[VAL_25]]] : memref<2x3x4xf64>
+// CHECK: scf.yield
+// CHECK: }
+// CHECK: call @delSparseTensorIteratorF64(%[[VAL_17]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: %[[VAL_27:.*]] = bufferization.to_tensor %[[VAL_21]] : memref<2x3x4xf64>
+// CHECK: return %[[VAL_27]] : tensor<2x3x4xf64>
+// CHECK: }
func.func @sparse_convert_3d(%arg0: tensor<2x3x4xf64, #SparseTensor>) -> tensor<2x3x4xf64> {
%0 = sparse_tensor.convert %arg0 : tensor<2x3x4xf64, #SparseTensor> to tensor<2x3x4xf64>
return %0 : tensor<2x3x4xf64>
diff --git a/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir b/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
index c373fd23bbef492..e8e69dc861015ce 100644
--- a/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
+++ b/mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir
@@ -1,13 +1,4 @@
-// First use with `kViaCOO` for sparse2sparse conversion (the old way).
-// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=1" \
-// RUN: --canonicalize --cse | FileCheck %s -check-prefix=CHECK-COO
-//
-// Now again with `kAuto` (the new default).
-// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=0" \
-// RUN: --canonicalize --cse | FileCheck %s -check-prefixes=CHECK-AUTO,CHECK
-
-// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-foreach=false" \
-// RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
+// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
#SparseVector64 = #sparse_tensor.encoding<{
map = (d0) -> (d0 : compressed),
@@ -42,71 +33,67 @@
map = (d0 : #sparse_tensor<slice(2, 2, 1)>, d1 : #sparse_tensor<slice(12, 13, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
}>
-// CHECK-LABEL: func @sparse_nop_convert(
-// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8>
-// CHECK: return %[[A]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_nop_convert(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK: return %[[VAL_0]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_nop_convert(%arg0: tensor<64xf32, #SparseVector>) -> tensor<64xf32, #SparseVector> {
%0 = sparse_tensor.convert %arg0 : tensor<64xf32, #SparseVector> to tensor<64xf32, #SparseVector>
return %0 : tensor<64xf32, #SparseVector>
}
-// CHECK-LABEL: func @sparse_hidden_nop_cast(
-// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8>
-// CHECK: return %[[A]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_hidden_nop_cast(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK: return %[[VAL_0]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_hidden_nop_cast(%arg0: tensor<32xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
%0 = sparse_tensor.convert %arg0 : tensor<32xf32, #SparseVector> to tensor<?xf32, #SparseVector>
return %0 : tensor<?xf32, #SparseVector>
}
-// CHECK-LABEL: func @sparse_convert_1d_ss(
-// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-// CHECK-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
-// CHECK: return %[[T]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_convert_1d_ss(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 3 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_6:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_3]], %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<1xi8>
+// CHECK: %[[VAL_8:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = call @newSparseTensor(%[[VAL_9]], %[[VAL_9]], %[[VAL_7]], %[[VAL_11]], %[[VAL_11]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: return %[[VAL_12]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_1d_ss(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
return %0 : tensor<?xf32, #SparseVector32>
}
-// CHECK-COO-LABEL: func @sparse_convert(
-// CHECK-COO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-// CHECK-COO-DAG: %[[ToCOO:.*]] = arith.constant 5 : i32
-// CHECK-COO-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-COO-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-COO-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-COO-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ToCOO]], %[[A]])
-// CHECK-COO: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK-COO: call @delSparseTensorCOOF32(%[[C]])
-// CHECK-COO: return %[[T]] : !llvm.ptr<i8>
-//
-// CHECK-AUTO-LABEL: func @sparse_convert(
-// CHECK-AUTO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-// CHECK-AUTO-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
-// CHECK-AUTO-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-AUTO-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-AUTO-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
-// CHECK-AUTO: return %[[T]] : !llvm.ptr<i8>
-
+// CHECK-LABEL: func.func @sparse_convert(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 3 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_6:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_3]], %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<1xi8>
+// CHECK: %[[VAL_8:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = call @newSparseTensor(%[[VAL_9]], %[[VAL_9]], %[[VAL_7]], %[[VAL_11]], %[[VAL_11]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: return %[[VAL_12]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
return %0 : tensor<?xf32, #SparseVector32>
@@ -124,87 +111,107 @@ func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32
crdWidth = 32
}>
-// CHECK-COO-LABEL: func @sparse_convert_singleton(
-// CHECK-COO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-// CHECK-COO-DAG: %[[ToCOO:.*]] = arith.constant 5 : i32
-// CHECK-COO-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
-// CHECK-COO-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-COO-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-COO-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-COO-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-COO: %[[C:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ToCOO]], %[[A]])
-// CHECK-COO: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
-// CHECK-COO: call @delSparseTensorCOOF32(%[[C]])
-// CHECK-COO: return %[[T]] : !llvm.ptr<i8>
//
-// CHECK-AUTO-LABEL: func @sparse_convert_singleton(
-// CHECK-AUTO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
-// CHECK-AUTO-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
-// CHECK-AUTO-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
-// CHECK-AUTO-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[Iota:.*]] = memref.alloca() : memref<1xindex>
-// CHECK-AUTO-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<1xi8> to memref<?xi8>
-// CHECK-AUTO-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<1xindex> to memref<?xindex>
-// CHECK-AUTO: %[[T:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
-// CHECK-AUTO: return %[[T]] : !llvm.ptr<i8>
+// CHECK-LABEL: func.func @sparse_convert_singleton(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 3 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : i8
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_6:.*]] = memref.alloca() : memref<1xi8>
+// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<1xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_3]], %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<1xi8>
+// CHECK: %[[VAL_8:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<1xindex>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<1xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<1xindex>
+// CHECK: %[[VAL_12:.*]] = call @newSparseTensor(%[[VAL_9]], %[[VAL_9]], %[[VAL_7]], %[[VAL_11]], %[[VAL_11]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: return %[[VAL_12]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_singleton(%arg0: tensor<?xf32, #SparseSingleton64>) -> tensor<?xf32, #SparseSingleton32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseSingleton64> to tensor<?xf32, #SparseSingleton32>
return %0 : tensor<?xf32, #SparseSingleton32>
}
-// CHECK-RWT-LABEL: func.func @sparse_convert_permuted(
-// CHECK-RWT-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #{{.*}}>>) -> tensor<?x?x?xf32, #{{.*}}>> {
-// CHECK-RWT-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
-// CHECK-RWT-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
-// CHECK-RWT-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
-// CHECK-RWT-DAG: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]]
-// CHECK-RWT-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]]
-// CHECK-RWT-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]]
-// CHECK-RWT-DAG: %[[VAL_7:.*]] = sparse_tensor.number_of_entries %[[VAL_0]]
-// CHECK-RWT: %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_4]], %[[VAL_5]], %[[VAL_6]]) size_hint=%[[VAL_7]]
-// CHECK-RWT: %[[VAL_9:.*]] = sparse_tensor.foreach in %[[VAL_0]] init(%[[VAL_8]])
-// CHECK-RWT: ^bb0(%[[VAL_10:.*]]: index, %[[VAL_11:.*]]: index, %[[VAL_12:.*]]: index, %[[VAL_13:.*]]: f32, %[[VAL_14:.*]]: tensor<?x?x?xf32, #{{.*}}>>):
-// CHECK-RWT: %[[VAL_15:.*]] = sparse_tensor.insert %[[VAL_13]] into %[[VAL_14]]{{\[}}%[[VAL_12]], %[[VAL_10]], %[[VAL_11]]]
-// CHECK-RWT: sparse_tensor.yield %[[VAL_15]] : tensor<?x?x?xf32, #{{.*}}>>
-// CHECK-RWT: }
-// CHECK-RWT: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_17:.*]] hasInserts : tensor<?x?x?xf32, #{{.*}}>>
-// CHECK-RWT: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_16]] : tensor<?x?x?xf32, #{{.*}}>> to memref<?xf32>
-// CHECK-RWT: %[[VAL_19:.*]] = sparse_tensor.coordinates_buffer %[[VAL_16]] : tensor<?x?x?xf32, #{{.*}}>> to memref<?xindex>
-// CHECK-RWT: sparse_tensor.sort hybrid_quick_sort %[[VAL_7]], %[[VAL_19]] jointly %[[VAL_18]] {ny = 0 : index, perm_map = #map}
-// CHECK-RWT: %[[VAL_20:.*]] = bufferization.alloc_tensor(%[[VAL_4]], %[[VAL_5]], %[[VAL_6]]) size_hint=%[[VAL_7]]
-// CHECK-RWT: %[[VAL_21:.*]] = sparse_tensor.foreach in %[[VAL_16]] init(%[[VAL_20]])
-// CHECK-RWT: ^bb0(%[[VAL_22:.*]]: index, %[[VAL_23:.*]]: index, %[[VAL_24:.*]]: index, %[[VAL_25:.*]]: f32, %[[VAL_26:.*]]: tensor<?x?x?xf32, #{{.*}}>>):
-// CHECK-RWT: %[[VAL_27:.*]] = sparse_tensor.insert %[[VAL_25]] into %[[VAL_26]]{{\[}}%[[VAL_24]], %[[VAL_22]], %[[VAL_23]]]
-// CHECK-RWT: sparse_tensor.yield %[[VAL_27]]
-// CHECK-RWT: }
-// CHECK-RWT: bufferization.dealloc_tensor %[[VAL_16]]
-// CHECK-RWT: %[[VAL_28:.*]] = sparse_tensor.load %[[VAL_29:.*]] hasInserts
-// CHECK-RWT: %[[VAL_30:.*]] = sparse_tensor.convert %[[VAL_28]]
-// CHECK-RWT: return %[[VAL_30]]
+// CHECK-LABEL: func.func @sparse_convert_permuted(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 5 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : i8
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_8:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_7]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_9:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_10:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> index
+// CHECK: %[[VAL_11:.*]] = memref.alloca() : memref<3xi8>
+// CHECK: %[[VAL_12:.*]] = memref.cast %[[VAL_11]] : memref<3xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_11]]{{\[}}%[[VAL_7]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<3xi8>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<3xi8>
+// CHECK: %[[VAL_13:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_14:.*]] = memref.cast %[[VAL_13]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_13]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: %[[VAL_18:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_19:.*]] = memref.cast %[[VAL_18]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_18]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_18]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_18]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_20:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_20]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_20]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_20]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_22:.*]] = memref.alloca() : memref<3xindex>
+// CHECK: %[[VAL_23:.*]] = memref.cast %[[VAL_22]] : memref<3xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_15]], %[[VAL_22]]{{\[}}%[[VAL_7]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_16]], %[[VAL_22]]{{\[}}%[[VAL_6]]] : memref<3xindex>
+// CHECK: memref.store %[[VAL_17]], %[[VAL_22]]{{\[}}%[[VAL_5]]] : memref<3xindex>
+// CHECK: %[[VAL_24:.*]] = call @newSparseTensor(%[[VAL_14]], %[[VAL_23]], %[[VAL_12]], %[[VAL_19]], %[[VAL_21]], %[[VAL_3]], %[[VAL_3]], %[[VAL_2]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: %[[VAL_25:.*]] = call @newSparseTensor(%[[VAL_14]], %[[VAL_23]], %[[VAL_12]], %[[VAL_19]], %[[VAL_21]], %[[VAL_3]], %[[VAL_3]], %[[VAL_2]], %[[VAL_2]], %[[VAL_24]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: call @delSparseTensorCOOF32(%[[VAL_24]]) : (!llvm.ptr<i8>) -> ()
+// CHECK: return %[[VAL_25]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_permuted(%arg0: tensor<?x?x?xf32, #SortedCOO3D>) -> tensor<?x?x?xf32, #TsssPermuted> {
%0 = sparse_tensor.convert %arg0 : tensor<?x?x?xf32, #SortedCOO3D> to tensor<?x?x?xf32, #TsssPermuted>
return %0 : tensor<?x?x?xf32, #TsssPermuted>
}
-// CHECK-RWT-LABEL: func.func @sparse_convert_slice(
-// CHECK-RWT-SAME: %[[VAL_0:.*]]: tensor<2x13xi32, #{{.*}}>) -> tensor<2x13xi32, #{{.*}}> {
-// CHECK-RWT: %[[VAL_1:.*]] = sparse_tensor.number_of_entries %[[VAL_0]] : tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: %[[VAL_2:.*]] = bufferization.alloc_tensor() size_hint=%[[VAL_1]] : tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: %[[VAL_3:.*]] = sparse_tensor.foreach in %[[VAL_0]] init(%[[VAL_2]]) : tensor<2x13xi32, #{{.*}}>, tensor<2x13xi32, #{{.*}}> -> tensor<2x13xi32, #{{.*}}> do {
-// CHECK-RWT: ^bb0(%[[VAL_4:.*]]: index, %[[VAL_5:.*]]: index, %[[VAL_6:.*]]: i32, %[[VAL_7:.*]]: tensor<2x13xi32, #{{.*}}>):
-// CHECK-RWT: %[[VAL_8:.*]] = sparse_tensor.insert %[[VAL_6]] into %[[VAL_7]]{{\[}}%[[VAL_4]], %[[VAL_5]]] : tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: sparse_tensor.yield %[[VAL_8]] : tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: }
-// CHECK-RWT: %[[VAL_9:.*]] = sparse_tensor.load %[[VAL_10:.*]] hasInserts : tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: %[[VAL_11:.*]] = sparse_tensor.convert %[[VAL_9]] : tensor<2x13xi32, #{{.*}}> to tensor<2x13xi32, #{{.*}}>
-// CHECK-RWT: return %[[VAL_11]] : tensor<2x13xi32, #{{.*}}>
+// CHECK-LABEL: func.func @sparse_convert_slice(
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 3 : i32
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 6 : i32
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 13 : index
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 9 : i8
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 16 : i8
+// CHECK: %[[VAL_10:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_11:.*]] = memref.cast %[[VAL_10]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_8]], %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<2xi8>
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_7]], %[[VAL_12]]{{\[}}%[[VAL_4]]] : memref<2xindex>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_4]], %[[VAL_14]]{{\[}}%[[VAL_4]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = call @newSparseTensor(%[[VAL_13]], %[[VAL_13]], %[[VAL_11]], %[[VAL_15]], %[[VAL_15]], %[[VAL_3]], %[[VAL_3]], %[[VAL_2]], %[[VAL_1]], %[[VAL_0]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK: return %[[VAL_16]] : !llvm.ptr<i8>
+// CHECK: }
func.func @sparse_convert_slice(%arg0: tensor<2x13xi32, #COOSlice>) -> (tensor<2x13xi32, #SortedCOO2D>) {
%0 = sparse_tensor.convert %arg0 : tensor<2x13xi32, #COOSlice> to tensor<2x13xi32, #SortedCOO2D>
return %0 : tensor<2x13xi32, #SortedCOO2D>
diff --git a/mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir b/mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir
index e1fe5d85e72ec28..7d852ca9cc1aa26 100644
--- a/mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir
+++ b/mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir
@@ -3,64 +3,60 @@
#DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>
// CHECK-LABEL: func.func @fill_zero_after_alloc(
-// CHECK-SAME: %[[Arg0:.*]]: !llvm.ptr<i8>,
-// CHECK-SAME: %[[Arg1:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
-// CHECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f64
-// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : i32
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : i32
-// CHECK-DAG: %[[I0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[I1:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[False:.*]] = arith.constant false
-// CHECK-DAG: %[[True:.*]] = arith.constant true
-// CHECK-DAG: %[[I100:.*]] = arith.constant 100 : index
-// CHECK-DAG: %[[I300:.*]] = arith.constant 300 : index
-// CHECK-DAG: %[[CompressedDLT:.*]] = arith.constant 8 : i8
-// CHECK-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<2xi8>
-// CHECK-DAG: %[[LvlTypesP:.*]] = memref.cast %[[LvlTypes]] : memref<2xi8> to memref<?xi8>
-// CHECK-DAG: memref.store %[[CompressedDLT]], %[[LvlTypes]]{{\[}}%[[I0]]] : memref<2xi8>
-// CHECK-DAG: memref.store %[[CompressedDLT]], %[[LvlTypes]]{{\[}}%[[I1]]] : memref<2xi8>
-// CHECK-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[DimSizesP:.*]] = memref.cast %[[DimSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I100]], %[[DimSizes]]{{\[}}%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I300]], %[[DimSizes]]{{\[}}%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[LvlSizes:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[LvlSizesP:.*]] = memref.cast %[[LvlSizes]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I100]], %[[LvlSizes]]{{\[}}%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I300]], %[[LvlSizes]]{{\[}}%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[Iota:.*]] = memref.alloca() : memref<2xindex>
-// CHECK-DAG: %[[IotaP:.*]] = memref.cast %[[Iota]] : memref<2xindex> to memref<?xindex>
-// CHECK-DAG: memref.store %[[I0]], %[[Iota]]{{\[}}%[[I0]]] : memref<2xindex>
-// CHECK-DAG: memref.store %[[I1]], %[[Iota]]{{\[}}%[[I1]]] : memref<2xindex>
-// CHECK-DAG: %[[NullPtr:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
-// CHECK: %[[VAL_19:.*]] = call @newSparseTensor(%[[DimSizesP]], %[[LvlSizesP]], %[[LvlTypesP]], %[[IotaP]], %[[IotaP]], %[[C0]], %[[C0]], %[[C1]], %[[C0]], %[[NullPtr]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
+// CHECK-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
+// CHECK-SAME: %[[VAL_1:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8> {
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : i32
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : i32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant false
+// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true
+// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 100 : index
+// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 300 : index
+// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 8 : i8
+// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<2xi8>
+// CHECK: %[[VAL_13:.*]] = memref.cast %[[VAL_12]] : memref<2xi8> to memref<?xi8>
+// CHECK: memref.store %[[VAL_11]], %[[VAL_12]]{{\[}}%[[VAL_5]]] : memref<2xi8>
+// CHECK: memref.store %[[VAL_11]], %[[VAL_12]]{{\[}}%[[VAL_6]]] : memref<2xi8>
+// CHECK: %[[VAL_14:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_9]], %[[VAL_14]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_10]], %[[VAL_14]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_16:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_5]], %[[VAL_16]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: memref.store %[[VAL_6]], %[[VAL_16]]{{\[}}%[[VAL_6]]] : memref<2xindex>
+// CHECK: %[[VAL_18:.*]] = llvm.mlir.zero : !llvm.ptr<i8>
+// CHECK: %[[VAL_19:.*]] = call @newSparseTensor(%[[VAL_15]], %[[VAL_15]], %[[VAL_13]], %[[VAL_17]], %[[VAL_17]], %[[VAL_4]], %[[VAL_4]], %[[VAL_3]], %[[VAL_4]], %[[VAL_18]]) : (memref<?xindex>, memref<?xindex>, memref<?xi8>, memref<?xindex>, memref<?xindex>, i32, i32, i32, i32, !llvm.ptr<i8>) -> !llvm.ptr<i8>
// CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<300xf64>
// CHECK: %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<300xf64> to memref<?xf64>
// CHECK: %[[VAL_22:.*]] = memref.alloc() : memref<300xi1>
// CHECK: %[[VAL_23:.*]] = memref.cast %[[VAL_22]] : memref<300xi1> to memref<?xi1>
// CHECK: %[[VAL_24:.*]] = memref.alloc() : memref<300xindex>
// CHECK: %[[VAL_25:.*]] = memref.cast %[[VAL_24]] : memref<300xindex> to memref<?xindex>
-// CHECK: linalg.fill ins(%[[F0]] : f64) outs(%[[VAL_20]] : memref<300xf64>)
-// CHECK: linalg.fill ins(%[[False]] : i1) outs(%[[VAL_22]] : memref<300xi1>)
-// CHECK: %[[VAL_26:.*]] = call @sparsePositions0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_27:.*]] = call @sparseCoordinates0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_28:.*]] = call @sparsePositions0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_29:.*]] = call @sparseCoordinates0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_30:.*]] = call @sparseValuesF64(%[[Arg0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
-// CHECK: %[[VAL_31:.*]] = call @sparsePositions0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_32:.*]] = call @sparseCoordinates0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_33:.*]] = call @sparsePositions0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_34:.*]] = call @sparseCoordinates0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK: %[[VAL_35:.*]] = call @sparseValuesF64(%[[Arg1]]) : (!llvm.ptr<i8>) -> memref<?xf64>
-// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_26]]{{\[}}%[[I0]]] : memref<?xindex>
-// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_26]]{{\[}}%[[I1]]] : memref<?xindex>
-// CHECK: scf.for %[[VAL_38:.*]] = %[[VAL_36]] to %[[VAL_37]] step %[[I1]] {
+// CHECK: linalg.fill ins(%[[VAL_2]] : f64) outs(%[[VAL_20]] : memref<300xf64>)
+// CHECK: linalg.fill ins(%[[VAL_7]] : i1) outs(%[[VAL_22]] : memref<300xi1>)
+// CHECK: %[[VAL_26:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_27:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_28:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_29:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_30:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
+// CHECK: %[[VAL_31:.*]] = call @sparsePositions0(%[[VAL_1]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_32:.*]] = call @sparseCoordinates0(%[[VAL_1]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_33:.*]] = call @sparsePositions0(%[[VAL_1]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_34:.*]] = call @sparseCoordinates0(%[[VAL_1]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK: %[[VAL_35:.*]] = call @sparseValuesF64(%[[VAL_1]]) : (!llvm.ptr<i8>) -> memref<?xf64>
+// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_26]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_26]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK: scf.for %[[VAL_38:.*]] = %[[VAL_36]] to %[[VAL_37]] step %[[VAL_6]] {
// CHECK: %[[VAL_39:.*]] = memref.load %[[VAL_27]]{{\[}}%[[VAL_38]]] : memref<?xindex>
// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_28]]{{\[}}%[[VAL_38]]] : memref<?xindex>
-// CHECK: %[[VAL_41:.*]] = arith.addi %[[VAL_38]], %[[I1]] : index
+// CHECK: %[[VAL_41:.*]] = arith.addi %[[VAL_38]], %[[VAL_6]] : index
// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_28]]{{\[}}%[[VAL_41]]] : memref<?xindex>
-// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_31]]{{\[}}%[[I0]]] : memref<?xindex>
-// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_31]]{{\[}}%[[I1]]] : memref<?xindex>
-// CHECK: %[[VAL_45:.*]]:3 = scf.while (%[[VAL_46:.*]] = %[[VAL_40]], %[[VAL_47:.*]] = %[[VAL_43]], %[[VAL_48:.*]] = %[[I0]]) : (index, index, index) -> (index, index, index) {
+// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_31]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_31]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK: %[[VAL_45:.*]]:3 = scf.while (%[[VAL_46:.*]] = %[[VAL_40]], %[[VAL_47:.*]] = %[[VAL_43]], %[[VAL_48:.*]] = %[[VAL_5]]) : (index, index, index) -> (index, index, index) {
// CHECK: %[[VAL_49:.*]] = arith.cmpi ult, %[[VAL_46]], %[[VAL_42]] : index
// CHECK: %[[VAL_50:.*]] = arith.cmpi ult, %[[VAL_47]], %[[VAL_44]] : index
// CHECK: %[[VAL_51:.*]] = arith.andi %[[VAL_49]], %[[VAL_50]] : i1
@@ -77,50 +73,50 @@
// CHECK: %[[VAL_62:.*]] = scf.if %[[VAL_61]] -> (index) {
// CHECK: %[[VAL_63:.*]] = memref.load %[[VAL_30]]{{\[}}%[[VAL_52]]] : memref<?xf64>
// CHECK: %[[VAL_64:.*]] = memref.load %[[VAL_33]]{{\[}}%[[VAL_53]]] : memref<?xindex>
-// CHECK: %[[VAL_65:.*]] = arith.addi %[[VAL_53]], %[[I1]] : index
+// CHECK: %[[VAL_65:.*]] = arith.addi %[[VAL_53]], %[[VAL_6]] : index
// CHECK: %[[VAL_66:.*]] = memref.load %[[VAL_33]]{{\[}}%[[VAL_65]]] : memref<?xindex>
-// CHECK: %[[VAL_67:.*]] = scf.for %[[VAL_68:.*]] = %[[VAL_64]] to %[[VAL_66]] step %[[I1]] iter_args(%[[VAL_69:.*]] = %[[VAL_54]]) -> (index) {
+// CHECK: %[[VAL_67:.*]] = scf.for %[[VAL_68:.*]] = %[[VAL_64]] to %[[VAL_66]] step %[[VAL_6]] iter_args(%[[VAL_69:.*]] = %[[VAL_54]]) -> (index) {
// CHECK: %[[VAL_70:.*]] = memref.load %[[VAL_34]]{{\[}}%[[VAL_68]]] : memref<?xindex>
// CHECK: %[[VAL_71:.*]] = memref.load %[[VAL_20]]{{\[}}%[[VAL_70]]] : memref<300xf64>
// CHECK: %[[VAL_72:.*]] = memref.load %[[VAL_35]]{{\[}}%[[VAL_68]]] : memref<?xf64>
// CHECK: %[[VAL_73:.*]] = arith.mulf %[[VAL_63]], %[[VAL_72]] : f64
// CHECK: %[[VAL_74:.*]] = arith.addf %[[VAL_71]], %[[VAL_73]] : f64
// CHECK: %[[VAL_75:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_70]]] : memref<300xi1>
-// CHECK: %[[VAL_76:.*]] = arith.cmpi eq, %[[VAL_75]], %[[False]] : i1
+// CHECK: %[[VAL_76:.*]] = arith.cmpi eq, %[[VAL_75]], %[[VAL_7]] : i1
// CHECK: %[[VAL_77:.*]] = scf.if %[[VAL_76]] -> (index) {
-// CHECK: memref.store %[[True]], %[[VAL_22]]{{\[}}%[[VAL_70]]] : memref<300xi1>
-// CHECK: memref.store %[[VAL_70]], %[[VAL_24]]{{\[}}%[[VAL_69]]] : memref<300xindex>
-// CHECK: %[[VAL_78:.*]] = arith.addi %[[VAL_69]], %[[I1]] : index
-// CHECK: scf.yield %[[VAL_78]] : index
+// CHECK: memref.store %[[VAL_8]], %[[VAL_22]]{{\[}}%[[VAL_70]]] : memref<300xi1>
+// CHECK: memref.store %[[VAL_70]], %[[VAL_24]]{{\[}}%[[VAL_69]]] : memref<300xindex>
+// CHECK: %[[VAL_78:.*]] = arith.addi %[[VAL_69]], %[[VAL_6]] : index
+// CHECK: scf.yield %[[VAL_78]] : index
// CHECK: } else {
-// CHECK: scf.yield %[[VAL_69]] : index
+// CHECK: scf.yield %[[VAL_69]] : index
// CHECK: }
// CHECK: memref.store %[[VAL_74]], %[[VAL_20]]{{\[}}%[[VAL_70]]] : memref<300xf64>
-// CHECK: scf.yield %[[VAL_79:.*]] : index
+// CHECK: scf.yield %[[VAL_77]] : index
// CHECK: }
-// CHECK: scf.yield %[[VAL_80:.*]] : index
+// CHECK: scf.yield %[[VAL_67]] : index
// CHECK: } else {
// CHECK: scf.yield %[[VAL_54]] : index
// CHECK: }
-// CHECK: %[[VAL_81:.*]] = arith.addi %[[VAL_52]], %[[I1]] : index
-// CHECK: %[[VAL_82:.*]] = arith.select %[[VAL_59]], %[[VAL_81]], %[[VAL_52]] : index
-// CHECK: %[[VAL_83:.*]] = arith.addi %[[VAL_53]], %[[I1]] : index
-// CHECK: %[[VAL_84:.*]] = arith.select %[[VAL_60]], %[[VAL_83]], %[[VAL_53]] : index
-// CHECK: scf.yield %[[VAL_82]], %[[VAL_84]], %[[VAL_85:.*]] : index, index, index
+// CHECK: %[[VAL_79:.*]] = arith.addi %[[VAL_52]], %[[VAL_6]] : index
+// CHECK: %[[VAL_80:.*]] = arith.select %[[VAL_59]], %[[VAL_79]], %[[VAL_52]] : index
+// CHECK: %[[VAL_81:.*]] = arith.addi %[[VAL_53]], %[[VAL_6]] : index
+// CHECK: %[[VAL_82:.*]] = arith.select %[[VAL_60]], %[[VAL_81]], %[[VAL_53]] : index
+// CHECK: scf.yield %[[VAL_80]], %[[VAL_82]], %[[VAL_62]] : index, index, index
// CHECK: }
-// CHECK: %[[VAL_86:.*]] = memref.alloca() : memref<2xindex>
-// CHECK: %[[VAL_87:.*]] = memref.cast %[[VAL_86]] : memref<2xindex> to memref<?xindex>
-// CHECK: memref.store %[[VAL_39]], %[[VAL_86]]{{\[}}%[[I0]]] : memref<2xindex>
-// CHECK: func.call @expInsertF64(%[[VAL_19]], %[[VAL_87]], %[[VAL_21]], %[[VAL_23]], %[[VAL_25]], %[[VAL_88:.*]]#2) : (!llvm.ptr<i8>, memref<?xindex>, memref<?xf64>, memref<?xi1>, memref<?xindex>, index) -> ()
+// CHECK: %[[VAL_83:.*]] = memref.alloca() : memref<2xindex>
+// CHECK: %[[VAL_84:.*]] = memref.cast %[[VAL_83]] : memref<2xindex> to memref<?xindex>
+// CHECK: memref.store %[[VAL_39]], %[[VAL_83]]{{\[}}%[[VAL_5]]] : memref<2xindex>
+// CHECK: func.call @expInsertF64(%[[VAL_19]], %[[VAL_84]], %[[VAL_21]], %[[VAL_23]], %[[VAL_25]], %[[VAL_85:.*]]#2) : (!llvm.ptr<i8>, memref<?xindex>, memref<?xf64>, memref<?xi1>, memref<?xindex>, index) -> ()
// CHECK: }
// CHECK: memref.dealloc %[[VAL_20]] : memref<300xf64>
// CHECK: memref.dealloc %[[VAL_22]] : memref<300xi1>
// CHECK: memref.dealloc %[[VAL_24]] : memref<300xindex>
// CHECK: call @endInsert(%[[VAL_19]]) : (!llvm.ptr<i8>) -> ()
// CHECK: return %[[VAL_19]] : !llvm.ptr<i8>
-// CHECK: }
+// CHECK: }
func.func @fill_zero_after_alloc(%arg0: tensor<100x200xf64, #DCSR>,
- %arg1: tensor<200x300xf64, #DCSR>) -> tensor<100x300xf64, #DCSR> {
+ %arg1: tensor<200x300xf64, #DCSR>) -> tensor<100x300xf64, #DCSR> {
%0 = tensor.empty() : tensor<100x300xf64, #DCSR>
%cst = arith.constant 0.000000e+00 : f64
%1 = linalg.fill ins(%cst : f64)
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