[mlir][sparse] deduplicate non-unique coordinates when coiterating COO tensors

[Peiming]

fix typo.

[aartbik]

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

133

De-duplicates?

585

const? also, just spell out the type?
{I can never remember if we prefer auto in the simple or in the hard cases ;-)

761

typo: level

789

same?

913

fast forward (no s)

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

193

a non-unique (no an)
to fast forward (missing r)

mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp

1453–1454

maybe move this TODO:sort into the loop emitter, since it is probably going to be inserted there?

[Peiming]

revert outdated change

[Peiming]

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

585

You are right, I reverted it. (It is left from some experimental change that I did not fully revert)

[Peiming]

address comments.

[Peiming]

fix memory leak

[Peiming]

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_coo_test.mlir

175

Why do I need to free the dense return here? @springerm

[aartbik]

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

763

it almost feels you can do this unconditionally, since it won't be set if not unique
but perhaps not worth it ;-)

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

struct LoopLevelInfo {
     Block *const userCodeBlock; // the block holding users' generated code.
     const Value iv;             // the induction variable for the loop
   };
 
   /// Linearizes address for dense dimension (i.e., p = (i * d0) + j).
   Value genAddress(OpBuilder &builder, Location loc, size_t tid, size_t dim,
                    Value iv);
 
+  /// Generates the segment high for an non-unique level (to fast foward
+  /// duplicated coordinates).
+  Value genSegmentHigh(OpBuilder &builder, Location loc, size_t tid, size_t lvl,
+                       Value pos, Value pHi);
+
   /// Generates instructions to compute the coordinate of tensors[tid][lvl]
   /// under the current loop context.  The final argument is the
   /// collapsed-output level, whereas this function handles converting
   /// that to the uncollapsed-input level
   Value genSparseCrd(OpBuilder &builder, Location loc, size_t tid,
                      size_t dstLvl);
 
   bool isOutputTensor(size_t tid) {
private:
   /// Input and (optional) output tensors.
   std::vector<Value> tensors;
   /// The dim type array for each tensor.
   std::vector<std::vector<DimLevelType>> dimTypes;
   /// Sparse iteration information (by tensor and dim). These arrays
   /// are updated to remain current within the current loop.
   // TODO: we may want to rename "pidx(s)" to `posCursor(s)` or similar.
   std::vector<std::vector<Value>> pidxs;
+  // The segment upper bound for non-uniques level after de-duplication.
+  std::vector<std::vector<Value>> segHi;
   std::vector<std::vector<Value>> coord;
   std::vector<std::vector<Value>> highs;
   std::vector<std::vector<Value>> lvlSizes;
   std::vector<std::vector<Value>> posBuffer; // to_positions
   std::vector<std::vector<Value>> crdBuffer; // to_coordinates
   std::vector<Value> valBuffer;              // to_value
 
   /// Whether the sparse input is a slice.

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

Value LoopEmitter::genAddress(OpBuilder &builder, Location loc, size_t tid,
   if (isSparseSlices[tid]) {
     auto enc = getSparseTensorEncoding(tensors[tid].getType());
     iv = toSliceCoord(builder, loc, iv, enc, dim);
   }
   Value add = builder.create<arith::AddIOp>(loc, mul, iv);
   return add;
 }
 
+Value LoopEmitter::genSegmentHigh(OpBuilder &builder, Location loc, size_t tid,
+                                  size_t lvl, Value pos, Value pHi) {
+  Value prevCrd = genIndexLoad(builder, loc, crdBuffer[tid][lvl], pos);
+  // De-deplicates repeated elements.
+  //
+  // while (pos < pHi && coord[pos] == prev_coord)
+  //    pos++;
+  // return pos;
+  auto whileOp = builder.create<scf::WhileOp>(
+      loc, builder.getIndexType(), pos,
+      /*beforeBuilder=*/
+      [this, tid, lvl, pHi, prevCrd](OpBuilder &builder, Location loc,
+                                     ValueRange ivs) {
+        Value inBound = builder.create<arith::CmpIOp>(
+            loc, arith::CmpIPredicate::ult, ivs[0], pHi);
+        auto ifOp =
+            builder.create<scf::IfOp>(loc, builder.getI1Type(), inBound, true);
+        {
+          OpBuilder::InsertionGuard guard(builder);
+          // Load the next coordinates only when inbound (to avoid OOB
+          // acccesses).
+          builder.setInsertionPointToStart(ifOp.thenBlock());
+          Value nxCrd = genIndexLoad(builder, loc, crdBuffer[tid][lvl], ivs[0]);
+          Value cont = builder.create<arith::CmpIOp>(
+              loc, arith::CmpIPredicate::eq, nxCrd, prevCrd);
+          builder.create<scf::YieldOp>(loc, cont);
+          // Else, the position is out of bound, yield false to terminate the
+          // loop.
+          builder.setInsertionPointToStart(ifOp.elseBlock());
+          builder.create<scf::YieldOp>(loc, constantI1(builder, loc, false));
+        }
+        builder.create<scf::ConditionOp>(loc, ifOp.getResults()[0], ivs);
+      },
+      /*afterBuilder=*/
+      [](OpBuilder &builder, Location loc, ValueRange ivs) {
+        // pos ++
+        Value nxPos = builder.create<arith::AddIOp>(
+            loc, ivs[0], constantIndex(builder, loc, 1));
+        builder.create<scf::YieldOp>(loc, nxPos);
+      });
+  // Return the segment high.
+  return whileOp.getResult(0);
+}
+
 Value LoopEmitter::genSparseCrd(OpBuilder &builder, Location loc, size_t tid,
                                 size_t dstLvl) {
   Value crd = constantIndex(builder, loc, 0);
   const auto reassoc = getCollapseReassociation(tid, dstLvl);
   for (unsigned i = 0; i < reassoc.size(); i++) {
     const auto srcLvl = reassoc[i];
     // A load on the coordinates array yields the coordinate.
     const Value mem = crdBuffer[tid][srcLvl];
void LoopEmitter::initialize(ValueRange ts, StringAttr loopTag, bool hasOutput,
   // First initializes fields.
   this->loopTag = loopTag;
   this->hasOutput = hasOutput;
   this->isSparseOut = isSparseOut;
   this->tensors.assign(ts.begin(), ts.end());
   this->isSparseSlices.assign(tensors.size(), false);
   this->dimTypes.assign(tensors.size(), std::vector<DimLevelType>());
   this->pidxs.assign(tensors.size(), std::vector<Value>());
+  this->segHi.assign(tensors.size(), std::vector<Value>());
   this->coord.assign(tensors.size(), std::vector<Value>());
   this->highs.assign(tensors.size(), std::vector<Value>());
   this->lvlSizes.assign(tensors.size(), std::vector<Value>());
   this->posBuffer.assign(tensors.size(), std::vector<Value>());
   this->crdBuffer.assign(tensors.size(), std::vector<Value>());
   this->valBuffer.assign(tensors.size(), nullptr);
   this->loopStack.reserve(topSort.size());
   this->sparsiferLoopLvlMap.assign(topSort.size(), 0);
if (enc && !(isOutputTensor(tid) && isSparseOut)) {
       isSparseSlices[tid] = enc.isSlice();
       for (auto dimTp : enc.getDimLevelType())
         dimTypes[tid].push_back(dimTp);
     } else
       dimTypes[tid].assign(rank, DimLevelType::Dense);
 
     // Initialize using empty value.
     pidxs[tid].assign(rank, Value());
+    segHi[tid].assign(rank, Value());
     coord[tid].assign(rank, Value());
     highs[tid].assign(rank, Value());
     lvlSizes[tid].assign(rank, Value());
     posBuffer[tid].assign(rank, Value());
     crdBuffer[tid].assign(rank, Value());
   }
 
   // FIXME: This map should be maintained outside loop emitter.
Operation *LoopEmitter::enterCoIterationOverTensorsAtDims(
     OpBuilder &builder, Location loc, ArrayRef<size_t> tids,
     ArrayRef<size_t> dims, bool needsUniv, MutableArrayRef<Value> reduc) {
   assert(tids.size() == dims.size());
   SmallVector<Type> types;
   SmallVector<Value> operands;
   // Construct the while-loop with a parameter for each coordinate.
   Type indexType = builder.getIndexType();
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     if (isCompressedDLT(dimTypes[tid][dim]) ||
         isSingletonDLT(dimTypes[tid][dim])) {
       assert(pidxs[tid][dim]);
       types.push_back(indexType);
       operands.push_back(pidxs[tid][dim]);
     }
   }
   // The position where user-supplied reduction variable starts.
   for (Value rec : reduc) {
if (!needsUniv) {
     min = after->getArguments().back();
   }
 
   // Sets up the loop stack.
   loopStack.emplace_back(tids, dims, whileOp, builder.getInsertionBlock(), min,
                          loopTag);
   assert(loopStack.size() == loopSeqStack.size());
 
+  for (auto [tid, dim] : llvm::zip(tids, dims)) {
+    if (!isUniqueDLT(dimTypes[tid][dim])) {
+      segHi[tid][dim] = genSegmentHigh(builder, loc, tid, dim, pidxs[tid][dim],
+                                       highs[tid][dim]);
+    }
+  }
+
   // Emits extra locals
   emitExtraLocalsForTensorsAtDenseDims(builder, loc, tids, dims);
 
   // Updates reduction variables
   assert(after->getNumArguments() == o + reduc.size() + (needsUniv ? 1 : 0));
   // In-place update on reduction variable.
   for (unsigned i = 0, e = reduc.size(); i < e; i++)
     reduc[i] = after->getArgument(o + i);
if (isCompressedDLT(dimType)) {
       pidxs[tid][lvl] = genIndexLoad(builder, loc, mem, pLo);
 
       Value pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
       highs[tid][lvl] = genIndexLoad(builder, loc, mem, pHi);
       return;
     }
     if (isSingletonDLT(dimType)) {
       Value pLo = lvl == 0 ? c0 : pidxs[tid][lvl - 1];
-      Value pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
-
+      Value pHi;
+      // If this is non-unique, the pHi is bound by the segment high of the
+      // previous levvel.
+      if (!isUniqueDLT(dimTypes[tid][lvl - 1]))
+        pHi = segHi[tid][lvl - 1];
+
+      // If pHi is still uninitialized, we set it to one as it is a singleton
+      // level.
+      // NOTE: Even if the level is non-unique, the pHi might not have been set
+      // in the previous statement, as we only compute segment high when we are
+      // coiterating non-unique levels.
+      if (!pHi)
+        pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
       pidxs[tid][lvl] = pLo;
       highs[tid][lvl] = pHi;
       return;
     }
   }
 
   llvm_unreachable("Unrecognizable dimesion type!");
 }
 
 void LoopEmitter::emitExtraLocalsForTensorsAtDenseDims(OpBuilder &builder,
                                                        Location loc,
                                                        ArrayRef<size_t> tids,
                                                        ArrayRef<size_t> dims) {
   // Initialize dense positions. Note that we generate dense coordinates of the
   // output tensor unconditionally, since they may not appear in the lattice,
   // but may be needed for linearized codegen.
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     if (isDenseDLT(dimTypes[tid][dim])) {
       auto enc = getSparseTensorEncoding(tensors[tid].getType());
       if (enc && !isSparseOutput(tid)) {
         bool validPidx = dim == 0 || pidxs[tid][dim - 1];
         if (!validPidx) {
           // We might not find the pidx for the sparse output tensor as it is
           // unconditionally required by the sparsification.
           assert(isOutputTensor(tid));
           continue;
void LoopEmitter::exitCoIterationLoop(OpBuilder &builder, Location loc,
   Value one = constantIndex(builder, loc, 1);
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     if (isCompressedDLT(dimTypes[tid][dim]) ||
         isSingletonDLT(dimTypes[tid][dim])) {
       Value op1 = coord[tid][dim];
       Value op3 = pidxs[tid][dim];
       Value cmp =
           builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq, op1, iv);
-      Value add = builder.create<arith::AddIOp>(loc, op3, one);
+      // If the loop contains a coiteration with non-unique level, we fast
+      // forwards all the duplicated coords by setting the position to the
+      // segment high.
+      Value add = !isUniqueDLT(dimTypes[tid][dim])
+                      ? segHi[tid][dim]
+                      : builder.create<arith::AddIOp>(loc, op3, one);
       operands.push_back(builder.create<arith::SelectOp>(loc, cmp, add, op3));
       // Following loops continue iteration from the break point of the
       // current while loop.
       pidxs[tid][dim] = whileOp->getResult(o++);
       // The coordinates are invalid now.
       coord[tid][dim] = nullptr;
+      // The segment high are invalid now
+      segHi[tid][dim] = nullptr;
       // highs remains unchanged.
     }
   }
 
   // Reduction value from users.
   for (auto &i : reduc) {
     operands.push_back(i);
     // In place update reduction variable.

mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp

if (at == env.topSortSize()) {
     return;
   }
 
   // Construct iteration lattices for current loop index, with L0 at top.
   unsigned idx = env.topSortAt(at);
   unsigned ldx = at == 0 ? -1u : env.topSortAt(at - 1);
   unsigned lts = env.merger().optimizeSet(env.merger().buildLattices(exp, idx));
 
   // TODO: sort
-  // TODO: dedup
 
   // Start a loop sequence.
   bool needsUniv = startLoopSeq(env, rewriter, exp, at, idx, ldx, lts);
 
   // Emit a loop for every lattice point L0 >= Li in this loop sequence.
   unsigned lsize = env.set(lts).size();
   for (unsigned i = 0; i < lsize; i++) {
     // Start a loop.
     unsigned li = env.set(lts)[i];

mlir/test/Dialect/SparseTensor/sorted_coo.mlir

%0 = linalg.generic #trait_matvec
     ^bb(%A: f64, %b: f64, %x: f64):
       %0 = arith.mulf %A, %b : f64
       %1 = arith.addf %x, %0 : f64
       linalg.yield %1 : f64
   } -> tensor<32xf64>
   return %0 : tensor<32xf64>
 }
 
-// CHECK-LABEL: func.func @mateltmul(
-// CHECK-SAME:    %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
-// CHECK-SAME:    %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
-// CHECK-SAME:    %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
-// CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f64
-// CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 0 : index
-// CHECK-DAG:     %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG:     %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG:     %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
-// CHECK-DAG:     linalg.fill ins(%[[VAL_3]] : f64) outs(%[[VAL_14]] : memref<32x64xf64>)
-// CHECK-DAG:     %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
-// CHECK-DAG:     %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
-// CHECK-DAG:     %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
-// CHECK-DAG:     %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>
-// CHECK:         %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_15]], %[[VAL_21:.*]] = %[[VAL_17]]) : (index, index) -> (index, index) {
-// CHECK:           %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_16]] : index
-// CHECK:           %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_18]] : index
-// CHECK:           %[[VAL_24:.*]] = arith.andi %[[VAL_22]], %[[VAL_23]] : i1
-// CHECK:           scf.condition(%[[VAL_24]]) %[[VAL_20]], %[[VAL_21]] : index, index
-// CHECK:         } do {
-// CHECK:         ^bb0(%[[VAL_25:.*]]: index, %[[VAL_26:.*]]: index):
-// CHECK:           %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:           %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:           %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index
-// CHECK:           %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index
-// CHECK:           %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
-// CHECK:           %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
-// CHECK:           %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1
-// CHECK:           scf.if %[[VAL_33]] {
-// CHECK:             %[[VAL_34:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
-// CHECK:             %[[VAL_35:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
-// CHECK:             %[[VAL_36:.*]]:2 = scf.while (%[[VAL_37:.*]] = %[[VAL_25]], %[[VAL_38:.*]] = %[[VAL_26]]) : (index, index) -> (index, index) {
-// CHECK:               %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_34]] : index
-// CHECK:               %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_38]], %[[VAL_35]] : index
-// CHECK:               %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1
-// CHECK:               scf.condition(%[[VAL_41]]) %[[VAL_37]], %[[VAL_38]] : index, index
-// CHECK:             } do {
-// CHECK:             ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index):
-// CHECK:               %[[VAL_44:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_42]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:               %[[VAL_45:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_43]]] : memref<?xindex, strided<[?], offset: ?>>
-// CHECK:               %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index
-// CHECK:               %[[VAL_47:.*]] = arith.select %[[VAL_46]], %[[VAL_45]], %[[VAL_44]] : index
-// CHECK:               %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
-// CHECK:               %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
-// CHECK:               %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1
-// CHECK:               scf.if %[[VAL_50]] {
-// CHECK:                 %[[VAL_51:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_42]]] : memref<?xf64>
-// CHECK:                 %[[VAL_52:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_43]]] : memref<?xf64>
-// CHECK:                 %[[VAL_53:.*]] = arith.mulf %[[VAL_51]], %[[VAL_52]] : f64
-// CHECK:                 memref.store %[[VAL_53]], %[[VAL_14]]{{\[}}%[[VAL_30]], %[[VAL_47]]] : memref<32x64xf64>
+// CHECK-LABEL:   func.func @mateltmul(
+// CHECK-SAME:      %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME:      %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
+// CHECK-SAME:      %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
+// CHECK-DAG:       %[[VAL_3:.*]] = arith.constant false
+// CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f64
+// CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
+// CHECK:           %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
+// CHECK:           linalg.fill ins(%[[VAL_4]] : f64) outs(%[[VAL_15]] : memref<32x64xf64>)
+// CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
+// CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<?xindex>
+// CHECK:           %[[VAL_20:.*]]:2 = scf.while (%[[VAL_21:.*]] = %[[VAL_16]], %[[VAL_22:.*]] = %[[VAL_18]]) : (index, index) -> (index, index) {
+// CHECK:             %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_17]] : index
+// CHECK:             %[[VAL_24:.*]] = arith.cmpi ult, %[[VAL_22]], %[[VAL_19]] : index
+// CHECK:             %[[VAL_25:.*]] = arith.andi %[[VAL_23]], %[[VAL_24]] : i1
+// CHECK:             scf.condition(%[[VAL_25]]) %[[VAL_21]], %[[VAL_22]] : index, index
+// CHECK:           } do {
+// CHECK:           ^bb0(%[[VAL_26:.*]]: index, %[[VAL_27:.*]]: index):
+// CHECK:             %[[VAL_28:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_29]], %[[VAL_28]] : index
+// CHECK:             %[[VAL_31:.*]] = arith.select %[[VAL_30]], %[[VAL_29]], %[[VAL_28]] : index
+// CHECK:             %[[VAL_32:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_26]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_33:.*]] = scf.while (%[[VAL_34:.*]] = %[[VAL_26]]) : (index) -> index {
+// CHECK:               %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_17]] : index
+// CHECK:               %[[VAL_36:.*]] = scf.if %[[VAL_35]] -> (i1) {
+// CHECK:                 %[[VAL_37:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_34]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_38:.*]] = arith.cmpi eq, %[[VAL_37]], %[[VAL_32]] : index
+// CHECK:                 scf.yield %[[VAL_38]] : i1
 // CHECK:               } else {
+// CHECK:                 scf.yield %[[VAL_3]] : i1
 // CHECK:               }
-// CHECK:               %[[VAL_54:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
-// CHECK:               %[[VAL_55:.*]] = arith.addi %[[VAL_42]], %[[VAL_5]] : index
-// CHECK:               %[[VAL_56:.*]] = arith.select %[[VAL_54]], %[[VAL_55]], %[[VAL_42]] : index
-// CHECK:               %[[VAL_57:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
-// CHECK:               %[[VAL_58:.*]] = arith.addi %[[VAL_43]], %[[VAL_5]] : index
-// CHECK:               %[[VAL_59:.*]] = arith.select %[[VAL_57]], %[[VAL_58]], %[[VAL_43]] : index
-// CHECK:               scf.yield %[[VAL_56]], %[[VAL_59]] : index, index
-// CHECK:             }
-// CHECK:           } else {
-// CHECK:           }
-// CHECK:           %[[VAL_60:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
-// CHECK:           %[[VAL_61:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
-// CHECK:           %[[VAL_62:.*]] = arith.select %[[VAL_60]], %[[VAL_61]], %[[VAL_25]] : index
-// CHECK:           %[[VAL_63:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
-// CHECK:           %[[VAL_64:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
-// CHECK:           %[[VAL_65:.*]] = arith.select %[[VAL_63]], %[[VAL_64]], %[[VAL_26]] : index
-// CHECK:           scf.yield %[[VAL_62]], %[[VAL_65]] : index, index
-// CHECK:         }
-// CHECK:         %[[VAL_66:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x64xf64>
-// CHECK:         return %[[VAL_66]] : tensor<32x64xf64>
-// CHECK:       }
+// CHECK:               scf.condition(%[[VAL_39:.*]]) %[[VAL_34]] : index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_40:.*]]: index):
+// CHECK:               %[[VAL_41:.*]] = arith.addi %[[VAL_40]], %[[VAL_6]] : index
+// CHECK:               scf.yield %[[VAL_41]] : index
+// CHECK:             }
+// CHECK:             %[[VAL_42:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_27]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:             %[[VAL_43:.*]] = scf.while (%[[VAL_44:.*]] = %[[VAL_27]]) : (index) -> index {
+// CHECK:               %[[VAL_45:.*]] = arith.cmpi ult, %[[VAL_44]], %[[VAL_19]] : index
+// CHECK:               %[[VAL_46:.*]] = scf.if %[[VAL_45]] -> (i1) {
+// CHECK:                 %[[VAL_47:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_44]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_47]], %[[VAL_42]] : index
+// CHECK:                 scf.yield %[[VAL_48]] : i1
+// CHECK:               } else {
+// CHECK:                 scf.yield %[[VAL_3]] : i1
+// CHECK:               }
+// CHECK:               scf.condition(%[[VAL_49:.*]]) %[[VAL_44]] : index
+// CHECK:             } do {
+// CHECK:             ^bb0(%[[VAL_50:.*]]: index):
+// CHECK:               %[[VAL_51:.*]] = arith.addi %[[VAL_50]], %[[VAL_6]] : index
+// CHECK:               scf.yield %[[VAL_51]] : index
+// CHECK:             }
+// CHECK:             %[[VAL_52:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_31]] : index
+// CHECK:             %[[VAL_53:.*]] = arith.cmpi eq, %[[VAL_29]], %[[VAL_31]] : index
+// CHECK:             %[[VAL_54:.*]] = arith.andi %[[VAL_52]], %[[VAL_53]] : i1
+// CHECK:             scf.if %[[VAL_54]] {
+// CHECK:               %[[VAL_55:.*]]:2 = scf.while (%[[VAL_56:.*]] = %[[VAL_26]], %[[VAL_57:.*]] = %[[VAL_27]]) : (index, index) -> (index, index) {
+// CHECK:                 %[[VAL_58:.*]] = arith.cmpi ult, %[[VAL_56]], %[[VAL_59:.*]] : index
+// CHECK:                 %[[VAL_60:.*]] = arith.cmpi ult, %[[VAL_57]], %[[VAL_61:.*]] : index
+// CHECK:                 %[[VAL_62:.*]] = arith.andi %[[VAL_58]], %[[VAL_60]] : i1
+// CHECK:                 scf.condition(%[[VAL_62]]) %[[VAL_56]], %[[VAL_57]] : index, index
+// CHECK:               } do {
+// CHECK:               ^bb0(%[[VAL_63:.*]]: index, %[[VAL_64:.*]]: index):
+// CHECK:                 %[[VAL_65:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_63]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_66:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_64]]] : memref<?xindex, strided<[?], offset: ?>>
+// CHECK:                 %[[VAL_67:.*]] = arith.cmpi ult, %[[VAL_66]], %[[VAL_65]] : index
+// CHECK:                 %[[VAL_68:.*]] = arith.select %[[VAL_67]], %[[VAL_66]], %[[VAL_65]] : index
+// CHECK:                 %[[VAL_69:.*]] = arith.cmpi eq, %[[VAL_65]], %[[VAL_68]] : index
+// CHECK:                 %[[VAL_70:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_68]] : index
+// CHECK:                 %[[VAL_71:.*]] = arith.andi %[[VAL_69]], %[[VAL_70]] : i1
+// CHECK:                 scf.if %[[VAL_71]] {
+// CHECK:                   %[[VAL_72:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_63]]] : memref<?xf64>
+// CHECK:                   %[[VAL_73:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_64]]] : memref<?xf64>
+// CHECK:                   %[[VAL_74:.*]] = arith.mulf %[[VAL_72]], %[[VAL_73]] : f64
+// CHECK:                   memref.store %[[VAL_74]], %[[VAL_15]]{{\[}}%[[VAL_31]], %[[VAL_68]]] : memref<32x64xf64>
+// CHECK:                 } else {
+// CHECK:                 }
+// CHECK:                 %[[VAL_75:.*]] = arith.cmpi eq, %[[VAL_65]], %[[VAL_68]] : index
+// CHECK:                 %[[VAL_76:.*]] = arith.addi %[[VAL_63]], %[[VAL_6]] : index
+// CHECK:                 %[[VAL_77:.*]] = arith.select %[[VAL_75]], %[[VAL_76]], %[[VAL_63]] : index
+// CHECK:                 %[[VAL_78:.*]] = arith.cmpi eq, %[[VAL_66]], %[[VAL_68]] : index
+// CHECK:                 %[[VAL_79:.*]] = arith.addi %[[VAL_64]], %[[VAL_6]] : index
+// CHECK:                 %[[VAL_80:.*]] = arith.select %[[VAL_78]], %[[VAL_79]], %[[VAL_64]] : index
+// CHECK:                 scf.yield %[[VAL_77]], %[[VAL_80]] : index, index
+// CHECK:               } attributes {"Emitted from" = "linalg.generic"}
+// CHECK:             } else {
+// CHECK:             }
+// CHECK:             %[[VAL_81:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_31]] : index
+// CHECK:             %[[VAL_82:.*]] = arith.select %[[VAL_81]], %[[VAL_83:.*]], %[[VAL_26]] : index
+// CHECK:             %[[VAL_84:.*]] = arith.cmpi eq, %[[VAL_29]], %[[VAL_31]] : index
+// CHECK:             %[[VAL_85:.*]] = arith.select %[[VAL_84]], %[[VAL_86:.*]], %[[VAL_27]] : index
+// CHECK:             scf.yield %[[VAL_82]], %[[VAL_85]] : index, index
+// CHECK:           } attributes {"Emitted from" = "linalg.generic"}
+// CHECK:           %[[VAL_87:.*]] = bufferization.to_tensor %[[VAL_15]] : memref<32x64xf64>
+// CHECK:           return %[[VAL_87]] : tensor<32x64xf64>
+// CHECK:         }
 func.func @mateltmul(%argx: tensor<32x64xf64, #SortedCOO>,
                      %argy: tensor<32x64xf64, #SortedCOO>,
                      %argz: tensor<32x64xf64>) -> tensor<32x64xf64> {
   %0 = linalg.generic #trait_mul
       ins(%argx, %argy : tensor<32x64xf64, #SortedCOO>, tensor<32x64xf64, #SortedCOO>)
       outs(%argz: tensor<32x64xf64>) {
     ^bb(%x: f64, %y: f64, %z: f64):
       %1 = arith.mulf %x, %y : f64
       linalg.yield %1 : f64
   } -> tensor<32x64xf64>
   return %0 : tensor<32x64xf64>
 }

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_coo_test.mlir

+// DEFINE: %{option} = enable-runtime-library=true
+// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
+// DEFINE: %{run} = mlir-cpu-runner \
+// DEFINE:  -e entry -entry-point-result=void  \
+// DEFINE:  -shared-libs=%mlir_c_runner_utils | \
+// DEFINE: FileCheck %s
+//
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{option} = enable-runtime-library=false
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation and vectorization.
+// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
+// RUN: %{compile} | %{run}
+//
+// Do the same run, but now with direct IR generation and, if available, VLA
+// vectorization.
+// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
+// REDEFINE: %{run} = %lli \
+// REDEFINE:   --entry-function=entry_lli \
+// REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
+// REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
+// REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
+// REDEFINE: FileCheck %s
+// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
+
+#SortedCOO = #sparse_tensor.encoding<{
+  dimLevelType = [ "compressed-nu", "singleton" ]
+}>
+
+#CSR = #sparse_tensor.encoding<{
+  dimLevelType = [ "dense", "compressed" ]
+}>
+
+#trait = {
+  indexing_maps = [
+    affine_map<(i,j) -> (i,j)>,  // A
+    affine_map<(i,j) -> (i,j)>,  // B
+    affine_map<(i,j) -> (i,j)>   // X (out)
+  ],
+  iterator_types = ["parallel", "parallel"],
+  doc = "X(i,j) = A(i,j) + B(i,j)"
+}
+
+module {
+  func.func @add_coo_csr(%arga: tensor<8x8xf32, #CSR>,
+                         %argb: tensor<8x8xf32, #SortedCOO>)
+             -> tensor<8x8xf32> {
+    %empty = tensor.empty() : tensor<8x8xf32>
+    %zero = arith.constant 0.000000e+00 : f32
+    %init = linalg.fill
+        ins(%zero : f32)
+        outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+    %0 = linalg.generic #trait
+      ins(%arga, %argb: tensor<8x8xf32, #CSR>,
+                        tensor<8x8xf32, #SortedCOO>)
+      outs(%init: tensor<8x8xf32>) {
+        ^bb(%a: f32, %b: f32, %x: f32):
+          %0 = arith.addf %a, %b : f32
+          linalg.yield %0 : f32
+        } -> tensor<8x8xf32>
+    return %0 : tensor<8x8xf32>
+  }
+
+  func.func @add_coo_coo(%arga: tensor<8x8xf32, #SortedCOO>,
+                         %argb: tensor<8x8xf32, #SortedCOO>)
+             -> tensor<8x8xf32> {
+    %empty = tensor.empty() : tensor<8x8xf32>
+    %zero = arith.constant 0.000000e+00 : f32
+    %init = linalg.fill
+        ins(%zero : f32)
+        outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+    %0 = linalg.generic #trait
+      ins(%arga, %argb: tensor<8x8xf32, #SortedCOO>,
+                        tensor<8x8xf32, #SortedCOO>)
+      outs(%init: tensor<8x8xf32>) {
+        ^bb(%a: f32, %b: f32, %x: f32):
+          %0 = arith.addf %a, %b : f32
+          linalg.yield %0 : f32
+        } -> tensor<8x8xf32>
+    return %0 : tensor<8x8xf32>
+  }
+
+  func.func @add_coo_dense(%arga: tensor<8x8xf32>,
+                           %argb: tensor<8x8xf32, #SortedCOO>)
+                   -> tensor<8x8xf32> {
+    %empty = tensor.empty() : tensor<8x8xf32>
+    %zero = arith.constant 0.000000e+00 : f32
+    %init = linalg.fill
+        ins(%zero : f32)
+        outs(%empty : tensor<8x8xf32>) -> tensor<8x8xf32>
+    %0 = linalg.generic #trait
+      ins(%arga, %argb: tensor<8x8xf32>,
+                        tensor<8x8xf32, #SortedCOO>)
+      outs(%init: tensor<8x8xf32>) {
+        ^bb(%a: f32, %b: f32, %x: f32):
+          %0 = arith.addf %a, %b : f32
+          linalg.yield %0 : f32
+        } -> tensor<8x8xf32>
+    return %0 : tensor<8x8xf32>
+  }
+
+  func.func @entry() {
+    %c0  = arith.constant 0 : index
+    %c1  = arith.constant 1 : index
+    %c8  = arith.constant 8 : index
+
+    %A = arith.constant dense<
+        [ [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ],
+          [ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1 ],
+          [ 2.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2 ],
+          [ 3.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 ],
+          [ 4.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 ],
+          [ 5.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5 ],
+          [ 6.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6 ],
+          [ 7.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7 ] ]
+    > : tensor<8x8xf32>
+    %B = arith.constant dense<
+        [ [ 7.8, 2.8, 3.8, 0.8, 3.8, 0.1, 7.8, 8.8 ],
+          [ 3.3, 2.3, 1.3, 4.3, 3.3, 6.3, 9.3, 8.3 ],
+          [ 6.6, 2.6, 3.6, 4.6, 3.6, 6.6, 7.6, 7.6 ],
+          [ 1.0, 3.0, 3.0, 4.0, 3.0, 6.0, 7.0, 8.0 ],
+          [ 0.1, 2.1, 3.1, 4.1, 3.1, 6.1, 7.1, 8.1 ],
+          [ 4.4, 2.4, 3.4, 4.4, 3.4, 6.4, 8.4, 8.4 ],
+          [ 5.5, 3.5, 1.5, 4.5, 3.5, 6.5, 7.5, 8.5 ],
+          [ 7.7, 2.7, 3.7, 0.7, 5.7, 3.7, 3.7, 0.7 ] ]
+    > : tensor<8x8xf32>
+
+    // Stress test with a "sparse" version of A and B.
+    %CSR_A = sparse_tensor.convert %A
+      : tensor<8x8xf32> to tensor<8x8xf32, #CSR>
+    %COO_A = sparse_tensor.convert %A
+      : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>
+    %COO_B = sparse_tensor.convert %B
+      : tensor<8x8xf32> to tensor<8x8xf32, #SortedCOO>
+
+    %C1 = call @add_coo_dense(%A, %COO_B) : (tensor<8x8xf32>,
+                                             tensor<8x8xf32, #SortedCOO>)
+                                          -> tensor<8x8xf32>
+    %C2 = call @add_coo_csr(%CSR_A, %COO_B) : (tensor<8x8xf32, #CSR>,
+                                               tensor<8x8xf32, #SortedCOO>)
+                                            -> tensor<8x8xf32>
+    %C3 = call @add_coo_coo(%COO_A, %COO_B) : (tensor<8x8xf32, #SortedCOO>,
+                                               tensor<8x8xf32, #SortedCOO>)
+                                            -> tensor<8x8xf32>
+    //
+    // Verify computed matrix C.
+    //
+    // CHECK-COUNT-3:      ( 8.8, 4.8, 6.8, 4.8, 8.8, 6.1, 14.8, 16.8 )
+    // CHECK-NEXT-COUNT-3: ( 4.4, 4.4, 4.4, 8.4, 8.4, 12.4, 16.4, 16.4 )
+    // CHECK-NEXT-COUNT-3: ( 8.8, 4.8, 6.8, 8.8, 8.8, 12.8, 14.8, 15.8 )
+    // CHECK-NEXT-COUNT-3: ( 4.3, 5.3, 6.3, 8.3, 8.3, 12.3, 14.3, 16.3 )
+    // CHECK-NEXT-COUNT-3: ( 4.5, 4.5, 6.5, 8.5, 8.5, 12.5, 14.5, 16.5 )
+    // CHECK-NEXT-COUNT-3: ( 9.9, 4.9, 6.9, 8.9, 8.9, 12.9, 15.9, 16.9 )
+    // CHECK-NEXT-COUNT-3: ( 12.1, 6.1, 5.1, 9.1, 9.1, 13.1, 15.1, 17.1 )
+    // CHECK-NEXT-COUNT-3: ( 15.4, 5.4, 7.4, 5.4, 11.4, 10.4, 11.4, 9.4 )
+    //
+    %f0  = arith.constant 0.0 : f32
+    scf.for %i = %c0 to %c8 step %c1 {
+      %v1 = vector.transfer_read %C1[%i, %c0], %f0
+        : tensor<8x8xf32>, vector<8xf32>
+      %v2 = vector.transfer_read %C2[%i, %c0], %f0
+        : tensor<8x8xf32>, vector<8xf32>
+      %v3 = vector.transfer_read %C3[%i, %c0], %f0
+        : tensor<8x8xf32>, vector<8xf32>
+      vector.print %v1 : vector<8xf32>
+      vector.print %v2 : vector<8xf32>
+      vector.print %v3 : vector<8xf32>
+    }
+
+    // Release resources.
+    bufferization.dealloc_tensor %CSR_A : tensor<8x8xf32, #CSR>
+    bufferization.dealloc_tensor %COO_A : tensor<8x8xf32, #SortedCOO>
+    bufferization.dealloc_tensor %COO_B : tensor<8x8xf32, #SortedCOO>
+
+
+    return
+  }
+}
 No newline at end of file