[mlir][sparse] Improve ConcatenateOp rewriting for annotated all dense result.

[bixia]

Summary

Previously, we rely on InsertOp to add values to the result, in the same way we
add values to a sparse tensor with compressed dimensions. We now direct store
values to the values buffer.

[Peiming]

mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir

277

Why this cast is needed? is it possible to allocate a static-shaped memref L274 at the first place?

[bixia]

Allocate buffer with static shape to avoid memref.cast.
rebase.

mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir

277

Good point. Modify genAlloca to support this.

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

LogicalResult matchAndRewrite(ConcatenateOp op,
     //   %tmp = memref.alloc : dense tensor
     // foreach in %s1 : insert d0, d1, %tmp
     // foreach in %s2 : insert d0, d1 + size(s1), %tmp
     // foreach in %s3 : insert d0, d1 + size(s1) + size(s2), %tmp
     // %t = convert_to_dest_tensor(%tmp)
     SparseTensorEncodingAttr encDst = getSparseTensorEncoding(dstTp);
     Value dst; // Destination tensor for inserting source tensor values.
     bool needTmpCOO = true;
+    bool allDense = false;
+    Value annotatedDenseDst;
+    int64_t rank = dstTp.getRank();
     if (encDst) {
-      bool allDense = encDst.isAllDense();
+      allDense = encDst.isAllDense();
       bool allOrdered = false;
       // When concatenating on dimension 0, and all inputs are sorted and have
       // an identity dimOrdering, the concatenate will generate coords in
       // lexOrder thus no need for the tmp COO buffer.
       // TODO: When conDim != 0, as long as conDim is the first dimension
       // in all input/output buffers, and all input/output buffers have the same
       // dimOrdering, the tmp COO buffer is still unnecessary (e.g, concatenate
       // CSC matrices along column).
if (encDst) {
       SmallVector<Value> dynSizes;
       getDynamicSizes(dstTp, sizes, dynSizes);
       RankedTensorType tp = dstTp;
       if (needTmpCOO) {
         tp = getUnorderedCOOFromType(dstTp);
         encDst = getSparseTensorEncoding(tp);
       }
       dst = rewriter.create<AllocTensorOp>(loc, tp, dynSizes).getResult();
+      if (allDense) {
+        // Create a view of the values buffer to match the unannotated dense
+        // tensor.
+        Value valuesBuffer = genToValues(rewriter, loc, dst);
+        Value idxBuffer =
+            genAlloca(rewriter, loc, rank, rewriter.getIndexType());
+        annotatedDenseDst = dst;
+        dst = reshapeValuesToLevels(rewriter, loc, encDst, sizes, valuesBuffer,
+                                    idxBuffer);
+      }
     } else {
       // TODO: Dense buffers should be allocated/deallocated via the callback
       // in BufferizationOptions.
       dst = allocDenseTensor(rewriter, loc, dstTp, sizes);
     }
 
-    int64_t rank = dstTp.getRank();
     Value offset = constantIndex(rewriter, loc, 0);
     SmallVector<Value> initArgs;
-    if (encDst)
+    if (encDst && !allDense)
       initArgs.push_back(dst);
     ForeachOp foreachOp;
     for (Value input : op.getInputs()) {
       // Build a for op for each input tensor to append new values into the
       // output tensor.
       foreachOp = rewriter.create<ForeachOp>(
           loc, input, initArgs,
           [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
               ValueRange reduc) {
             SmallVector<Value> indices(rank, Value());
             for (int64_t i = 0; i < rank; i++) {
               Value idx = args[i];
               if (i == static_cast<int64_t>(conDim))
                 // Transform coordinates for the concatenating dim.
                 idx = builder.create<arith::AddIOp>(loc, idx, offset);
               indices[toStoredDim(encDst, i)] = idx;
             }
-            if (encDst) {
+            if (encDst && !allDense) {
               Value cond = genIsNonzero(rewriter, loc, v);
               scf::IfOp ifOp = builder.create<scf::IfOp>(
                   loc, TypeRange(reduc.front().getType()), cond, /*else*/ true);
               builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
               Value t =
                   builder.create<InsertOp>(loc, v, reduc.front(), indices);
               rewriter.create<scf::YieldOp>(loc, t);
               rewriter.setInsertionPointToStart(&ifOp.getElseRegion().front());
               rewriter.create<scf::YieldOp>(loc, reduc.front());
               rewriter.setInsertionPointAfter(ifOp);
               rewriter.create<sparse_tensor::YieldOp>(loc, ifOp.getResult(0));
             } else {
               builder.create<memref::StoreOp>(loc, v, dst, indices);
               builder.create<sparse_tensor::YieldOp>(loc);
             }
           });
       // Accumulates the offset. Note that only static-shaped inputs are allowed
       // by concatenate op verifier, which saves us from computing the offset
       // dynamically.
       int64_t d = input.getType().cast<RankedTensorType>().getShape()[conDim];
       assert(!ShapedType::isDynamic(d));
       offset = rewriter.create<arith::AddIOp>(loc, offset,
                                               constantIndex(rewriter, loc, d));
-      if (encDst) {
+      if (encDst && !allDense) {
         dst = foreachOp.getResult(0);
         initArgs[0] = dst;
       }
     }
 
     if (encDst) {
+      if (!allDense) {
         dst = rewriter.create<LoadOp>(loc, dst, true);
         if (needTmpCOO) {
           Value tmpCoo = dst;
           dst = rewriter.create<ConvertOp>(loc, dstTp, tmpCoo).getResult();
           rewriter.create<DeallocTensorOp>(loc, tmpCoo);
         }
+      } else {
+        dst = rewriter.create<ConvertOp>(loc, dstTp, annotatedDenseDst)
+                  .getResult();
+      }
       rewriter.replaceOp(op, dst);
     } else {
       rewriter.replaceOpWithNewOp<bufferization::ToTensorOp>(op, dstTp, dst);
     }
     return success();
   }
 };
 

mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir

 //  CHECK-SAME:  %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
 //   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //   CHECK-DAG:  %[[TMP_c5:.*]] = arith.constant 5 : index
 //   CHECK-DAG:  %[[TMP_c2:.*]] = arith.constant 2 : index
 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
+//       CHECK:  %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf64>
+//       CHECK:  %[[DIM_0:.*]] = memref.alloca(%[[TMP_c2]]) : memref<?xindex>
+//       CHECK:  memref.store %[[TMP_c9]], %[[DIM_0]][%[[TMP_c0]]] : memref<?xindex>
+//       CHECK:  memref.store %[[TMP_c4]], %[[DIM_0]][%[[TMP_c1]]] : memref<?xindex>
+//       CHECK:  %[[DIM_1:.*]] = memref.cast %[[DIM_0]] : memref<?xindex> to memref<2xindex>
+//       CHECK:  %[[VAL_1:.*]] = memref.reshape %[[VAL_0]](%[[DIM_1]]) : (memref<?xf64>, memref<2xindex>) -> memref<?x?xf64>
 //       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
-//       CHECK:      %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_23]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_1]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_23]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_4]]
 //       CHECK:  }
 //       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
-//       CHECK:      %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_2]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_5]]
 //       CHECK:  }
 //       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
-//       CHECK:      %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_3]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_6]]
 //       CHECK:  }
-//       CHECK:  %[[R:.*]] = sparse_tensor.load %[[RET_3:.*]] hasInserts : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
+//       CHECK:  %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
 //       CHECK:  return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
 func.func @concat_sparse_sparse_annotated_dense(%arg0: tensor<2x4xf64, #DCSR>,
                                 %arg1: tensor<3x4xf64, #DCSR>,
                                 %arg2: tensor<4x4xf64, #DCSR>)
                                 -> tensor<?x?xf64, #DENSE> {
     %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
          : tensor<2x4xf64, #DCSR>,
            tensor<3x4xf64, #DCSR>,
            tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DENSE>
     return %0 : tensor<?x?xf64, #DENSE>
 }
 
 // CHECK-LABEL: @concat_sparse_sparse_annotated_dense_permute(
 //  CHECK-SAME:  %[[TMP_arg0:.*]]: tensor<2x4xf64, #sparse_tensor
 //  CHECK-SAME:  %[[TMP_arg1:.*]]: tensor<3x4xf64, #sparse_tensor
 //  CHECK-SAME:  %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
 //   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //   CHECK-DAG:  %[[TMP_c5:.*]] = arith.constant 5 : index
 //   CHECK-DAG:  %[[TMP_c2:.*]] = arith.constant 2 : index
 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
+//       CHECK:  %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor
+//       CHECK:  %[[DIM_0:.*]] = memref.alloca(%[[TMP_c2]]) : memref<?xindex>
+//       CHECK:  memref.store %[[TMP_c4]], %[[DIM_0]][%[[TMP_c0]]] : memref<?xindex>
+//       CHECK:  memref.store %[[TMP_c9]], %[[DIM_0]][%[[TMP_c1]]] : memref<?xindex>
+//       CHECK:  %[[DIM_1:.*]] = memref.cast %[[DIM_0]] : memref<?xindex> to memref<2xindex>
+//       CHECK:  %[[VAL_1:.*]] = memref.reshape %[[VAL_0]](%[[DIM_1]]) : (memref<?xf64>, memref<2xindex>) -> memref<?x?xf64>
 //       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
-//       CHECK:      %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_27]], %[[TMP_23]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_1]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_23]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_4]]
 //       CHECK:  }
 //       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
-//       CHECK:      %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_27]], %[[TMP_29]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_2]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_29]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_5]]
 //       CHECK:  }
 //       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
-//       CHECK:  %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]])
+//       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
-//       CHECK:    %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]])
+//       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
-//       CHECK:      %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_27]], %[[TMP_29]]] : tensor<?x?xf64, #sparse_tensor
-//       CHECK:      scf.yield %[[NEW_3]]
+//       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_29]]] : memref<?x?xf64>
 //       CHECK:    }
-//       CHECK:    scf.yield %[[RET_6]]
 //       CHECK:  }
-//       CHECK:  %[[R:.*]] = sparse_tensor.load %[[RET_3:.*]] hasInserts : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
+//       CHECK:  %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
 //       CHECK:  return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
 func.func @concat_sparse_sparse_annotated_dense_permute(%arg0: tensor<2x4xf64, #DCSR>,
                                 %arg1: tensor<3x4xf64, #DCSR>,
                                 %arg2: tensor<4x4xf64, #DCSR>)
                                 -> tensor<?x?xf64, #DENSE_P> {
     %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
          : tensor<2x4xf64, #DCSR>,
            tensor<3x4xf64, #DCSR>,
            tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DENSE_P>
     return %0 : tensor<?x?xf64, #DENSE_P>
 }
 No newline at end of file