--- /dev/null
+// RUN: mlir-opt %s -sparsification -cse | FileCheck %s
+
+#Dense = #sparse_tensor.encoding<{
+ dimLevelType = [ "dense" , "dense" ]
+}>
+
+#trait_scale = {
+ indexing_maps = [
+ affine_map<(i,j) -> (i,j)> // X (out)
+ ],
+ iterator_types = ["parallel", "parallel"],
+ doc = "X(i,j) = X(i,j) * 2.0"
+}
+
+// CHECK-LABEL: func.func @sparse_scale(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>)
+// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f32
+// CHECK: %[[VAL_3:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf32>
+// CHECK: %[[VAL_4:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
+// CHECK: %[[VAL_5:.*]] = arith.mulf %[[VAL_4]], %[[VAL_2]] : f32
+// CHECK: memref.store %[[VAL_5]], %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xf32>
+// CHECK: %[[VAL_6:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
+// CHECK: return %[[VAL_6]] : tensor<1x1xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
+func.func @sparse_scale(%argx: tensor<1x1xf32, #Dense>) -> tensor<1x1xf32, #Dense> {
+ %c = arith.constant 2.0 : f32
+ %0 = linalg.generic #trait_scale
+ outs(%argx: tensor<1x1xf32, #Dense>) {
+ ^bb(%x: f32):
+ %1 = arith.mulf %x, %c : f32
+ linalg.yield %1 : f32
+ } -> tensor<1x1xf32, #Dense>
+ return %0 : tensor<1x1xf32, #Dense>
+}