diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_unary.mlir
@@ -71,6 +71,30 @@
     return %0 : tensor<?xf64, #SparseVector>
   }
 
+  // Performs B[i] = i * A[i].
+  func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
+    %c = arith.constant 0 : index
+    %d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
+    %xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
+    %0 = linalg.generic #trait_vec_scale
+       ins(%arga: tensor<?xf64, #SparseVector>)
+        outs(%xv: tensor<?xf64, #SparseVector>) {
+        ^bb(%a: f64, %x: f64):
+          %idx = linalg.index 0 : index
+          %1 = sparse_tensor.unary %a : f64 to f64
+            present={
+              ^bb0(%x0: f64):
+                %tmp = arith.index_cast %idx : index to i64
+                %idxf = arith.uitofp %tmp : i64 to f64
+                %ret = arith.mulf %x0, %idxf : f64
+                sparse_tensor.yield %ret : f64
+            }
+            absent={} 
+          linalg.yield %1 : f64
+    } -> tensor<?xf64, #SparseVector>
+    return %0 : tensor<?xf64, #SparseVector>
+  }
+
   // Clips values to the range [3, 7].
   func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
     %c0 = arith.constant 0 : index
@@ -99,6 +123,40 @@
     return %0 : tensor<?x?xf64, #DCSR>
   }
 
+  // Slices matrix and only keep the value of the lower-right corner of the original
+  // matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99.
+  func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
+    %c0 = arith.constant 0 : index
+    %c1 = arith.constant 1 : index
+    %d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
+    %d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
+    %xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
+    %0 = linalg.generic #trait_mat_scale
+       ins(%argx: tensor<?x?xf64, #DCSR>)
+        outs(%xv: tensor<?x?xf64, #DCSR>) {
+        ^bb(%a: f64, %x: f64):
+          %row = linalg.index 0 : index
+          %col = linalg.index 1 : index
+          %1 = sparse_tensor.unary %a: f64 to f64
+            present={
+              ^bb0(%x0: f64):
+                %v = arith.constant 99.0 : f64
+                %two = arith.constant 2 : index
+                %r = arith.muli %two, %row : index
+                %c = arith.muli %two, %col : index
+                %cmp1 = arith.cmpi "ult", %r, %d0 : index
+                %tmp = arith.select %cmp1, %v, %x0 : f64
+                %cmp2 = arith.cmpi "ult", %c, %d1 : index
+                %result = arith.select %cmp2, %v, %tmp : f64
+                sparse_tensor.yield %result : f64
+            }
+            absent={}
+          linalg.yield %1 : f64
+    } -> tensor<?x?xf64, #DCSR>
+    return %0 : tensor<?x?xf64, #DCSR>
+  }
+  
+
   // Dumps a sparse vector of type f64.
   func.func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
     // Dump the values array to verify only sparse contents are stored.
@@ -171,10 +229,14 @@
        : (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>
     %1 = call @vector_negation(%sv1)
        : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
-
+    %2 = call @vector_magnify(%sv1)
+       : (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
+    
 
     // Call sparse matrix kernels.
-    %2 = call @matrix_clip(%sm1)
+    %3 = call @matrix_clip(%sm1)
+      : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
+    %4 = call @matrix_slice(%sm1)
       : (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
 
     //
@@ -186,20 +248,28 @@
     // CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 )
     // CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
     // CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
+    // CHECK-NEXT: ( 0, 6, 33, 68, 100, 126, 196, 232, 279, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 )
+    // CHECK-NEXT: ( 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 68, 0, 0, 100, 126, 0, 0, 0, 0, 0, 0, 196, 232, 0, 279 )
     // CHECK-NEXT: ( 3, 3, 3, 4, 5, 6, 7, 7, 7, -1, -1, -1, -1, -1, -1, -1 )
     // CHECK-NEXT: ( ( 3, 3, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 3 ), ( 0, 0, 4, 0, 5, 0, 0, 6 ), ( 7, 0, 7, 7, 0, 0, 0, 0 ) )
+    // CHECK-NEXT: ( 99, 99, 99, 99, 5, 6, 99, 99, 99, -1, -1, -1, -1, -1, -1, -1 )
+    // CHECK-NEXT: ( ( 99, 99, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 99 ), ( 0, 0, 99, 0, 5, 0, 0, 6 ), ( 99, 0, 99, 99, 0, 0, 0, 0 ) )
     //
     call @dump_vec_f64(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
     call @dump_vec_i32(%0) : (tensor<?xi32, #SparseVector>) -> ()
     call @dump_vec_f64(%1) : (tensor<?xf64, #SparseVector>) -> ()
-    call @dump_mat(%2) : (tensor<?x?xf64, #DCSR>) -> ()
-
+    call @dump_vec_f64(%2) : (tensor<?xf64, #SparseVector>) -> ()
+    call @dump_mat(%3) : (tensor<?x?xf64, #DCSR>) -> ()
+    call @dump_mat(%4) : (tensor<?x?xf64, #DCSR>) -> ()
+    
     // Release the resources.
     sparse_tensor.release %sv1 : tensor<?xf64, #SparseVector>
     sparse_tensor.release %sm1 : tensor<?x?xf64, #DCSR>
     sparse_tensor.release %0 : tensor<?xi32, #SparseVector>
     sparse_tensor.release %1 : tensor<?xf64, #SparseVector>
-    sparse_tensor.release %2 : tensor<?x?xf64, #DCSR>
+    sparse_tensor.release %2 : tensor<?xf64, #SparseVector>
+    sparse_tensor.release %3 : tensor<?x?xf64, #DCSR>
+    sparse_tensor.release %4 : tensor<?x?xf64, #DCSR>
     return
   }
 }