diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir
index f7975e0738fa81..73dddefb0e4aa5 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir
@@ -44,7 +44,7 @@ module {
     %cst = arith.constant 0.000000e+00 : f32
     %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
     %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
-    %expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
+    %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>
     %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
 
     // Note: tensor.collapse_shape is a metadata-only operation on dense tensors
@@ -60,7 +60,7 @@ module {
     %cst = arith.constant 0.000000e+00 : f32
     %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
     %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
-    %expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
+    %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>
     %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
 
     // Note: tensor.collapse_shape is a metadata-only operation on dense tensors
@@ -76,7 +76,7 @@ module {
     %cst = arith.constant 0.000000e+00 : f32
     %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
     %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
-    %expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
+    %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>
     %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
     return %ret1 :  tensor<?x?x?xf32>
   }
@@ -88,7 +88,7 @@ module {
     %cst = arith.constant 0.000000e+00 : f32
     %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
     %2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
-    %expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
+    %expanded = tensor.expand_shape %2 [[0], [1, 2]] output_shape [5,2,3]: tensor<5x6xf32> into tensor<5x2x3xf32>
     %ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
 
     // Note: tensor.collapse_shape is a metadata-only operation on dense tensors
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir
index 6679a81c74088b..3932424845763d 100644
--- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir
@@ -53,62 +53,86 @@
 module {
 
   func.func @expand_dense(%arg0: tensor<12xf64>) -> tensor<3x4xf64> {
-    %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64>
+    %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [3, 4] : tensor<12xf64> into tensor<3x4xf64>
     return %0 : tensor<3x4xf64>
   }
 
   func.func @expand_from_sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64> {
-    %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64>
+    %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [3, 4] : tensor<12xf64, #SparseVector> into tensor<3x4xf64>
     return %0 : tensor<3x4xf64>
   }
 
   func.func @expand_to_sparse(%arg0: tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix> {
-    %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64, #SparseMatrix>
+    %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [3, 4] : tensor<12xf64> into tensor<3x4xf64, #SparseMatrix>
     return %0 : tensor<3x4xf64, #SparseMatrix>
   }
 
   func.func @expand_sparse2sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix> {
-    %0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64, #SparseMatrix>
+    %0 = tensor.expand_shape %arg0 [[0, 1]] output_shape [3, 4] : tensor<12xf64, #SparseVector> into tensor<3x4xf64, #SparseMatrix>
     return %0 : tensor<3x4xf64, #SparseMatrix>
   }
 
   func.func @expand_dense_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64>
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [3, 2, 2] : tensor<3x4xf64> into tensor<3x2x2xf64>
     return %0 : tensor<3x2x2xf64>
   }
 
   func.func @expand_from_sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64>
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [3, 2, 2] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64>
     return %0 : tensor<3x2x2xf64>
   }
 
   func.func @expand_to_sparse_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64, #Sparse3dTensor>
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [3, 2, 2] : tensor<3x4xf64> into tensor<3x2x2xf64, #Sparse3dTensor>
     return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
   }
 
   func.func @expand_sparse2sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64, #Sparse3dTensor>
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [3, 2, 2] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64, #Sparse3dTensor>
     return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
   }
 
   func.func @expand_dense_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64>
+    %c0 = arith.constant 0 : index
+    %c1 = arith.constant 1 : index
+    %c2 = arith.constant 2 : index
+    %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf64>
+    %d1 = tensor.dim %arg0, %c1 : tensor<?x?xf64>
+    %d2 = arith.divui %d1, %c2 : index
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%d0, 2, %d2]  : tensor<?x?xf64> into tensor<?x2x?xf64>
     return %0 : tensor<?x2x?xf64>
   }
 
   func.func @expand_from_sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64>
+    %c0 = arith.constant 0 : index
+    %c1 = arith.constant 1 : index
+    %c2 = arith.constant 2 : index
+    %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf64, #SparseMatrix>
+    %d1 = tensor.dim %arg0, %c1 : tensor<?x?xf64, #SparseMatrix>
+    %d2 = arith.divui %d1, %c2 : index
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%d0, 2, %d2]  : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64>
     return %0 : tensor<?x2x?xf64>
   }
 
   func.func @expand_to_sparse_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64, #Sparse3dTensor>
+    %c0 = arith.constant 0 : index
+    %c1 = arith.constant 1 : index
+    %c2 = arith.constant 2 : index
+    %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf64>
+    %d1 = tensor.dim %arg0, %c1 : tensor<?x?xf64>
+    %d2 = arith.divui %d1, %c2 : index
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%d0, 2, %d2]  : tensor<?x?xf64> into tensor<?x2x?xf64, #Sparse3dTensor>
     return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
   }
 
   func.func @expand_sparse2sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
-    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64, #Sparse3dTensor>
+    %c0 = arith.constant 0 : index
+    %c1 = arith.constant 1 : index
+    %c2 = arith.constant 2 : index
+    %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf64, #SparseMatrix>
+    %d1 = tensor.dim %arg0, %c1 : tensor<?x?xf64, #SparseMatrix>
+    %d2 = arith.divui %d1, %c2 : index
+    %0 = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [%d0, 2, %d2]  : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64, #Sparse3dTensor>
     return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
   }