diff --git a/mlir/test/Integration/Dialect/SparseTensor/taco/test_Tensor.py b/mlir/test/Integration/Dialect/SparseTensor/taco/test_Tensor.py
--- a/mlir/test/Integration/Dialect/SparseTensor/taco/test_Tensor.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/taco/test_Tensor.py
@@ -17,9 +17,10 @@
 # Set up scalar and sparse tensors.
 alpha = pt.tensor(42.0)
 S = pt.tensor([8, 8, 8],
-              pt.format([pt.compressed, pt.compressed, pt.compressed]))
+              pt.format([pt.compressed, pt.dense, pt.compressed], [1, 0, 2]))
 X = pt.tensor([8, 8, 8],
-              pt.format([pt.compressed, pt.compressed, pt.compressed]))
+              pt.format([pt.compressed, pt.compressed, pt.compressed],
+                        [1, 0, 2]))
 S.insert([0, 0, 0], 2.0)
 S.insert([1, 1, 1], 3.0)
 S.insert([4, 4, 4], 4.0)
diff --git a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py
--- a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py
@@ -325,6 +325,13 @@
     """Returns the number of dimensions represented by the format."""
     return self.format_pack.rank()
 
+  def get_permutation_and_sparsity(self) -> Tuple[np.ndarray, np.ndarray]:
+    """Constructs the numpy arrays for the permutation and sparsity."""
+    perm = np.array(self.ordering.ordering, dtype=np.ulonglong)
+    a = [0 if s == ModeFormat.DENSE else 1 for s in self.format_pack.formats]
+    sparse = np.array(a, dtype=np.uint8)
+    return (perm, sparse)
+
   def mlir_tensor_attr(self) -> Optional[sparse_tensor.EncodingAttr]:
     """Constructs the MLIR attributes for the tensor format."""
     order = (
@@ -1017,7 +1024,9 @@
       shape = np.array(self._shape, np.int64)
       indices = np.array(self._coords, np.int64)
       values = np.array(self._values, self._dtype.value)
-      ptr = utils.coo_tensor_to_sparse_tensor(shape, values, indices)
+      perm, sparse = self.format.get_permutation_and_sparsity()
+      ptr = utils.coo_tensor_to_sparse_tensor(shape, values, indices, perm,
+                                              sparse)
     else:
       ptr = self._packed_sparse_value
 
diff --git a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
--- a/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
+++ b/mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco_utils.py
@@ -133,7 +133,8 @@
 
 
 def coo_tensor_to_sparse_tensor(np_shape: np.ndarray, np_values: np.ndarray,
-                                np_indices: np.ndarray) -> int:
+                                np_indices: np.ndarray, np_perm: np.ndarray,
+                                np_sparse: np.ndarray) -> int:
   """Converts a COO-flavored format sparse tensor to an MLIR sparse tensor.
 
   Args:
@@ -141,6 +142,10 @@
      np_values: A 1D numpy array, for the non-zero values in the tensor.
      np_indices: A 2D numpy array of integers, representing the indices for the
        non-zero values in the tensor.
+     np_perm: A 1D numpy array of integers, representing the storage ordering
+       for the dimensions.
+     np_sparse: A 1D numpy array of uint8, representing the sparsity values
+       for the dimensions.
 
   Returns:
      An integer for the non-null ctypes.c_void_p to the MLIR sparse tensor
@@ -159,8 +164,6 @@
       ctypes.POINTER(np.ctypeslib.as_ctypes_type(np_values.dtype)))
   indices = np_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
 
-  np_perm = np.arange(r, dtype=np.ulonglong)
-  np_sparse = np.full(r, 1, dtype=np.uint8)
   perm = np_perm.ctypes.data_as(ctypes.POINTER(ctypes.c_ulonglong))
   sparse = np_sparse.ctypes.data_as(ctypes.POINTER(ctypes.c_uint8))