diff --git a/.jenkins/check/config/filter_pylint.txt b/.jenkins/check/config/filter_pylint.txt
index c484bbb9378..f57108b15ce 100644
--- a/.jenkins/check/config/filter_pylint.txt
+++ b/.jenkins/check/config/filter_pylint.txt
@@ -128,7 +128,8 @@
 "mindspore/tests/ut/python/nn/test_cell_method_attribute.py"                                            "no-self-argument"
 "mindspore/tests/ut/python/onnx/test_onnx.py"                                                           "unused-variable"
 "mindspore/tests/ut/python/ops"                                                                         "super-init-not-called"
-"mindspore/tests/ut/python/ops/test_tensor_slice.py"                                                    "redefined-outer-name"
+"mindspore/tests/st/ops/test_tensor_slice.py"                                                           "redefined-outer-name"
+"mindspore/tests/st/ops/test_tensor_slice.py"                                                           "useless-super-delegation"
 "mindspore/tests/ut/python/optimizer/test_debug_location.py"                                            "super-init-not-called"
 "mindspore/tests/ut/python/parallel/"                                                                   "protected-access"
 "mindspore/tests/ut/python/parameter_feature/test_var_grad.py"                                          "bad-super-call"
diff --git a/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py b/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
index 6d65df11ac9..5300e3e833a 100644
--- a/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
+++ b/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
@@ -16,7 +16,6 @@
 """array_ops vmap impl."""
 from __future__ import absolute_import
 
-import numpy as np
 import mindspore
 import mindspore.numpy as mnp
 from mindspore import ops
@@ -147,14 +146,13 @@ def _get_prefix(indices_shape, axis_size, indices_dtype):
     the generated prefix is a Tensor([[[0], [0]],
                                       [[1], [1]]])
     """
-    if not indices_shape:
-        raise ValueError("indices_shape is empty in _get_prefix.")
 
     indices_len = len(indices_shape)
 
     if indices_len == 1:
-        prefix = np.arange(axis_size)
-        return Tensor(prefix, indices_dtype)
+        prefix = P.Range()(Tensor(0, indices_dtype), P.Fill()(
+            indices_dtype, (), axis_size), Tensor(1, indices_dtype))
+        return prefix
 
     indices_end = indices_len - 1
     prefix_shape = ()
@@ -169,8 +167,9 @@ def _get_prefix(indices_shape, axis_size, indices_dtype):
         else:
             expand_shape = expand_shape + (1,)
 
-    prefix = np.broadcast_to(np.arange(axis_size).reshape(expand_shape), prefix_shape)
-    return Tensor(prefix, indices_dtype)
+    prefix = P.BroadcastTo(prefix_shape)(P.Reshape()(P.Range()(Tensor(
+        0, indices_dtype), Tensor(axis_size, indices_dtype), Tensor(1, indices_dtype)), expand_shape))
+    return prefix
 
 
 @vmap_rules_getters.register(P.Transpose)
@@ -352,6 +351,7 @@ def get_unstack_vmap_rule(prim, axis_size):
 def get_reshape_vmap_rule(prim, axis_size):
     """VmapRule for `Reshape` operation."""
 
+
     @constexpr
     def get_batch_shape(x_shape, x_dim, target_shape, axis_size):
         if x_dim == 0:
@@ -364,19 +364,20 @@ def get_reshape_vmap_rule(prim, axis_size):
         dim_prod = 1
         for i, shp_i in enumerate(target_shape):
             if shp_i == -1:
-                if neg_index != -1:
-                    raise ValueError(f'The shape can only has one -1 at most, but {target_shape}.')
                 neg_index = i
             else:
                 dim_prod *= shp_i
-        arr_prod = np.prod(x_shape)
+        arr_prod = 1
+        for i in x_shape:
+            arr_prod *= i
         target_shape_list = list(target_shape)
         if neg_index != -1:
             neg_index_size = int(arr_prod // (dim_prod * axis_size))
             target_shape_list[neg_index] = neg_index_size
 
-        arr_prod_before_dim = np.prod(x_shape[:x_dim])
-
+        arr_prod_before_dim = 1
+        for i in x_shape[:x_dim]:
+            arr_prod_before_dim *= i
         dim_prod = 1
         for i, shp_i in enumerate(target_shape_list, start=1):
             dim_prod *= shp_i
@@ -565,12 +566,11 @@ def get_scatter_nd_vmap_rule(prim, axis_size):
     @constexpr
     def _gen_indices_offset(shape, offset):
         # original rank(indices.shape) is required >= 2, so indices with batch dim's rank >= 3.
-        shape = [shape[0]] + [1] * (len(shape) - 2) + [shape[-1]]
-        val = np.zeros(shape, np.int32)  # the dtype will be changed when creating Tensor
-        val = np.reshape(val, (shape[0], shape[-1]))
+        shape = (shape[0],) + (1,) * (len(shape) - 2) + (shape[-1],)
+        val = P.Zeros()((shape[0], shape[-1]), mindspore.int32)
         for i in range(shape[0]):
             val[i, 0] = i * offset
-        return np.reshape(val, shape)
+        return P.Reshape()(val, shape)
 
     if isinstance(prim, str):
         prim = Primitive(prim)
@@ -591,7 +591,7 @@ def get_scatter_nd_vmap_rule(prim, axis_size):
         indices_shape = F.shape(indices)
         indices_dtype = F.dtype(indices)
         offset_val = _gen_indices_offset(indices_shape, offset)
-        indices_offset = Tensor(offset_val, indices_dtype)
+        indices_offset = P.Cast()(offset_val, indices_dtype)
         new_indices = P.Add()(indices, indices_offset)
         out = prim(new_indices, updates, new_shape)
         real_out = P.Reshape()(out, out_shape)
diff --git a/mindspore/python/mindspore/ops/_vmap/vmap_base.py b/mindspore/python/mindspore/ops/_vmap/vmap_base.py
index 439ab26d4f1..c832b404053 100644
--- a/mindspore/python/mindspore/ops/_vmap/vmap_base.py
+++ b/mindspore/python/mindspore/ops/_vmap/vmap_base.py
@@ -135,9 +135,6 @@ def _get_broadcast_shape(x_shape, dst, axis_size):
     x_ndim = len(x_shape)
     broadcast_ndim = x_ndim + 1
 
-    if dst < -broadcast_ndim or dst >= broadcast_ndim:
-        _raise_value_error("Destination axis {} is out of bounds for array of dimension"
-                           " [{}, {}).".format(dst, -broadcast_ndim, broadcast_ndim))
     if dst < 0:
         dst = broadcast_ndim + dst
 
diff --git a/mindspore/python/mindspore/ops/_vmap/vmap_convolution_ops.py b/mindspore/python/mindspore/ops/_vmap/vmap_convolution_ops.py
index ee4655b37d6..876344cf0d7 100644
--- a/mindspore/python/mindspore/ops/_vmap/vmap_convolution_ops.py
+++ b/mindspore/python/mindspore/ops/_vmap/vmap_convolution_ops.py
@@ -16,7 +16,6 @@
 """convolution vmap impl"""
 from __future__ import absolute_import
 
-import numpy as np
 import mindspore.numpy as mnp
 from mindspore.ops import constexpr
 from mindspore.ops import operations as P
@@ -174,10 +173,7 @@ def _reshape_merge_dims(src_dim, dst_dim, target):
 @constexpr
 def _get_expand_shape(src_dim, dst_size, shape, prim_name):
     """Get new shape for splitting src_dim into dst_size parts."""
-    dst_size2, remainder = np.divmod(shape[src_dim], dst_size)
-    if remainder != 0:
-        _raise_value_error("The remainder of {} / {} should be 0, "
-                           "but got {} in {}.".format(shape[src_dim], dst_size, remainder, prim_name))
+    dst_size2 = shape[src_dim] // dst_size
     new_shape = list(shape)
     new_shape[src_dim:(src_dim + 1)] = [dst_size, dst_size2]
     return tuple(new_shape)
diff --git a/mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py b/mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
index b2c87b53e79..15cc9c13046 100644
--- a/mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
+++ b/mindspore/python/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
@@ -71,41 +71,35 @@ complex_priority_map = {
 complex_types = [mstype.complex64, mstype.complex128]
 
 
-#TODO: remove comment
 @constexpr
 def raise_value_error(msg):
     """Constexpr for raise_value_error."""
     raise ValueError(msg)
 
 
-#TODO: remove comment
 @constexpr
 def raise_index_error(msg):
     """Constexpr for raise_index_error."""
     raise IndexError(msg)
 
 
-#TODO: remove comment
 @constexpr
 def raise_type_error(msg):
     """Constexpr for raise_type_error."""
     raise TypeError(msg)
 
 
-#TODO: remove comment
 @constexpr
 def raise_unimplemented_error(msg):
     raise NotImplementedError(msg)
 
 
-#TODO: remove comment
 @constexpr
 def log_warning(msg):
     """Adds warning to logger."""
     logger.warning(msg)
 
 
-#TODO: remove comment
 @constexpr
 def check_equal(param1, param2, msg="{},{}"):
     """Checks whether the two parameters are equal or not."""
@@ -114,14 +108,12 @@ def check_equal(param1, param2, msg="{},{}"):
     return param1
 
 
-#TODO: remove comment
 @constexpr
 def make_empty_slice():
     """Creates a empty slice."""
     return slice(None, None, None)
 
 
-#TODO: remove comment
 @constexpr
 def _deep_list(array_like, dim_size=None):
     """convert nested tuple/list mixtures to pure nested list"""
@@ -132,7 +124,6 @@ def _deep_list(array_like, dim_size=None):
     return array_like
 
 
-#TODO: remove comment
 @constexpr
 def deep_tuple(array_like):
     """convert nested tuple/list mixtures to pure nested tuple"""
@@ -165,8 +156,6 @@ def _deep_tensor_to_nparray(array_like):
     return array_like
 
 
-#TODO: remove comment
-#@constexpr(run_graph=False)
 @constexpr
 def check_range(x, dim_size):
     if dim_size is None:
@@ -178,7 +167,6 @@ def check_range(x, dim_size):
     return x
 
 
-#TODO: remove comment
 @constexpr
 def make_tensor(a, dtype=mstype.int64, data_shape=None, dim_size=None):
     """
@@ -225,7 +213,6 @@ def make_tensor(a, dtype=mstype.int64, data_shape=None, dim_size=None):
 tensor_operator_registry.register('make_tensor', make_tensor)
 
 
-#TODO: remove comment
 @constexpr
 def judge_data_dim(data_dim, min_data_dim=0, max_data_dim=8):
     """Judges whether the data dim is valid."""
@@ -241,7 +228,6 @@ def get_source_shape(data_shape, value_shape):
     return value_shape
 
 
-#TODO: remove comment
 @constexpr
 def check_tensor_setitem_index(index, element_type=None):
     """Checks tuple index type of tensor assignment."""
@@ -268,7 +254,6 @@ def check_tensor_setitem_index(index, element_type=None):
         "Index of type '{}' is not supported yet.".format(type(index)))
 
 
-#TODO: remove comment
 @constexpr
 def is_same_type(inst, type_):
     """
@@ -284,7 +269,6 @@ def is_same_type(inst, type_):
     return inst == type_
 
 
-#TODO: remove comment
 @constexpr
 def check_valid_dim(dim, name):
     """Checks whether the dim is valid."""
@@ -292,7 +276,6 @@ def check_valid_dim(dim, name):
         raise ValueError(f"For '{name}', the dimension of inputs must be 1d or 2d, but got {dim}.")
 
 
-#TODO: remove comment
 @constexpr
 def judge_index_type(index_type, target_type):
     """Judges whether the index type is valid."""
@@ -301,7 +284,6 @@ def judge_index_type(index_type, target_type):
     return False
 
 
-#TODO: remove comment
 @constexpr
 def judge_indexes_types(dtypes, target_type):
     """Check a tuple of tensor data type."""
@@ -315,7 +297,6 @@ def judge_indexes_types(dtypes, target_type):
     return True
 
 
-#TODO: remove comment
 @constexpr
 def check_type_isinstance(dtype, target_type):
     """Checks whether the dtype is instance of target type."""
@@ -324,14 +305,12 @@ def check_type_isinstance(dtype, target_type):
     return isinstance(dtype, target_type)
 
 
-#TODO: remove comment
 @constexpr
 def check_type_invalid(dtype, target_type):
     """Checks whether the dtype is valid."""
     return dtype != target_type and (isinstance(target_type, (list, tuple)) and dtype not in target_type)
 
 
-#TODO: remove comment
 @constexpr
 def check_type_valid(dtype, target_type, op_name):
     """Checks whether the dtype is valid."""
@@ -343,7 +322,6 @@ def check_type_valid(dtype, target_type, op_name):
             f"The '{op_name}' doesn't support '{dtype}' and expect to receive {target_type}.")
 
 
-#TODO: remove comment
 @constexpr
 def check_types_valid(dtypes, target_type, op_name):
     """Check a tuple of tensor data type."""
@@ -351,7 +329,6 @@ def check_types_valid(dtypes, target_type, op_name):
         check_type_valid(dtype, target_type, op_name)
 
 
-#TODO: remove comment
 @constexpr
 def get_pos_of_indexes_types(indexes_types, op_name):
     """Separate the position information of tensor and slice and ellipsis from the mixed tensors index."""
@@ -403,7 +380,7 @@ def ellipsis2slice(input_, shape):
     return tuple(result)
 
 
-#remove constexpr
+@constexpr
 def slice2indices(input_slice, shape):
     """
     Converts slice to indices.
@@ -435,7 +412,6 @@ def slice2indices(input_slice, shape):
     return P.Stack(-1)(mesh_arrays)
 
 
-#TODO: remove comment
 @constexpr
 def check_indices(indices_size, index):
     """Checks indices whether is empty."""
@@ -445,7 +421,6 @@ def check_indices(indices_size, index):
     return indices_size
 
 
-#TODO: remove comment
 @constexpr
 def check_indices_value_size(indices_size, value_size):
     """Checks if the sizes are already matched."""
@@ -459,7 +434,6 @@ def check_indices_value_size(indices_size, value_size):
     return value_size
 
 
-#TODO: remove comment
 @constexpr
 def tuple_index_type_cnt(types, op_name):
     """count the tensor type of types which contains the tuple elements' type."""
@@ -470,7 +444,6 @@ def tuple_index_type_cnt(types, op_name):
     return MIXED
 
 
-#TODO: remove comment
 @constexpr
 def check_value_elements(types):
     """Judges the type of all elements of the tuple."""
@@ -485,7 +458,6 @@ def check_value_elements(types):
     return CONTAIN_TENSOR
 
 
-#TODO: remove comment
 @constexpr
 def get_index_tensor_dtype(dtype):
     """Check a tuple of tensor data type."""
@@ -497,7 +469,6 @@ def get_index_tensor_dtype(dtype):
         f"For '{TENSOR_SETITEM}', the index tensor data type '{dtype}' is not supported.")
 
 
-#TODO: remove comment
 @constexpr
 def check_tensors_dtype_same(data_dtype, value_dtype, op_name):
     """Check tensors data type same."""
@@ -540,7 +511,7 @@ def get_broadcast_shape(x_shape, y_shape, prim_name):
     return broadcast_shape
 
 
-#remove constexpr
+@constexpr
 def generate_broadcast_shape(shapes, op_name):
     """Generate broadcast shape for a tuple of shape."""
     if not shapes:
@@ -551,20 +522,20 @@ def generate_broadcast_shape(shapes, op_name):
     return tuple(broadcast_shape)
 
 
-#remove constexpr
+@constexpr
 def check_two_shapes_need_broadcast(shape_x, shape_y):
     """Check shape_y needs to be broadcast to shape_x."""
     return shape_y != shape_x
 
 
-#remove constexpr
+@constexpr
 def compute_multiples(origin_shape, broadcast_shape):
     """Compute multiples between origin shape with broadcast shape."""
     len_gap = len(broadcast_shape) - len(origin_shape)
     return broadcast_shape[0:len_gap] + tuple(map(lambda x, y: x // y, broadcast_shape[len_gap:], tuple(origin_shape)))
 
 
-#remove constexpr
+@constexpr
 def convert_scalar_to_tensor(data_shape, data_dtype, indices_shape, value, op_type):
     """Convert a scalar to a tensor."""
     if op_type == SET_ITEM_BY_ONE_TENSOR:
@@ -589,7 +560,6 @@ def generate_updates_shape(data_shape, index_shape, op_type, is_dynamic):
     return updates_shape
 
 
-#TODO: remove comment
 @constexpr
 def transform_slice_to_ele_list(slice_index, dim_len):
     """Transforms slice to element list."""
@@ -601,7 +571,6 @@ def transform_slice_to_ele_list(slice_index, dim_len):
     return slice_ele_list
 
 
-#TODO: remove comment
 @constexpr
 def generate_index_info_from_tuple_of_mixed_tensors(tensor_positions, tensor_indexes_shapes,
                                                     slice_shapes, op_name, fancy_position=None):
@@ -631,14 +600,12 @@ def _judge_order_continuous(order_sequence):
     return True
 
 
-#TODO: remove comment
 @constexpr
 def scalar_in_sequence(x, y):
     """Determine whether the scalar in the sequence."""
     return x in y
 
 
-#TODO: remove comment
 @constexpr
 def get_np_eps(input_dtype):
     """Get numpy eps."""
@@ -647,7 +614,6 @@ def get_np_eps(input_dtype):
     return float(eps)
 
 
-#TODO: remove comment
 @constexpr
 def check_number_index_type(number):
     """Check if it is int or bool number"""
@@ -659,7 +625,7 @@ def check_number_index_type(number):
                      .format(number, type(number)))
 
 
-#remove constexpr
+@constexpr
 def get_stride_info_from_slice(data_shape, slice_index):
     """Get stride info from a python slice"""
     begin, end, step = get_slice_stride(slice_index, data_shape[0])
@@ -673,7 +639,7 @@ def get_stride_info_from_slice(data_shape, slice_index):
     return tuple(begin_strides), tuple(end_strides), tuple(step_strides)
 
 
-#remove constexpr
+@constexpr
 def get_stride_info_from_integer(data_shape, number):
     """Get stride info from an integer"""
     begin_strides = [number]
@@ -686,7 +652,6 @@ def get_stride_info_from_integer(data_shape, number):
     return tuple(begin_strides), tuple(end_strides), tuple(step_strides)
 
 
-#TODO: remove comment
 @constexpr
 def get_slice_stride(index_slice, dim_size):
     """Get slice stride info"""
@@ -748,14 +713,12 @@ def get_stride_info_from_tuple(data_shape, tuple_index):
     return strides_v, shrink_axis
 
 
-#TODO: remove comment
 @constexpr
 def scalar_to_tensor(x):
     """Convert a scalar to a tensor"""
     return Tensor(x)
 
 
-#TODO: remove comment
 @constexpr
 def unpack(x):
     if isinstance(x, (tuple, list)) and len(x) == 1:
@@ -763,7 +726,6 @@ def unpack(x):
     return x
 
 
-#TODO: remove comment
 @constexpr
 def normalize_start(start, dim_size):
     """
@@ -779,7 +741,6 @@ def normalize_start(start, dim_size):
     return start if start < dim_size else dim_size
 
 
-#TODO: remove comment
 @constexpr
 def normalize_stop(stop, dim_size):
     """
@@ -797,7 +758,6 @@ def normalize_stop(stop, dim_size):
     return stop if stop < dim_size else dim_size
 
 
-#TODO: remove comment
 @constexpr
 def get_step_from_slice(input_slice):
     """get step in a slice."""
@@ -807,7 +767,6 @@ def get_step_from_slice(input_slice):
     return step
 
 
-#TODO: remove comment
 @constexpr
 def normalize_slice(input_slice, dim_size):
     """Normalizes start, stop, step in a slice."""
@@ -823,7 +782,6 @@ def normalize_slice(input_slice, dim_size):
     return start, stop, step
 
 
-#TODO: remove comment
 @constexpr
 def tuple_slice(tup, start, end):
     """get sliced tuple from start and end."""
@@ -834,7 +792,6 @@ def expanded_shape(shape, expand_size):
     return (1,)*expand_size + shape
 
 
-#TODO: remove comment
 @constexpr
 def sequence_mul_int(seq, number):
     """
@@ -852,14 +809,12 @@ def sequence_mul_int(seq, number):
     return seq * number
 
 
-#TODO: remove comment
 @constexpr
 def check_in_sequence(x, y):
     """Determine whether the input `x` is in the sequence `y`."""
     return x in y
 
 
-#TODO: remove comment
 @constexpr
 def is_slice(x):
     return isinstance(x, slice)
@@ -874,7 +829,6 @@ def filter_expanded_dims(shape, not_expanded_dim):
     return tuple(res)
 
 
-#TODO: remove comment
 @constexpr
 def sequence_to_index(sequence, dim_size):
     """Transforms sequence to tensor index."""
@@ -892,7 +846,7 @@ def sequence_to_index(sequence, dim_size):
     return make_tensor(sequence, mstype.int64, None, dim_size)
 
 
-#remove constexpr
+@constexpr
 def int_to_index(i, shape):
     """Converts integer to tensor indices."""
     dim_size = shape[0]
@@ -913,7 +867,6 @@ def int_to_index(i, shape):
     return P.Concat(-1)((P.Fill()(mstype.int64, P.Shape()(index)[:-1] + (1,), i), index))
 
 
-#TODO: remove comment
 @constexpr
 def rem_not_expanded_dims(idx_advanced, expand_true, tensor_index_ndim, rem_ndim, not_expanded_dim):
     """Adds remaining dimensions not indexed to not_expanded_dim"""
@@ -933,13 +886,11 @@ def rem_not_expanded_dims(idx_advanced, expand_true, tensor_index_ndim, rem_ndim
     return not_expanded_dim, idx_advanced
 
 
-#TODO: remove comment
 @constexpr
 def check_slice_empty(start, stop, step):
     return (start - stop)*step >= 0
 
 
-#TODO: remove comment
 @constexpr
 def real_axes(ndim_orig, ndim_out, axes_orig):
     """Returns the real axes to be reduced after performing broadcast"""
@@ -952,7 +903,7 @@ def real_axes(ndim_orig, ndim_out, axes_orig):
 check_axis_valid_const = constexpr(validator.check_axis_valid)
 
 
-#remove constexpr
+@constexpr
 def compute_slice_shape(slice_shape, broadcast_shape_len, slice_cnt, fancy_position):
     """Computes slice tensor shapes"""
     shape = [1] * len(slice_shape)
@@ -961,7 +912,7 @@ def compute_slice_shape(slice_shape, broadcast_shape_len, slice_cnt, fancy_posit
     return shape
 
 
-#remove constexpr
+@constexpr
 def infer_out_shape(*shapes):
     """
     Returns shape of output after broadcasting. Raises ValueError if shapes cannot be broadcast.
@@ -977,7 +928,6 @@ def infer_out_shape(*shapes):
     return tuple(shape_out)
 
 
-#TODO: remove comment
 @constexpr
 def use_copy_slice(tuple_index):
     if tuple_index is not None and len(tuple_index) >= 2:
@@ -987,7 +937,6 @@ def use_copy_slice(tuple_index):
     return False
 
 
-#TODO: remove comment
 @constexpr
 def is_ascend():
     return context.get_context('device_target') == "Ascend"
@@ -998,7 +947,6 @@ def gen_exception_msg(msg_format, *args):
     return msg_format.format(*args)
 
 
-#TODO: remove comment
 @constexpr
 def get_output_dtype(dtype_1, dtype_2, use_complex=False):
     """Returns output dtype after type promotion."""
@@ -1017,7 +965,6 @@ def get_output_dtype(dtype_1, dtype_2, use_complex=False):
     return dtype_2
 
 
-#TODO: remove comment
 @constexpr
 def promote_binary_dtype(dtype_1, dtype_2):
     if dtype_1 == dtype_2:
diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py
index 15bb573f3d7..3342c5fc26e 100644
--- a/mindspore/python/mindspore/ops/function/array_func.py
+++ b/mindspore/python/mindspore/ops/function/array_func.py
@@ -4412,7 +4412,6 @@ def index_fill(x, axis, index, value):
     return index_fill_(x, axis, index, value)
 
 
-#TODO: remove comment
 @constexpr
 def _check_check_axis_in_range(axis, ndim):
     """Checks axes are with the bounds of ndim"""
diff --git a/tests/ut/python/numpy_native/test_numpy_ops.py b/tests/st/numpy_native/test_numpy_ops.py
similarity index 98%
rename from tests/ut/python/numpy_native/test_numpy_ops.py
rename to tests/st/numpy_native/test_numpy_ops.py
index e507ad5ac2a..1b5e35def2a 100644
--- a/tests/ut/python/numpy_native/test_numpy_ops.py
+++ b/tests/st/numpy_native/test_numpy_ops.py
@@ -19,12 +19,13 @@ import mindspore.numpy as mnp
 from mindspore import Tensor
 from mindspore.nn import Cell
 import mindspore.context as context
-from ....mindspore_test_framework.mindspore_test import mindspore_test
-from ....mindspore_test_framework.pipeline.forward.compile_forward \
+from ...mindspore_test_framework.mindspore_test import mindspore_test
+from ...mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config
 
 context.set_context(mode=context.GRAPH_MODE)
 
+
 class MeshGrid(Cell):
     def construct(self, a, b, c, d):
         ret = mnp.meshgrid(a, b, c, d)
diff --git a/tests/ut/python/ops/test_tensor_slice.py b/tests/st/ops/test_tensor_slice.py
similarity index 99%
rename from tests/ut/python/ops/test_tensor_slice.py
rename to tests/st/ops/test_tensor_slice.py
index 1436bc87e1b..9ca43d99ff8 100644
--- a/tests/ut/python/ops/test_tensor_slice.py
+++ b/tests/st/ops/test_tensor_slice.py
@@ -20,8 +20,8 @@ from mindspore import Tensor, Parameter
 from mindspore import context
 from mindspore import dtype as mstype
 from mindspore.nn import Cell
-from ....mindspore_test_framework.mindspore_test import mindspore_test
-from ....mindspore_test_framework.pipeline.forward.compile_forward \
+from ...mindspore_test_framework.mindspore_test import mindspore_test
+from ...mindspore_test_framework.pipeline.forward.compile_forward \
     import pipeline_for_compile_forward_ge_graph_for_case_by_case_config, \
     pipeline_for_compile_forward_ge_graph_for_case_by_case_config_exception