diff --git a/mindspore/ops/composite/multitype_ops/_compile_utils.py b/mindspore/ops/composite/multitype_ops/_compile_utils.py
index b90a5e35063..6bd37fef4b0 100644
--- a/mindspore/ops/composite/multitype_ops/_compile_utils.py
+++ b/mindspore/ops/composite/multitype_ops/_compile_utils.py
@@ -18,12 +18,14 @@ from . import _constexpr_utils as const_utils
 from ... import functional as F
 from ... import operations as P
 from ...composite import base
+from ...operations._inner_ops import TensorCopySlices
 from ....common.tensor import Tensor
 from ....common import dtype as mstype
 from ....common._register_for_tensor import tensor_operator_registry
 
 hyper_map = base.HyperMap()
 stack = P.Stack(axis=-1)
+copy_slice = TensorCopySlices()
 
 
 def _tensor_getitem(self, index):
@@ -671,6 +673,14 @@ def tensor_setitem_by_slice_with_tensor(data, input_slice, value):
     result = None
     check_result = const_utils.check_tensor_setitem_index(input_slice)
     if check_result:
+        start, stop, step = const_utils.normalize_slice(input_slice, data.shape[0])
+        if step == 1:
+            dim0_size = stop - start
+            if dim0_size <= 0:
+                return data
+            value_shape = (dim0_size,) + const_utils.tuple_slice(data.shape, 1, None)
+            value = _broadcast(value_shape, value)
+            return copy_slice(data, value, (start,), (stop,), (step,))
         data_shape = F.shape(data)
         indices = const_utils.slice2indices(input_slice, data_shape)
         if indices is False:
@@ -691,6 +701,18 @@ def tensor_setitem_by_tuple_with_tensor(data, tuple_index, value):
     """Assigns the tensor by tuple with tensor value."""
     op_name = const_utils.TENSOR_SETITEM
     tuple_index = _transform_ellipsis_to_slice(data, tuple_index, op_name)
+
+    if const_utils.use_copy_slice(tuple_index):
+        dim1_start, dim1_stop, _ = const_utils.normalize_slice(tuple_index[1], data.shape[1])
+        if dim1_stop - dim1_start <= 0:
+            return data
+        start = (tuple_index[0], dim1_start)
+        stop = (tuple_index[0] + 1, dim1_stop)
+        step = (1, 1)
+        value_shape = (dim1_stop - dim1_start,) + const_utils.tuple_slice(data.shape, 2, None)
+        value = _broadcast(value_shape, value)
+        return copy_slice(data, value, start, stop, step)
+
     tuple_index, value, idx_advanced = remove_expanded_dims(tuple_index, F.shape(data), value)
 
     if tuple_index is False:
diff --git a/mindspore/ops/composite/multitype_ops/_constexpr_utils.py b/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
index ed9683511a7..9c075be7072 100644
--- a/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
+++ b/mindspore/ops/composite/multitype_ops/_constexpr_utils.py
@@ -825,3 +825,12 @@ def infer_out_shape(*shapes):
             raise ValueError(f'operands could not be broadcast together with shapes {*shapes,}')
         shape_out.appendleft(max_size)
     return tuple(shape_out)
+
+
+@constexpr
+def use_copy_slice(tuple_index):
+    if tuple_index is not None and len(tuple_index) >= 2:
+        return (isinstance(tuple_index[0], int) and
+                isinstance(tuple_index[1], slice) and tuple_index[1].step in (1, None) and
+                all(x == slice(None, None, None) for x in tuple_index[2:]))
+    return False