diff --git a/mindspore/core/ops/clip_by_norm_no_div_dum.cc b/mindspore/core/ops/clip_by_norm_no_div_sum.cc
similarity index 100%
rename from mindspore/core/ops/clip_by_norm_no_div_dum.cc
rename to mindspore/core/ops/clip_by_norm_no_div_sum.cc
diff --git a/mindspore/python/mindspore/ops/_vmap/vmap_grad_nn_ops.py b/mindspore/python/mindspore/ops/_vmap/vmap_grad_nn_ops.py
index 29911c22779..891a90dd948 100644
--- a/mindspore/python/mindspore/ops/_vmap/vmap_grad_nn_ops.py
+++ b/mindspore/python/mindspore/ops/_vmap/vmap_grad_nn_ops.py
@@ -494,3 +494,48 @@ def get_instance_norm_grad_rule(prim, axis_size):
         return (output_x, 0), (updated_moving_mean, 0), (updated_moving_variance, 0)
 
     return vmap_rule
+
+
+@vmap_rules_getters.register(G.MirrorPadGrad)
+def get_mirror_pad_grad_grad_vmap_rule(prim, axis_size):
+    """VmapRule for `MirrorPadGrad` operation."""
+    input_max_dim = 4
+
+    def vmap_rule(*params_bdim):
+        is_all_none, result = vmap_general_preprocess(prim, params_bdim)
+        if is_all_none:
+            return result
+        if len(params_bdim) < 2:
+            _raise_value_error("The input params in `{}` must >= 2, but got {}.".format(prim.name, len(params_bdim)))
+        input_x, input_x_dim = params_bdim[0]
+        paddings, paddings_dim = params_bdim[1]
+
+        out = None
+        x = _bdim_at_front(input_x, input_x_dim, axis_size)
+        if paddings_dim is not None:
+            _raise_value_error(
+                "The source axis of `paddings` in `{}` must be None, but got {}.".format(prim.name, paddings_dim))
+        pad_dim = F.shape(paddings)[0]
+        x_ndim = F.rank(x)
+
+        if pad_dim == x_ndim and x_ndim <= input_max_dim:
+            out = prim(x, paddings)
+        elif x_ndim > input_max_dim:
+            # reshape to 4 dims
+            x_shape = F.shape(x)
+            diff_dim = x_ndim - input_max_dim
+            first_shape = 1
+            for i in range(diff_dim + 1):
+                first_shape *= x_shape[i]
+            input_shape = (first_shape,) + x_shape[(-input_max_dim + 1):]
+            x = F.reshape(x, input_shape)
+            out = prim(x, paddings)
+            out_shape = F.shape(out)
+            real_out_shape = x_shape[:diff_dim + 1] + out_shape[1:]
+            out = F.reshape(out, real_out_shape)
+        else:
+            _raise_value_error("The dim of `input_x` in `{}` must be bigger than {}, "
+                               "but got {}.".format(prim.name, pad_dim, x_ndim))
+        return (out, 0)
+
+    return vmap_rule