diff --git a/tests/st/dynamic_shape/test_dynamic_wenet_ascend.py b/tests/st/dynamic_shape/test_dynamic_wenet_ascend.py
index b36ef0335b8..982fc39bd1e 100644
--- a/tests/st/dynamic_shape/test_dynamic_wenet_ascend.py
+++ b/tests/st/dynamic_shape/test_dynamic_wenet_ascend.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2022 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -576,13 +576,8 @@ class CustomDense(nn.Dense):
         """Initialize CustomDense."""
         super(CustomDense, self).__init__(in_channels, out_channels,
                                           weight_init, bias_init, has_bias, activation)
-        self.scatterupdate = ops.TensorScatterUpdate()
         self.dyn_shape = ops.TensorShape()
-        self.mul = ops.Mul()
         self.cast = ops.Cast()
-        self.indices_0 = Tensor(np.array([[0]]), mstype.int32)
-        self.indices_1 = Tensor(np.array([[-1]]), mstype.int32)
-        self.indices_2 = Tensor(np.array([[2]]), mstype.int32)
 
     def construct(self, x):
         x_shape = self.shape_op(x)
@@ -591,9 +586,7 @@ class CustomDense(nn.Dense):
             x_dyn_shape = self.cast(x_dyn_shape, mstype.float32)
             if len(x_dyn_shape) != 2:
                 new_shape = x_dyn_shape[1:]
-                updates = self.mul(x_dyn_shape[0:1], x_dyn_shape[1:2])
-                new_shape = self.scatterupdate(
-                    new_shape, self.indices_0, updates)
+                new_shape[0] = x_dyn_shape[0:1] * x_dyn_shape[1:2]
                 new_shape = self.cast(new_shape, mstype.int64)
                 x = self.reshape(x, new_shape)
             x = self.matmul(x, self.weight)
@@ -604,9 +597,7 @@ class CustomDense(nn.Dense):
             if len(x_dyn_shape) != 2:
                 out_shape = self.dyn_shape(x)
                 out_shape = self.cast(out_shape, mstype.float32)
-                updates = out_shape[1:2]
-                x_dyn_shape = self.scatterupdate(
-                    x_dyn_shape, self.indices_2, updates)
+                x_dyn_shape[2] = out_shape[1:2]
                 x_dyn_shape = self.cast(x_dyn_shape, mstype.int64)
                 x = self.reshape(x, x_dyn_shape)
         else:
@@ -1342,25 +1333,12 @@ class PositionalEncoding(nn.Cell):
         self.pe = Tensor(np.expand_dims(self.pe, 0), mstype.float32)
         self.get_shape = ops.Shape()
         self.dyn_shape = ops.TensorShape()
-        self.stridedslice = ops.StridedSlice()
-        self.scatterupdate = ops.TensorScatterUpdate()
-        self.indices_1 = Tensor(([[1]]), mstype.int32)
-        self.end = Tensor(
-            (self.pe.shape[0], 0, self.pe.shape[2]), mstype.float32)
 
     def construct(self, x, offset=0) -> Tuple[mindspore.Tensor, mindspore.Tensor]:
         x_shape = self.get_shape(x)
-        if -1 not in x_shape:
-            pos_emb = self.pe[:, offset: offset + x_shape[1]]
-        else:
-            x_dyn_shape = self.dyn_shape(x)
-            x_dyn_shape = self.cast(x_dyn_shape, mstype.float32)
-            begin = (0, offset, 0)
-            end = self.scatterupdate(
-                self.end, self.indices_1, offset + x_dyn_shape[1:2])
-            end = self.cast(end, mstype.int64)
-            step = (1, 1, 1)
-            pos_emb = self.stridedslice(self.pe, begin, end, step)
+        if -1 in x_shape:
+            x_shape = self.dyn_shape(x)
+        pos_emb = self.pe[:, offset: offset + x_shape[1]]
         x = x * self.xscale + pos_emb
         return self.dropout(x), self.dropout(pos_emb)
 
@@ -1387,17 +1365,9 @@ class RelPositionalEncoding(PositionalEncoding):
         """
         x = x * self.xscale
         x_shape = self.get_shape(x)
-        if -1 not in x_shape:
-            pos_emb = self.pe[:, offset: offset + x_shape[1]]
-        else:
-            x_dyn_shape = self.dyn_shape(x)
-            x_dyn_shape = self.cast(x_dyn_shape, mstype.float32)
-            begin = (0, offset, 0)
-            end = self.scatterupdate(
-                self.end, self.indices_1, offset + x_dyn_shape[1:2])
-            end = self.cast(end, mstype.int64)
-            step = (1, 1, 1)
-            pos_emb = self.stridedslice(self.pe, begin, end, step)
+        if -1 in x_shape:
+            x_shape = self.dyn_shape(x)
+        pos_emb = self.pe[:, offset: offset + x_shape[1]]
         return self.dropout(x), self.dropout(pos_emb)
 
 
@@ -1840,11 +1810,10 @@ def test_train():
     logging.info("Training start.")
 
     model.train(
-        MAX_EPOCH * steps_size,
+        MAX_EPOCH,
         train_dataset,
         callbacks=callback,
-        dataset_sink_mode=True,
-        sink_size=1,
+        dataset_sink_mode=True
     )
 
     train_loss = callback.loss