diff --git a/tests/st/pynative/dynamic_shape/test_pynative_control_flow.py b/tests/st/pynative/dynamic_shape/test_pynative_control_flow.py
new file mode 100644
index 0000000000..9a1ee9fa13
--- /dev/null
+++ b/tests/st/pynative/dynamic_shape/test_pynative_control_flow.py
@@ -0,0 +1,121 @@
+# Copyright 2020 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.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+import pytest
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+import mindspore.common.dtype as mstype
+import mindspore.ops as P
+from mindspore.common import ParameterTuple
+import torch
+import torch.nn as nn_pt
+
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
+class GradofAllInputsAndParams(nn.Cell):
+    def __init__(self, net, sens=False):
+        super().__init__()
+        self.grad = P.GradOperation(get_all=True, get_by_list=True, sens_param=sens)
+        self.net = net
+        self.params = ParameterTuple(self.net.trainable_params())
+
+    def construct(self, *x):
+        out = self.grad(self.net, self.params)(*x)
+        return out
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_pynative_diff_shape_with_while_in_construct():
+    class WhileNetMs(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(1, 1, 3, weight_init='ones', pad_mode='pad')
+
+        def construct(self, x, flag):
+            while flag:
+                if flag > 1:
+                    x = self.conv(x)
+                else:
+                    x = x + 1
+                flag = flag - 1
+            return x
+
+    class WhileNetPt(nn_pt.Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = nn_pt.Conv2d(in_channels=1, out_channels=1, kernel_size=(3, 3),
+                                     stride=1, padding=0, bias=False)
+            self.weight = nn_pt.Parameter(torch.from_numpy(np.ones([1, 1, 3, 3]).astype(np.float32)))
+            self.conv.register_parameter('weight', self.weight)
+
+        def forward(self, x, flag):
+            while flag:
+                if flag > 1:
+                    x = self.conv(x)
+                else:
+                    x = x + 1
+                flag = flag - 1
+            return x
+
+    net = WhileNetMs()
+    input_ms = Tensor(np.random.rand(1, 1, 224, 224).astype(np.float32))
+    flag = 2
+    out = net(input_ms, flag)
+    backnet = GradofAllInputsAndParams(net)
+    backout = backnet(input_ms, Tensor(flag, mstype.int32))
+
+    comparenet = WhileNetPt()
+    torch_input = torch.from_numpy(input_ms.asnumpy())
+    torch_input.requires_grad = True
+    torch_flag = torch.from_numpy(np.array(flag))
+    torch_flag.requires_grad = False
+    out_good = comparenet(torch_input, torch_flag)
+    grad = torch.from_numpy(np.ones_like(out_good.detach().numpy()).astype(np.float32))
+    out_good.backward(gradient=grad)
+    assert np.allclose(out_good.detach().numpy(), out.asnumpy(), 0.01, 0.01)
+    assert np.allclose(torch_input.grad.numpy(), backout[0][0].asnumpy(), 0.01, 0.01)
+    assert np.allclose(comparenet.weight.grad.numpy(), backout[1][0].asnumpy(), 0.01, 0.01)
+
+    flag = 3
+    out = net(input_ms, flag)
+    backout = backnet(input_ms, Tensor(flag, mstype.int32))
+    torch_flag = torch.from_numpy(np.array(flag))
+    torch_flag.requires_grad = False
+    comparenet.zero_grad()
+    torch_input.grad.zero_()
+    out_good = comparenet(torch_input, torch_flag)
+    grad = torch.from_numpy(np.ones_like(out_good.detach().numpy()).astype(np.float32))
+    out_good.backward(gradient=grad)
+    assert np.allclose(out_good.detach().numpy(), out.asnumpy(), 0.01, 0.01)
+    assert np.allclose(torch_input.grad.numpy(), backout[0][0].asnumpy(), 0.01, 0.01)
+    assert np.allclose(comparenet.weight.grad.numpy(), backout[1][0].asnumpy(), 0.01, 0.01)
+
+    input_ms = Tensor(np.random.rand(1, 1, 112, 112).astype(np.float32))
+    flag = 4
+    backout = backnet(input_ms, Tensor(flag, mstype.int32))
+    torch_input = torch.from_numpy(input_ms.asnumpy())
+    torch_input.requires_grad = True
+    torch_flag = torch.from_numpy(np.array(flag))
+    torch_flag.requires_grad = False
+    comparenet.zero_grad()
+    out_good = comparenet(torch_input, torch_flag)
+    grad = torch.from_numpy(np.ones_like(out_good.detach().numpy()).astype(np.float32))
+    out_good.backward(gradient=grad)
+    assert np.allclose(torch_input.grad.numpy(), backout[0][0].asnumpy(), 0.01, 0.01)
+    assert np.allclose(comparenet.weight.grad.numpy(), backout[1][0].asnumpy(), 0.01, 0.01)