From 906f3fa22230f102bce0c926578852f67f63d122 Mon Sep 17 00:00:00 2001
From: chenfei <chefei52@huawei.com>
Date: Fri, 19 Mar 2021 15:52:03 +0800
Subject: [PATCH] add test case for while grad

---
 tests/st/control/test_while_grad.py | 48 +++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 tests/st/control/test_while_grad.py

diff --git a/tests/st/control/test_while_grad.py b/tests/st/control/test_while_grad.py
new file mode 100644
index 0000000000..870d46a1ef
--- /dev/null
+++ b/tests/st/control/test_while_grad.py
@@ -0,0 +1,48 @@
+# Copyright 2021 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 pytest
+from mindspore.ops import composite as C
+import mindspore.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore.common.tensor import Tensor
+
+class Net(nn.Cell):
+    def construct(self, x, y):
+        while x < y:
+            x = x * x + 1
+        return x
+
+
+class GradNet(nn.Cell):
+    def __init__(self, net):
+        super().__init__()
+        self.net = net
+        self.grad_op = C.GradOperation(get_all=True)
+
+    def construct(self, x, y):
+        gradient_function = self.grad_op(self.net)
+        return gradient_function(x, y)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_while_grad():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
+    x = Tensor([2.0], dtype=mstype.float32)
+    y = Tensor([2.0], dtype=mstype.float32)
+    GradNet(Net())(x, y)