add some control sink testcases

tests/st/control/inner/test_002_single_for.py

@@ -0,0 +1,65 @@
+# 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.
+# ============================================================================
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_signle_for():
+    class SignleForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.add = P.Add()
+            self.mul = P.Mul()
+
+        def construct(self, x, y, z):
+            x = self.add(x, y)
+            for _ in range(0, 3):
+                z = self.add(z, x)
+            y = self.mul(z, y)
+            return y
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([2], mstype.int32)
+    y = Tensor([5], mstype.int32)
+    z = Tensor([4], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_net = SignleForNet()
+    net = GradNet(for_net)
+    graph_forward_res = for_net(x, y, z)
+    graph_backward_res = net(x, y, z)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_net = SignleForNet()
+    net = GradNet(for_net)
+    pynative_forward_res = for_net(x, y, z)
+    pynative_backward_res = net(x, y, z)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_030_for_in_if.py

@@ -0,0 +1,70 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_in_if():
+    class ForInIfNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 4, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            if self.param_a > self.param_b:
+                x = self.mul(x, 2)
+                for _ in range(0, 5):
+                    x = self.add(x, x)
+                    self.param_b += 1
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([10], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    graph_forward_res = for_in_if_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_if_net = ForInIfNet()
+    net = GradNet(for_in_if_net)
+    pynative_forward_res = for_in_if_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_031_for_in_while.py

@@ -0,0 +1,75 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_in_while():
+    class ForInWhileNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            while self.param_a > self.param_b:
+                x = self.mul(x, 2)
+                for _ in range(0, 5):
+                    x = self.add(x, x)
+                    self.param_b = self.param_b + 1
+            y = self.sub(x, self.param_b)
+            self.assign(self.param_a, y)
+            return x
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([2], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_while_net = ForInWhileNet()
+    net = GradNet(for_in_while_net)
+    graph_forward_res = for_in_while_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_while_net = ForInWhileNet()
+    net = GradNet(for_in_while_net)
+    pynative_forward_res = for_in_while_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_032_for_in_for.py

@@ -0,0 +1,75 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_in_for():
+    class ForInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            for _ in range(0, 10):
+                x = self.mul(x, 2)
+                for _ in range(0, 5):
+                    x = self.add(x, x)
+                    self.param_b += 1
+            y = self.sub(x, self.param_b)
+            z = self.relu(x + y)
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([2], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_in_for_net = ForInForNet()
+    net = GradNet(for_in_for_net)
+    graph_forward_res = for_in_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_in_for_net = ForInForNet()
+    net = GradNet(for_in_for_net)
+    pynative_forward_res = for_in_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_102_if_after_for.py

@@ -0,0 +1,77 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_if_after_for():
+    class IfAfterForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(x, self.param_b)
+            for _ in range(0, 2):
+                x = self.sub(x, 2)
+            self.param_b = self.add(self.param_b, 2)
+            if x < self.param_b:
+                y = self.mul(x, self.param_a)
+
+            z = self.relu(x + y)
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    if_after_for_net = IfAfterForNet()
+    net = GradNet(if_after_for_net)
+    graph_forward_res = if_after_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    if_after_for_net = IfAfterForNet()
+    net = GradNet(if_after_for_net)
+    pynative_forward_res = if_after_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_302_for_after_for.py

@@ -0,0 +1,79 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_after_for():
+    class ForAfterForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+
+        def construct(self, x):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(x, self.param_b)
+            for _ in range(0, 2):
+                x = self.sub(x, 3)
+                y = x + self.param_b
+            self.param_a = x + y
+            for _ in range(0, 5):
+                y = self.mul(x, self.param_a)
+
+            z = self.relu(y + self.param_a)
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_net = ForAfterForNet()
+    net = GradNet(for_after_for_net)
+    graph_forward_res = for_after_for_net(x)
+    graph_backward_res = net(x)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_net = ForAfterForNet()
+    net = GradNet(for_after_for_net)
+    pynative_forward_res = for_after_for_net(x)
+    pynative_backward_res = net(x)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_320_for_after_while_in_if.py

@@ -0,0 +1,92 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_after_while_in_if():
+    class ForAfterWhileInIfNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.softmax = nn.Softmax()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 16, dtype=np.float32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+        def construct(self, x, y):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(y, self.param_b)
+
+            if self.param_b == y - self.param_a:
+                self.param_c = self.div(self.param_c, self.param_b)
+                while self.param_a > x:
+                    self.param_c = self.param_a + 2
+                    x = x + 1
+                y = self.softmax(self.param_c)
+                self.param_b = self.sub(y, self.param_b)
+
+            x = self.mul(self.param_b, self.param_c)
+
+            for _ in range(0, 4):
+                x = self.sub(x, 3)
+                y = y + self.param_b
+
+            self.param_a = x + y
+            z = self.relu(y + self.param_a)
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_while_in_if_net = ForAfterWhileInIfNet()
+    net = GradNet(for_after_while_in_if_net)
+    graph_forward_res = for_after_while_in_if_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_while_in_if_net = ForAfterWhileInIfNet()
+    net = GradNet(for_after_while_in_if_net)
+    pynative_forward_res = for_after_while_in_if_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_322_for_after_while_in_for.py

@@ -0,0 +1,93 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_after_while_in_for():
+    class ForAfterWhileInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.softmax = nn.Softmax()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 16, dtype=np.float32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+        def construct(self, x, y):
+            self.assign(self.param_a, x + self.param_a)
+            y = self.add(y, self.param_b)
+
+            for _ in range(0, 3):
+                self.param_b = self.add(self.param_c, self.param_b)
+                while self.param_c > x:
+                    self.param_b = self.param_a + 2
+                    x = x + 1
+                y = self.softmax(self.param_c) + self.param_a
+                self.param_b = self.sub(y, self.param_b)
+
+            x = self.mul(self.param_b, self.param_c)
+
+            for _ in range(0, 4):
+                x = self.mul(x, 3)
+                y = y + self.param_b
+                x = self.relu(self.param_c)
+
+            self.param_a = x - y
+            z = y + self.param_b
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_while_in_for_net = ForAfterWhileInForNet()
+    net = GradNet(for_after_while_in_for_net)
+    graph_forward_res = for_after_while_in_for_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_while_in_for_net = ForAfterWhileInForNet()
+    net = GradNet(for_after_while_in_for_net)
+    pynative_forward_res = for_after_while_in_for_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_331_for_after_for_in_while.py

@@ -0,0 +1,88 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_after_for_in_while():
+    class ForAfterForInWhileNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.softmax = nn.Softmax()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 16, dtype=np.float32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+        def construct(self, x, y):
+            while self.param_c > x:
+                self.param_b = self.add(self.param_c, self.param_b)
+                for _ in range(0, 20):
+                    self.param_b = self.param_a + 2
+                self.param_c = self.param_c - 1
+                x = x + 2
+                y = self.softmax(self.param_c) + self.param_a
+                self.param_b = self.sub(y, self.param_b)
+            x = self.mul(self.param_b, self.param_a)
+            for _ in range(0, 4):
+                x = self.mul(x, 3)
+                y = y + self.param_b
+                x = self.relu(self.param_c)
+            self.param_a = x - y
+            z = y + self.param_b
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_in_while_net = ForAfterForInWhileNet()
+    net = GradNet(for_after_for_in_while_net)
+    graph_forward_res = for_after_for_in_while_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_in_while_net = ForAfterForInWhileNet()
+    net = GradNet(for_after_for_in_while_net)
+    pynative_forward_res = for_after_for_in_while_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res

tests/st/control/inner/test_332_for_after_for_in_for.py

@@ -0,0 +1,84 @@
+# 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 numpy as np
+from mindspore import context
+from mindspore import Tensor, nn
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+grad_all = C.GradOperation(get_all=True)
+context.set_context(device_target="Ascend")
+
+def test_for_after_for_in_for():
+    class ForAfterForInForNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.relu = nn.ReLU()
+            self.softmax = nn.Softmax()
+            self.mul = P.Mul()
+            self.add = P.Add()
+            self.sub = P.Sub()
+            self.div = P.Div()
+            self.assign = P.Assign()
+            param_a = np.full((1,), 5, dtype=np.float32)
+            self.param_a = Parameter(Tensor(param_a), name='a')
+            param_b = np.full((1,), 2, dtype=np.float32)
+            self.param_b = Parameter(Tensor(param_b), name='b')
+            param_c = np.full((1,), 20, dtype=np.float32)
+            self.param_c = Parameter(Tensor(param_c), name='c')
+
+        def construct(self, x, y):
+            for _ in range(0, 4):
+                self.param_b = self.add(self.param_c, self.param_b)
+                for _ in range(0, 8):
+                    self.param_b = self.param_a + j
+                self.param_c = self.param_a * self.param_b
+
+            for _ in range(0, 3):
+                y = y + self.param_b
+                x = self.relu(self.param_c * 3)
+                self.param_a = x - y
+            z = y + self.param_b
+            return z
+
+    class GradNet(nn.Cell):
+        def __init__(self, net):
+            super(GradNet, self).__init__()
+            self.net = net
+
+        def construct(self, *inputs):
+            return grad_all(self.net)(*inputs)
+
+    x = Tensor([11], mstype.int32)
+    y = Tensor([7], mstype.int32)
+
+    # graph mode
+    context.set_context(mode=context.GRAPH_MODE)
+    for_after_for_in_for_net = ForAfterForInForNet()
+    net = GradNet(for_after_for_in_for_net)
+    graph_forward_res = for_after_for_in_for_net(x, y)
+    graph_backward_res = net(x, y)
+
+    # pynative mode
+    context.set_context(mode=context.PYNATIVE_MODE)
+    for_after_for_in_for_net = ForAfterForInForNet()
+    net = GradNet(for_after_for_in_for_net)
+    pynative_forward_res = for_after_for_in_for_net(x, y)
+    pynative_backward_res = net(x, y)
+
+    assert graph_forward_res == pynative_forward_res
+    assert graph_backward_res == pynative_backward_res