!14641 [ME][ControlFlow]Add some testcases

From: @chenfei52
Reviewed-by: @ginfung
Signed-off-by:

tests/st/control/inner/test_001_single_while.py

@@ -0,0 +1,60 @@
+# 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.
+# ============================================================================
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=True, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def construct(self, x, y):
+        y = y + 10
+        while x < y:
+            x = (x + 2) * (y - 9)
+            y = y + 2
+        x = x + 5
+        return x
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, forward_net):
+        super(BackwardNet, self).__init__()
+        self.forward_net = forward_net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    c1 = Tensor([0], mstype.int32)
+    c2 = Tensor([0], mstype.int32)
+    expect = Tensor([75], mstype.int32)
+    forward_net = ForwardNet()
+    output = forward_net(c1, c2)
+    assert expect == output
+
+
+def test_backward():
+    c1 = Tensor([0], mstype.int32)
+    c2 = Tensor([0], mstype.int32)
+    expect = Tensor([75], mstype.int32)
+    forward_net = ForwardNet()
+    output = forward_net(c1, c2)
+    assert expect == output

tests/st/control/inner/test_011_if_in_while.py

@@ -0,0 +1,67 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles:
+            if out <= 20:
+                out = x * y + out
+            i = i + 1
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_011_if_in_while_break.py

@@ -0,0 +1,114 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles:
+            out = x * y + out
+            if out > 20:
+                break
+            i = i + 1
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+class ForwardNetReplaceBreak(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNetReplaceBreak, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles and out <= 20:
+            out = x * y + out
+            i = i + 1
+        return out
+
+
+class BackwardNetReplaceBreak(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNetReplaceBreak, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+# Problem: Exceed function call depth limit 1000.
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)
+
+
+def test_forward_replace_break():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNetReplaceBreak(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+# Problem: Exceed function call depth limit 1000.
+def test_backward_replace_break():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNetReplaceBreak(max_cycles=10)
+    backward_net = BackwardNetReplaceBreak(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_012_if_in_for.py

@@ -0,0 +1,64 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        for _ in range(self.max_cycles):
+            if out <= 20:
+                out = x * y + out
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_012_if_in_for_break.py

@@ -0,0 +1,65 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        for _ in range(self.max_cycles):
+            out = x * y + out
+            if out > 20:
+                break
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_020_while_in_if.py

@@ -0,0 +1,68 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+        self.i = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        i = self.i
+        if x > y:
+            while i < self.max_cycles:
+                out = x * y + out
+                i = i + 1
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_101_if_after_while.py

@@ -0,0 +1,68 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles:
+            out = x * y + out
+            i = i + 1
+        if out >= 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_111_if_after_if_in_while.py

@@ -0,0 +1,69 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles:
+            if out <= 20:
+                out = x * y + out
+            i = i + 1
+        if out >= 30:
+            out = out - 30
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_120_if_after_while_in_if.py

@@ -0,0 +1,70 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+        self.i = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        i = self.i
+        if x > y:
+            while i < self.max_cycles:
+                out = x * y + out
+                i = i + 1
+        if out > 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_121_if_after_while_in_while.py

@@ -0,0 +1,71 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+        self.i = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        i = self.i
+        while x < y:
+            while i < self.max_cycles:
+                out = x * y + out
+                i = i + 1
+            x = x + 1
+        if out > 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_122_if_after_while_in_for.py

@@ -0,0 +1,68 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        while x < y:
+            for _ in range(0, self.max_cycles):
+                out = x * y + out
+            x = x + 1
+        if out > 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_200_while_after_if.py

@@ -0,0 +1,68 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        if out >= 20:
+            out = out - 20
+        while i < self.max_cycles:
+            out = x * y + out
+            i = i + 1
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_210_while_after_if_in_if.py

@@ -0,0 +1,69 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        if x < y:
+            if out >= 20:
+                out = out - 20
+        while i < self.max_cycles:
+            out = x * y + out
+            i = i + 1
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_211_while_after_if_in_while.py

@@ -0,0 +1,69 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        while i < self.max_cycles:
+            if out <= 20:
+                out = x * y + out
+            i = i + 1
+        while i < self.max_cycles:
+            out = out + 10
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_212_while_after_if_in_for.py

@@ -0,0 +1,68 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.i = Tensor(np.array(0), mstype.int32)
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        i = self.i
+        out = self.zero
+        for _ in range(0, self.max_cycles):
+            if out <= 20:
+                out = x * y + out
+        while i < self.max_cycles:
+            out = out + 10
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=10)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_220_while_after_while_in_if.py

@@ -0,0 +1,70 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+        self.i = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        i = self.i
+        if x > y:
+            while i < self.max_cycles:
+                out = x * y + out
+                i = i + 1
+        while out > 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)

tests/st/control/inner/test_230_while_after_for_in_if.py

@@ -0,0 +1,67 @@
+# 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
+from mindspore.common import dtype as mstype
+from mindspore import nn
+from mindspore import Tensor
+from mindspore.ops import composite as C
+from mindspore import context
+
+context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend")
+
+
+class ForwardNet(nn.Cell):
+    def __init__(self, max_cycles=10):
+        super(ForwardNet, self).__init__()
+        self.max_cycles = max_cycles
+        self.zero = Tensor(np.array(0), mstype.int32)
+
+    def construct(self, x, y):
+        out = self.zero
+        if x > y:
+            for _ in range(0, self.max_cycles):
+                out = x * y + out
+        while out > 20:
+            out = out - 20
+        return out
+
+
+class BackwardNet(nn.Cell):
+    def __init__(self, net):
+        super(BackwardNet, self).__init__(auto_prefix=False)
+        self.forward_net = net
+        self.grad = C.GradOperation()
+
+    def construct(self, *inputs):
+        grads = self.grad(self.forward_net)(*inputs)
+        return grads
+
+
+def test_forward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    out = forward_net(x, y)
+    print("forward out:", out)
+
+
+def test_backward():
+    x = Tensor(np.array(1), mstype.int32)
+    y = Tensor(np.array(3), mstype.int32)
+    forward_net = ForwardNet(max_cycles=3)
+    backward_net = BackwardNet(forward_net)
+    grads = backward_net(x, y)
+    print("grads:", grads)