while test cases

tests/st/control/inner/test_021_while_while_normal.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
+        j = self.i
+        while j < self.max_cycles:
+            i = self.i
+            while i < self.max_cycles:
+                out = x * y + out
+                i = i + 1
+            j = j + 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_022_for_while_normal.py

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+# 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
+        for _ in range(0, self.max_cycles):
+            i = self.i
+            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)