add dde err log watching testcase

tests/st/control/test_dde_error_log.py

@@ -0,0 +1,49 @@
+# Copyright 2022 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 os
+import subprocess
+import pytest
+
+
+def run_watch_dde_network(file_name, log_file_name):
+    _cur_dir = os.path.dirname(os.path.realpath(__file__))
+    file_name = os.path.join(_cur_dir, file_name)
+    assert os.path.exists(file_name)
+
+    log_file_name = os.path.join(_cur_dir, log_file_name)
+    if os.path.exists(log_file_name):
+        os.remove(log_file_name)
+    assert not os.path.exists(log_file_name)
+    cmd_first = f"GLOG_v=2 python " + file_name + " > " + log_file_name + " 2>&1"
+    subprocess.check_output(cmd_first, shell=True)
+    assert os.path.exists(log_file_name)
+    with open(log_file_name, "r") as f_first:
+        data_first = f_first.read()
+    assert "Purify elements failed" not in data_first
+
+    # Clean files
+    os.remove(log_file_name)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_watch_dde_error_log():
+    """
+    Feature: DDE.
+    Description: Some error raised in DDE process unexpected, so add this case to watch it.
+    Expectation: No error raised in DDE process .
+    """
+    run_watch_dde_network("./watch_dde_error_log.py", "watch_dde_error_log.log")

tests/st/control/watch_dde_error_log.py

@@ -0,0 +1,90 @@
+# 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.nn import Cell
+from mindspore.common import Tensor
+import mindspore.ops.operations as P
+import mindspore.ops.functional as F
+import numpy as np
+
+
+def test_switch_simplify_avoid_dead_node():
+    """
+    Feature: Switch simplify pass.
+    Description: If switch simplify pass can't simplify constant tensor condition,
+                 dead node will exist in backend.
+    Expectation: output correct.
+    """
+
+    class Net(Cell):
+        def __init__(self):
+            super().__init__()
+            self.op = P.Add()
+
+        def construct(self, x, y):
+            if y != x:
+                x = y - 3
+            elif x == 4:
+                for r in range(2):
+                    x = 1 / y
+                    if x > 2:
+                        y = y + 3
+                        y = y - y
+                        y = y * x
+                    elif y >= x:
+                        x = x * x
+                    elif x > y:
+                        x = y - r
+                    else:
+                        y = 2 + x
+                    for _ in range(2):
+                        x = x * y
+                        x = x - 3
+                        y = y + 2
+                        if x > 3:
+                            break
+                    if x > 2:
+                        break
+            elif x == y:
+                if y <= x:
+                    y = x / 2
+                    x = 3 + y
+                    x = x * 2
+                elif x == 2:
+                    x = y * y
+                elif x < y:
+                    y = 2 * y
+                elif x != 2:
+                    y = x * y
+            while x != 5:
+                break
+            return self.op(x, y)
+
+    x = np.array([4], np.float32)
+    y = np.array([4], np.float32)
+    net = Net()
+    out = net(Tensor(x), Tensor(y))
+    grad_net = F.grad(net, grad_position=(0, 1))
+    fgrad = grad_net(Tensor(x), Tensor(y))
+    sgrad_net = F.grad(grad_net)
+    sgrad = sgrad_net(Tensor(x), Tensor(y))
+    assert np.allclose(out.asnumpy(), np.array([-19.75], np.float32))
+    assert np.allclose(fgrad[0].asnumpy(), np.array([0.], np.float32))
+    assert np.allclose(fgrad[1].asnumpy(), np.array([-2.03125], np.float32))
+    assert np.allclose(sgrad.asnumpy(), np.array([0.], np.float32))
+
+
+if __name__ == "__main__":
+    test_switch_simplify_avoid_dead_node()