list add list

mindspore/ops/composite/multitype_ops/add_impl.py

@@ -179,6 +179,23 @@ def _tensor_add_list(x, y):
     return F.tensor_add(x, y)
 
 
+@add.register("List", "List")
+def _list_add_list(x, y):
+    """
+        list is added to list.
+
+        Args:
+            x (list): x
+            y (list): y.
+
+        Returns:
+            list, has the same dtype as x.
+    """
+    for i in y:
+        x.append(i)
+    return x
+
+
 @add.register("Tensor", "Tensor")
 def _tensor_add_tensor(x, y):
     """

tests/st/profiler/test_profiler.py

@@ -134,7 +134,7 @@ class TestProfiler:
         """ Run after class end."""
         cleanup()
 
-    @pytest.mark.level0
+    @pytest.mark.level1
     @pytest.mark.platform_x86_gpu_training
     @pytest.mark.env_onecard
     def test_gpu_profiler(self):

tests/ut/python/pipeline/parse/test_list_add_list.py

@@ -0,0 +1,37 @@
+# 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.
+# ============================================================================
+""" test list add list """
+
+import numpy as np
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.value1 = [Tensor([1, 2, 3]), Tensor([4, 5, 6])]
+        self.value2 = [Tensor([7, 8, 9]), Tensor([10, 11, 12])]
+
+    def construct(self):
+        return self.value1 + self.value2
+
+def test_list_add_list():
+    context.set_context(mode=context.GRAPH_MODE)
+    net = Net()
+    expect_ret = (Tensor([1, 2, 3]), Tensor([4, 5, 6]), Tensor([7, 8, 9]), Tensor([10, 11, 12]))
+    for i in range(len(net())):
+        assert (np.array_equal(net()[i].asnumpy(), expect_ret[i].asnumpy()))