add UT

python/paddle/fluid/tests/unittests/test_communicator_async2.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 __future__ import print_function
+
+import unittest
+import time
+import threading
+import numpy
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.communicator import Communicator
+
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
+
+
+class TestCommunicator(unittest.TestCase):
+    def net(self):
+        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+        cost = fluid.layers.square_error_cost(input=x, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        return avg_cost
+
+    def test_communicator_async(self):
+        role = role_maker.UserDefinedRoleMaker(
+            current_id=0,
+            role=role_maker.Role.WORKER,
+            worker_num=2,
+            server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
+
+        fleet.init(role)
+        avg_cost = self.net()
+
+        optimizer = fluid.optimizer.Adam(0.01)
+        strategy = StrategyFactory.create_async_strategy()
+        optimizer = fleet.distributed_optimizer(optimizer, strategy)
+        optimizer.minimize(avg_cost)
+
+        fleet.init_worker()
+        time.sleep(10)
+        fleet.stop_worker()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_communicator_async3.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 __future__ import print_function
+
+import unittest
+import time
+import threading
+import numpy
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.communicator import Communicator
+
+import paddle.fluid.incubate.fleet.base.role_maker as role_maker
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
+from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
+
+
+class TestCommunicator(unittest.TestCase):
+    def net(self):
+        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+
+        cost = fluid.layers.square_error_cost(input=x, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        return avg_cost
+
+    def test_communicator_async(self):
+        role = role_maker.UserDefinedRoleMaker(
+            current_id=0,
+            role=role_maker.Role.WORKER,
+            worker_num=2,
+            server_endpoints=["127.0.0.1:6001", "127.0.0.1:6002"])
+
+        fleet.init(role)
+        avg_cost = self.net()
+
+        optimizer = fluid.optimizer.Adagrad(0.01)
+        strategy = StrategyFactory.create_async_strategy()
+        optimizer = fleet.distributed_optimizer(optimizer, strategy)
+        optimizer.minimize(avg_cost)
+
+        fleet.init_worker()
+        time.sleep(10)
+        fleet.stop_worker()
+
+
+if __name__ == '__main__':
+    unittest.main()