fix UT

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -27,8 +27,6 @@ list(APPEND MIXED_DIST_TEST_OPS test_launch)
 list(APPEND MIXED_DIST_TEST_OPS test_c_comm_init_op)
 list(APPEND MIXED_DIST_TEST_OPS test_launch_ps)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_async)
-list(APPEND MIXED_DIST_TEST_OPS test_communicator_async2)
-list(APPEND MIXED_DIST_TEST_OPS test_communicator_async3)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_geo)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_half_async)
 list(APPEND MIXED_DIST_TEST_OPS test_communicator_sync)

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.SERVER,
-            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)
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/fluid/tests/unittests/test_dist_fleet_ps4.py

@@ -94,15 +94,13 @@ class TestPSPassWithBow(unittest.TestCase):
         pt = fluid.layers.data(
             name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
         # embedding
-        pt_emb = fluid.layers.embedding(
+        pt_emb = fluid.contrib.layers.sparse_embedding(
             input=pt,
-            is_distributed=is_distributed,
             size=[dict_dim, emb_dim],
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Constant(value=0.01),
                 name="__emb__",
-                learning_rate=emb_lr),
-            is_sparse=is_sparse)
+                learning_rate=emb_lr))
         pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
         # vsum
         pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')

python/paddle/fluid/tests/unittests/test_dist_fleet_ps5.py

@@ -165,12 +165,13 @@ class TestPSPassWithBow(unittest.TestCase):
         fleet.init(role)
         loss, acc, _ = self.net()
 
-        optimizer = fluid.optimizer.SGD(
+        optimizer = fluid.optimizer.Adagrad(
             learning_rate=fluid.layers.exponential_decay(
                 learning_rate=base_lr,
                 decay_steps=500,
                 decay_rate=0.969,
                 staircase=True))
+
         strategy = StrategyFactory.create_async_strategy()
         optimizer = fleet.distributed_optimizer(optimizer, strategy)
         optimizer.minimize(loss)