add UT

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

@@ -31,7 +31,6 @@ from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distribu
 class TestCommunicator(unittest.TestCase):
     def net(self):
         x = fluid.layers.data(name='x', shape=[1], dtype='float32')
-        #        y_predict = fluid.layers.fc(input=x, size=1, act=None)
         y = fluid.layers.data(name='y', shape=[1], dtype='float32')
 
         cost = fluid.layers.square_error_cost(input=x, label=y)

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

@@ -28,9 +28,7 @@ import paddle.distributed.fleet as fleet
 class TestCommunicator(unittest.TestCase):
     def net(self):
         x = fluid.layers.data(name='x', shape=[1], dtype='float32')
-        #        y_predict = fluid.layers.fc(input=x, size=1, act=None)
         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

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

@@ -42,16 +42,11 @@ class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
         paddle.fluid.framework.switch_startup_program(startup_program)
 
         fleet.init(role_maker.PaddleCloudRoleMaker())
-        input_x = paddle.fluid.layers.data(
-            name="x", shape=[32], dtype='float32')
-        input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
 
-        fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
-        fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
-        prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
-        cost = paddle.fluid.layers.cross_entropy(
-            input=prediction, label=input_y)
-        avg_cost = paddle.fluid.layers.mean(x=cost)
+        x = paddle.fluid.layers.data(name='x', shape=[1], dtype='float32')
+        y = paddle.fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = paddle.fluid.layers.square_error_cost(input=x, label=y)
+        avg_cost = paddle.fluid.layers.mean(cost)
 
         strategy = paddle.distributed.fleet.DistributedStrategy()
         strategy.a_sync = True

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

@@ -34,16 +34,11 @@ class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
 
     def test_gradient_merge_optimizer(self):
         fleet.init(role_maker.PaddleCloudRoleMaker())
-        input_x = paddle.fluid.layers.data(
-            name="x", shape=[32], dtype='float32')
-        input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
 
-        fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
-        fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
-        prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
-        cost = paddle.fluid.layers.cross_entropy(
-            input=prediction, label=input_y)
-        avg_cost = paddle.fluid.layers.mean(x=cost)
+        x = paddle.fluid.layers.data(name='x', shape=[1], dtype='float32')
+        y = paddle.fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = paddle.fluid.layers.square_error_cost(input=x, label=y)
+        avg_cost = paddle.fluid.layers.mean(cost)
 
         strategy = paddle.distributed.fleet.DistributedStrategy()
         strategy.a_sync = False