[fluid_ops] clean listen_and_serv (#71884)

Author: co63oc

paddle/fluid/operators/pscore/listen_and_serv_op.cc

@@ -11,91 +11,3 @@
 # 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 unittest
-
-import paddle
-from paddle.distributed.fleet.base import role_maker
-
-paddle.enable_static()
-
-
-class TestFleetGradientMergeMetaOptimizer(unittest.TestCase):
-    def setUp(self):
-        os.environ["PADDLE_PSERVER_NUMS"] = "2"
-        os.environ["PADDLE_TRAINERS_NUM"] = "2"
-        os.environ["POD_IP"] = "127.0.0.1"
-        os.environ["PADDLE_PORT"] = "36001"
-        os.environ["PADDLE_TRAINER_ID"] = "0"
-        os.environ["PADDLE_TRAINERS_NUM"] = "2"
-        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = (
-            "127.0.0.1:36001,127.0.0.2:36001"
-        )
-
-    def test_a_sync_optimizer_trainer(self):
-        os.environ["TRAINING_ROLE"] = "TRAINER"
-        from paddle.distributed import fleet
-
-        main_program = paddle.base.Program()
-        startup_program = paddle.base.Program()
-
-        paddle.base.framework.switch_main_program(main_program)
-        paddle.base.framework.switch_startup_program(startup_program)
-
-        fleet.init(role_maker.PaddleCloudRoleMaker())
-        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
-        input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
-
-        fc_1 = paddle.static.nn.fc(x=input_x, size=64, activation='tanh')
-        fc_2 = paddle.static.nn.fc(x=fc_1, size=64, activation='tanh')
-        prediction = paddle.static.nn.fc(x=[fc_2], size=2, activation='softmax')
-        cost = paddle.nn.functional.cross_entropy(
-            input=prediction, label=input_y, reduction='none', use_softmax=False
-        )
-        avg_cost = paddle.mean(x=cost)
-
-        strategy = paddle.distributed.fleet.DistributedStrategy()
-        strategy.a_sync = True
-        strategy.a_sync_configs = {"k_steps": 100, "launch_barrier": False}
-
-        optimizer = paddle.optimizer.SGD(learning_rate=0.01)
-        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
-
-        optimizer.minimize(avg_cost)
-
-    def test_a_sync_optimizer_pserver(self):
-        os.environ["TRAINING_ROLE"] = "PSERVER"
-        from paddle.distributed import fleet
-
-        main_program = paddle.base.Program()
-        startup_program = paddle.base.Program()
-
-        paddle.base.framework.switch_main_program(main_program)
-        paddle.base.framework.switch_startup_program(startup_program)
-
-        fleet.init(role_maker.PaddleCloudRoleMaker())
-        input_x = paddle.static.data(name="x", shape=[-1, 32], dtype='float32')
-        input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
-
-        fc_1 = paddle.static.nn.fc(x=input_x, size=64, activation='tanh')
-        fc_2 = paddle.static.nn.fc(x=fc_1, size=64, activation='tanh')
-        prediction = paddle.static.nn.fc(x=[fc_2], size=2, activation='softmax')
-        cost = paddle.nn.functional.cross_entropy(
-            input=prediction, label=input_y, reduction='none', use_softmax=False
-        )
-        avg_cost = paddle.mean(x=cost)
-
-        strategy = paddle.distributed.fleet.DistributedStrategy()
-        strategy.a_sync = True
-        strategy.a_sync_configs = {"k_steps": 100, "launch_barrier": False}
-
-        optimizer = paddle.optimizer.SGD(learning_rate=0.01)
-        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
-        optimizer.minimize(avg_cost)
-
-        prog = paddle.base.default_main_program()
-        self.assertEqual(prog.global_block().ops[0].type, "listen_and_serv")
-
-
-if __name__ == "__main__":
-    unittest.main()