# 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. """Test cases for role makers.""" import os import tempfile import unittest import paddle class TestCloudRoleMaker2(unittest.TestCase): """ Test cases for paddle cloud role makers. """ def setUp(self): """Set up, set envs.""" self.temp_dir = tempfile.TemporaryDirectory() def tearDown(self): self.temp_dir.cleanup() def test_pslib_2(self): """Test cases for pslib.""" import paddle.fluid as fluid from paddle.fluid.incubate.fleet.base.role_maker import ( GeneralRoleMaker, RoleMakerBase, ) from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import ( fleet, ) paddle.enable_static() os.environ["POD_IP"] = "127.0.0.1" os.environ["PADDLE_PORT"] = "36001" os.environ["TRAINING_ROLE"] = "TRAINER" os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002" os.environ["PADDLE_TRAINER_ID"] = "0" os.environ["PADDLE_TRAINERS_NUM"] = "1" place = fluid.CPUPlace() exe = fluid.Executor(place) try: fleet.init(None) except: print("no mpi4py, skip test_pslib_2") return train_program = fluid.Program() startup_program = fluid.Program() scope = fluid.Scope() with fluid.program_guard(train_program, startup_program): show = fluid.layers.data( name="show", shape=[-1, 1], dtype="float32", lod_level=1, append_batch_size=False, ) fc = fluid.layers.fc(input=show, size=1, act=None) label = fluid.layers.data( name="click", shape=[-1, 1], dtype="int64", lod_level=1, append_batch_size=False, ) label_cast = fluid.layers.cast(label, dtype='float32') cost = fluid.layers.log_loss(fc, label_cast) try: adam = fluid.optimizer.Adam(learning_rate=0.000005) adam = fleet.distributed_optimizer(adam) adam.minimize([cost], [scope]) fleet.run_server() except: print("do not support pslib test, skip") return os.environ["TRAINING_ROLE"] = "wrong" try: role1 = GeneralRoleMaker(path="./test_gloo_1") role1.generate_role() except: print("catch expected error of wrong TRAINING_ROLE") os.environ["TRAINING_ROLE"] = "PSERVER" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001" role2 = GeneralRoleMaker(path="./test_gloo_2") role2._finalize() role2._all_gather(1) role2._all_gather(1) role2._barrier_server() role2._all_gather(1) role3 = GeneralRoleMaker(path="./test_gloo_3") role3._worker_gather(1) role3._worker_gather(1) os.environ["TRAINING_ROLE"] = "TRAINER" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002" role4 = GeneralRoleMaker(path="./test_gloo_4") role4._worker_gather(1) role4._get_rank() role4._get_size() role4._all_comm.init() role5 = GeneralRoleMaker(path="./test_gloo_5") role5.get_local_endpoint() role5.get_local_endpoint() role6 = GeneralRoleMaker(path="./test_gloo_6") role6.get_trainer_endpoints() role6.get_trainer_endpoints() role7 = GeneralRoleMaker(path="./test_gloo_7") role7.get_pserver_endpoints() role7.get_pserver_endpoints() role8 = GeneralRoleMaker(path="./test_gloo_8") role8.is_worker() role8.is_worker() role9 = GeneralRoleMaker(path="./test_gloo_9") role9.is_server() role9.is_server() role10 = GeneralRoleMaker(path="./test_gloo_10") role10.is_first_worker() role10.is_first_worker() role11 = GeneralRoleMaker(path="./test_gloo_11") role11.worker_index() role11.worker_index() role12 = GeneralRoleMaker(path="./test_gloo_12") role12.server_index() role12.server_index() role13 = GeneralRoleMaker(path="./test_gloo_13") role13.worker_num() role13.worker_num() role14 = GeneralRoleMaker(path="./test_gloo_14") role14.server_num() role14.server_num() role15 = GeneralRoleMaker(path="./test_gloo_15") role15._barrier_worker() role15._barrier_worker() role16 = GeneralRoleMaker(path="./test_gloo_16") role16._barrier_all() role16._barrier_all() role17 = GeneralRoleMaker(path="./test_gloo_17") role17._barrier_server() role17._barrier_server() role18 = GeneralRoleMaker(path="./test_gloo_18") role18._worker_num() role18._worker_num() role19 = GeneralRoleMaker(path="./test_gloo_19") role19._server_num() role19._server_num() role20 = GeneralRoleMaker(path="./test_gloo_20") a = [1] b = [0] role20._all_reduce(a, b) role21 = GeneralRoleMaker(path="./test_gloo_21") role21.all_reduce_worker([], []) role21.all_reduce_worker([], []) role21.barrier_worker() role21.barrier_all() role22 = GeneralRoleMaker(path="./test_gloo_22") role22._get_rank() role22._get_rank() os.environ["PADDLE_PSERVER_ID"] = "0" role23 = GeneralRoleMaker(path="./test_gloo_23") role23._get_size() role23._get_size() path = os.path.join( self.temp_dir.name, "test_fleet_gloo_role_maker_1.txt" ) with open(path, "w") as f: data = "1 1 1 1\n" f.write(data) dataset = paddle.distributed.InMemoryDataset() dataset.set_filelist([path]) dataset._set_use_var([show, label]) dataset.load_into_memory() dataset.get_memory_data_size(fleet) dataset.get_shuffle_data_size(fleet) class TmpClass: """ dummy tmp class """ def __init__(self): pass def all_reduce_worker(self, input, output): """ dummy all reduce worker Args: input(None): fake input output(None): fale output """ pass def barrier_worker(self): """ dummy barrier worker """ pass from paddle.fluid.incubate.fleet.base.fleet_base import Fleet class TmpFleet(Fleet): """ dummy tmp fleet """ def __init__(self): super().__init__() self._role_maker = None def init_worker(self): """ dummy init worker """ pass def init_server(self, model_dir=None): """ dummy init server Args: model_dir(None): fake model_dir """ pass def run_server(self): """ dummy run server """ pass def stop_worker(self): """ dummy stop worker """ pass def distributed_optimizer(self, optimizer, strategy=None): """ dummy distributed optimizer Args: optimizer(None): fake optimizer strategy(None): fake strategy """ pass def save_inference_model(self): """ dummy save inference model """ pass def save_persistables(self): """ dummy save persistables """ pass os.environ["TRAINING_ROLE"] = "TRAINER" tmp = TmpFleet() tmp._role_maker = TmpClass() tmp.all_reduce_worker([], []) tmp.barrier_worker() from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker tmp = RoleMakerBase() tmp.all_gather(1) tmp.all_reduce_worker([], []) tmp.barrier_worker() tmp.barrier_all() from paddle.fluid.incubate.fleet.base.role_maker import ( MPISymetricRoleMaker, ) tmp1 = MPISymetricRoleMaker() tmp1.all_gather(1) tmp1.all_gather(1) tmp2 = MPISymetricRoleMaker() tmp2.all_reduce_worker([], []) tmp3 = MPISymetricRoleMaker() tmp3.barrier_worker() tmp3.barrier_worker() tmp4 = MPISymetricRoleMaker() tmp4.barrier_all() tmp4.barrier_all() if __name__ == "__main__": unittest.main()