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130 lines
4.3 KiB
130 lines
4.3 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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import paddle.distributed.fleet as fleet
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import paddle.distributed.fleet.base.role_maker as role_maker
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import os
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import paddle.fluid as fluid
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class TestFleetBase(unittest.TestCase):
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def setUp(self):
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os.environ["POD_IP"] = "127.0.0.1"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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os.environ["PADDLE_TRAINERS_NUM"] = "2"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
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"127.0.0.1:36001,127.0.0.2:36001"
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def test_init(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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def test_is_first_worker(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_first_worker():
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print("test fleet first worker done.")
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def test_worker_index(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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print(fleet.worker_index())
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def test_worker_num(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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print(fleet.worker_num())
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def test_is_worker(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_worker():
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print("test fleet is worker")
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def test_worker_endpoints(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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print(fleet.worker_endpoints(to_string=True))
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def test_server_num(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_server():
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print("fleet server num: {}".format(fleet.server_num()))
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def test_server_index(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_server():
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print("fleet server index: {}".format(fleet.server_index()))
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def test_server_endpoints(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_server():
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print("fleet server index: {}".format(
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fleet.server_endpoints(to_string=True)))
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def test_is_server(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_server():
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print("test fleet is server")
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def test_util(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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self.assertEqual(fleet.util, None)
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def test_barrier_worker(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_worker():
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fleet.barrier_worker()
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def test_init_worker(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_worker():
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fleet.init_worker()
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def test_run_server(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_worker():
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fleet.run_worker()
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def test_stop_worker(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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if fleet.is_worker():
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fleet.stop_worker()
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def test_distributed_optimizer(self):
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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optimizer = paddle.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer)
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def test_exception(self):
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import paddle.distributed.fleet as fleet
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self.assertRaises(Exception, fleet.init_worker)
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if __name__ == "__main__":
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unittest.main()
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