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93 lines
3.6 KiB
93 lines
3.6 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 os
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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 paddle.fluid as fluid
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paddle.enable_static()
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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_collective_minimize(self):
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input_x = paddle.fluid.layers.data(
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name="x", shape=[32], dtype='float32')
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input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
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fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
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fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
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cost = paddle.fluid.layers.cross_entropy(
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input=prediction, label=input_y)
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avg_cost = paddle.fluid.layers.mean(x=cost)
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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strategy = fleet.DistributedStrategy()
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optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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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_fleet_get_applied_optimizer(self):
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input_x = paddle.fluid.layers.data(
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name="x", shape=[32], dtype='float32')
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input_y = paddle.fluid.layers.data(name="y", shape=[1], dtype='int64')
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fc_1 = paddle.fluid.layers.fc(input=input_x, size=64, act='tanh')
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fc_2 = paddle.fluid.layers.fc(input=fc_1, size=64, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc_2], size=2, act='softmax')
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cost = paddle.fluid.layers.cross_entropy(
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input=prediction, label=input_y)
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avg_cost = paddle.fluid.layers.mean(x=cost)
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fleet.init(is_collective=True)
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meta_list = fleet._get_applied_meta_list()
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graph_list = fleet._get_applied_graph_list()
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# not called minimize function
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self.assertEqual(len(meta_list), 0)
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self.assertEqual(len(graph_list), 0)
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strategy = fleet.DistributedStrategy()
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optimizer = paddle.fluid.optimizer.SGD(learning_rate=0.001)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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meta_list = fleet._get_applied_meta_list()
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graph_list = fleet._get_applied_graph_list()
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self.assertEqual(len(meta_list), 0)
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self.assertEqual(len(graph_list), 1)
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if __name__ == "__main__":
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unittest.main()
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