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# Copyright (c) 2018 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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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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import os
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import signal
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import subprocess
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import time
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import unittest
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from multiprocessing import Process
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from op_test import OpTest
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def run_pserver(use_cuda, sync_mode, ip, port, trainers, trainer_id):
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x = fluid.layers.data(name='x', shape=[1], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None, bias_attr=False)
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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# loss function
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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# optimizer
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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ps1 = ip + ":" + str(int(port) + 1)
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ps2 = ip + ":" + port
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pserver_endpoints = ps1 + "," + ps2
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config = fluid.DistributeTranspilerConfig()
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config.slice_var_up = False
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t = fluid.DistributeTranspiler(config=config)
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t.transpile(
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trainer_id,
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pservers=pserver_endpoints,
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trainers=trainers,
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sync_mode=sync_mode)
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pserver_prog = t.get_pserver_program(ps2)
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# pserver2 have no parameter
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assert (len(pserver_prog.blocks), 2)
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assert (len(pserver_prog.blocks[1].ops), 0)
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pserver_startup = t.get_startup_program(ps2, pserver_prog)
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exe.run(pserver_startup)
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exe.run(pserver_prog)
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class TestListenAndServOp(OpTest):
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def setUp(self):
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self.ps_timeout = 5
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self.ip = "127.0.0.1"
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self.port = "0"
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self.trainers = 1
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self.trainer_id = 0
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def _start_pserver(self, use_cuda, sync_mode):
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p = Process(
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target=run_pserver,
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args=(use_cuda, sync_mode, self.ip, self.port, self.trainers,
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self.trainer_id))
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p.daemon = True
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p.start()
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return p
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def _wait_ps_ready(self, pid):
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start_left_time = self.ps_timeout
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sleep_time = 0.5
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while True:
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assert start_left_time >= 0, "wait ps ready failed"
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time.sleep(sleep_time)
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try:
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# the listen_and_serv_op would touch a file which contains the listen port
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# on the /tmp directory until it was ready to process all the RPC call.
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os.stat("/tmp/paddle.%d.port" % pid)
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return
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except os.error:
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start_left_time -= sleep_time
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def test_handle_signal_in_serv_op(self):
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# run pserver on CPU in sync mode
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p1 = self._start_pserver(False, True)
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self._wait_ps_ready(p1.pid)
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# raise SIGTERM to pserver
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os.kill(p1.pid, signal.SIGINT)
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p1.join()
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# run pserver on CPU in async mode
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p2 = self._start_pserver(False, False)
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self._wait_ps_ready(p2.pid)
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# raise SIGTERM to pserver
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os.kill(p2.pid, signal.SIGTERM)
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p2.join()
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if __name__ == '__main__':
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unittest.main()
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