Paddle/python/paddle/fluid/tests/unittests/test_dist_train.py

#   Copyright (c) 2018 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.

import unittest

import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.layers as layers
import numpy
from multiprocessing import Process
from threading import Thread
import os, sys
import time


class TestSendOp(unittest.TestCase):
    def test_send(self):
        # Run init_serv in a thread
        place = fluid.CPUPlace()
        # NOTE: python thread will not work here due to GIL.
        p = Process(target=self.init_serv, args=(place, ))
        p.daemon = True
        p.start()

        time.sleep(10)
        with open("/tmp/paddle.%d.selected_port" % p.pid, "r") as fn:
            selected_port = int(fn.readlines()[0])
        self.init_client(place, selected_port)

        self.run_local(place)
        self.assertTrue(numpy.allclose(self.local_out, self.dist_out))

        # FIXME(typhoonzero): find a way to gracefully shutdown the server.
        os.system("kill -9 %d" % p.pid)
        p.join()

    def init_serv(self, place):
        main = fluid.Program()

        with fluid.program_guard(main):
            serv = layers.ListenAndServ(
                "127.0.0.1:0", ["X"], optimizer_mode=False)
            with serv.do():
                x = layers.data(
                    shape=[32, 32],
                    dtype='float32',
                    name="X",
                    append_batch_size=False)
                fluid.initializer.Constant(value=1.0)(x, main.global_block())
                o = layers.scale(x=x, scale=10.0)
            main.global_block().create_var(
                name=o.name, psersistable=False, dtype=o.dtype, shape=o.shape)

        self.server_exe = fluid.Executor(place)
        self.server_exe.run(main)

    def init_client(self, place, port):
        main = fluid.Program()
        with fluid.program_guard(main):
            x = layers.data(
                shape=[32, 32],
                dtype='float32',
                name='X',
                append_batch_size=False)
            fluid.initializer.Constant(value=2.3)(x, main.global_block())
            get_var = main.global_block().create_var(
                name="scale_0.tmp_0",  # server side var
                dtype="float32",
                persistable=False,
                shape=[32, 32])
            o = layers.Send("127.0.0.1:%d" % port, [x], [get_var])
        exe = fluid.Executor(place)
        self.dist_out = exe.run(main, fetch_list=o)  # o is a list

    def run_local(self, place):
        main = fluid.Program()
        with fluid.program_guard(main):
            x = layers.data(
                shape=[32, 32],
                dtype='float32',
                name='X',
                append_batch_size=False)
            fluid.initializer.Constant(value=2.3)(x, main.global_block())
            o = layers.scale(x=x, scale=10.0)
        exe = fluid.Executor(place)
        self.local_out = exe.run(main, fetch_list=[o])


if __name__ == "__main__":
    unittest.main()