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 os
import time
import unittest
from multiprocessing import Process
import signal

import numpy

import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers.io import ListenAndServ
from paddle.fluid.layers.io import Recv
from paddle.fluid.layers.io import Send


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()

        self.ps_timeout = 5
        self._wait_ps_ready(p.pid)

        with open("/tmp/paddle.%d.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.kill(p.pid, signal.SIGKILL)
        p.join()

    def _wait_ps_ready(self, pid):
        start_left_time = self.ps_timeout
        sleep_time = 0.5
        while True:
            assert start_left_time >= 0, "wait ps ready failed"
            time.sleep(sleep_time)
            try:
                # the listen_and_serv_op would touch a file which contains the listen port
                # on the /tmp directory until it was ready to process all the RPC call.
                os.stat("/tmp/paddle.%d.port" % pid)
                return
            except os.error:
                start_left_time -= sleep_time

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

        with fluid.program_guard(main):
            serv = ListenAndServ("127.0.0.1:0", ["X"], optimizer_mode=False)
            with serv.do():
                out_var = main.global_block().create_var(
                    name="scale_0.tmp_0",
                    psersistable=True,
                    dtype="float32",
                    shape=[32, 32])
                x = layers.data(
                    shape=[32, 32],
                    dtype='float32',
                    name="X",
                    append_batch_size=False)
                fluid.initializer.Constant(value=1.0)(x, main.global_block())
                layers.scale(x=x, scale=10.0, out=out_var)

        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])
            fluid.initializer.Constant(value=2.3)(get_var, main.global_block())
            Send("127.0.0.1:%d" % port, [x])
            o = Recv("127.0.0.1:%d" % port, [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()