# 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 as np 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 import paddle.fluid.layers.ops as ops from dist_test_utils import remove_ps_flag from paddle.fluid import core RPC_OP_ROLE_ATTR_NAME = op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName( ) RPC_OP_ROLE_ATTR_VALUE = core.op_proto_and_checker_maker.OpRole.RPC class TestSendOp(unittest.TestCase): def test_send(self): remove_ps_flag(os.getpid()) # 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) np.testing.assert_allclose(self.local_out, self.dist_out, rtol=1e-05) os.kill(p.pid, signal.SIGINT) 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()) ops._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): main.global_block().append_op(type="fetch_barrier", inputs={}, outputs={"Out": []}, attrs={ "endpoints": ["127.0.0.1:{0}".format(port)], RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE }) x = layers.data(shape=[32, 32], dtype='float32', name='X', append_batch_size=False) x.persistable = True 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()) # NOTE(zjl): `Send` is async send, which means that the sent # variable would be needed even though `Send` op runs. # Is it a right design? If I do not set `x.persistable = True`, # this unittest would hang in rpc client after x is deleted. # # BTW, `Send` is not a public API to users. So I set # `x.persistable = True` to be a hot fix of this unittest. 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()