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346 lines
12 KiB
346 lines
12 KiB
# 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 time
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import unittest
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import os
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import sys
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import six
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import signal
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import subprocess
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import argparse
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class TestDistRunnerBase(object):
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def get_model(self, batch_size=2):
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raise NotImplementedError(
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"get_model should be implemented by child classes.")
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def get_transpiler(self, trainer_id, main_program, pserver_endpoints,
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trainers, sync_mode):
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# NOTE: import fluid until runtime, or else forking processes will cause error.
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import paddle
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import paddle.fluid as fluid
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t = fluid.DistributeTranspiler()
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t.transpile(
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trainer_id=trainer_id,
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program=main_program,
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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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return t
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def run_pserver(self, args):
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import paddle
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import paddle.fluid as fluid
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self.get_model(batch_size=2)
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if args.mem_opt:
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fluid.memory_optimize(fluid.default_main_program())
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t = self.get_transpiler(args.trainer_id,
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fluid.default_main_program(), args.endpoints,
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args.trainers, args.sync_mode)
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pserver_prog = t.get_pserver_program(args.current_endpoint)
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startup_prog = t.get_startup_program(args.current_endpoint,
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pserver_prog)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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exe.run(pserver_prog)
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def run_trainer(self, use_cuda, args):
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import paddle
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import paddle.fluid as fluid
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
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self.get_model(batch_size=2)
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if args.mem_opt:
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fluid.memory_optimize(fluid.default_main_program())
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if args.is_dist:
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t = self.get_transpiler(args.trainer_id,
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fluid.default_main_program(),
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args.endpoints, args.trainers,
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args.sync_mode)
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trainer_prog = t.get_trainer_program()
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else:
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trainer_prog = fluid.default_main_program()
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startup_exe = fluid.Executor(place)
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startup_exe.run(fluid.default_startup_program())
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strategy = fluid.ExecutionStrategy()
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strategy.num_threads = 1
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strategy.allow_op_delay = False
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build_stra = fluid.BuildStrategy()
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if args.use_reduce:
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build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
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else:
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build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
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exe = fluid.ParallelExecutor(
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use_cuda,
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loss_name=avg_cost.name,
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exec_strategy=strategy,
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build_strategy=build_stra)
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feed_var_list = [
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var for var in trainer_prog.global_block().vars.values()
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if var.is_data
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]
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feeder = fluid.DataFeeder(feed_var_list, place)
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reader_generator = test_reader()
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data = next(reader_generator)
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first_loss, = exe.run(fetch_list=[avg_cost.name],
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feed=feeder.feed(data))
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print(first_loss)
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for i in six.moves.xrange(5):
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data = next(reader_generator)
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loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
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data = next(reader_generator)
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last_loss, = exe.run(fetch_list=[avg_cost.name], feed=feeder.feed(data))
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print(last_loss)
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def runtime_main(test_class):
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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parser = argparse.ArgumentParser(description='Run dist test.')
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parser.add_argument(
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'--role', type=str, required=True, choices=['pserver', 'trainer'])
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parser.add_argument('--endpoints', type=str, required=False, default="")
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parser.add_argument('--is_dist', action='store_true')
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parser.add_argument('--trainer_id', type=int, required=False, default=0)
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parser.add_argument('--trainers', type=int, required=False, default=1)
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parser.add_argument(
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'--current_endpoint', type=str, required=False, default="")
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parser.add_argument('--sync_mode', action='store_true')
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parser.add_argument('--mem_opt', action='store_true')
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parser.add_argument('--use_reduce', action='store_true')
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args = parser.parse_args()
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model = test_class()
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if args.role == "pserver" and args.is_dist:
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model.run_pserver(args)
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else:
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use_cuda = True if core.is_compiled_with_cuda() else False
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model.run_trainer(use_cuda, args)
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import paddle.compat as cpt
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import socket
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from contextlib import closing
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class TestDistBase(unittest.TestCase):
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def _setup_config(self):
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raise NotImplementedError("tests should have _setup_config implemented")
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def setUp(self):
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self._trainers = 2
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self._pservers = 2
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self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
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self._find_free_port(), self._find_free_port())
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self._python_interp = "python"
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self._sync_mode = True
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self._mem_opt = False
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self._use_reduce = False
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self._setup_config()
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def _find_free_port(self):
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with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
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s.bind(('', 0))
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return s.getsockname()[1]
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def start_pserver(self, model_file, check_error_log):
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ps0_ep, ps1_ep = self._ps_endpoints.split(",")
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ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
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ps0_cmd = ps_cmd % \
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(self._python_interp, model_file, self._ps_endpoints, ps0_ep,
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self._trainers)
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ps1_cmd = ps_cmd % \
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(self._python_interp, model_file, self._ps_endpoints, ps1_ep,
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self._trainers)
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if self._sync_mode:
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ps0_cmd += " --sync_mode"
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ps1_cmd += " --sync_mode"
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if self._mem_opt:
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ps0_cmd += " --mem_opt"
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ps1_cmd += " --mem_opt"
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ps0_pipe = subprocess.PIPE
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ps1_pipe = subprocess.PIPE
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if check_error_log:
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print(ps0_cmd)
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print(ps1_cmd)
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ps0_pipe = open("/tmp/ps0_err.log", "wb")
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ps1_pipe = open("/tmp/ps1_err.log", "wb")
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ps0_proc = subprocess.Popen(
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ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe)
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ps1_proc = subprocess.Popen(
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ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe)
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if not check_error_log:
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return ps0_proc, ps1_proc, None, None
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else:
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return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
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def _wait_ps_ready(self, pid):
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retry_times = 50
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while True:
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assert retry_times >= 0, "wait ps ready failed"
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time.sleep(3)
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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 as e:
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sys.stderr.write('waiting for pserver: %s, left retry %d\n' %
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(e, retry_times))
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retry_times -= 1
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def check_with_place(self, model_file, delta=1e-3, check_error_log=False):
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# TODO(typhoonzero): should auto adapt GPU count on the machine.
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required_envs = {
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"PATH": os.getenv("PATH", ""),
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"PYTHONPATH": os.getenv("PYTHONPATH", ""),
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"LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""),
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"FLAGS_fraction_of_gpu_memory_to_use": "0.15",
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"FLAGS_cudnn_deterministic": "1",
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"CPU_NUM": "1"
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}
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if check_error_log:
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required_envs["GLOG_v"] = "7"
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required_envs["GLOG_logtostderr"] = "1"
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# Run local to get a base line
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env_local = {"CUDA_VISIBLE_DEVICES": "0"}
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env_local.update(required_envs)
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local_cmd = "%s %s --role trainer" % (self._python_interp, model_file)
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if not check_error_log:
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local_proc = subprocess.Popen(
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local_cmd.split(" "),
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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env=env_local)
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else:
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err_log = open("/tmp/trainer.err.log", "wb")
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local_proc = subprocess.Popen(
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local_cmd.split(" "),
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stdout=subprocess.PIPE,
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stderr=err_log,
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env=env_local)
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local_proc.wait()
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out, err = local_proc.communicate()
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local_ret = cpt.to_text(out)
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sys.stderr.write('local_loss: %s\n' % local_ret)
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sys.stderr.write('local_stderr: %s\n' % err)
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# Run dist train to compare with local results
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ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model_file,
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check_error_log)
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self._wait_ps_ready(ps0.pid)
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self._wait_ps_ready(ps1.pid)
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ps0_ep, ps1_ep = self._ps_endpoints.split(",")
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tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
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tr0_cmd = tr_cmd % \
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(self._python_interp, model_file, self._ps_endpoints,
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0, ps0_ep, self._trainers)
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tr1_cmd = tr_cmd % \
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(self._python_interp, model_file, self._ps_endpoints,
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1, ps1_ep, self._trainers)
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if self._sync_mode:
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tr0_cmd += " --sync_mode"
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tr1_cmd += " --sync_mode"
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if self._mem_opt:
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tr0_cmd += " --mem_opt"
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tr1_cmd += " --mem_opt"
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if self._use_reduce:
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tr0_cmd += " --use_reduce"
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tr1_cmd += " --use_reduce"
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env0 = {"CUDA_VISIBLE_DEVICES": "0"}
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env1 = {"CUDA_VISIBLE_DEVICES": "1"}
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env0.update(required_envs)
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env1.update(required_envs)
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FNULL = open(os.devnull, 'w')
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tr0_pipe = subprocess.PIPE
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tr1_pipe = subprocess.PIPE
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if check_error_log:
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print("tr0_cmd:", tr0_cmd)
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print("tr1_cmd:", tr1_cmd)
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tr0_pipe = open("/tmp/tr0_err.log", "wb")
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tr1_pipe = open("/tmp/tr1_err.log", "wb")
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tr0_proc = subprocess.Popen(
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tr0_cmd.strip().split(" "),
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stdout=subprocess.PIPE,
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stderr=tr0_pipe,
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env=env0)
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tr1_proc = subprocess.Popen(
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tr1_cmd.strip().split(" "),
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stdout=subprocess.PIPE,
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stderr=tr1_pipe,
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env=env1)
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tr0_proc.wait()
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tr1_proc.wait()
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out, err = tr0_proc.communicate()
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sys.stderr.write('dist_stderr: %s\n' % err)
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loss_data0 = cpt.to_text(out)
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sys.stderr.write('dist_loss: %s\n' % loss_data0)
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lines = loss_data0.split("\n")
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dist_first_loss = eval(lines[0].replace(" ", ","))[0]
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dist_last_loss = eval(lines[1].replace(" ", ","))[0]
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local_lines = local_ret.split("\n")
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local_first_loss = eval(local_lines[0])[0]
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local_last_loss = eval(local_lines[1])[0]
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# close trainer file
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if check_error_log:
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tr0_pipe.close()
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tr1_pipe.close()
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ps0_pipe.close()
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ps1_pipe.close()
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# FIXME: use terminate() instead of sigkill.
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os.kill(ps0.pid, signal.SIGKILL)
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os.kill(ps1.pid, signal.SIGKILL)
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ps0.terminate()
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ps1.terminate()
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ps0.wait()
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ps1.wait()
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FNULL.close()
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self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)
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self.assertAlmostEqual(local_last_loss, dist_last_loss, delta=delta)
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