# 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. from __future__ import print_function """ high level unit test for distribute fleet. """ import argparse import os import pickle import subprocess import sys import time import traceback import math import collections import socket from contextlib import closing import six import unittest import numpy as np import tempfile import paddle.fluid as fluid import paddle.fluid.incubate.fleet.base.role_maker as role_maker from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet from paddle.fluid.transpiler.distribute_transpiler import DistributeTranspilerConfig from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory __all__ = ['FleetDistRunnerBase', 'TestFleetBase', 'runtime_main'] RUN_STEP = 5 LEARNING_RATE = 0.01 DIST_UT_PORT = 0 class FleetDistRunnerBase(object): """ run_pserver,run_trainer : after init role, using transpiler split program net : implment by child class, the network of model do training : exe run program """ def generate_strategy(self, args): self.strategy = None if args.mode == "async": self.strategy = StrategyFactory.create_async_strategy() elif args.mode == "sync": self.strategy = StrategyFactory.create_sync_strategy() elif args.mode == "half_async": self.strategy = StrategyFactory.create_half_async_strategy() elif args.mode == "geo": self.strategy = StrategyFactory.create_geo_strategy( args.geo_sgd_need_push_nums) return self.strategy def run_pserver(self, args): if args.role.upper() != "PSERVER": raise ValueError("args role must be PSERVER") role = role_maker.UserDefinedRoleMaker( current_id=args.current_id, role=role_maker.Role.SERVER, worker_num=args.trainers, server_endpoints=args.endpoints.split(",")) fleet.init(role) strategy = self.generate_strategy(args) avg_cost = self.net() use_grad_clip = int(os.getenv('GRAD_CLIP', 0)) if use_grad_clip: # 1: clip_by_value; 2: clip_by_norm; 3:clip_by_global_norm if use_grad_clip == 1: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByValue(2.0)) elif use_grad_clip == 2: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByNorm(2.0)) elif use_grad_clip == 3: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByGlobalNorm(2.0)) optimizer = fluid.optimizer.SGD(LEARNING_RATE) optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) fleet.init_server() fleet.run_server() def run_dataset_trainer(self, args): if args.role.upper() != "TRAINER": raise ValueError("args role must be TRAINER") role = role_maker.UserDefinedRoleMaker( current_id=args.current_id, role=role_maker.Role.WORKER, worker_num=args.trainers, server_endpoints=args.endpoints.split(",")) fleet.init(role) strategy = self.generate_strategy(args) avg_cost = self.net() use_grad_clip = int(os.getenv('GRAD_CLIP', 0)) if use_grad_clip: # 1: clip_by_value; 2: clip_by_norm; 3:clip_by_global_norm if use_grad_clip == 1: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByValue(2.0)) elif use_grad_clip == 2: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByNorm(2.0)) elif use_grad_clip == 3: fluid.clip.set_gradient_clip( clip=fluid.clip.GradientClipByGlobalNorm(2.0)) optimizer = fluid.optimizer.SGD(LEARNING_RATE) optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) out = self.do_dataset_training(fleet) def run_pyreader_trainer(self, args): if args.role.upper() != "TRAINER": raise ValueError("args role must be TRAINER") role = role_maker.UserDefinedRoleMaker( current_id=args.current_id, role=role_maker.Role.WORKER, worker_num=args.trainers, server_endpoints=args.endpoints.split(",")) fleet.init(role) strategy = self.generate_strategy(args) avg_cost = self.net() self.reader = fluid.io.PyReader( feed_list=self.feeds, capacity=64, iterable=False, use_double_buffer=False) optimizer = fluid.optimizer.SGD(LEARNING_RATE) optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(avg_cost) out = self.do_pyreader_training(fleet) def net(self, batch_size=4, lr=0.01): raise NotImplementedError( "get_model should be implemented by child classes.") def do_dataset_training(self, fleet): raise NotImplementedError( "do_dataset_training should be implemented by child classes.") def do_pyreader_training(self, fleet): raise NotImplementedError( "do_pyreader_training should be implemented by child classes.") class TestFleetBase(unittest.TestCase): """ start_pserver,start_trainer : add start cmd to test run_cluster : using multi process to test distribute program """ def _setup_config(self): raise NotImplementedError("tests should have _setup_config implemented") def setUp(self): self._mode = "sync" self._reader = "pyreader" self._trainers = 2 self._pservers = 2 self._port_set = set() global DIST_UT_PORT if DIST_UT_PORT == 0 and os.getenv("PADDLE_DIST_UT_PORT"): DIST_UT_PORT = int(os.getenv("PADDLE_DIST_UT_PORT")) if DIST_UT_PORT: print("set begin_port:", DIST_UT_PORT) self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( DIST_UT_PORT, DIST_UT_PORT + 1) DIST_UT_PORT += 2 else: self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( self._find_free_port(), self._find_free_port()) self._python_interp = sys.executable self._geo_sgd_need_push_nums = 5 self._grad_clip_mode = 0 self._setup_config() def _find_free_port(self): def __free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) return s.getsockname()[1] while True: port = __free_port() if port not in self._port_set: self._port_set.add(port) return port def _start_pserver(self, cmd, required_envs): ps0_cmd, ps1_cmd = cmd.format(0), cmd.format(1) ps0_pipe = open(tempfile.gettempdir() + "/ps0_err.log", "wb+") ps1_pipe = open(tempfile.gettempdir() + "/ps1_err.log", "wb+") ps0_proc = subprocess.Popen( ps0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps0_pipe, env=required_envs) ps1_proc = subprocess.Popen( ps1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=ps1_pipe, env=required_envs) return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe def _start_trainer(self, cmd, required_envs): tr0_cmd, tr1_cmd = cmd.format(0), cmd.format(1) tr0_pipe = open(tempfile.gettempdir() + "/tr0_err.log", "wb+") tr1_pipe = open(tempfile.gettempdir() + "/tr1_err.log", "wb+") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr0_pipe, env=required_envs) tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=subprocess.PIPE, stderr=tr1_pipe, env=required_envs) return tr0_proc, tr1_proc, tr0_pipe, tr1_pipe def _run_cluster(self, model, envs): env = {'CPU_NUM': '1', 'GRAD_CLIP': str(self._grad_clip_mode)} env.update(envs) python_path = self._python_interp if os.getenv('WITH_COVERAGE', 'OFF') == 'ON': envs['COVERAGE_FILE'] = os.getenv('COVERAGE_FILE', '') python_path += " -m coverage run --branch -p" tr_cmd = "{0} {1} --role trainer --endpoints {2} --current_id {{}} --trainers {3} --mode {4} --geo_sgd_need_push_nums {5} --reader {6}".format( python_path, model, self._ps_endpoints, self._trainers, self._mode, self._geo_sgd_need_push_nums, self._reader) ps_cmd = "{0} {1} --role pserver --endpoints {2} --current_id {{}} --trainers {3} --mode {4} --geo_sgd_need_push_nums {5} --reader {6}".format( python_path, model, self._ps_endpoints, self._trainers, self._mode, self._geo_sgd_need_push_nums, self._reader) # Run dist train to compare with local results ps0, ps1, ps0_pipe, ps1_pipe = self._start_pserver(ps_cmd, env) tr0, tr1, tr0_pipe, tr1_pipe = self._start_trainer(tr_cmd, env) # Wait until trainer process terminate while True: stat0 = tr0.poll() time.sleep(0.1) if stat0 is not None: break while True: stat1 = tr1.poll() time.sleep(0.1) if stat1 is not None: break tr0_out, tr0_err = tr0.communicate() tr1_out, tr1_err = tr1.communicate() # close trainer file tr0_pipe.close() tr1_pipe.close() ps0_pipe.close() ps1_pipe.close() ps0.terminate() ps1.terminate() ''' with open("/tmp/tr0_out.log", "wb+") as wn: wn.write(tr0_out) with open("/tmp/tr1_out.log", "wb+") as wn: wn.write(tr1_out) # print server log ''' # print server log ''' with open("/tmp/ps0_err.log", "r") as fn: sys.stderr.write("ps0 stderr: %s\n" % fn.read()) with open("/tmp/ps1_err.log", "r") as fn: sys.stderr.write("ps1 stderr: %s\n" % fn.read()) ''' # print log ''' with open("/tmp/tr0_err.log", "r") as fn: sys.stderr.write('trainer 0 stderr: %s\n' % fn.read()) with open("/tmp/tr1_err.log", "r") as fn: sys.stderr.write('trainer 1 stderr: %s\n' % fn.read()) ''' return 0, 0 def check_with_place(self, model_file, delta=1e-3, check_error_log=False, need_envs={}): required_envs = { "PATH": os.getenv("PATH", ""), "PYTHONPATH": os.getenv("PYTHONPATH", ""), "LD_LIBRARY_PATH": os.getenv("LD_LIBRARY_PATH", ""), "FLAGS_rpc_deadline": "5000", # 5sec to fail fast "http_proxy": "" } required_envs.update(need_envs) if check_error_log: required_envs["GLOG_v"] = "3" required_envs["GLOG_logtostderr"] = "1" tr0_losses, tr1_losses = self._run_cluster(model_file, required_envs) def runtime_main(test_class): parser = argparse.ArgumentParser(description='Run Fleet test.') parser.add_argument( '--role', type=str, required=True, choices=['pserver', 'trainer']) parser.add_argument('--endpoints', type=str, required=False, default="") parser.add_argument('--current_id', type=int, required=False, default=0) parser.add_argument('--trainers', type=int, required=False, default=1) parser.add_argument('--mode', type=str, required=False, default='geo') parser.add_argument( '--geo_sgd_need_push_nums', type=int, required=False, default=2) parser.add_argument('--reader', type=str, required=False, default='dataset') args = parser.parse_args() model = test_class() if args.role == "pserver": model.run_pserver(args) else: if args.reader == "dataset": model.run_dataset_trainer(args) else: model.run_pyreader_trainer(args)