# 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 from paddle.distributed.fleet.utils.ps_util import DistributedInfer from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory import paddle.distributed.fleet as fleet import paddle.distributed.fleet.base.role_maker as role_maker import paddle.fluid as fluid import paddle """ high level unit test for distribute fleet. """ import os import sys import subprocess import six import shutil import numpy as np import argparse from contextlib import closing import socket import time import tempfile import unittest import paddle paddle.enable_static() __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 __init__(self): self._exe = None def build_role(self, args): if args.role.upper() == "PSERVER": role = role_maker.UserDefinedRoleMaker( is_collective=False, init_gloo=False, path=args.gloo_path, current_id=args.current_id, role=role_maker.Role.SERVER, worker_endpoints=args.trainer_endpoints.split(","), server_endpoints=args.endpoints.split(",")) else: role = role_maker.UserDefinedRoleMaker( is_collective=False, init_gloo=False, path=args.gloo_path, current_id=args.current_id, role=role_maker.Role.WORKER, worker_endpoints=args.trainer_endpoints.split(","), server_endpoints=args.endpoints.split(",")) self.role = role return role def build_strategy(self, args): if args.mode == "sync": self.strategy = paddle.distributed.fleet.DistributedStrategy() self.strategy.a_sync = False elif args.mode == "async": self.strategy = paddle.distributed.fleet.DistributedStrategy() self.strategy.a_sync = True elif args.mode == "geo": self.strategy = paddle.distributed.fleet.DistributedStrategy() self.strategy.a_sync = True self.strategy.a_sync_configs = { "k_steps": args.geo_sgd_need_push_nums } elif args.mode == "auto": self.strategy = paddle.distributed.fleet.DistributedStrategy() self.strategy.auto = True self.dump_param = os.getenv("dump_param", "").split(",") self.dump_fields = os.getenv("dump_fields", "").split(",") self.dump_fields_path = os.getenv("dump_fields_path", "") debug = int(os.getenv("Debug", "0")) # TODO(update strategy to support dump params) if False: # debug: self.strategy.set_debug_opt({ "dump_param": self.dump_param, "dump_fields": self.dump_fields, "dump_fields_path": self.dump_fields_path }) return self.strategy def build_optimizer(self, avg_cost, strategy): use_grad_clip = int(os.getenv('GRAD_CLIP', 0)) grad_clip = None if use_grad_clip: # 1: clip_by_value; 2: clip_by_norm; 3:clip_by_global_norm if use_grad_clip == 1: grad_clip = paddle.nn.ClipGradByValue(min=-5.0, max=5.0) elif use_grad_clip == 2: grad_clip = paddle.nn.ClipGradByNorm(2.0) elif use_grad_clip == 3: grad_clip = paddle.nn.ClipGradByGlobalNorm(2.0) use_decay = int(os.getenv("USE_DECAY", "0")) if use_decay: scheduler = paddle.optimizer.lr.ExponentialDecay( learning_rate=LEARNING_RATE, gamma=0.999, verbose=True) optimizer = fluid.optimizer.SGD(scheduler, grad_clip=grad_clip) """ # learning rate decay method before 2.0 optimizer = fluid.optimizer.SGD( learning_rate=fluid.layers.exponential_decay( learning_rate=LEARNING_RATE, decay_steps=500, decay_rate=0.969, staircase=True)) """ else: optimizer = fluid.optimizer.SGD(LEARNING_RATE, grad_clip=grad_clip) optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy) optimizer.minimize(avg_cost) def run_pserver(self, args): fleet.init_server() fleet.run_server() def run_dataset_trainer(self, args): out = self.do_dataset_training(fleet) def run_pyreader_trainer(self, args): out = self.do_pyreader_training(fleet) def net(self, args, batch_size=4, lr=0.01): raise NotImplementedError( "get_model should be implemented by child classes.") def get_executor(self): if self._exe is None: device_env = os.getenv("DEVICE", 'cpu') if device_env == 'cpu': device = fluid.CPUPlace() elif device_env == 'gpu': device = fluid.CUDAPlace(0) self._exe = fluid.Executor(device) return self._exe 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.") def do_distributed_testing(self, fleet): raise NotImplementedError( "do_distributed_testing 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 tearDown(self): t = time.time() - self.startTime print('%s: %.3f' % (self.__class__.__name__, t)) def setUp(self): self.startTime = time.time() self._mode = "sync" self._reader = "pyreader" self._trainers = 2 self._pservers = 2 self._need_test = 0 self._model_dir = "" 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) self._tr_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( DIST_UT_PORT + 2, DIST_UT_PORT + 3) DIST_UT_PORT += 4 else: self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % ( self._find_free_port(), self._find_free_port()) self._tr_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) log_dirname = required_envs.get("LOG_DIRNAME", tempfile.gettempdir()) log_prename = required_envs.get("LOG_PREFIX", "") if log_dirname: log_prename += "_" ps0_err_log = os.path.join(log_dirname, log_prename + "ps0_stderr.log") ps1_err_log = os.path.join(log_dirname, log_prename + "ps1_stderr.log") ps0_out_log = os.path.join(log_dirname, log_prename + "ps0_stdout.log") ps1_out_log = os.path.join(log_dirname, log_prename + "ps1_stdout.log") ps0_err = open(ps0_err_log, "wb+") ps1_err = open(ps1_err_log, "wb+") ps0_out = open(ps0_out_log, "wb+") ps1_out = open(ps1_out_log, "wb+") ps0_proc = subprocess.Popen( ps0_cmd.strip().split(" "), stdout=ps0_out, stderr=ps0_err, env=required_envs) ps1_proc = subprocess.Popen( ps1_cmd.strip().split(" "), stdout=ps1_out, stderr=ps1_err, env=required_envs) return ((ps0_proc, ps0_out, ps0_err, ps0_out_log, ps0_err_log), (ps1_proc, ps1_out, ps1_err, ps1_out_log, ps1_err_log)) def _start_trainer(self, cmd, required_envs): tr0_cmd, tr1_cmd = cmd.format(0), cmd.format(1) log_dirname = required_envs.get("LOG_DIRNAME", tempfile.gettempdir()) log_prename = required_envs.get("LOG_PREFIX", "") if log_dirname: log_prename += "_" tr0_err_log = os.path.join(log_dirname, log_prename + "tr0_stderr.log") tr1_err_log = os.path.join(log_dirname, log_prename + "tr1_stderr.log") tr0_out_log = os.path.join(log_dirname, log_prename + "tr0_stdout.log") tr1_out_log = os.path.join(log_dirname, log_prename + "tr1_stdout.log") tr0_err = open(tr0_err_log, "wb+") tr1_err = open(tr1_err_log, "wb+") tr0_out = open(tr0_out_log, "wb+") tr1_out = open(tr1_out_log, "wb+") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=tr0_out, stderr=tr0_err, env=required_envs) tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=tr1_out, stderr=tr1_err, env=required_envs) return ((tr0_proc, tr0_out, tr0_err, tr0_out_log, tr0_err_log), (tr1_proc, tr1_out, tr1_err, tr1_out_log, tr1_err_log)) def _run_cluster(self, model, envs): env = {'GRAD_CLIP': str(self._grad_clip_mode), 'WITH_DISTRIBUTE': 'ON'} python_path = self._python_interp gloo_path = tempfile.mkdtemp() if os.getenv('WITH_COVERAGE', 'OFF') == 'ON': envs['COVERAGE_FILE'] = os.getenv('COVERAGE_FILE', '') python_path += " -m coverage run --branch -p" env.update(envs) tr_cmd = "{0} {1} --role trainer --endpoints {2} --trainer_endpoints {3} --current_id {{}} --trainers {4} --mode {5} --geo_sgd_need_push_nums {6} --reader {7} --gloo_path {8} --test {9}".format( python_path, model, self._ps_endpoints, self._tr_endpoints, self._trainers, self._mode, self._geo_sgd_need_push_nums, self._reader, gloo_path, self._need_test) ps_cmd = "{0} {1} --role pserver --endpoints {2} --trainer_endpoints {3} --current_id {{}} --trainers {4} --mode {5} --geo_sgd_need_push_nums {6} --reader {7} --gloo_path {8} --test {9}".format( python_path, model, self._ps_endpoints, self._tr_endpoints, self._trainers, self._mode, self._geo_sgd_need_push_nums, self._reader, gloo_path, self._need_test) if self._model_dir: tr_cmd += " --model_dir {}".format(self._model_dir) ps_cmd += " --model_dir {}".format(self._model_dir) # Run dist train to compare with local results ps0, ps1 = self._start_pserver(ps_cmd, env) tr0, tr1 = self._start_trainer(tr_cmd, env) ps0_proc, ps0_out, ps0_err, ps0_out_log, ps0_err_log = ps0 ps1_proc, ps1_out, ps1_err, ps1_out_log, ps1_err_log = ps1 tr0_proc, tr0_out, tr0_err, tr0_out_log, tr0_err_log = tr0 tr1_proc, tr1_out, tr1_err, tr1_out_log, tr1_err_log = tr1 # Wait until trainer process terminate #time_out = 120 time_out = 60 cur_time = 0 while True: stat0 = tr0_proc.poll() stat1 = tr1_proc.poll() if stat0 is not None and stat1 is not None: break else: time.sleep(0.5) cur_time += 0.5 if cur_time >= time_out: tr0_proc.terminate() tr1_proc.terminate() tr0_proc.wait() tr1_proc.wait() break tr0_ret = tr0_proc.returncode tr1_ret = tr1_proc.returncode ps0_proc.kill() ps1_proc.kill() ps0_proc.wait() ps1_proc.wait() def is_listen_failed(logx): is_lf = False listen_rgx = "Fail to listen" with open(logx, "r") as rb: for line in rb.readlines(): if listen_rgx in line: is_lf = True break return is_lf def catlog(logx): basename = os.path.basename(logx) print("\n================== Error {} begin =====================". format(basename)) os.system("cat {}".format(logx)) print("================== Error {} end =====================\n". format(basename)) if tr0_ret != 0 or tr1_ret != 0: if is_listen_failed(ps0_err) or is_listen_failed(ps1_err): print("find parameter server port bind failed, skip the error") tr0_ret, tr1_ret = 0, 0 else: for out, err in [ (ps0_out_log, ps0_err_log), (ps1_out_log, ps1_err_log), (tr0_out_log, tr0_err_log), (tr1_out_log, tr1_err_log) ]: catlog(out) catlog(err) for pipe in [ tr0_err, tr0_out, tr1_err, tr1_out, ps0_err, ps0_out, ps1_err, ps1_out ]: pipe.close() shutil.rmtree(gloo_path) self.assertEqual(tr0_ret, 0, "something wrong in tr0, please check") self.assertEqual(tr1_ret, 0, "something wrong in tr1, please check") 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( '--trainer_endpoints', type=str, required=False, default="") parser.add_argument('--gloo_path', 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') parser.add_argument('--test', type=int, required=False, default=0) parser.add_argument('--model_dir', type=str, required=False, default="") args = parser.parse_args() model = test_class() role = model.build_role(args) # for distributed inference if args.test and args.model_dir != "": avg_cost = model.net(args, is_train=False) dist_infer = DistributedInfer() dist_infer.init_distributed_infer_env( exe=model.get_executor(), loss=model.avg_cost, role_maker=role, dirname=args.model_dir) if fleet.is_worker(): with paddle.static.program_guard( main_program=dist_infer.get_dist_infer_program()): model.do_distributed_testing(fleet) fleet.stop_worker() return if fleet.is_server(): return fleet.init(role) strategy = model.build_strategy(args) avg_cost = model.net(args) model.build_optimizer(avg_cost, strategy) if args.role == "pserver": model.run_pserver(args) else: if args.reader == "dataset": model.run_dataset_trainer(args) else: model.run_pyreader_trainer(args) if args.test: test_origin_program = paddle.static.Program() test_startup_program = paddle.static.Program() with paddle.static.program_guard( main_program=test_origin_program, startup_program=test_startup_program): with paddle.utils.unique_name.guard(): avg_cost = model.net(args, is_train=False) dist_infer = DistributedInfer( main_program=test_origin_program, startup_program=test_startup_program) with paddle.static.program_guard( main_program=dist_infer.get_dist_infer_program()): model.do_distributed_testing(fleet) fleet.stop_worker()