# 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 Distributed 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 """ 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 import paddle.fluid as fluid import paddle.distributed.fleet.base.role_maker as role_maker import paddle.distributed.fleet as fleet from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory from paddle.distributed.fleet.utils.ps_util import Distributed 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 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)) 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)) 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) """ # 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) 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 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._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) 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_out = open(tempfile.gettempdir() + "/tr0_stdout.log", "wb+") tr1_out = open(tempfile.gettempdir() + "/tr1_stdout.log", "wb+") tr0_proc = subprocess.Popen( tr0_cmd.strip().split(" "), stdout=tr0_out, stderr=tr0_pipe, env=required_envs) tr1_proc = subprocess.Popen( tr1_cmd.strip().split(" "), stdout=tr1_out, stderr=tr1_pipe, env=required_envs) return tr0_proc, tr1_proc, tr0_pipe, tr1_pipe def _run_cluster(self, model, envs): env = {'GRAD_CLIP': str(self._grad_clip_mode)} 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) # 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() tr0_ret = tr0.returncode tr1_ret = tr0.returncode if tr0_ret != 0: print( "========================Error tr0_err begin===========================" ) os.system("cat {}".format(tempfile.gettempdir() + "/tr0_err.log")) print( "========================Error tr0_err end===========================" ) if tr1_ret != 0: print( "========================Error tr1_err begin===========================" ) os.system("cat {}".format(tempfile.gettempdir() + "/tr1_err.log")) print( "========================Error tr1_err end===========================" ) # close trainer file tr0_pipe.close() tr1_pipe.close() ps0_pipe.close() ps1_pipe.close() ps0.terminate() ps1.terminate() 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) args = parser.parse_args() model = test_class() role = model.build_role(args) 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 = fluid.Program() test_startup_program = fluid.Program() with fluid.program_guard( main_program=test_origin_program, startup_program=test_startup_program): with fluid.unique_name.guard(): avg_cost = model.net(args, is_train=False) send_ctx = fleet.fleet._runtime_handle._communicator.send_ctx_ varname2tables = {} for gradname, ctx in send_ctx.items(): if ctx.is_sparse: param = gradname.strip("@GRAD") varname2tables[param] = ctx.table_id() else: continue ps_util = Distributed() test_main_program = ps_util.estimate(test_origin_program, varname2tables) print(str(test_main_program)) print(str(test_startup_program)) model.do_distributed_testing(args, test_main_program, test_startup_program) fleet.stop_worker()