# Copyright (c) 2019 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 functools import logging import socket import time import os import signal import copy import sys import six import subprocess from contextlib import closing import socket logger = logging.getLogger("root") logger.propagate = False def get_cluster_from_args(args, selected_gpus): node_ips = [x.strip() for x in args.cluster_node_ips.split(',')] node_ip = args.node_ip node_rank = node_ips.index(node_ip) logger.debug("parsed from args:node_ips:{} node_ip:{} node_rank:{}".format( node_ips, node_ip, node_rank)) free_ports = None if not args.use_paddlecloud and len( node_ips) <= 1 and args.started_port is None: free_ports = find_free_ports(len(selected_gpus)) if free_ports is not None: free_ports = list(free_ports) else: started_port = 6070 if args.started_port is not None: started_port = args.started_port free_ports = [ x for x in range(started_port, started_port + len(selected_gpus)) ] trainer_endpoints = [] for ip in node_ips: trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) def get_gpus(selected_gpus): if selected_gpus is None: from paddle.fluid import core gpus_num = core.get_cuda_device_count() gpus = [str(x) for x in range(0, gpus_num)] else: cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES") if cuda_visible_devices is None or cuda_visible_devices == "": gpus = [x.strip() for x in selected_gpus.split(',')] else: # change selected_gpus into relative values # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.selected_gpus=4,5,6,7; # therefore selected_gpus=0,1,2,3 cuda_visible_devices_list = cuda_visible_devices.split(',') for x in selected_gpus.split(','): assert x in cuda_visible_devices_list, "Can't find "\ "your selected_gpus %s in CUDA_VISIBLE_DEVICES[%s]."\ % (x, cuda_visible_devices) gpus = [ cuda_visible_devices_list.index(x.strip()) for x in selected_gpus.split(',') ] logger.info("Change selected_gpus into reletive values. --ips:{} " "will change into relative_ips:{} according to your " "CUDA_VISIBLE_DEVICES:{}".format( selected_gpus, gpus, cuda_visible_devices_list)) return gpus def _print_arguments(args): print("----------- Configuration Arguments -----------") for arg, value in sorted(six.iteritems(vars(args))): print("%s: %s" % (arg, value)) print("------------------------------------------------") class Hdfs(object): def __init__(self): self.hdfs_ugi = None self.hdfs_name = None self.hdfs_path = None def is_valid(self): return self.hdfs_ugi is not None and \ self.hdfs_name is not None and \ self.hdfs_path is not None def __str__(self): return "hdfs_ugi:{} hdfs_name:{} hdfs_path{}".format( self.hdfs_ugi, self.hdfs_name, self.hdfs_path) def __eq__(self, n): return self.hdfs_ugi == n.hdfs_ugi and \ self.hdfs_name == n.hdfs_name and \ self.hdfs_path == n.hdfs_path def __ne__(self, n): return not self == n class Cluster(object): def __init__(self, hdfs): self.job_server = None self.pods = [] self.hdfs = None self.job_stage_flag = None def __str__(self): return "job_server:{} pods:{} job_stage_flag:{} hdfs:{}".format( self.job_server, [str(pod) for pod in self.pods], self.job_stage_flag, self.hdfs) def __eq__(self, cluster): if len(self.pods) != len(cluster.pods): return False for a, b in zip(self.pods, cluster.pods): if a != b: return False if self.job_stage_flag != cluster.job_stage_flag: return False return True def __ne__(self, cluster): return not self.__eq__(cluster) def update_pods(cluster): self.pods = copy.copy(cluster.pods) def trainers_nranks(self): return len(self.trainers_endpoints()) def pods_nranks(self): return len(self.pods) def trainers_endpoints(self): r = [] for pod in self.pods: for t in pod.trainers: r.append(t.endpoint) return r def pods_endpoints(self): r = [] for pod in self.pods: ep = "{}:{}".format(pod.addr, pod.port) assert pod.port != None and pod.addr != None, "{} not a valid endpoint".format( ep) r.append(ep) return r def get_pod_by_id(self, pod_id): for pod in self.pods: if str(pod_id) == str(pod.id): return pod return None class JobServer(object): def __init__(self): self.endpoint = None def __str__(self): return "{}".format(self.endpoint) def __eq__(self, j): return self.endpint == j.endpoint def __ne__(self, j): return not self == j class Trainer(object): def __init__(self): self.gpus = [] self.endpoint = None self.rank = None def __str__(self): return "gpu:{} endpoint:{} rank:{}".format(self.gpus, self.endpoint, self.rank) def __eq__(self, t): if len(self.gpus) != len(t.gpus): return False if self.endpoint != t.endpoint or \ self.rank != t.rank: return False for a, b in zip(self.gpus, t.gpus): if a != b: return False return True def __ne__(self, t): return not self == t def rank(self): return self.rank class Pod(object): def __init__(self): self.rank = None self.id = None self.addr = None self.port = None self.trainers = [] self.gpus = [] def __str__(self): return "rank:{} id:{} addr:{} port:{} visible_gpu:{} trainers:{}".format( self.rank, self.id, self.addr, self.port, self.gpus, [str(t) for t in self.trainers]) def __eq__(self, pod): if self.rank != pod.rank or \ self.id != pod.id or \ self.addr != pod.addr or \ self.port != pod.port: logger.debug("pod {} != pod".format(self, pod)) return False if len(self.trainers) != len(pod.trainers): logger.debug("trainers {} != {}".format(self.trainers, pod.trainers)) return False for i in range(len(self.trainers)): if self.trainers[i] != pod.trainers[i]: logger.debug("trainer {} != {}".format(self.trainers[i], pod.trainers[i])) return False return True def __ne__(self, pod): return not self == pod def parse_response(self, res_pods): pass def rank(self): return self.rank def get_visible_gpus(self): r = "" for g in self.gpus: r += "{},".format(g) assert r != "", "this pod {} can't see any gpus".format(self) r = r[:-1] return r def get_logger(log_level, name="root"): logger = logging.getLogger(name) logger.setLevel(log_level) log_handler = logging.StreamHandler() log_format = logging.Formatter( '%(levelname)s %(asctime)s %(filename)s:%(lineno)d] %(message)s') log_handler.setFormatter(log_format) logger.addHandler(log_handler) return logger def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus): assert type(trainer_endpoints) is list, "trainer_endpoints must be list" cluster = Cluster(hdfs=None) trainer_rank = 0 for node_rank, ip in enumerate(node_ips): pod = Pod() pod.rank = node_rank pod.addr = ip cur_node_endpoints = trainer_endpoints[node_rank] # when use paddlecloud, endpoints may > selected_gpus(user_defined) assert len(cur_node_endpoints) >= len( selected_gpus ), "current trainer_endpoints size should be greater equal than selected_gpus size." for i in range(len(selected_gpus)): trainer = Trainer() trainer.gpus.append(selected_gpus[i]) trainer.endpoint = "%s" % (cur_node_endpoints[i]) trainer.rank = trainer_rank trainer_rank += 1 pod.trainers.append(trainer) cluster.pods.append(pod) pod_rank = node_ips.index(node_ip) return cluster, cluster.pods[pod_rank] def terminate_local_procs(procs): for p in procs: if p.proc.poll() is None: p.proc.terminate() if p.log_fn: p.log_fn.close() logger.debug("terminate process id:{}".format(p.proc.pid)) #wait all process terminiated time.sleep(3) for step in range(0, 50): alive = False for p in procs: if p.proc.poll() is None: # not termniate os.kill(p.proc.pid, signal.SIGKILL) alive = True if not alive: logger.info("terminate all the procs") return time.sleep(3) logger.fatal("can't kill all process and exit") exit(1) def get_host_name_ip(): try: host_name = socket.gethostname() host_ip = socket.gethostbyname(host_name) return host_name, host_ip except: return None def add_arguments(argname, type, default, help, argparser, **kwargs): """Add argparse's argument. Usage: .. code-block:: python parser = argparse.ArgumentParser() add_argument("name", str, "Jonh", "User name.", parser) args = parser.parse_args() """ type = distutils.util.strtobool if type == bool else type argparser.add_argument( "--" + argname, default=default, type=type, help=help + ' Default: %(default)s.', **kwargs) def find_free_ports(num): def __free_port(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) return s.getsockname()[1] port_set = set() step = 0 while True: port = __free_port() if port not in port_set: port_set.add(port) if len(port_set) >= num: return port_set step += 1 if step > 100: print( "can't find avilable port and use the specified static port now!" ) return None return None def _prepare_trainer_env(cluster, trainer): proc_env = { "FLAGS_selected_gpus": "%s" % ",".join([str(g) for g in trainer.gpus]), "PADDLE_TRAINER_ID": "%d" % trainer.rank, "PADDLE_CURRENT_ENDPOINT": "%s" % trainer.endpoint, "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()) } return proc_env class TrainerProc(object): def __init__(self): self.proc = None self.log_fn = None self.log_offset = None self.rank = None self.local_rank = None self.cmd = None def start_local_trainers(cluster, pod, training_script, training_script_args, log_dir=None): current_env = copy.copy(os.environ.copy()) #paddle broadcast ncclUniqueId use socket, and #proxy maybe make trainers unreachable, so delete them. #if we set them to "", grpc will log error message "bad uri" #so just delete them. current_env.pop("http_proxy", None) current_env.pop("https_proxy", None) procs = [] for idx, t in enumerate(pod.trainers): proc_env = _prepare_trainer_env(cluster, t) current_env.update(proc_env) logger.debug("trainer proc env:{}".format(current_env)) cmd = [sys.executable, "-u", training_script] + training_script_args logger.info("start trainer proc:{} env:{}".format(cmd, proc_env)) fn = None if log_dir is not None: os.system("mkdir -p {}".format(log_dir)) fn = open("%s/workerlog.%d" % (log_dir, idx), "a") proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) else: proc = subprocess.Popen(cmd, env=current_env) tp = TrainerProc() tp.proc = proc tp.rank = t.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd procs.append(tp) return procs def pull_worker_log(tp): if tp.log_fn: with open(tp.log_fn.name, 'r') as fin: fin.seek(tp.log_offset, 0) for line in fin: try: sys.stdout.write(line) except UnicodeEncodeError: sys.stdout.write( 'UnicodeEncodeError occurs at this line. ' 'Please refer to the original log file "%s"\n' % tp.log_fn.name) tp.log_offset = fin.tell() def watch_local_trainers(procs, nranks): try: error = False error_rank = [] # wait all process finish or one error alive = False for p in procs: if p.log_fn and p.local_rank == 0: pull_worker_log(p) ret = p.proc.poll() if ret is None: alive = True elif ret != 0: error = True error_rank.append(p.rank) if error: terminate_local_procs(procs) exit(1) except KeyboardInterrupt: logger.warning("KeyboardInterrupt, exit") terminate_local_procs(procs) raise except SystemExit: logger.error( "ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.". format(nranks, error_rank)) terminate_local_procs(procs) raise except: logger.error( "ABORT!!! Out of all {} trainers, the trainer process with rank={} was aborted. Please check its log.". format(nranks, error_rank)) terminate_local_procs(procs) raise return alive