# 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 logging import time import os import signal import copy import sys import subprocess import tempfile import shutil from contextlib import closing import multiprocessing import socket import warnings import six import struct import json import paddle import paddle.fluid as fluid from distutils.util import strtobool import paddle.utils.cpp_extension.extension_utils as utils logger = logging.getLogger("root") logger.propagate = False class DistributeMode(): """ There are various mode for fleetrun, each of them is designed for different model. """ COLLECTIVE = 0 PS = 1 PS_HETER = 2 class DeviceMode(): """ Training devices type """ UNKNOWN = -1 CPU = 0 GPU = 1 KUNLUN = 2 XPU = 2 ASCEND_NPU = 3 UNKNOWN = 3 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(self, 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 world_device_ids(self): r = [] for pod in self.pods: for t in pod.trainers: str_accelerators = [str(acc) for acc in t.accelerators] r.append(str_accelerators) 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.accelerators = [] self.endpoint = None self.rank = None self.stage = None def __str__(self): return "accelerator:{} endpoint:{} rank:{}".format( self.accelerators, self.endpoint, self.rank) def __eq__(self, t): if len(self.accelerators) != len(t.accelerators): return False if self.endpoint != t.endpoint or \ self.rank != t.rank: return False for a, b in zip(self.accelerators, t.accelerators): 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.servers = [] self.workers = [] self.heter_workers = [] self.accelerators = [] self.device_mode = None def __str__(self): return "rank:{} id:{} addr:{} port:{} visible_accelerator:{} trainers:{} servers:{} \ workers:{} heter_workers:{}".format( self.rank, self.id, self.addr, self.port, self.accelerators, [ str(t) for t in self.trainers ], [str(s) for s in self.servers], [str(w) for w in self.workers], [str(h) for h in self.heter_workers]) 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 {} != {}".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 if len(self.servers) != len(pod.servers): logger.debug("servers {} != {}".format(self.servers, pod.servers)) return False for i in range(len(self.servers)): if self.servers[i] != pod.servers[i]: logger.debug("servers {} != {}".format(self.servers[i], pod.servers[i])) return False if len(self.workers) != len(pod.workers): logger.debug("workers {} != {}".format(self.workers, pod.workers)) return False for i in range(len(self.workers)): if self.workers[i] != pod.workers[i]: logger.debug("workers {} != {}".format(self.workers[i], pod.workers[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_accelerators(self): r = "" for g in self.accelerators: r += "{},".format(g) assert r != "", "this pod {} can't see any accelerators".format(self) r = r[:-1] return r def get_logger(log_level=20, 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, device_mode, devices_per_proc): 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 pod.device_mode = device_mode cur_node_endpoints = trainer_endpoints[node_rank] # when use paddlecloud, endpoints may > devices_per_proc(user_defined) assert len(cur_node_endpoints) >= len( devices_per_proc ), "current trainer_endpoints size should be greater equal than acclerators size." for i in range(len(devices_per_proc)): trainer = Trainer() if device_mode == DeviceMode.GPU or device_mode == DeviceMode.ASCEND_NPU: if isinstance(devices_per_proc[i], (list, tuple)): trainer.accelerators.extend(devices_per_proc[i]) pod.accelerators.extend(devices_per_proc[i]) else: trainer.accelerators.append(devices_per_proc[i]) pod.accelerators.append(devices_per_proc[i]) elif device_mode == DeviceMode.XPU: if isinstance(devices_per_proc[i], (list, tuple)): trainer.accelerators.extend(devices_per_proc[i]) else: trainer.accelerators.append(devices_per_proc[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): # try to terminate process by group, this happend in multiprocess senario in user process if os.name != 'nt': for p in procs: if p.proc.poll() is None: os.killpg(os.getpgid(p.proc.pid), signal.SIGTERM) if p.log_fn: p.log_fn.close() logger.info("terminate process group gid:{}".format(p.proc.pid)) time.sleep(1) 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 = 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: # Note(wangxi): Close the connection with a TCP RST instead # of a TCP FIN, to avoid time_wait state. s.setsockopt(socket.SOL_SOCKET, socket.SO_LINGER, struct.pack('ii', 1, 0)) 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 > 400: print( "can't find avilable port and use the specified static port now!" ) return None return None def get_ports(num, offset): if os.environ.get('FLAGS_START_PORT') is None: ports = find_free_ports(num) if ports is not None: ports = list(ports) else: start_port = int(os.environ.get('FLAGS_START_PORT')) ports = range(start_port + offset, start_port + offset + num, 1) return ports def pretty_print_envs(envs, header=None): spacing = 2 max_k = 40 max_v = 45 for k, v in envs.items(): max_k = max(max_k, len(k)) h_format = " " + "|{{:>{}s}}{}{{:^{}s}}|\n".format(max_k, " " * spacing, max_v) l_format = " " + "|{{:>{}s}}{{}}{{:^{}s}}|\n".format(max_k, max_v) length = max_k + max_v + spacing border = " +" + "".join(["="] * length) + "+" line = " +" + "".join(["-"] * length) + "+" draws = "" draws += border + "\n" if header: draws += h_format.format(header[0], header[1]) else: draws += h_format.format("fleetrun Distributed Envs", "Value") draws += line + "\n" for k, v in envs.items(): if isinstance(v, str) and len(v) >= max_v: str_v = "... " + v[-41:] else: str_v = v draws += l_format.format(k, " " * spacing, str(str_v)) draws += border _str = "\n{}\n".format(draws) return _str 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 _run_with_coverage = False def run_with_coverage(*args): global _run_with_coverage assert len(args) <= 1, "len(args) {} should <= 1".format(len(args)) if len(args) == 1: assert isinstance(args[0], bool) _run_with_coverage = args[0] return _run_with_coverage def start_local_trainers(cluster, pod, training_script, training_script_args, log_dir=None, envs=None): if envs is None: current_env = copy.copy(os.environ.copy()) else: current_env = copy.copy(envs) # 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) ids = cluster.world_device_ids() res = [':'.join(ele) for ele in ids] procs = [] for idx, t in enumerate(pod.trainers): proc_env = { "PADDLE_TRAINER_ID": "%d" % t.rank, "PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint, "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), "PADDLE_RANK_IN_NODE": str(idx), "PADDLE_LOCAL_DEVICE_IDS": ",".join([str(acc) for acc in t.accelerators]), "PADDLE_WORLD_DEVICE_IDS": ",".join(res), } # The following three environnement variables are used for auto mapping if current_env.get("PADDLE_CLUSTER_TOPO_PATH", None) is not None: proc_env["PADDLE_CLUSTER_TOPO_PATH"] = current_env[ "PADDLE_CLUSTER_TOPO_PATH"] if current_env.get("PADDLE_RANK_MAPPING_PATH", None) is not None: proc_env["PADDLE_RANK_MAPPING_PATH"] = current_env[ "PADDLE_RANK_MAPPING_PATH"] if current_env.get("PADDLE_ENABLE_AUTO_MAPPING", None) is not None: proc_env["PADDLE_ENABLE_AUTO_MAPPING"] = current_env[ "PADDLE_ENABLE_AUTO_MAPPING"] if len(t.accelerators) > 0 and pod.device_mode == DeviceMode.GPU: proc_env["FLAGS_selected_gpus"] = "%s" % ",".join( [str(g) for g in t.accelerators]) elif len(t. accelerators) > 0 and pod.device_mode == DeviceMode.ASCEND_NPU: proc_env["FLAGS_selected_npus"] = "%s" % ",".join( [str(g) for g in t.accelerators]) if len(t.accelerators) > 0: proc_env["FLAGS_selected_accelerators"] = "%s" % ",".join( [str(g) for g in t.accelerators]) # to do: same code style in future if fluid.core.is_compiled_with_xpu() and len(t.accelerators) > 0: proc_env["FLAGS_selected_xpus"] = "%s" % ",".join( [str(g) for g in t.accelerators]) current_env.update(proc_env) coverage_args = [] if run_with_coverage() or os.environ.get("WITH_COVERAGE", "OFF") == "ON": coverage_args = ["-m", "coverage", "run", "--branch", "-p"] cmd = [sys.executable, "-u"] + coverage_args + [training_script ] + training_script_args logger.debug("start trainer proc{} env:{}".format(cmd, current_env)) if idx == 0: logger.info("Local start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.trainers), pretty_print_envs(proc_env, ("Distributed Envs", "Value")))) logger.info( "details about PADDLE_TRAINER_ENDPOINTS can be found in " "{}/endpoints.log, and detail running logs maybe found in " "{}/workerlog.0".format(log_dir, log_dir)) fn = None pre_fn = None if os.name == 'nt' else os.setsid if log_dir is not None: os.system("mkdir -p {}".format(log_dir)) if os.path.exists("%s/endpoints.log" % log_dir): os.system("rm -f {}/endpoints.log".format(log_dir)) with open("%s/endpoints.log" % log_dir, "w") as f: f.write("PADDLE_TRAINER_ENDPOINTS: \n") f.write("\n".join(cluster.trainers_endpoints())) if current_env.get("PADDLE_ENABLE_AUTO_MAPPING") is not None \ and current_env.get("PADDLE_NEED_RANK_MAPPING").lower() == "true": fn = open("%s/prelaunchlog.%d" % (log_dir, idx), "a") else: fn = open("%s/workerlog.%d" % (log_dir, idx), "a") proc = subprocess.Popen( cmd, env=current_env, stdout=fn, stderr=fn, preexec_fn=pre_fn) else: proc = subprocess.Popen(cmd, env=current_env, preexec_fn=pre_fn) 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) return 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) return 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) return return alive def get_gpus(gpus): if gpus is None: gpus_num = fluid.core.get_cuda_device_count() res_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 == "": res_gpus = [x.strip() for x in gpus.split(',')] else: # change gpus into relative values # e.g. CUDA_VISIBLE_DEVICES=4,5,6,7; args.gpus=4,5,6,7; # therefore gpus=0,1,2,3 cuda_visible_devices_list = cuda_visible_devices.split(',') for x in gpus.split(','): assert x in cuda_visible_devices_list, "Can't find "\ "your gpus %s in CUDA_VISIBLE_DEVICES[%s]."\ % (x, cuda_visible_devices) res_gpus = [ cuda_visible_devices_list.index(x.strip()) for x in gpus.split(',') ] logger.info("Change selected_gpus into reletive values. --ips:{} " "will change into relative_ips:{} according to your " "CUDA_VISIBLE_DEVICES:{}".format( gpus, res_gpus, cuda_visible_devices_list)) return res_gpus def get_xpus(xpus): if xpus is None: xpus_num = fluid.core.get_xpu_device_count() res_xpus = [str(x) for x in range(0, xpus_num)] else: xpu_visible_devices = os.getenv("XPU_VISIBLE_DEVICES") if xpu_visible_devices is None or xpu_visible_devices == "": res_xpus = [x.strip() for x in xpus.split(',')] else: # change xpus into relative values # e.g. XPU_VISIBLE_DEVICES=4,5,6,7; args.xpus=4,5,6,7; # therefore xpus=0,1,2,3 xpu_visible_devices_list = xpu_visible_devices.split(',') for x in xpus.split(','): assert x in xpu_visible_devices_list, "Can't find "\ "your xpus %s in XPU_VISIBLE_DEVICES[%s]."\ % (x, xpu_visible_devices) res_xpus = [ xpu_visible_devices_list.index(x.strip()) for x in xpus.split(',') ] logger.info("Change selected_xpus into reletive values. --ips:{} " "will change into relative_ips:{} according to your " "XPU_VISIBLE_DEVICES:{}".format( xpus, res_xpus, xpu_visible_devices_list)) return res_xpus def get_npus(npus): if npus is None: npus_num = fluid.core.get_npu_device_count() res_npus = [str(x) for x in range(0, npus_num)] else: npu_visible_devices = os.getenv("ASCEND_VISIBLE_DEVICES") if npu_visible_devices is None or npu_visible_devices == "": res_npus = [x.strip() for x in npus.split(',')] else: # change npus into relative values # e.g. ASCEND_VISIBLE_DEVICES=4,5,6,7; args.npus=4,5,6,7; # therefore npus=0,1,2,3 npu_visible_devices_list = npu_visible_devices.split(',') for x in npus.split(','): assert x in npu_visible_devices_list, "Can't find "\ "your npus %s in ASCEND_VISIBLE_DEVICES[%s]."\ % (x, npu_visible_devices) res_npus = [ npu_visible_devices_list.index(x.strip()) for x in npus.split(',') ] logger.info("Change selected_npus into reletive values. --ips:{} " "will change into relative_ips:{} according to your " "ASCEND_VISIBLE_DEVICES:{}".format( npus, res_npus, npu_visible_devices_list)) return res_npus def get_device_mode(backend): if backend == 'heter': if fluid.core.is_compiled_with_cuda() and \ fluid.core.get_cuda_device_count() > 0: print("launch train in heter mode with GPU device.") return DeviceMode.GPU if fluid.core.is_compiled_with_xpu() and \ fluid.core.get_xpu_device_count() > 0: print("launch train in heter mode with XPU device.") return DeviceMode.XPU if fluid.core.is_compiled_with_npu() and \ fluid.core.get_npu_device_count() > 0: print("launch train in heter mode with NPU device.") return DeviceMode.ASCEND_NPU if backend == 'hccl' and fluid.core.get_npu_device_count() > 0: print("launch train in ascend npu mode!") return DeviceMode.ASCEND_NPU if backend == 'nccl' and \ fluid.core.get_cuda_device_count() > 0: print("launch train in GPU mode!") return DeviceMode.GPU if backend == 'bkcl' and fluid.core.get_xpu_device_count() > 0: print("launch train in XPU mode") return DeviceMode.XPU if backend == 'gloo': print("launch train in CPU mode") return DeviceMode.CPU raise RuntimeError("Don't supported devices") def get_device_proc_info(args): # device_mode device_mode = get_device_mode(args.backend) # devices devices_per_proc = [] if device_mode == DeviceMode.GPU: gpus = get_gpus(args.gpus) if args.nproc_per_node is not None: assert (len(gpus) % int(args.nproc_per_node)) ==0, \ "gpus' number:{} mod args.nproc_per_node:{} must == 0".format(len(gpus), args.nproc_per_node) n = int(len(gpus) / int(args.nproc_per_node)) devices_per_proc = [ gpus[i:i + n] for i in six.moves.range(0, len(gpus), n) ] else: devices_per_proc = gpus elif device_mode == DeviceMode.ASCEND_NPU: npus = get_npus(args.npus) if args.nproc_per_node is not None: assert (len(npus) % int(args.nproc_per_node)) ==0, \ "npus' number:{} mod args.nproc_per_node:{} must == 0".format(len(npus), args.nproc_per_node) n = int(len(npus) / int(args.nproc_per_node)) devices_per_proc = [ npus[i:i + n] for i in six.moves.range(0, len(npus), n) ] else: devices_per_proc = npus elif device_mode == DeviceMode.XPU: xpus = get_xpus(args.xpus) if args.nproc_per_node is not None: assert (len(xpus) % int(args.nproc_per_node)) == 0, \ "xpus' number:{} mod args.nproc_per_node:{} must == 0".format(len(xpus), args.nproc_per_node) n = int(len(xpus) / int(args.nproc_per_node)) devices_per_proc = [ xpus[i:i + n] for i in six.moves.range(0, len(xpus), n) ] else: devices_per_proc = xpus elif device_mode == DeviceMode.CPU: if hasattr(args, "paddle_cpuonly") and args.nproc_per_node is None: #NOTE (xiongkun03) set it to cpu core number args.nproc_per_node = multiprocessing.cpu_count() if args.nproc_per_node is None: devices_per_proc = [0] else: devices_per_proc = [x for x in range(0, args.nproc_per_node)] else: assert False, "Can't support device_mode:{}, support only cpu|gpu|xpu now.".format( device_mode) return (device_mode, devices_per_proc) def direct_start(args): # run ps-cpu mode on paddlecloud, using given envs cmd = [sys.executable, "-u", args.training_script] + \ args.training_script_args proc = subprocess.Popen(cmd) proc.wait() return def get_custom_endpoints(origin_endpoints, offset=0): """ origin_endpoint: ip:port user_define_endpoint: ip:(port+offset) """ assert origin_endpoints != None paddle_user_define_endpoints_list = [] for ip_port in origin_endpoints.split(","): ip = ip_port.split(":")[0] port = ip_port.split(":")[1] new_port = int(port) + offset paddle_user_define_endpoints_list.append(":".join((ip, str(new_port)))) paddle_user_define_endpoints = ",".join(paddle_user_define_endpoints_list) return paddle_user_define_endpoints #def cloud_ps_heter_env_set(args): # environs = {} # # paddle_trainer_endpoints = os.getenv("TRAINER_IP_PORT_LIST", "") # assert paddle_trainer_endpoints != None # # paddle_pserver_endpoints = os.getenv("PSERVER_IP_PORT_LIST", "") # assert paddle_pserver_endpoints != None # # # hard code for paddlecloud custom-framework # avilable_ports = os.getenv("TRAINER_PORTS", "").split(",") # assert len( # avilable_ports # ) >= 2, "set paddle_ports_num >= 2 in config.ini for paddlecloud job submit" # # # hard code for paddlecloud custom-framework # trainers_num = len(paddle_pserver_endpoints.split(",")) # assert trainers_num != 0 # environs["PADDLE_TRAINERS_NUM"] = trainers_num # environs["TRAINERS_NUM"] = trainers_num # # # hard code for paddlecloud custom-framework # environs["PADDLE_HETER_TRAINER_IP_PORT_LIST"] = paddle_trainer_endpoints # environs["PADDLE_PSERVERS_IP_PORT_LIST"] = paddle_pserver_endpoints # environs["PADDLE_TRAINER_ENDPOINTS"] = get_custom_endpoints( # paddle_pserver_endpoints, 1) # heter_worker_num = len(paddle_trainer_endpoints.split(",")) # if (args.heter_worker_num != None) and ( # heter_worker_num != args.heter_worker_num): # warnings.warn( # "Your fleetrun setting: heter_worker_num is {}, but we find {} device can be used, this setting has been changed.". # format(args.heter_worker_num, heter_worker_num)) # args.heter_worker_num = heter_worker_num # # for k, v in environs.items(): # os.environ[k] = str(v) # logger.info("Set heter parameter server env: {}".format( # pretty_print_envs(environs))) def get_mapped_cluster_without_rank_mapping( node_ips, node_ip, trainer_endpoints, device_mode, node_ranks): assert type(trainer_endpoints) is list, "trainer_endpoints must be list" assert device_mode == DeviceMode.GPU, \ "Only support get mapped cluster for gpu now." cluster = Cluster(hdfs=None) for node_rank, ip in enumerate(node_ips): pod = Pod() pod.rank = node_rank pod.addr = ip pod.device_mode = device_mode cur_node_endpoints = trainer_endpoints[node_rank] # choose rank from global mapped ranks and set it to the trainer. ranks_per_node = node_ranks[node_rank] assert len(ranks_per_node) == 1 for i in range(len(ranks_per_node)): trainer = Trainer() trainer.endpoint = "%s" % (cur_node_endpoints[i]) trainer.rank = ranks_per_node[i] pod.trainers.append(trainer) cluster.pods.append(pod) pod_rank = node_ips.index(node_ip) return cluster, cluster.pods[pod_rank] def get_mapped_cluster_from_args_without_rank_mapping(args, device_mode): assert device_mode == DeviceMode.GPU, \ "Only support get mapped cluster for gpu now." gpus_num = fluid.core.get_cuda_device_count() # parse ip-ranks json file cluster_topo = None with open(args.cluster_topo_path, "r") as json_file: cluster_topo = json.load(json_file) node_ips = [] node_ranks = [] for idx, cur_cluster_topo in enumerate(cluster_topo["machines"]): node_ips.append(cur_cluster_topo['addr']) node_ranks.append([idx]) if len(node_ips) == 1: node_ip = node_ips[0] else: if args.host: node_ip = args.host else: _, node_ip = get_host_name_ip() assert node_ip in node_ips, \ "Can't find your local ip {%s} in node_ips: {%s}" % (node_ip, node_ips) node_rank = node_ips.index(node_ip) assert len(node_ranks) == len(node_ips), \ "ranks length should be equal to ips length." logger.debug("parsed from args: node_ips:{} node_ip:{} " "node_rank:{} node_ranks:{}".format( node_ips, node_ip, node_rank, node_ranks[node_rank])) # NOTE: there are different number of global mapped ranks on each node. free_ports = [] trainer_endpoints = [] for ip in node_ips: node_rank = node_ips.index(ip) if os.environ.get('PADDLE_PORT') is not None: start_port = int(os.getenv("PADDLE_PORT", "")) free_ports = [ x for x in range(start_port, start_port + len(node_ranks[ node_rank])) ] elif os.environ.get('FLAGS_START_PORT') is not None: start_port = int(os.environ.get('FLAGS_START_PORT')) free_ports = [ x for x in range(start_port, start_port + len(node_ranks[ node_rank])) ] else: free_ports = find_free_ports(len(node_ranks[node_rank])) trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) return get_mapped_cluster_without_rank_mapping( node_ips, node_ip, trainer_endpoints, device_mode, node_ranks) def get_mapped_cluster_with_rank_mapping(node_ips, node_ip, trainer_endpoints, device_mode, node_ranks, node_rank_mappings): assert type(trainer_endpoints) is list, "trainer_endpoints must be list" assert device_mode == DeviceMode.GPU, \ "Only support get mapped cluster for gpu now." def get_relative_gpu_id(gpu_id): cuda_visible_devices = os.getenv("CUDA_VISIBLE_DEVICES") if cuda_visible_devices is None or cuda_visible_devices == "": return gpu_id else: cuda_visible_devices_list = cuda_visible_devices.split(',') relative_id = cuda_visible_devices_list.index(str(gpu_id)) logger.info( "Change gpu id from {} to {} based on CUDA_VISIBLE_DEVICES {}". format(gpu_id, relative_id, cuda_visible_devices_list)) return relative_id cluster = Cluster(hdfs=None) for node_rank, ip in enumerate(node_ips): pod = Pod() pod.rank = node_rank pod.addr = ip pod.device_mode = device_mode cur_node_endpoints = trainer_endpoints[node_rank] # choose rank from global mapped ranks and set it to the trainer. ranks_per_node = node_ranks[node_rank] cur_node_rank_mapping = node_rank_mappings[node_rank] for i in range(len(ranks_per_node)): trainer = Trainer() local_device_ids = cur_node_rank_mapping["ranks"][str( ranks_per_node[i])] assert len(local_device_ids) == 1, \ "Only support one process to one device mapping" trainer.accelerators.append( get_relative_gpu_id(local_device_ids[0])) trainer.endpoint = "%s" % (cur_node_endpoints[i]) trainer.rank = ranks_per_node[i] pod.trainers.append(trainer) cluster.pods.append(pod) pod_rank = node_ips.index(node_ip) return cluster, cluster.pods[pod_rank] def get_mapped_cluster_from_args_with_rank_mapping(args, device_mode): assert device_mode == DeviceMode.GPU, \ "Only support get mapped cluster for gpu now." gpus_num = fluid.core.get_cuda_device_count() # parse ip-ranks json file rank_mapping_path = args.rank_mapping_path or os.getenv( "PADDLE_RANK_MAPPING_PATH") rank_mapping = None with open(rank_mapping_path, "r") as json_file: rank_mapping = json.load(json_file) # reset PADDLE_RANK_MAPPING_PATH env os.environ["PADDLE_RANK_MAPPING_PATH"] = "" node_ips = [] node_ranks = [] node_rank_mappings = [] for cur_rank_mapping in rank_mapping: node_ips.append(cur_rank_mapping['addr']) cur_node_rank_list = [ int(i) for i in list(cur_rank_mapping['ranks'].keys()) ] cur_node_rank_list.sort() node_ranks.append(cur_node_rank_list) node_rank_mappings.append(cur_rank_mapping) if len(node_ips) == 1: node_ip = node_ips[0] else: if args.host: node_ip = args.host else: _, node_ip = get_host_name_ip() assert node_ip in node_ips, \ "Can't find your local ip {%s} in node_ips: {%s}" % (node_ip, node_ips) node_rank = node_ips.index(node_ip) assert len(node_ranks[node_rank]) <= gpus_num, \ "number of ranks mapped to one node should not exceed the avaiable ones." assert len(node_ranks) == len(node_ips), \ "ranks length should be equal to ips length." logger.debug("parsed from args: node_ips:{} node_ip:{} " "node_rank:{} node_ranks:{}".format( node_ips, node_ip, node_rank, node_ranks[node_rank])) # NOTE: there are different number of global mapped ranks on each node. free_ports = [] trainer_endpoints = [] for ip in node_ips: node_rank = node_ips.index(ip) if os.environ.get('PADDLE_PORT') is not None: start_port = int(os.getenv("PADDLE_PORT", "")) free_ports = [ x for x in range(start_port, start_port + len(node_ranks[ node_rank])) ] elif os.environ.get('FLAGS_START_PORT') is not None: start_port = int(os.environ.get('FLAGS_START_PORT')) free_ports = [ x for x in range(start_port, start_port + len(node_ranks[ node_rank])) ] else: free_ports = find_free_ports(len(node_ranks[node_rank])) trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) return get_mapped_cluster_with_rank_mapping(node_ips, node_ip, trainer_endpoints, device_mode, node_ranks, node_rank_mappings) class ParameterServerLauncher(object): def __init__(self, args, distribute_mode): self.args = args self.distribute_mode = distribute_mode self.server_num = 0 self.worker_num = 0 self.heter_worker_num = 0 self.server_endpoints = "" self.server_endpoints_ips = [] self.server_endpoints_port = [] self.worker_endpoints = "" self.worker_endpoints_ips = [] self.worker_endpoints_port = [] self.heter_worker_endpoints = "" self.heter_worker_endpoints_ips = [] self.heter_worker_endpoints_port = [] self.is_local = True self.current_node_ip = "" self.stage_trainer_num = [] self.stage_heter_map = {} self.stage_list = [] self.stage_device_map = {} self.stage_num = 0 self.get_role_endpoints(args) def get_role_endpoints(self, args): if args.server_num: self.server_num = args.server_num if args.servers: assert len( args.servers.split(",") ) == self.server_num, "The server_num and servers doesn't match. Expect servers endpoints num epual to server_num, but received servers enpoint num: {} and server_num {}".format( len(args.servers.split(",")), self.server_num) self.server_endpoints = args.servers else: ports = get_ports(self.server_num, 0) self.server_endpoints = ",".join( ["127.0.0.1:" + str(x) for x in ports]) else: assert args.servers != "", "The setting of Parameter-Server must has server_num or servers." self.server_endpoints = args.servers self.server_num = len(self.server_endpoints.split(",")) # get worker envs if args.worker_num: self.worker_num = args.worker_num if args.workers: assert len( args.workers.split(",") ) == self.worker_num, "The worker_num and workers doesn't match. Expect workers endpoints num epual to worker_num, but received workers enpoint num: {} and worker_num {}".format( len(args.workers.split(",")), self.worker_num) self.worker_endpoints = args.workers else: ports = get_ports(self.worker_num, self.server_num) self.worker_endpoints = ",".join( ["127.0.0.1:" + str(x) for x in ports]) else: assert args.workers != "", "The setting of Parameter-Server must has worker_num or workers." worker_endpoints_ips = [ x.strip().split(":")[0] for x in args.workers.split(",") ] self.worker_num = len(worker_endpoints_ips) worker_endpoints_len = [ len(x.strip().split(":")) for x in args.workers.split(",") ] if 1 in worker_endpoints_len: # if no port value in worker_endpoints, will set default port values. start_port = 6170 worker_endpoints_port = range( start_port + self.server_num, start_port + self.server_num + self.worker_num, 1) # create endpoints str worker_endpoints = [] for i in range(self.worker_num): worker_endpoints.append(":".join((worker_endpoints_ips[ i], str(worker_endpoints_port[i])))) self.worker_endpoints = ",".join(worker_endpoints) else: self.worker_endpoints = args.workers # get heter worker envs if self.distribute_mode == DistributeMode.PS_HETER: assert args.heter_devices != "", "The setting of Parameter-Server heter mode must has heter_devices." self.stage_device_map[1] = "cpu" # for cpu trainer heter_devices_list = args.heter_devices.split(";") for i in range(len(heter_devices_list)): self.stage_device_map[i + 2] = heter_devices_list[i] self.stage_heter_map[1] = self.worker_endpoints if args.heter_worker_num: self.stage_heter_trainer_num = args.heter_worker_num.split(";") self.stage_heter_trainer_num = [ int(trainer_num) for trainer_num in self.stage_heter_trainer_num ] if args.heter_workers: assert len(args.heter_workers.split(";")) == len( self.stage_heter_trainer_num ), "The stage_num and heter_workers doesn't match. Expect heter_workers endpoints stage num epual to heter_worker_num stage, but received heter_workers enpoint stage num: {} and heter_worker_num stage {}".format( len(args.heter_workers.split(";")), len(self.stage_heter_trainer_num)) heter_worker_endpoints_list = args.heter_workers.split(";") self.heter_worker_endpoints = "" for i in range(len(self.stage_heter_trainer_num)): if self.heter_worker_endpoints != "": self.heter_worker_endpoints += "," heter_worker_endpoints = heter_worker_endpoints_list[ i].split(",") assert len( heter_worker_endpoints ) == self.stage_heter_trainer_num[ i], "The heter trainer num in stage {} is not equal in args.heter_worker_num and args.heter_workers".format( i) heter_worker_endpoints_ips = [ x.strip().split(":")[0] for x in heter_worker_endpoints ] heter_worker_endpoints_len = [ len(x.strip().split(":")) for x in heter_worker_endpoints ] if 1 in heter_worker_endpoints_len: # if no port value in heter_worker_endpoint, will set default port values. heter_worker_endpoints_port = get_ports( len(heter_worker_endpoints_ips), self.worker_num + self.server_num + self.heter_worker_num) new_heter_worker_endpoints = [] for j in range(len(heter_worker_endpoints_ips)): new_heter_worker_endpoints.append(":".join(( heter_worker_endpoints_ips[j], str( heter_worker_endpoints_port[j])))) ip_port_list = ",".join(new_heter_worker_endpoints) else: ip_port_list = ",".join(heter_worker_endpoints) self.stage_heter_map[i + 2] = ip_port_list self.stage_list.extend([i + 2] * len(ip_port_list.split(','))) self.heter_worker_num += self.stage_heter_trainer_num[i] self.heter_worker_endpoints += ip_port_list else: for i in range(len(self.stage_heter_trainer_num)): heter_trainer_num = self.stage_heter_trainer_num[i] ports = get_ports(heter_trainer_num, self.server_num + self.worker_num + self.heter_worker_num) ip_port_list = ",".join( ["127.0.0.1:" + str(x) for x in ports]) self.stage_heter_map[i + 2] = ip_port_list self.stage_list.extend([i + 2] * len(ip_port_list.split(','))) self.heter_worker_num += heter_trainer_num if self.heter_worker_endpoints != "": self.heter_worker_endpoints += "," self.heter_worker_endpoints += ip_port_list else: assert args.heter_workers != "", "The setting of Parameter-Server heter mode must has heter_worker_num or heter_workers." self.stage_heter_trainer_num = [] heter_worker_endpoints_list = args.heter_workers.split(";") self.heter_worker_endpoints = "" for i in range(len(heter_worker_endpoints_list)): heter_worker_endpoints = heter_worker_endpoints_list[ i].split(",") self.stage_heter_trainer_num.append( len(heter_worker_endpoints)) heter_worker_endpoints_ips = [ x.strip().split(":")[0] for x in heter_worker_endpoints ] heter_worker_endpoints_len = [ len(x.strip().split(":")) for x in heter_worker_endpoints ] if 1 in heter_worker_endpoints_len: # if no port value in heter_worker_endpoint, will set default port values. heter_worker_endpoints_port = get_ports( len(heter_worker_endpoints_ips), self.worker_num + self.server_num + self.heter_worker_num) new_heter_worker_endpoints = [] for j in range(len(heter_worker_endpoints_ips)): new_heter_worker_endpoints.append(":".join(( heter_worker_endpoints_ips[j], str( heter_worker_endpoints_port[j])))) ip_port_list = ",".join(new_heter_worker_endpoints) else: ip_port_list = ",".join(heter_worker_endpoints) self.stage_heter_map[i + 2] = ip_port_list self.stage_list.extend([i + 2] * len(ip_port_list.split(','))) self.heter_worker_num += self.stage_heter_trainer_num[-1] if self.heter_worker_endpoints != "": self.heter_worker_endpoints += "," self.heter_worker_endpoints += ip_port_list self.stage_trainer_num = [self.worker_num ] + self.stage_heter_trainer_num self.stage_num = len(self.stage_trainer_num) # get http_port if args.http_port: http_port = [args.http_port] else: http_port = get_ports( 1, self.server_num + self.worker_num + self.heter_worker_num) http_ip = self.server_endpoints.split(",")[0].split(":")[0] self.http_port = http_ip + ":" + str(http_port[0]) # check local or user define self.server_endpoints_ips = [ x.strip().split(":")[0] for x in self.server_endpoints.split(",") ] self.worker_endpoints_ips = [ x.strip().split(":")[0] for x in self.worker_endpoints.split(",") ] self.server_endpoints_port = [ x.strip().split(":")[1] for x in self.server_endpoints.split(",") ] self.worker_endpoints_port = [ x.strip().split(":")[1] for x in self.worker_endpoints.split(",") ] self.node_ips = [] for ip in self.server_endpoints_ips: if ip not in self.node_ips: self.node_ips.append(ip) for ip in self.worker_endpoints_ips: if ip not in self.node_ips: self.node_ips.append(ip) if self.distribute_mode == DistributeMode.PS_HETER: self.heter_worker_endpoints_ips = [ x.strip().split(":")[0] for x in self.heter_worker_endpoints.split(",") ] self.heter_worker_endpoints_port = [ x.strip().split(":")[1] for x in self.heter_worker_endpoints.split(",") ] for ip in self.heter_worker_endpoints_ips: if ip not in self.node_ips: self.node_ips.append(ip) if len(set(self.node_ips)) == 1: self.is_local = True self.current_node_ip = self.node_ips[0] else: self.is_local = False pod_ip = os.getenv("POD_IP", None) if pod_ip == None: _, self.current_node_ip = get_host_name_ip() else: self.current_node_ip = pod_ip if not self.distribute_mode == DistributeMode.PS_HETER: assert self.current_node_ip in self.node_ips, "Can't find your local ip {%s} in args.servers and args.workers ips: {%s}" \ % (self.current_node_ip, self.node_ips) if self.current_node_ip in self.node_ips: self.node_rank = self.node_ips.index(self.current_node_ip) logger.debug( "parsed from args: node_ips:{} current_node_ip:{} node_rank:{}". format(self.node_ips, self.current_node_ip, self.node_rank)) def start_ps(self): if not self.current_node_ip in self.node_ips: return cluster = Cluster(hdfs=None) server_rank = 0 worker_rank = 0 heter_worker_rank = 0 for node_rank, ip in enumerate(self.node_ips): pod = Pod() pod.rank = node_rank pod.addr = ip for i in range(len(self.server_endpoints_ips)): if ip == self.server_endpoints_ips[i]: server = Trainer() server.endpoint = "%s:%s" % (ip, self.server_endpoints_port[i]) server.rank = server_rank server_rank += 1 pod.servers.append(server) for j in range(len(self.worker_endpoints_ips)): if ip == self.worker_endpoints_ips[j]: worker = Trainer() worker.endpoint = "%s:%s" % (ip, self.worker_endpoints_port[j]) worker.rank = worker_rank worker.stage = 1 worker_rank += 1 pod.workers.append(worker) for k in range(len(self.heter_worker_endpoints_ips)): if ip == self.heter_worker_endpoints_ips[k]: heter_worker = Trainer() heter_worker.endpoint = "%s:%s" % ( ip, self.heter_worker_endpoints_port[k]) heter_worker.rank = heter_worker_rank heter_worker.stage = self.stage_list[k] heter_worker_rank += 1 pod.heter_workers.append(heter_worker) cluster.pods.append(pod) pod = cluster.pods[self.node_rank] self.gloo_rendezvous_dir = tempfile.mkdtemp() # 3. subproces start self.procs = {"worker": [], "server": [], "heter_worker": []} self.cmds = {"worker": [], "server": [], "heter_worker": []} self.log_fns = {"worker": [], "server": [], "heter_worker": []} self.start_pod_server(self.args, pod) self.start_pod_worker(self.args, pod) if self.distribute_mode == DistributeMode.PS_HETER: self.start_pod_heter_worker(self.args, pod) logger.info( "Please check servers, workers and heter_worker logs in {}/workerlog.*, {}/serverlog.* and {}/heterlog.*". format(self.args.log_dir, self.args.log_dir, self.args.log_dir)) # 4. wait for finish training if len(self.procs["worker"]) > 0: # if node has worker procs # only wait worker to finish here for i, proc in enumerate(self.procs["worker"]): self.procs["worker"][i].proc.wait() if len(self.log_fns["worker"]) > 0: self.log_fns["worker"][i].close() logger.info( "all workers exit, going to finish parameter server and heter_worker." ) if len(self.procs["heter_worker"]) > 0: for i, proc in enumerate(self.procs["heter_worker"]): self.log_fns["heter_worker"][i].close() self.procs["heter_worker"][i].proc.terminate() logger.info("all heter_worker are killed") if len(self.procs["server"]) > 0: for i, proc in enumerate(self.procs["server"]): self.log_fns["server"][i].close() self.procs["server"][i].proc.terminate() logger.info("all parameter server are killed") else: # if node has not worker procs # blocking training process if len(self.procs["server"]) > 0: for i, proc in enumerate(self.procs["server"]): self.procs["server"][i].proc.wait() if len(self.procs["heter_worker"]) > 0: for i, proc in enumerate(self.procs["heter_worker"]): self.procs["heter_worker"][i].proc.wait() if os.path.exists(self.gloo_rendezvous_dir): shutil.rmtree(self.gloo_rendezvous_dir) def start_pod_server(self, args, pod): default_env = os.environ.copy() current_env = copy.copy(default_env) current_env.pop("http_proxy", None) current_env.pop("https_proxy", None) for idx, cur_server in enumerate(pod.servers): if self.distribute_mode == DistributeMode.PS_HETER: proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": self.server_endpoints, "PADDLE_TRAINER_ENDPOINTS": self.worker_endpoints, "PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST": self.heter_worker_endpoints, "PADDLE_PORT": cur_server.endpoint.split(":")[1], "TRAINING_ROLE": "PSERVER", "PADDLE_TRAINERS_NUM": str(self.worker_num), "POD_IP": cur_server.endpoint.split(":")[0], "PADDLE_WITH_GLOO": str(os.getenv("PADDLE_WITH_GLOO", "0")), "PADDLE_GLOO_RENDEZVOUS": "3", "PADDLE_GLOO_FS_PATH": self.gloo_rendezvous_dir, "PADDLE_GLOO_HTTP_ENDPOINT": self.http_port } else: proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": self.server_endpoints, "PADDLE_TRAINER_ENDPOINTS": self.worker_endpoints, "PADDLE_PORT": cur_server.endpoint.split(":")[1], "TRAINING_ROLE": "PSERVER", "PADDLE_TRAINERS_NUM": str(self.worker_num), "POD_IP": cur_server.endpoint.split(":")[0], "PADDLE_WITH_GLOO": str(os.getenv("PADDLE_WITH_GLOO", "0")), "PADDLE_GLOO_RENDEZVOUS": "3", "PADDLE_GLOO_FS_PATH": self.gloo_rendezvous_dir, "PADDLE_GLOO_HTTP_ENDPOINT": self.http_port } current_env.update(proc_env) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args self.cmds["server"].append(cmd) if idx == 0: logger.info( "Local server start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.servers), pretty_print_envs(proc_env, ("Distributed Envs", "Value" )))) if args.log_dir is not None: os.system("mkdir -p {}".format(args.log_dir)) fn = open("%s/serverlog.%d" % (args.log_dir, idx), "w") self.log_fns["server"].append(fn) 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 = cur_server.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd self.procs["server"].append(tp) def start_pod_worker(self, args, pod): default_env = os.environ.copy() current_env = copy.copy(default_env) current_env.pop("http_proxy", None) current_env.pop("https_proxy", None) heter_device_num = 0 device_list = [] if fluid.core.is_compiled_with_cuda(): device_list = get_gpus(args.gpus) heter_device_num = len(device_list) elif fluid.core.is_compiled_with_xpu(): heter_device_num = fluid.core.get_xpu_device_count() device_list = [str(x) for x in range(0, heter_device_num)] for idx, cur_worker in enumerate(pod.workers): device_id = "0" if heter_device_num == 0 else str(device_list[( idx) % heter_device_num]) if self.distribute_mode == DistributeMode.PS_HETER: proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": self.server_endpoints, "PADDLE_TRAINER_ENDPOINTS": self.worker_endpoints, "PADDLE_TRAINERS_NUM": str(self.worker_num), "PADDLE_STAGE_TRAINERS_NUM": str(self.stage_trainer_num), "STAGE_ID": "1", "STAGE_NUM": str(self.stage_num), "PADDLE_PREVIOUS_HETER_TRAINER_IP_PORT_LIST": "", "PADDLE_NEXT_HETER_TRAINER_IP_PORT_LIST": self.stage_heter_map[2], "PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST": self.heter_worker_endpoints, "HETER_DEVICE_TYPE": self.stage_device_map[1], "TRAINING_ROLE": "TRAINER", "POD_IP": cur_worker.endpoint.split(":")[0], "PADDLE_PORT": cur_worker.endpoint.split(":")[1], "PADDLE_TRAINER_ID": str(cur_worker.rank), "PADDLE_WITH_GLOO": str(os.getenv("PADDLE_WITH_GLOO", "0")), "PADDLE_GLOO_RENDEZVOUS": "3", "PADDLE_GLOO_FS_PATH": self.gloo_rendezvous_dir, "FLAGS_selected_gpus": "0", "FLAGS_selected_xpus": "0", "CUDA_VISIBLE_DEVICES": device_id, "XPU_VISIBLE_DEVICES": device_id, "PADDLE_GLOO_HTTP_ENDPOINT": self.http_port } else: proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": self.server_endpoints, "PADDLE_TRAINER_ENDPOINTS": self.worker_endpoints, "PADDLE_TRAINERS_NUM": str(self.worker_num), "TRAINING_ROLE": "TRAINER", "POD_IP": cur_worker.endpoint.split(":")[0], "PADDLE_PORT": cur_worker.endpoint.split(":")[1], "PADDLE_TRAINER_ID": str(cur_worker.rank), "PADDLE_WITH_GLOO": str(os.getenv("PADDLE_WITH_GLOO", "0")), "PADDLE_GLOO_RENDEZVOUS": "3", "PADDLE_GLOO_FS_PATH": self.gloo_rendezvous_dir, "FLAGS_selected_gpus": "0", "FLAGS_selected_xpus": "0", "CUDA_VISIBLE_DEVICES": device_id, "XPU_VISIBLE_DEVICES": device_id, "PADDLE_GLOO_HTTP_ENDPOINT": self.http_port } current_env.update(proc_env) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args self.cmds["worker"].append(cmd) if idx == 0: logger.info( "Local worker start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.workers), pretty_print_envs(proc_env, ("Distributed Envs", "Value" )))) if args.log_dir is not None: os.system("mkdir -p {}".format(args.log_dir)) fn = open("%s/workerlog.%d" % (args.log_dir, idx), "w") self.log_fns["worker"].append(fn) 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 = cur_worker.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd self.procs["worker"].append(tp) def start_pod_heter_worker(self, args, pod): default_env = os.environ.copy() current_env = copy.copy(default_env) current_env.pop("http_proxy", None) current_env.pop("https_proxy", None) heter_device_num = 0 device_list = [] if fluid.core.is_compiled_with_cuda(): device_list = get_gpus(args.gpus) heter_device_num = len(device_list) elif fluid.core.is_compiled_with_xpu(): heter_device_num = fluid.core.get_xpu_device_count() device_list = [str(x) for x in range(0, heter_device_num)] for idx, cur_heter_worker in enumerate(pod.heter_workers): device_id = "0" if heter_device_num == 0 else str(device_list[( idx) % heter_device_num]) stage_id = cur_heter_worker.stage proc_env = { "PADDLE_PSERVERS_IP_PORT_LIST": self.server_endpoints, "PADDLE_TRAINER_ENDPOINTS": self.worker_endpoints, "PADDLE_NEXT_HETER_TRAINER_IP_PORT_LIST": self.stage_heter_map[stage_id + 1] if stage_id <= self.stage_num - 1 else "", "PADDLE_PREVIOUS_HETER_TRAINER_IP_PORT_LIST": self.stage_heter_map[stage_id - 1], "PADDLE_ALL_HETER_TRAINER_IP_PORT_LIST": self.heter_worker_endpoints, "HETER_DEVICE_TYPE": self.stage_device_map[stage_id], "STAGE_ID": str(stage_id), "STAGE_NUM": str(self.stage_num), "PADDLE_PORT": cur_heter_worker.endpoint.split(":")[1], "TRAINING_ROLE": "HETER_TRAINER", "PADDLE_TRAINERS_NUM": str(self.worker_num), "PADDLE_STAGE_TRAINERS_NUM": str(self.stage_trainer_num), "POD_IP": cur_heter_worker.endpoint.split(":")[0], "PADDLE_WITH_GLOO": str(os.getenv("PADDLE_WITH_GLOO", "0")), "PADDLE_GLOO_RENDEZVOUS": "3", "PADDLE_GLOO_FS_PATH": self.gloo_rendezvous_dir, "FLAGS_selected_gpus": "0", "FLAGS_selected_xpus": "0", "CUDA_VISIBLE_DEVICES": device_id, "XPU_VISIBLE_DEVICES": device_id, "PADDLE_GLOO_HTTP_ENDPOINT": self.http_port } current_env.update(proc_env) cmd = [sys.executable, "-u", args.training_script ] + args.training_script_args self.cmds["heter_worker"].append(cmd) if idx == 0: logger.info( "Local heter_worker start {} processes. First process distributed " "environment info (Only For Debug): {}".format( len(pod.heter_workers), pretty_print_envs(proc_env, ("Distributed Envs", "Value" )))) if args.log_dir is not None: os.system("mkdir -p {}".format(args.log_dir)) fn = open("%s/heterlog.%d" % (args.log_dir, idx), "w") self.log_fns["heter_worker"].append(fn) 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 = cur_heter_worker.rank tp.local_rank = idx tp.log_fn = fn tp.log_offset = fn.tell() if fn else None tp.cmd = cmd self.procs["heter_worker"].append(tp) def check_backend(backend): if backend not in ['nccl', 'gloo', 'bkcl', 'auto', 'hccl', 'heter']: raise ValueError("paddle.distributed initialize error, " "backend argument can only be one of " "'nccl', 'gloo', 'bkcl', 'auto', 'hccl', 'heter' " "but got %s" % backend) if backend == 'nccl' and not fluid.core.is_compiled_with_cuda(): raise ValueError( "paddle.distributed initialize error, " "your paddle is not compiled with cuda but you assign 'nccl' as backend." ) if backend == 'bkcl' and not fluid.core.is_compiled_with_xpu(): raise ValueError( "paddle.distributed initialize error, " "your paddle is not compiled with xpu but you assign 'bkcl' as backend." ) if backend == 'hccl' and not fluid.core.is_compiled_with_npu(): raise ValueError( "paddle.distributed initialize error, " "your paddle is not compiled with npu but you assign 'hccl' as backend." ) def block_windows_and_macos(backend): if backend != 'gloo': return if utils.OS_NAME.startswith('darwin'): # MACOS , block raise ValueError( "You are going to using gloo on macos, but currently is not supported" ) if utils.IS_WINDOWS: # MACOS , block raise ValueError( "You are going to using gloo on windows, but currently is not supported" ) def get_backend_by_compile_flag(): if fluid.core.is_compiled_with_cuda(): return 'nccl' if fluid.core.is_compiled_with_xpu(): return 'bkcl' if fluid.core.is_compiled_with_npu(): return 'hccl' return 'gloo'