wehub-resource-sync
324 lines
12 KiB
Python
324 lines
12 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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from ..context.device import DeviceType
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from .controller import Controller, ControllerMode
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class CollectiveController(Controller):
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def __init__(self, ctx):
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self._tuner_run_mode = None # 'tuner_only', 'run_only', 'tuner_and_run'
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super().__init__(ctx)
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@classmethod
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def enable(cls, ctx):
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# collective is the default mode
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if ctx:
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ctx.logger.debug(f"{cls.__name__} enabled")
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ctx.args.run_mode = ControllerMode.COLLECTIVE
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return True
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else:
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return False
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def build_pod(self):
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skip_run = self._build_pod_with_tuner()
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if skip_run:
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return
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if (
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self.ctx.args.master is None
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and self.ctx.args.start_port
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and self.ctx.args.ips
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):
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return self._build_pod_with_args()
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else:
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if self.ctx.args.auto_parallel_config is None:
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skip_run = True
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# only when skip_run is False, should not reset pod
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return self._build_pod_with_master(skip_run)
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def _build_pod_with_tuner(self):
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auto_parallel_config = self.ctx.args.auto_parallel_config
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if auto_parallel_config is not None:
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if not os.path.exists(auto_parallel_config):
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self.ctx.logger.warning("auto_parallel_conf not exists!")
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if not auto_parallel_config.endswith(".json"):
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self.ctx.logger.warning(
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"auto_parallel_config should be a json format file!"
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)
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with open(auto_parallel_config, 'r') as robj:
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auto_parallel_data = json.loads(robj.read())
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self._tuner_run_mode = auto_parallel_data.get(
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"tuner_run_mode", 'tuner_and_run'
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)
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self.ctx.logger.info(f"tuner_run_mode is: {self._tuner_run_mode}")
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endpoint = f"127.0.0.1:{self.ctx.node.get_free_port()}"
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pod_replicas = self.pod_replicas()
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if self._tuner_run_mode in ['tuner_only', 'tuner_and_run']:
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e = {
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"PADDLE_AUTO_PARALLEL_CONFIG": self.ctx.args.auto_parallel_config,
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"PADDLE_TRAINERS_NUM": "1",
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"PADDLE_TRAINER_ENDPOINTS": endpoint,
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"PADDLE_TRAINER_ID": "0",
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"PADDLE_CURRENT_ENDPOINT": endpoint,
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"FLAGS_selected_gpus": "0",
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"PADDLE_AUTO_PARALLEL_STAGE": "tuner",
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"PADDLE_GLOBAL_SIZE": f"{pod_replicas * int(self.ctx.args.nnodes)}",
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"PADDLE_LOCAL_SIZE": f"{pod_replicas}",
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}
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log_file = "tuner.log"
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self.add_container(envs=e, log_file=log_file, is_init=True)
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if self._tuner_run_mode == 'tuner_only':
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return True
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return False
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def _build_pod_with_args(self):
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self.pod.replicas = self.pod_replicas()
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start_port = int(self.ctx.args.start_port)
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ips = self.ctx.args.ips.split(',')
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job_endpoints = [
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f"{h}:{p + start_port}"
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for h in ips
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for p in range(self.pod.replicas)
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]
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self.ctx.logger.debug(f"job endpoints: {job_endpoints}")
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self.ctx.logger.warning(
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f"master is set by args, it will be overwritten by {job_endpoints[0]}."
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)
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# this is necessary for tcp store to work when endpoints cannot be passed to sub processes.
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self.ctx.args.master = job_endpoints[0]
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rank_offset = (
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ips.index(self.ctx.node.ip) * self.pod.replicas
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if self.ctx.node.ip in ips
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else 0
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)
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self.save_pod_log(job_endpoints)
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selected_dev_key = self.ctx.node.device.get_selected_device_key()
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selected_dev_list = self.ctx.node.device.get_selected_devices(
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self.ctx.args.devices
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)
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for i in range(self.pod.replicas):
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e = {
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"PADDLE_MASTER": self.ctx.args.master,
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"PADDLE_GLOBAL_SIZE": f"{len(job_endpoints)}",
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"PADDLE_LOCAL_SIZE": f"{self.pod.replicas}",
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"PADDLE_GLOBAL_RANK": f"{i + rank_offset}",
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"PADDLE_LOCAL_RANK": f"{i}",
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"PADDLE_NNODES": f"{len(ips)}",
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# compatible env
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"PADDLE_CURRENT_ENDPOINT": job_endpoints[i + rank_offset],
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"PADDLE_TRAINER_ID": f"{i + rank_offset}",
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"PADDLE_TRAINERS_NUM": f"{len(job_endpoints)}",
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"PADDLE_RANK_IN_NODE": str(i),
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"PADDLE_AUTO_CLUSTER": str(self.ctx.args.auto_cluster_config),
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}
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e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)})
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if self._tuner_run_mode is not None:
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e.update(
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{
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"PADDLE_AUTO_PARALLEL_CONFIG": self.ctx.args.auto_parallel_config,
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"PADDLE_AUTO_PARALLEL_STAGE": "run",
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}
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)
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if len(selected_dev_list) > 0:
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if self.ctx.node.device.dtype == DeviceType.CUSTOM_DEVICE:
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e.update(self.ctx.node.device.get_custom_device_envs())
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if self.pod.replicas == 1:
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e.update({selected_dev_key: ",".join(selected_dev_list)})
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else:
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e.update({selected_dev_key: selected_dev_list[i]})
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else:
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e.update({'PADDLE_DISTRI_BACKEND': 'gloo'})
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log_file = f"workerlog.{i}"
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self.add_container(envs=e, log_file=log_file)
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return True
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def _build_pod_with_master(self, reset_pod=True):
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self.pod.replicas = self.pod_replicas()
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# rank will be reset when restart
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self.pod.rank = int(self.ctx.args.rank)
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port = self.ctx.node.get_free_port()
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# compatible
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endpoints = [
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f"{self.ctx.node.ip}:{p}"
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for p in self.ctx.node.get_free_ports(
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self.pod.replicas, self.pod.rank
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)
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]
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data = json.dumps(
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{
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'name': self.pod.name,
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'rank': self.pod.rank,
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'replicas': self.pod.replicas,
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'dtype': self.ctx.node.device.dtype,
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'candidate': f'{self.ctx.node.ip}:{port}',
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'endpoints': ",".join(endpoints),
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}
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)
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peer_list, rank = self.master.sync_peers(
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f'/{self.job.id}/info',
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self.pod.name,
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data,
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self.job.replicas,
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self.pod.rank,
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)
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self.pod.rank = rank
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if len(peer_list) < 1:
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return False
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peer_list = [json.loads(i) for i in peer_list]
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self.ctx.logger.debug(f"sync peers done {peer_list}")
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self.save_pod_log(peer_list)
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global_size = sum([i['replicas'] for i in peer_list])
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rank_offset = sum([i['replicas'] for i in peer_list[:rank]])
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'''
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The new designed collective need nothing but a master endpoint
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'''
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collective_master = peer_list[0]['candidate']
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# get collective master ip
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collective_master_ip = collective_master.split(':')[0].strip()
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os.environ["COLLECTIVE_MASTER_IP"] = collective_master_ip
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job_endpoints = [i['endpoints'] for i in peer_list]
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if reset_pod:
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self.pod.reset()
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selected_dev_key = self.ctx.node.device.get_selected_device_key()
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selected_dev_list = self.ctx.node.device.get_selected_devices(
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self.ctx.args.devices
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)
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for i in range(self.pod.replicas):
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e = {
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"PADDLE_MASTER": collective_master,
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"PADDLE_GLOBAL_SIZE": f"{global_size}",
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"PADDLE_LOCAL_SIZE": f"{self.pod.replicas}",
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"PADDLE_GLOBAL_RANK": f"{i + rank_offset}",
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"PADDLE_LOCAL_RANK": f"{i}",
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"PADDLE_NNODES": f"{self.job.replicas}",
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# compatible env
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"PADDLE_CURRENT_ENDPOINT": endpoints[i],
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"PADDLE_TRAINER_ID": f"{i + rank_offset}",
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"PADDLE_TRAINERS_NUM": f"{global_size}",
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"PADDLE_RANK_IN_NODE": str(i),
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"PADDLE_AUTO_CLUSTER": str(self.ctx.args.auto_cluster_config),
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}
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e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)})
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if self._tuner_run_mode is not None:
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e.update(
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{
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"PADDLE_AUTO_PARALLEL_CONFIG": self.ctx.args.auto_parallel_config,
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"PADDLE_AUTO_PARALLEL_STAGE": "run",
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}
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)
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if len(selected_dev_list) > 0:
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if self.ctx.node.device.dtype == DeviceType.CUSTOM_DEVICE:
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e.update(self.ctx.node.device.get_custom_device_envs())
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if self.pod.replicas == 1:
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e.update({selected_dev_key: ",".join(selected_dev_list)})
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else:
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e.update({selected_dev_key: selected_dev_list[i]})
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else:
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e.update({'PADDLE_DISTRI_BACKEND': 'gloo'})
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# log_file = "{}.{}.{}.log".format(self.job.id, self.pod.name, i)
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log_file = f"workerlog.{i}"
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self.add_container(envs=e, log_file=log_file)
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return True
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class CollectiveElasticController(CollectiveController):
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@classmethod
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def enable(cls, ctx):
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if ctx.args.master and ctx.args.master.startswith("etcd://"):
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ctx.logger.debug(f"{cls.__name__} enabled")
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ctx.args.run_mode = ControllerMode.COLLECTIVE
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return True
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else:
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return False
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def register(self):
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if self.job.id == 'default':
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self.ctx.logger.warning(
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'Using default job name may cause conflict, add --job_id in args'
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)
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self.master.register_heartbeat(self.job.id, self.pod.name)
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def run(self):
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timeout = int(self.ctx.args.elastic_timeout)
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timeout = timeout if self.job.elastic else timeout * 10
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self.register()
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while self.pod.restart <= self.ctx.args.max_restart:
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self.build_job()
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self.ctx.logger.info("Waiting peer ready...")
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ok, replicas = self.master.wait_peer_ready(
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self.job.replicas_min, self.job.replicas_max, timeout
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)
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if ok:
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self.job.replicas = replicas
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else:
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self.ctx.logger.warning(f"peer not ready {self.job}")
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if self.ctx.is_auto_tuner_mode():
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self.ctx.logger.info(
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"Failed to start peer, auto tuner exit."
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)
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import sys
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sys.exit(-1)
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break
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self.ctx.logger.debug(f"Run {self.job}")
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if not self.build_pod():
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continue
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self.master.set_status(self.ctx.status.RUNNING)
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self.deploy_pod()
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if self.watch():
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break
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self.ctx.logger.debug(f"Job done {self.job}")
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