New design for launch/run

python/paddle/distributed/run/__init__.py

@@ -0,0 +1,86 @@
+# Copyright (c) 2022 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 .job.container import Container
+from .job.pod import Pod
+from .job.job import Job
+from . import plugins
+
+#__all__ = [Container, Pod, Job]
+'''
+Paddle distribution training entry ``python -m paddle.distributed.run``.
+
+Help
+
+# for arg usage and explanation, try the following command
+# python -m paddle.distributed.run -h
+
+Collective Mode
+
+Case 1: 1 node
+
+use all visible devices
+# python -m paddle.distributed.run train.py
+
+use specified devices
+# python -m paddle.distributed.run --devices=0,1,2,3 train.py
+
+Case 2: multi-node, auto detect ip/port
+
+# python -m paddle.distributed.run --np 2 train.py
+# auto print following command
+# python -m paddle.distributed.run --master 10.0.0.1:13538 --np 2 demo.py
+# then copy and paste above command to other nodes
+
+Case 3: multi-node, specified master/rendezvous server
+
+# python -m paddle.distributed.run --np 2 --master 10.0.0.1:2379 train.py
+# the master ip must be one of the node and the port must available
+
+Parameter Server Mode
+
+Case 1.1: 1 node, 1 ps, 1 trainer
+
+# python -m paddle.distributed.run --mode ps train.py
+# python -m paddle.distributed.run --server_num=1 --trainer_num=1 train.py
+
+Case 1.2: 1 node, 2 ps, 2 trainer
+
+# python -m paddle.distributed.run --server_num=2 --trainer_num=2 train.py
+
+Case 2: 2 node, 2 ps, 2 trainer per node
+
+# python -m paddle.distributed.run --server_num=2 --trainer_num=2 --np 2 train.py
+# auto print following command
+# python -m paddle.distributed.run --master 10.0.0.1:13538 --server_num=2 --trainer_num=2 --np 2 train.py
+# then copy and paste above command to other nodes
+
+Case 3: multi-node, specified master/rendezvous server
+
+# python -m paddle.distributed.run --master 10.0.0.1:13538 --server_num=2 --trainer_num=2 --np 2 train.py
+# the master ip must be one of the node and the port must available
+
+Case 4: specified servers and trainers in each node
+
+python -m paddle.distributed.run --servers 127.0.0.1:8900,127.0.0.1:8901 --trainers 127.0.0.1:8902,127.0.0.1:8903 train.py
+
+
+Elastic Mode
+
+# run following command in 3 node to run immediately, or in 2 node to run after elastic_timeout
+# python -m paddle.distributed.run --master etcd://10.0.0.1:2379 --np 2:3 train.py
+
+# once the peer number changes between 2:3, the strategy holds
+
+'''

python/paddle/distributed/run/__main__.py

@@ -0,0 +1,28 @@
+# Copyright (c) 2022 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 .context import Context
+from . import controllers
+
+# initialize the context to run
+ctx = Context()
+
+# initialize the selected controller
+c = controllers.init(ctx)
+
+# run the pods
+c.run()
+
+# manager or just wait pod
+c.finalize()

python/paddle/distributed/run/context/__init__.py

@@ -0,0 +1,219 @@
+# Copyright (c) 2022 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 argparse import ArgumentParser, REMAINDER
+import os, copy
+
+from paddle.distributed.run import plugins
+
+from .node import Node
+from .status import Status
+
+import logging
+
+
+class Context(object):
+    def __init__(self, enable_plugin=True):
+        os.environ.pop('http_proxy', None)
+        os.environ.pop('https_proxy', None)
+
+        self.args = self.parse_args()
+        self.envs = self.fetch_envs()
+        self.logger = self.get_logger()
+
+        self.node = Node()
+        self.status = Status()
+
+        self.set_env_in_args()
+
+        # design for event queue, later
+        self.events = []
+
+        if enable_plugin:
+            self._enable_plugin()
+
+    def get_envs(self):
+        return self.envs.copy()
+
+    def _enable_plugin(self):
+        for pl in plugins.enabled_plugins:
+            pl(self)
+
+    def parse_args(self):
+        parser = ArgumentParser()
+
+        base_group = parser.add_argument_group("Base Parameters")
+
+        base_group.add_argument(
+            "--master",
+            type=str,
+            default=None,
+            help="the master/rendezvous server, ip:port")
+
+        base_group.add_argument(
+            "--rank", type=int, default=-1, help="the peer rank")
+
+        base_group.add_argument(
+            "--log", type=str, default="INFO", help="log level. Default INFO")
+
+        base_group.add_argument(
+            "--np",
+            type=str,
+            default="1",
+            help="the number of peers, i.e. pod/node number")
+
+        base_group.add_argument(
+            "--nproc_per_node",
+            type=int,
+            default=None,
+            help="the number of processes in a pod")
+
+        base_group.add_argument(
+            "--log_dir",
+            type=str,
+            default="log",
+            help="the path for each process's log. Default ./log")
+        base_group.add_argument(
+            "--mode",
+            type=str,
+            default="collective",
+            help="run mode of the job, collective/ps/ps-heter")
+
+        base_group.add_argument(
+            "--id",
+            type=str,
+            default="default",
+            help="unique id of the job. Default default")
+
+        base_group.add_argument(
+            "--devices",
+            type=str,
+            default=None,
+            help="accelerate devices. as --gpus,npus,xps")
+
+        base_group.add_argument(
+            "--host", type=str, default=None, help="host ip")
+
+        base_group.add_argument(
+            "training_script",
+            type=str,
+            help="the full path of py script,"
+            "followed by arguments for the "
+            "training script")
+
+        base_group.add_argument('training_script_args', nargs=REMAINDER)
+
+        ps_group = parser.add_argument_group("Parameter-Server Parameters")
+        # for parameter server
+        ps_group.add_argument(
+            "--servers",
+            type=str,
+            default='',
+            help="servers endpoints full list")
+        ps_group.add_argument(
+            "--trainers",
+            type=str,
+            default='',
+            help="trainers endpoints full list")
+
+        ps_group.add_argument(
+            "--trainer_num", type=int, default=None, help="number of trainers")
+        ps_group.add_argument(
+            "--server_num", type=int, default=None, help="number of servers")
+        ps_group.add_argument(
+            "--gloo_port", type=int, default=6767, help="gloo http port")
+        ps_group.add_argument(
+            "--with_gloo", type=str, default="0", help="use gloo or not")
+
+        # parameter elastic mode
+        elastic_group = parser.add_argument_group("Elastic Parameters")
+        elastic_group.add_argument(
+            "--max_restart",
+            type=int,
+            default=3,
+            help="the times can restart. Default 3")
+
+        elastic_group.add_argument(
+            "--elastic_level",
+            type=int,
+            default=-1,
+            help="elastic level: -1 disable, 0 failed exit, peers hold, 1 internal restart"
+        )
+
+        elastic_group.add_argument(
+            "--elastic_timeout",
+            type=int,
+            default=30,
+            help="seconds to wait before elastic perform training")
+        return parser.parse_args()
+
+    def _valide_env(self, key):
+        if key in ['POD_IP']:
+            return True
+        if key.endswith('_VISIBLE_DEVICES'):
+            return True
+        if key.startswith('PADDLE_'):
+            return True
+
+        return False
+
+    def fetch_envs(self):
+        ge = os.environ.copy()
+
+        black_env_list = ['http_proxy', 'https_proxy']
+        for key in black_env_list:
+            ge.pop(key, None)
+
+        return ge
+        '''
+        # use black list instead white list
+        return {k: ge[k] for k in ge if self._valide_env(k)}
+        '''
+
+    def get_logger(self, level=logging.INFO):
+        logger = logging.getLogger("PADDLERUN")
+        logger.setLevel(self.args.log.upper() or level)
+        formatter = logging.Formatter(
+            fmt='%(name)s %(levelname)s %(asctime)s %(message)s')
+        ch = logging.StreamHandler()
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+        return logger
+
+    def set_env_in_args(self):
+        env_args = {
+            'POD_IP': 'host',
+            'PADDLE_MASTER': 'master',
+            'PADDLE_DEVICES': 'devices',
+            'PADDLE_NP': 'np',
+            'PADDLE_MODE': 'mode',
+            'PADDLE_LOG': 'log',
+            'PADDLE_NPROC_PER_NODE': 'nproc_per_node',
+            'PADDLE_JOB_ID': 'id',
+            'PADDLE_RANK': 'rank',
+            'PADDLE_LOG_DIR': 'log_dir',
+            'PADDLE_MAX_RESTlRT': 'max_restart',
+            'PADDLE_ELASTIC_LEVEL': 'elastic_level',
+            'PADDLE_ELASTIC_TIMEOUT': 'elastic_timeout',
+            'PADDLE_SERVER_NUM': 'server_num',
+            'PADDLE_TRAINER_NUM': 'trainer_num',
+            'PADDLE_SERVERS_ENDPOINTS': 'servers',
+            'PADDLE_TRAINERS_ENDPOINTS': 'trainers',
+            'PADDLE_GLOO_PORT': 'gloo_port',
+            'PADDLE_WITH_GLOO': 'with_gloo',
+        }
+
+        for k, v in env_args.items():
+            if k in self.envs:
+                setattr(self.args, v, self.envs[k])