Paddle/python/paddle/distributed/launch.py

# 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.
"""
paddle.distributed.launch is a module that spawns multiple distributed 
process on each training node for gpu training.
Usage:
    In both of single node training or multiple node training, this module 
launch a process on each of the given gpu card.
    1. for single node training with all visible gpu cards:
       python -m paddle.distributed.launch \
         your_training_py (arg1 arg2 and all others)
    
    2. for single node training with [0,4) cards
       python -m paddle.distributed.launch --selected_gpus="0,1,2,3" \
         your_training_py (arg1 arg2 and all others)
    3. for multiple node training such as two node:192.168.0.16, 192.168.0.17
        on 192.168.0.16:
            python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \
                --node_ip=192.168.0.16 \
                your_training_py (arg1 arg2 and all others)
        on 192.168.0.17:
            python -m paddle.distributed.launch --cluster_node_ips="192.168.0.16,192.168.0.17" \
                --node_ip=192.168.0.17 \
                your_training_py (arg1 arg2 and all others)
"""

from __future__ import print_function
import sys
from sys import version
import subprocess
import os
import time
import six
import copy
from argparse import ArgumentParser, REMAINDER

from paddle.distributed.utils import *
from paddle.distributed import cloud_utils


def _print_arguments(args):
    print("-----------  Configuration Arguments -----------")
    for arg, value in sorted(six.iteritems(vars(args))):
        print("%s: %s" % (arg, value))
    print("------------------------------------------------")


def _parse_args():
    """
    Helper function parsing the command line options
    @retval ArgumentParser
    """
    parser = ArgumentParser(
        description='''start paddle training using multi-process mode.
NOTE: your train program ***must*** run as distributed nccl2 mode,
see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
And your train program must read environment variables below in order to let different
process init properly:
FLAGS_selected_gpus
PADDLE_TRAINER_ID
PADDLE_CURRENT_ENDPOINT
PADDLE_TRAINERS_NUM
PADDLE_TRAINER_ENDPOINTS
POD_IP (current node ip address, not needed for local training)
''')

    #Optional arguments for the launch helper
    parser.add_argument(
        "--cluster_node_ips",
        type=str,
        default="127.0.0.1",
        help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..")
    parser.add_argument(
        "--node_ip",
        type=str,
        default="127.0.0.1",
        help="The current node ip. ")
    parser.add_argument(
        "--use_paddlecloud",
        action='store_true',
        help="wheter to use paddlecloud platform to run your multi-process job. If false, no need to set this argument."
    )
    parser.add_argument(
        "--started_port",
        type=int,
        default=None,
        help="The trainer's started port on a single node")

    parser.add_argument(
        "--print_config",
        type=bool,
        default=True,
        help="Print the config or not")

    parser.add_argument(
        "--selected_gpus",
        type=str,
        default=None,
        help="It's for gpu training and the training process will run on the selected_gpus,"
        "each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training."
    )

    parser.add_argument(
        "--log_level",
        type=int,
        default=20,  # logging.INFO, details are here:https://docs.python.org/3/library/logging.html#levels
        help="Logging level, default is logging.INFO")

    parser.add_argument(
        "--log_dir",
        type=str,
        help="The path for each process's log.If it's not set, the log will printed to default pipe."
    )

    #positional
    parser.add_argument(
        "training_script",
        type=str,
        help="The full path to the single GPU training "
        "program/script to be launched in parallel, "
        "followed by all the arguments for the "
        "training script")

    #rest from the training program
    parser.add_argument('training_script_args', nargs=REMAINDER)
    return parser.parse_args()


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 get_cluster_and_pod(args):
    # parse arguments, used for cloud-single-machine and local
    selected_gpus = get_gpus(args.selected_gpus)
    trainers_num = cloud_utils.get_trainers_num()
    logger.debug("parsed from args trainerss_num:{} selected_gpus:{}".format(
        trainers_num, selected_gpus))

    cluster = None
    pod = None

    if args.use_paddlecloud and trainers_num != 1:
        cluster, pod = cloud_utils.get_cloud_cluster(
            args.cluster_node_ips, args.node_ip, args.started_port,
            selected_gpus)
        logger.info("get cluster from cloud:{}".format(cluster))
    else:
        cluster, pod = get_cluster_from_args(args, selected_gpus)
        logger.info("get cluster from args:{}".format(cluster))

    return cluster, pod


def launch(args):
    cluster, pod = get_cluster_and_pod(args)

    procs = start_local_trainers(
        cluster,
        pod,
        training_script=args.training_script,
        training_script_args=args.training_script_args,
        log_dir=args.log_dir)

    while True:
        alive = watch_local_trainers(procs, cluster.trainers_nranks())

        if not alive:
            logger.info("Local procs complete, POD info:{}".format(pod))
            break

        time.sleep(3)


if __name__ == "__main__":
    args = _parse_args()

    logger = get_logger(args.log_level)

    if args.print_config:
        _print_arguments(args)

    launch(args)