You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1004 lines
32 KiB
1004 lines
32 KiB
# 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.
|
|
"""Defination of Role Makers."""
|
|
|
|
from __future__ import print_function
|
|
import paddle.fluid as fluid
|
|
import os
|
|
import time
|
|
|
|
__all__ = [
|
|
'Role', 'RoleMakerBase', 'MPISymetricRoleMaker', 'UserDefinedRoleMaker',
|
|
'UserDefinedCollectiveRoleMaker', 'PaddleCloudRoleMaker', 'GeneralRoleMaker'
|
|
]
|
|
|
|
|
|
class Role:
|
|
WORKER = 1
|
|
SERVER = 2
|
|
|
|
|
|
class RoleMakerBase(object):
|
|
"""
|
|
RoleMakerBase is a base class for assigning a role to current process
|
|
in distributed training.
|
|
A paddle developer can implement RoleMakerBase to design a role maker
|
|
for worker or pserver assignment.
|
|
"""
|
|
|
|
def __init__(self):
|
|
self._worker_endpoints = []
|
|
self._server_endpoints = []
|
|
self._role_is_generated = False
|
|
self._role = None
|
|
self._current_id = -1
|
|
|
|
def is_worker(self):
|
|
"""
|
|
return is_worker() of current process
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def is_server(self):
|
|
"""
|
|
return is_server() of current process
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def is_first_worker(self):
|
|
"""
|
|
Check whether the node is the first instance of worker.
|
|
Returns:
|
|
bool: True if this is the first node of worker,
|
|
False if not.
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def worker_num(self):
|
|
"""
|
|
Get current total worker number.
|
|
|
|
Returns:
|
|
int: worker number
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def worker_index(self):
|
|
"""
|
|
Get current worker id.
|
|
|
|
Returns:
|
|
int: node id
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def server_index(self):
|
|
"""
|
|
Get current server id.
|
|
|
|
Returns:
|
|
int: node id
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
def get_trainer_endpoints(self):
|
|
"""
|
|
return trainer endpoints
|
|
"""
|
|
return self._worker_endpoints
|
|
|
|
def get_pserver_endpoints(self):
|
|
"""
|
|
return pserver endpoints
|
|
"""
|
|
return self._server_endpoints
|
|
|
|
def to_string(self):
|
|
return "role: {}, current_id: {}, worker_endpoints: {}, server_endpoints: {}".format(
|
|
self._role, self._current_id, self._worker_endpoints,
|
|
self._server_endpoints)
|
|
|
|
def all_gather(self, input):
|
|
"""
|
|
all gather between trainers and pservers
|
|
|
|
Args:
|
|
input(int|float): input value
|
|
|
|
Returns:
|
|
return a list of values
|
|
"""
|
|
print("warning: RoleMakerBase does not have all gather.")
|
|
return None
|
|
|
|
def all_reduce_worker(self, input, output, mode="sum"):
|
|
"""
|
|
all reduce between trainers if current role is TRAINER,
|
|
only support array of one dim.
|
|
|
|
Args:
|
|
input(list/numpy.array): array of one dim
|
|
output(list/numpy.array): array of one dim
|
|
mode(str): "sum" or "min" or "max"
|
|
"""
|
|
print("warning: RoleMakerBase does not have all reduce worker.")
|
|
|
|
def barrier_worker(self):
|
|
"""
|
|
barrier between trainers if current role is TRAINER
|
|
"""
|
|
print("warning: RoleMakerBase does not have barrier worker.")
|
|
|
|
def barrier_all(self):
|
|
"""
|
|
barrier between trainers if current role is PSERVER
|
|
"""
|
|
print("warning: RoleMakerBase does not have barrier all.")
|
|
|
|
|
|
class MPIRoleMaker(RoleMakerBase):
|
|
"""
|
|
MPIRoleMaker is a MPI-API based role maker which is a counter-part of K8SRoleMaker
|
|
mpi4py will be used if a developer inherits MPIRoleMaker
|
|
"""
|
|
|
|
def __init__(self):
|
|
"""Init."""
|
|
super(MPIRoleMaker, self).__init__()
|
|
from mpi4py import MPI
|
|
self.MPI = MPI
|
|
self._comm = MPI.COMM_WORLD
|
|
self._node_type_comm = None
|
|
self._ips = None
|
|
self._ip = None
|
|
|
|
def _get_rank(self):
|
|
"""Return rank."""
|
|
self._rank = self._comm.Get_rank()
|
|
return self._rank
|
|
|
|
def _get_size(self):
|
|
"""Return size."""
|
|
self._size = self._comm.Get_size()
|
|
return self._size
|
|
|
|
def _all_gather(self, obj):
|
|
"""
|
|
all_gather(obj) will call MPI's allgather function
|
|
"""
|
|
self._barrier_all()
|
|
return self._comm.allgather(obj)
|
|
|
|
def _worker_gather(self, obj):
|
|
"""
|
|
worker_gather(obj) will call MPI's allgather function
|
|
"""
|
|
if self.is_worker():
|
|
self._node_type_comm.barrier()
|
|
return self._node_type_comm.allgather(obj)
|
|
return None
|
|
|
|
def _barrier_all(self):
|
|
"""
|
|
barrier_all() will call MPI's barrier_all function
|
|
"""
|
|
self._comm.barrier()
|
|
|
|
def _finalize(self):
|
|
"""
|
|
finalize the current MPI instance.
|
|
"""
|
|
self.MPI.Finalize()
|
|
|
|
def _get_ips(self):
|
|
"""
|
|
collect current distributed job's ip list
|
|
"""
|
|
if not self._ips:
|
|
self._ips = self._comm.allgather(self.get_local_ip())
|
|
return self._ips
|
|
|
|
def get_local_ip(self):
|
|
"""Return get local ip."""
|
|
import socket
|
|
self._ip = socket.gethostbyname(socket.gethostname())
|
|
return self._ip
|
|
|
|
def generate_role(self):
|
|
"""
|
|
generate_role() should be called to identify current process's role
|
|
"""
|
|
raise NotImplementedError("Please implement this method in child class")
|
|
|
|
|
|
class MPISymetricRoleMaker(MPIRoleMaker):
|
|
"""
|
|
MPISymetricRoleMaker is designed for worker and server assignment
|
|
under MPI. Typically, a worker and a server node will be appointed
|
|
on each physical node. This role maker can be only used under MPI.
|
|
"""
|
|
|
|
def __init__(self):
|
|
"""Init."""
|
|
super(MPISymetricRoleMaker, self).__init__()
|
|
self._node_type = None
|
|
self._proc_per_node = 2
|
|
self._pserver_rand_port = 0
|
|
|
|
def _check_role_generation(self):
|
|
"""Check whether role has been generated."""
|
|
if not self._role_is_generated:
|
|
raise NameError("generate_role() should be called first")
|
|
return True
|
|
|
|
def all_gather(self, input):
|
|
"""
|
|
all gather between trainers and pservers
|
|
|
|
Args:
|
|
input(int|float): input value
|
|
|
|
Returns:
|
|
return a list of values
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._all_gather(input)
|
|
|
|
def all_reduce_worker(self, input, output, mode="sum"):
|
|
"""
|
|
all reduce between trainers if current role is TRAINER,
|
|
only support array of one dim.
|
|
|
|
Args:
|
|
input(list/numpy.array): array of one dim
|
|
output(list/numpy.array): array of one dim
|
|
mode(str): "sum" or "min" or "max"
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if not self.is_worker():
|
|
print("warning: current role is not worker in all_reduce_worker")
|
|
return
|
|
self._all_reduce(input, output, mode)
|
|
|
|
def barrier_worker(self):
|
|
"""
|
|
barrier between trainers if current role is TRAINER
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if self.is_worker():
|
|
self._node_type_comm.barrier()
|
|
else:
|
|
print("warning: current role is not worker in barrier_worker")
|
|
|
|
def barrier_all(self):
|
|
"""
|
|
barrier between trainers if current role is PSERVER
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
self._comm.barrier()
|
|
|
|
def is_first_worker(self):
|
|
"""
|
|
return whether current process is the first worker assigned by role maker
|
|
"""
|
|
if self._check_role_generation():
|
|
return self.is_worker() and 0 == self.worker_index()
|
|
return False
|
|
|
|
def get_pserver_endpoints(self):
|
|
"""
|
|
get pserver endpoints
|
|
|
|
Returns:
|
|
endpoints(list): pserver endpoints
|
|
"""
|
|
if self._pserver_rand_port <= 0:
|
|
import random
|
|
random.seed(self._server_num())
|
|
# port will be randomly generated from 60001 to 63999
|
|
# random seed is server num so that all nodes will get
|
|
# the same port
|
|
self._pserver_rand_port = random.randint(60001, 64000)
|
|
endpoints = [
|
|
x + ":" + str(self._pserver_rand_port)
|
|
for x in self._server_endpoints
|
|
]
|
|
return endpoints
|
|
|
|
def worker_num(self):
|
|
return self._worker_num()
|
|
|
|
def is_worker(self):
|
|
"""
|
|
return whether current process is worker assigned by role maker
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._node_type == 1
|
|
return False
|
|
|
|
def is_server(self):
|
|
"""
|
|
return whether current process is server assigned by role maker
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._node_type == 0
|
|
return False
|
|
|
|
def _worker_num(self):
|
|
"""
|
|
return the current number of worker
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._get_size() / self._proc_per_node
|
|
return 0
|
|
|
|
def _server_num(self):
|
|
"""
|
|
return the current number of server
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._get_size() / self._proc_per_node
|
|
else:
|
|
self.generate_role()
|
|
return self._get_size() / self._proc_per_node
|
|
|
|
def worker_index(self):
|
|
"""
|
|
return the index of worker
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._rank / self._proc_per_node
|
|
else:
|
|
self.generate_role()
|
|
return self._get_size() / 2
|
|
|
|
def server_index(self):
|
|
"""
|
|
return the index of server
|
|
"""
|
|
if self._check_role_generation():
|
|
return self._rank / self._proc_per_node
|
|
else:
|
|
self.generate_role()
|
|
return self._get_size() / self._proc_per_node
|
|
|
|
def _all_reduce(self, input, output, mode="sum"):
|
|
"""
|
|
all reduce between trainers if current role is TRAINER,
|
|
only support array of one dim.
|
|
|
|
Args:
|
|
input(list/numpy.array): array of one dim
|
|
output(list/numpy.array): array of one dim
|
|
mode(str): "sum" or "min" or "max"
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if mode == "sum":
|
|
mode = self.MPI.SUM
|
|
elif mode == "max":
|
|
mode = self.MPI.MAX
|
|
elif mode == "min":
|
|
mode = self.MPI.MIN
|
|
else:
|
|
raise ValueError("unknown mode: %s" % mode)
|
|
self._node_type_comm.Allreduce(input, output, op=mode)
|
|
|
|
def _barrier_worker(self):
|
|
"""
|
|
barrier all workers in current distributed job
|
|
"""
|
|
if self._check_role_generation():
|
|
if self.is_worker():
|
|
self._node_type_comm.barrier()
|
|
else:
|
|
raise Exception("You should check role generation first")
|
|
|
|
def _barrier_server(self):
|
|
"""
|
|
barrier all servers in current distributed job
|
|
"""
|
|
if self._check_role_generation():
|
|
if self.is_server():
|
|
self._node_type_comm.barrier()
|
|
else:
|
|
raise Exception("You should check role generation first")
|
|
|
|
def generate_role(self):
|
|
"""
|
|
generate currently process's role
|
|
"""
|
|
if not self._role_is_generated:
|
|
# TODO(guru4elephant): only allow to be called once
|
|
self._worker_endpoints = self._get_ips()[1::2]
|
|
self._server_endpoints = self._get_ips()[::2]
|
|
|
|
if 0 == self._get_rank() % self._proc_per_node % 2:
|
|
self._node_type = 0
|
|
else:
|
|
self._node_type = 1
|
|
self._node_type_comm = self._comm.Split(self._node_type)
|
|
self._role_is_generated = True
|
|
else:
|
|
raise Exception("You should check role generation first")
|
|
|
|
|
|
class PaddleCloudRoleMaker(RoleMakerBase):
|
|
"""
|
|
role maker for paddle cloud,
|
|
base class is RoleMakerBase
|
|
"""
|
|
|
|
def __init__(self, is_collective=False):
|
|
super(PaddleCloudRoleMaker, self).__init__()
|
|
self._role_is_generated = False
|
|
self._is_collective = is_collective
|
|
|
|
def generate_role(self):
|
|
"""Generate role."""
|
|
if not self._role_is_generated:
|
|
if not self._is_collective:
|
|
try:
|
|
# Environment variable PADDLE_PSERVERS_IP_PORT_LIST must be set
|
|
# format: string(ip:port), eg. 127.0.0.1:6001
|
|
eplist = os.environ["PADDLE_PSERVERS_IP_PORT_LIST"].split(
|
|
",")
|
|
# note that, we usually assign the same port to different ips
|
|
# if we run parameter server training in local mode
|
|
# port should be different in environment variables
|
|
|
|
trainers_num = int(os.environ["PADDLE_TRAINERS_NUM"])
|
|
training_role = os.environ["TRAINING_ROLE"]
|
|
|
|
if training_role not in ["TRAINER", "PSERVER"]:
|
|
raise ValueError(
|
|
"TRAINING_ROLE must be PSERVER or TRAINER")
|
|
|
|
if training_role == "TRAINER":
|
|
role = Role.WORKER
|
|
current_id = int(os.environ["PADDLE_TRAINER_ID"])
|
|
elif training_role == "PSERVER":
|
|
role = Role.SERVER
|
|
cur_ip = os.environ["POD_IP"]
|
|
curr_port = os.environ["PADDLE_PORT"]
|
|
curr_endpoint = ":".join([cur_ip, curr_port])
|
|
current_id = eplist.index(curr_endpoint)
|
|
else:
|
|
raise ValueError(
|
|
"TRAINING_ROLE must be PSERVER or TRAINER")
|
|
except ValueError as ve:
|
|
raise ValueError(
|
|
"something wrong with PaddleCloud, please check environment"
|
|
)
|
|
|
|
self._trainers_num = trainers_num
|
|
self._server_endpoints = eplist
|
|
self._role = role
|
|
self._current_id = current_id
|
|
else:
|
|
self._current_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
|
|
self._training_role = os.getenv("PADDLE_TRAINING_ROLE",
|
|
"TRAINER")
|
|
assert (self._training_role == "TRAINER")
|
|
self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
|
|
self._current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
|
|
assert self._worker_endpoints is not None, "can't find PADDLE_TRAINER_ENDPOINTS"
|
|
self._worker_endpoints = self._worker_endpoints.split(",")
|
|
self._trainers_num = len(self._worker_endpoints)
|
|
|
|
self._role_is_generated = True
|
|
|
|
def get_pserver_endpoints(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._server_endpoints
|
|
|
|
def is_worker(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.WORKER
|
|
|
|
def is_server(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.SERVER
|
|
|
|
def is_first_worker(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.WORKER and self._current_id == 0
|
|
|
|
def worker_index(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._current_id
|
|
|
|
def server_index(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._current_id
|
|
|
|
def worker_num(self):
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._trainers_num
|
|
|
|
|
|
class GeneralRoleMaker(RoleMakerBase):
|
|
"""
|
|
This role maker is for general use, you can set os.environ to customize:
|
|
PADDLE_PSERVERS_IP_PORT_LIST : all pservers' ip:port, seperated by ','
|
|
PADDLE_TRAINER_ENDPOINTS : all trainers' ip:port, seperated by ','
|
|
TRAINING_ROLE : TRAINER or PSERVER
|
|
PADDLE_TRAINER_ID : current trainer id (only for trainer),
|
|
it is index in PADDLE_TRAINER_ENDPOINTS
|
|
PADDLE_PSERVER_ID : current pserver id (only for pserver)
|
|
it is index in PADDLE_PSERVERS_IP_PORT_LIST
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
super(RoleMakerBase, self).__init__()
|
|
self._role_is_generated = False
|
|
self._hdfs_name = kwargs.get("hdfs_name", "")
|
|
self._hdfs_ugi = kwargs.get("hdfs_ugi", "")
|
|
self._hdfs_path = kwargs.get("path", "")
|
|
self._iface = self.__get_default_iface()
|
|
# this environment variable can be empty
|
|
self._prefix = os.getenv("SYS_JOB_ID", "")
|
|
|
|
def generate_role(self):
|
|
"""
|
|
generate role for general role maker
|
|
"""
|
|
if not self._role_is_generated:
|
|
eplist = os.environ["PADDLE_PSERVERS_IP_PORT_LIST"].split(",")
|
|
training_role = os.environ["TRAINING_ROLE"]
|
|
worker_endpoints = os.environ["PADDLE_TRAINER_ENDPOINTS"].split(",")
|
|
trainers_num = len(worker_endpoints)
|
|
if training_role not in ["TRAINER", "PSERVER"]:
|
|
raise ValueError("TRAINING_ROLE must be PSERVER or TRAINER")
|
|
if training_role == "TRAINER":
|
|
role = Role.WORKER
|
|
current_id = int(os.environ["PADDLE_TRAINER_ID"])
|
|
self._node_type = 1
|
|
self._cur_endpoint = worker_endpoints[current_id]
|
|
gloo = fluid.core.Gloo()
|
|
gloo.init(current_id,
|
|
len(worker_endpoints),
|
|
self._hdfs_path.rstrip("/") + "/trainer",
|
|
self._hdfs_name, self._hdfs_ugi, self._iface,
|
|
self._prefix)
|
|
self._node_type_comm = gloo
|
|
elif training_role == "PSERVER":
|
|
role = Role.SERVER
|
|
if os.environ.get("PADDLE_PSERVER_ID") is not None:
|
|
current_id = int(os.environ["PADDLE_PSERVER_ID"])
|
|
cur_endpoint = eplist[current_id]
|
|
else:
|
|
# this is for compatible with paddlecloud
|
|
cur_ip = os.environ["POD_IP"]
|
|
cur_port = os.environ["PADDLE_PORT"]
|
|
cur_endpoint = ":".join([cur_ip, cur_port])
|
|
current_id = eplist.index(cur_endpoint)
|
|
self._node_type = 0
|
|
self._cur_endpoint = cur_endpoint
|
|
gloo = fluid.core.Gloo()
|
|
gloo.init(current_id,
|
|
len(eplist),
|
|
self._hdfs_path.rstrip("/") + "/pserver",
|
|
self._hdfs_name, self._hdfs_ugi, self._iface,
|
|
self._prefix)
|
|
self._node_type_comm = gloo
|
|
|
|
gloo = fluid.core.Gloo()
|
|
all_list = worker_endpoints + eplist
|
|
gloo.init(
|
|
all_list.index(self._cur_endpoint),
|
|
len(all_list),
|
|
self._hdfs_path.rstrip("/") + "/all", self._hdfs_name,
|
|
self._hdfs_ugi, self._iface, self._prefix)
|
|
self._all_comm = gloo
|
|
self._trainers_num = trainers_num
|
|
self._server_endpoints = eplist
|
|
self._role = role
|
|
self._current_id = current_id
|
|
self._rank = all_list.index(self._cur_endpoint)
|
|
self._size = len(all_list)
|
|
self._worker_endpoints = worker_endpoints
|
|
self._role_is_generated = True
|
|
|
|
def all_gather(self, input):
|
|
"""
|
|
all gather between trainers and pservers
|
|
|
|
Args:
|
|
input(int|float): input value
|
|
|
|
Returns:
|
|
return a list of values
|
|
"""
|
|
return self._all_gather(input)
|
|
|
|
def all_reduce_worker(self, input, output, mode="sum"):
|
|
"""
|
|
all reduce between trainers if current role is TRAINER,
|
|
only support array of one dim.
|
|
|
|
Args:
|
|
input(list/numpy.array): array of one dim
|
|
output(list/numpy.array): array of one dim
|
|
mode(str): "sum" or "min" or "max"
|
|
"""
|
|
if not self.is_worker():
|
|
return
|
|
self._all_reduce(input, output, mode)
|
|
|
|
def barrier_worker(self):
|
|
"""
|
|
barrier between trainers if current role is TRAINER
|
|
"""
|
|
self._barrier_worker()
|
|
|
|
def barrier_all(self):
|
|
"""
|
|
barrier between trainers if current role is PSERVER
|
|
"""
|
|
self._barrier_all()
|
|
|
|
def get_local_endpoint(self):
|
|
"""
|
|
get local endpoint of current process
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._cur_endpoint
|
|
|
|
def get_trainer_endpoints(self):
|
|
"""
|
|
get endpoint of all trainers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._worker_endpoints
|
|
|
|
def get_pserver_endpoints(self):
|
|
"""
|
|
get endpoint of all pservers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._server_endpoints
|
|
|
|
def is_worker(self):
|
|
"""
|
|
whether current process is worker
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.WORKER
|
|
|
|
def is_server(self):
|
|
"""
|
|
whether current process is server
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.SERVER
|
|
|
|
def is_first_worker(self):
|
|
"""
|
|
whether current process is worker of rank 0
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._role == Role.WORKER and self._current_id == 0
|
|
|
|
def worker_index(self):
|
|
"""
|
|
get index of current worker
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._current_id
|
|
|
|
def server_index(self):
|
|
"""
|
|
get index of current server
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._current_id
|
|
|
|
def worker_num(self):
|
|
"""
|
|
retrun the current number of worker
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._worker_num()
|
|
|
|
def server_num(self):
|
|
"""
|
|
return the current number of server
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._server_num()
|
|
|
|
def _barrier_worker(self):
|
|
"""
|
|
barrier all workers in current distributed job
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if self.is_worker():
|
|
self._node_type_comm.barrier()
|
|
|
|
def _barrier_all(self):
|
|
"""
|
|
barrier all workers and servers in current distributed job
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
self._all_comm.barrier()
|
|
|
|
def _barrier_server(self):
|
|
"""
|
|
barrier all servers in current distributed job
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if self.is_server():
|
|
self._node_type_comm.barrier()
|
|
|
|
def _worker_num(self):
|
|
"""
|
|
return the current number of worker
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._trainers_num
|
|
|
|
def _server_num(self):
|
|
"""
|
|
return the current number of server
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return len(self._server_endpoints)
|
|
|
|
def _finalize(self):
|
|
"""Default do nothing."""
|
|
pass
|
|
|
|
def _all_reduce(self, input, output, mode="sum"):
|
|
"""
|
|
all reduce between all workers
|
|
|
|
Args:
|
|
input(list|numpy.array): array of one dim
|
|
output(list|numpy.array): array of one dim
|
|
mode(str): "sum" or "min" or "max"
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
input_list = [i for i in input]
|
|
ans = self._node_type_comm.all_reduce(input_list, mode)
|
|
for i in range(len(ans)):
|
|
output[i] = ans[i]
|
|
|
|
def _all_gather(self, obj):
|
|
"""
|
|
gather between all workers and pservers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
self._barrier_all()
|
|
return self._all_comm.all_gather(obj)
|
|
|
|
def _worker_gather(self, obj):
|
|
"""
|
|
gather between all workers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
if not self.is_worker():
|
|
return None
|
|
self._barrier_worker()
|
|
return self._node_type_comm.all_gather(obj)
|
|
|
|
def _get_rank(self):
|
|
"""
|
|
get current rank in all workers and pservers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._rank
|
|
|
|
def _get_size(self):
|
|
"""
|
|
get total num of all workers and pservers
|
|
"""
|
|
if not self._role_is_generated:
|
|
self.generate_role()
|
|
return self._size
|
|
|
|
def __get_default_iface(self):
|
|
"""
|
|
get default physical interface
|
|
"""
|
|
default1 = self.__get_default_iface_from_gateway()
|
|
default2 = self.__get_default_iface_from_interfaces()
|
|
return default2 if default1 == "lo" else default1
|
|
|
|
def __get_default_iface_from_gateway(self):
|
|
"""
|
|
get default physical interface
|
|
"""
|
|
import netifaces
|
|
gateways = netifaces.gateways()
|
|
if gateways.get(netifaces.AF_INET) != None:
|
|
gateway = gateways[netifaces.AF_INET]
|
|
if len(gateway) > 0 and len(gateway[0]) > 1:
|
|
return gateway[0][1]
|
|
return "lo"
|
|
|
|
def __get_default_iface_from_interfaces(self):
|
|
"""
|
|
get default physical interface
|
|
"""
|
|
import netifaces
|
|
for intf_name in netifaces.interfaces():
|
|
addresses = netifaces.ifaddresses(intf_name)
|
|
if netifaces.AF_INET in addresses:
|
|
ipv4_addresses = addresses[netifaces.AF_INET]
|
|
for ipv4_address in ipv4_addresses:
|
|
if 'broadcast' in ipv4_address:
|
|
return intf_name
|
|
return "lo"
|
|
|
|
|
|
class UserDefinedRoleMaker(RoleMakerBase):
|
|
"""
|
|
UserDefinedRoleMaker is designed for worker and server assignment
|
|
under manual. Typically, a worker and a server node will be appointed
|
|
on each physical node, It can be assign by user.
|
|
"""
|
|
|
|
def __init__(self,
|
|
current_id=0,
|
|
role=Role.WORKER,
|
|
worker_num=0,
|
|
server_endpoints=None):
|
|
super(UserDefinedRoleMaker, self).__init__()
|
|
|
|
if not isinstance(server_endpoints, list):
|
|
raise TypeError("server_endpoints must be as string list")
|
|
elif len(server_endpoints) <= 0:
|
|
raise ValueError(
|
|
"the length of server_endpoints list must be greater than 0")
|
|
elif len(server_endpoints) != len(set(server_endpoints)):
|
|
raise ValueError("server_endpoints can't have duplicate elements")
|
|
else:
|
|
for server_endpoint in server_endpoints:
|
|
if not isinstance(server_endpoint, str):
|
|
raise TypeError(
|
|
"every element in server_endpoints list must be as string"
|
|
)
|
|
self._server_endpoints = server_endpoints
|
|
|
|
if role != Role.WORKER and role != Role.SERVER:
|
|
raise TypeError("role must be as Role")
|
|
else:
|
|
self._role = role
|
|
|
|
if not isinstance(current_id, int):
|
|
raise TypeError("current_id must be as int")
|
|
else:
|
|
if current_id < 0:
|
|
raise ValueError(
|
|
"current_id must be greater than or equal to 0")
|
|
elif self._role == Role.SERVER and current_id >= len(
|
|
server_endpoints):
|
|
raise ValueError(
|
|
"if role is Role.SERVER, current_id must be less than or equal to len(server_endpoints) - 1"
|
|
)
|
|
self._current_id = current_id
|
|
|
|
if not isinstance(worker_num, int):
|
|
raise TypeError("worker_num must be as int")
|
|
else:
|
|
if worker_num <= 0:
|
|
raise ValueError("worker_num must be greater than 0")
|
|
self._worker_num = worker_num
|
|
|
|
def generate_role(self):
|
|
self._role_is_generated = True
|
|
|
|
def is_worker(self):
|
|
return self._role == Role.WORKER
|
|
|
|
def is_server(self):
|
|
return self._role == Role.SERVER
|
|
|
|
def is_first_worker(self):
|
|
return self._role == Role.WORKER and self._current_id == 0
|
|
|
|
def worker_index(self):
|
|
return self._current_id
|
|
|
|
def server_index(self):
|
|
return self._current_id
|
|
|
|
def worker_num(self):
|
|
return self._worker_num
|
|
|
|
|
|
class UserDefinedCollectiveRoleMaker(RoleMakerBase):
|
|
"""
|
|
UserDefinedCollectiveRoleMaker is designed for worker assignment
|
|
under manual for collective mode.
|
|
"""
|
|
|
|
def __init__(self, current_id=0, worker_endpoints=None):
|
|
super(UserDefinedCollectiveRoleMaker, self).__init__()
|
|
|
|
if not isinstance(worker_endpoints, list):
|
|
raise TypeError("worker_endpoints must be as string list")
|
|
elif len(worker_endpoints) <= 0:
|
|
raise ValueError(
|
|
"the length of worker_endpoints list must be greater than 0")
|
|
elif len(worker_endpoints) != len(set(worker_endpoints)):
|
|
raise ValueError("worker_endpoints can't have duplicate elements")
|
|
else:
|
|
for worker_endpoint in worker_endpoints:
|
|
if not isinstance(worker_endpoint, str):
|
|
raise TypeError(
|
|
"every element in worker_endpoints list must be as string"
|
|
)
|
|
self._worker_endpoints = worker_endpoints
|
|
|
|
if not isinstance(current_id, int):
|
|
raise TypeError("current_id must be as int")
|
|
else:
|
|
if current_id < 0:
|
|
raise ValueError(
|
|
"current_id must be greater than or equal to 0")
|
|
elif current_id >= len(worker_endpoints):
|
|
raise ValueError(
|
|
"current_id must be less than or equal to len(worker_endpoints) - 1"
|
|
)
|
|
self._current_id = current_id
|
|
|
|
self._worker_num = len(self._worker_endpoints)
|
|
|
|
def generate_role(self):
|
|
self._role_is_generated = True
|
|
|
|
def is_worker(self):
|
|
return True
|
|
|
|
def is_first_worker(self):
|
|
return self._current_id == 0
|
|
|
|
def worker_index(self):
|
|
return self._current_id
|
|
|
|
def worker_num(self):
|
|
return self._worker_num
|