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distributed.py
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distributed.py
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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE.txt file in the root directory of this source tree.
import logging
import multiprocessing as mp
from abc import ABC, abstractmethod
from datetime import timedelta
from typing import Callable, List, NamedTuple, Optional
import torch.distributed as td
import torch.multiprocessing
from torchbiggraph.types import Rank
from torchbiggraph.util import tag_logs_with_process_name
logger = logging.getLogger("torchbiggraph")
class ProcessRanks(NamedTuple):
"""Assign a unique ordinal rank to each process for distributed training.
torch.distributed requires that N communicating processes register
themselves with globally unique ranks [0, ..., N-1]. Distributed training
launches several communicating subprocesses on each machine. This class
manages the assignment from processes/subprocesses to ranks.
"""
world_size: int
trainers: List[Rank]
parameter_servers: List[Rank]
parameter_clients: List[Rank]
lock_server: Rank
partition_servers: List[Rank]
@classmethod
def from_num_invocations(
cls,
num_machines: int,
num_partition_servers: int,
) -> 'ProcessRanks':
world_size = 0
def add_group(group_size: int) -> List[Rank]:
nonlocal world_size
group = [Rank(world_size + r) for r in range(group_size)]
world_size += group_size
return group
trainers = add_group(num_machines)
parameter_servers = add_group(num_machines)
parameter_clients = add_group(num_machines)
lock_server, = add_group(1)
if num_partition_servers < 0:
# Use machines as partition servers
partition_servers = add_group(num_machines)
else:
partition_servers = add_group(num_partition_servers)
return cls(
world_size, trainers, parameter_servers,
parameter_clients, lock_server, partition_servers)
def init_process_group(
init_method: Optional[str],
world_size: int,
rank: Rank,
groups: List[List[Rank]],
backend: str = "gloo",
) -> List['td.ProcessGroup']:
# With the THD backend there were no timeouts so high variance in
# execution time between trainers was not a problem. With the new c10d
# implementation we do have to take timeouts into account. To simulate
# the old behavior we use a ridiculously high default timeout.
timeout = timedelta(days=365)
logger.info("init_process_group start")
if init_method is None:
raise RuntimeError("distributed_init_method must be set when num_machines > 1")
td.init_process_group(backend,
init_method=init_method,
world_size=world_size,
rank=rank,
timeout=timeout)
logger.info("init_process_group creating groups")
group_objs = []
for group in groups:
group_objs.append(td.new_group(group, timeout=timeout))
logger.info("init_process_group done")
return group_objs
class Startable(ABC):
@abstractmethod
def start(self) -> None:
pass
def _server_init(
server: Startable,
process_name: str,
init_method: Optional[str],
world_size: int,
server_rank: Rank,
groups: List[List[Rank]],
subprocess_init: Optional[Callable[[], None]] = None,
) -> None:
tag_logs_with_process_name(process_name)
if subprocess_init is not None:
subprocess_init()
init_process_group(
init_method=init_method,
world_size=world_size,
rank=server_rank,
groups=groups,
)
server.start()
def start_server(
server: Startable,
process_name: str,
init_method: Optional[str],
world_size: int,
server_rank: Rank,
groups: List[List[Rank]],
subprocess_init: Optional[Callable[[], None]] = None,
) -> mp.Process:
p = mp.get_context("spawn").Process(
name=process_name,
target=_server_init,
args=(
server,
process_name,
init_method,
world_size,
server_rank,
groups,
subprocess_init,
),
)
p.daemon = True
p.start()
return p