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# This is from open_clip.
import os
import logging
import torch
import torch.distributed as dist
def is_global_master(args):
return args.rank == 0
def is_local_master(args):
return args.local_rank == 0
def is_master(args, local=False):
return is_local_master(args) if local else is_global_master(args)
def is_using_distributed():
if "WORLD_SIZE" in os.environ:
return int(os.environ["WORLD_SIZE"]) > 1
if "SLURM_NTASKS" in os.environ:
return int(os.environ["SLURM_NTASKS"]) > 1
return False
def world_info_from_env():
local_rank = 0
for v in (
"LOCAL_RANK",
"MPI_LOCALRANKID",
"SLURM_LOCALID",
"OMPI_COMM_WORLD_LOCAL_RANK",
):
if v in os.environ:
local_rank = int(os.environ[v])
break
global_rank = 0
for v in ("RANK", "PMI_RANK", "SLURM_PROCID", "OMPI_COMM_WORLD_RANK"):
if v in os.environ:
global_rank = int(os.environ[v])
break
world_size = 1
for v in ("WORLD_SIZE", "PMI_SIZE", "SLURM_NTASKS", "OMPI_COMM_WORLD_SIZE"):
if v in os.environ:
world_size = int(os.environ[v])
break
return local_rank, global_rank, world_size
def init_distributed_device(args):
# Distributed training = training on more than one GPU.
# Works in both single and multi-node scenarios.
args.distributed = False
args.world_size = 1
args.rank = 0 # global rank
args.local_rank = 0
# For testing, allow forcing distributed mode to test distributed code path even on one gpu.
if is_using_distributed() or args.force_distributed:
if "SLURM_PROCID" in os.environ:
# DDP via SLURM
args.local_rank, args.rank, env_world_size = world_info_from_env()
if args.preset_world_size is None:
args.world_size = env_world_size
else:
args.world_size = args.preset_world_size
if args.rank >= args.world_size:
logging.info(f"Rank {args.rank} not needed with world size {args.world_size}. Exiting.")
exit(0)
# SLURM var -> torch.distributed vars in case needed
os.environ["LOCAL_RANK"] = str(args.local_rank)
os.environ["RANK"] = str(args.rank)
os.environ["WORLD_SIZE"] = str(args.world_size)
torch.distributed.init_process_group(
backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank,
)
else:
# DDP via torchrun, torch.distributed.launch
# Note that this currently assumes that the world size is all gpus in a node.
assert args.preset_world_size is None, "--preset_world_size with torchrun is not currently supported."
args.local_rank, _, _ = world_info_from_env()
torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url)
args.world_size = torch.distributed.get_world_size()
args.rank = torch.distributed.get_rank()
args.distributed = True
if torch.cuda.is_available():
if args.distributed and not args.no_set_device_rank:
device = "cuda:%d" % args.local_rank
else:
device = "cuda:0"
torch.cuda.set_device(device)
else:
device = "cpu"
args.device = device
device = torch.device(device)
return device
def broadcast_object(args, obj, src=0):
if args.rank == src:
objects = [obj]
else:
objects = [None]
dist.broadcast_object_list(objects, src=src)
return objects[0]
def all_gather_object(args, obj, dst=0):
# gather a pickle-able python object across all ranks
objects = [None for _ in range(args.world_size)]
dist.all_gather_object(objects, obj)
return objects
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