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import os
import warnings
from typing import Any, List, Optional
from torch import distributed as dist
__all__ = [
"init",
"is_initialized",
"size",
"rank",
"local_size",
"local_rank",
"is_main",
"barrier",
"gather",
"all_gather",
]
def init() -> None:
if "RANK" not in os.environ:
warnings.warn("Environment variable `RANK` is not set. Skipping distributed initialization.")
return
dist.init_process_group(backend="nccl", init_method="env://")
def is_initialized() -> bool:
return dist.is_initialized()
def size() -> int:
return int(os.environ.get("WORLD_SIZE", 1))
def rank() -> int:
return int(os.environ.get("RANK", 0))
def local_size() -> int:
return int(os.environ.get("LOCAL_WORLD_SIZE", 1))
def local_rank() -> int:
return int(os.environ.get("LOCAL_RANK", 0))
def is_main() -> bool:
return rank() == 0
def barrier() -> None:
dist.barrier()
def gather(obj: Any, dst: int = 0) -> Optional[List[Any]]:
if not is_initialized():
return [obj]
if is_main():
objs = [None for _ in range(size())]
dist.gather_object(obj, objs, dst=dst)
return objs
else:
dist.gather_object(obj, dst=dst)
return None
def all_gather(obj: Any) -> List[Any]:
if not is_initialized():
return [obj]
objs = [None for _ in range(size())]
dist.all_gather_object(objs, obj)
return objs
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