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"""
Helpers for distributed training.
"""
import io
import os
import socket
import blobfile as bf
import torch as th
import torch.distributed as dist
# Change this to reflect your cluster layout.
# The GPU for a given rank is (rank % GPUS_PER_NODE).
GPUS_PER_NODE = 1 # 8
SETUP_RETRY_COUNT = 3
def setup_dist(rank, world_size, port="12145"):
"""
Setup a distributed process group.
"""
if dist.is_initialized():
return
# comm = MPI.COMM_WORLD
# backend = "gloo" if not th.cuda.is_available() else "nccl"
# if backend == "gloo":
# hostname = "localhost"
# else:
# hostname = socket.gethostbyname(socket.getfqdn())
# os.environ["MASTER_ADDR"] = comm.bcast(hostname, root=0)
# os.environ["RANK"] = str(comm.rank)
# os.environ["WORLD_SIZE"] = str(comm.size)
# port = comm.bcast(_find_free_port(), root=0)
# os.environ["MASTER_PORT"] = str(port)
# dist.init_process_group(backend=backend, init_method="env://")
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = port
dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
# def dev():
# """
# Get the device to use for torch.distributed.
# """
# if th.cuda.is_available():
# return th.device(f"cuda:{MPI.COMM_WORLD.Get_rank() % GPUS_PER_NODE}")
# return th.device("cpu")
# def load_state_dict(path, **kwargs):
# """
# Load a PyTorch file without redundant fetches across MPI ranks.
# """
# if MPI.COMM_WORLD.Get_rank() == 0:
# with bf.BlobFile(path, "rb") as f:
# data = f.read()
# else:
# data = None
# data = MPI.COMM_WORLD.bcast(data)
# return th.load(io.BytesIO(data), **kwargs)
def sync_params(params):
"""
Synchronize a sequence of Tensors across ranks from rank 0.
"""
for p in params:
with th.no_grad():
dist.broadcast(p, 0)
def _find_free_port():
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("", 0))
s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
return s.getsockname()[1]
finally:
s.close()
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