I am not hitting either of those error messages when using NCCL or GLOO for torch=1.8.0, both are working fine. Here is the script I am using which is based off of yours (with a dummy model):
import torch
import torch.distributed as dist
import torch.nn as nn
import numpy as np
import random
import argparse
import os

os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "29500"

parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--local_rank', metavar='N', type=int,
                    help='rank')
parser.add_argument('--seed', metavar='N', type=int,
                    help='seed')
args = parser.parse_args()
args.device="cuda:{}".format(args.local_rank)

model = nn.Linear(1, 1).to(args.device)

torch.distributed.init_process_group(backend='nccl')
seed = args.seed + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.cuda.set_device(dist.get_rank())

model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank])
model.to(args.device)

and I am launching with python3 -m torch.distributed.launch --nproc_per_node=2 test.py --seed=42
Can you provide more detail about the model you are using and your torch version?