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#!/usr/bin/env python3
# Owner(s): ["oncall: r2p"]
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import torch
import torch.distributed as dist
import torch.nn.functional as F
def parse_args():
parser = argparse.ArgumentParser(description="test script")
parser.add_argument(
"--init-method",
"--init_method",
type=str,
required=True,
help="init_method to pass to `dist.init_process_group()` (e.g. env://)",
)
parser.add_argument(
"--world-size",
"--world_size",
type=int,
default=os.getenv("WORLD_SIZE", -1),
help="world_size to pass to `dist.init_process_group()`",
)
parser.add_argument(
"--rank",
type=int,
default=os.getenv("RANK", -1),
help="rank to pass to `dist.init_process_group()`",
)
return parser.parse_args()
def main():
args = parse_args()
dist.init_process_group(
backend="gloo",
init_method=args.init_method,
world_size=args.world_size,
rank=args.rank,
)
rank = dist.get_rank()
world_size = dist.get_world_size()
# one hot (by rank) tensor of size world_size
# example:
# rank 0, world_size 4 => [1, 0, 0, 0]
# rank 1, world_size 4 => [0, 1, 0, 0]
# ...
t = F.one_hot(torch.tensor(rank), num_classes=world_size)
# after all_reduce t = tensor.ones(size=world_size)
dist.all_reduce(t)
# adding all elements in t should equal world_size
derived_world_size = torch.sum(t).item()
if derived_world_size != world_size:
raise RuntimeError(
f"Wrong world size derived. Expected: {world_size}, Got: {derived_world_size}"
)
print("Done")
if __name__ == "__main__":
main()
|
