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# Copyright 2020 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" | |
A simple launcher script for TPU training | |
Inspired by https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py | |
:: | |
>>> python xla_spawn.py --num_cores=NUM_CORES_YOU_HAVE | |
YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other | |
arguments of your training script) | |
""" | |
import importlib | |
import sys | |
from argparse import REMAINDER, ArgumentParser | |
from pathlib import Path | |
import torch_xla.distributed.xla_multiprocessing as xmp | |
def parse_args(): | |
""" | |
Helper function parsing the command line options | |
@retval ArgumentParser | |
""" | |
parser = ArgumentParser( | |
description=( | |
"PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes" | |
) | |
) | |
# Optional arguments for the launch helper | |
parser.add_argument("--num_cores", type=int, default=1, help="Number of TPU cores to use (1 or 8).") | |
# positional | |
parser.add_argument( | |
"training_script", | |
type=str, | |
help=( | |
"The full path to the single TPU training " | |
"program/script to be launched in parallel, " | |
"followed by all the arguments for the " | |
"training script" | |
), | |
) | |
# rest from the training program | |
parser.add_argument("training_script_args", nargs=REMAINDER) | |
return parser.parse_args() | |
def main(): | |
args = parse_args() | |
# Import training_script as a module. | |
script_fpath = Path(args.training_script) | |
sys.path.append(str(script_fpath.parent.resolve())) | |
mod_name = script_fpath.stem | |
mod = importlib.import_module(mod_name) | |
# Patch sys.argv | |
sys.argv = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores)] | |
xmp.spawn(mod._mp_fn, args=(), nprocs=args.num_cores) | |
if __name__ == "__main__": | |
main() | |