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from argparse import ArgumentParser
from multiprocessing import Pool
from requests import HTTPError
from transformers import AutoModel, AutoTokenizer
def get_args():
parser = ArgumentParser()
# --experiments bigscience/tr3d-1B3-oscar-checkpoints,bigscience/tr3e-1B3-c4-checkpoints,bigscience/tr3m-1B3-pile-checkpoints
parser.add_argument('--experiments', type=lambda s: s.split(','), required=True, help='Experiments we want to download.')
# --steps 19500,28500,37500,48000,57000,66000,76500,85500,94500,105000,114000
parser.add_argument('--steps', type=lambda s: [int(item) for item in s.split(',')], required=True, help='Steps we should download the model checkpoints')
return parser.parse_args()
def _load_model(pretrain:str, revision: str):
try:
AutoModel.from_pretrained(pretrain, revision=revision)
AutoTokenizer.from_pretrained(pretrain, revision=revision)
return f"Loaded: {{pretrain:{pretrain}, revision:{revision}}}"
except HTTPError:
return f"Failed to load: {{pretrain:{pretrain}, revision:{revision}}}"
def load_model(kwargs):
return _load_model(**kwargs)
def main():
args = get_args()
pretrains = args.experiments
steps = args.steps
revisions = [f"global_step{step}" for step in steps]
# with Pool(10) as pool:
# results = pool.imap(
# load_model,
# [{"pretrain": pretrain, "revision": revision} for pretrain in pretrains for revision in revisions],
# chunksize=1
# )
#
# for result in results:
# print(result)
for kwargs in [{"pretrain": pretrain, "revision": revision} for pretrain in pretrains for revision in revisions]:
print(load_model(kwargs))
if __name__ == "__main__":
main()
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