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# Copyright (c) 2023, NVIDIA CORPORATION. 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.
# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
#
# 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.
"""
Example: python scripts/checkpoint_averaging/average_model_checkpoints.py \
--name_prefix=<checkpoint name> \
--checkpoint_dir=<folder with mp_rank_X subfolders containing checkpoints>
will generate a new file in each of the mp_rank_X subfolders named <checkpoint name>-averaged.ckpt
Typically you should follow up this script with a call to examples/nlp/language_modeling/megatron_ckpt_to_nemo.py
to convert .ckpt checkpoint to .nemo format.
"""
import argparse
import os
import torch
from nemo.utils import logging
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--name_prefix', help='Name of the final checkpoint. Will append -averaged.ckpt automatically.',
)
parser.add_argument(
'--checkpoint_dir', help='Folder containing all mp_rank_X subfolders.',
)
args = parser.parse_args()
# repeating for all ranks
for rank_dir in os.listdir(args.checkpoint_dir):
if not rank_dir.startswith('mp_rank_'):
continue
logging.info("Processing %s", rank_dir)
full_checkpoint_dir = os.path.join(args.checkpoint_dir, rank_dir)
checkpoint_paths = [
os.path.join(full_checkpoint_dir, x)
for x in os.listdir(full_checkpoint_dir)
if x.endswith('.ckpt') and not x.endswith('-last.ckpt')
]
# everything below is copied over from average_model_checkpoints.py
""" < Checkpoint Averaging Logic > """
# load state dicts
n = len(checkpoint_paths)
avg_state = None
logging.info(f"Averaging {n} checkpoints ...")
for ix, path in enumerate(checkpoint_paths):
checkpoint = torch.load(path, map_location='cpu')
if 'state_dict' in checkpoint:
checkpoint = checkpoint['state_dict']
if ix == 0:
# Initial state
avg_state = checkpoint
logging.info(f"Initialized average state dict with checkpoint : {path}")
else:
# Accumulated state
for k in avg_state:
avg_state[k] = avg_state[k] + checkpoint[k]
logging.info(f"Updated average state dict with state from checkpoint : {path}")
for k in avg_state:
if str(avg_state[k].dtype).startswith("torch.int"):
# For int type, not averaged, but only accumulated.
# e.g. BatchNorm.num_batches_tracked
pass
else:
avg_state[k] = avg_state[k] / n
# Save model
ckpt_name = os.path.join(full_checkpoint_dir, args.name_prefix + '-averaged.ckpt')
torch.save({'state_dict': avg_state}, ckpt_name)
logging.info(f"Averaged pytorch checkpoint saved as : {ckpt_name}")
if __name__ == '__main__':
main()
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