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on
Zero
Running
on
Zero
| # Copyright (c) 2020 Mobvoi Inc (Di Wu) | |
| # Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu) | |
| # | |
| # 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. | |
| import os | |
| import argparse | |
| import glob | |
| import yaml | |
| import torch | |
| def get_args(): | |
| parser = argparse.ArgumentParser(description='average model') | |
| parser.add_argument('--dst_model', required=True, help='averaged model') | |
| parser.add_argument('--src_path', | |
| required=True, | |
| help='src model path for average') | |
| parser.add_argument('--val_best', | |
| action="store_true", | |
| help='averaged model') | |
| parser.add_argument('--num', | |
| default=5, | |
| type=int, | |
| help='nums for averaged model') | |
| args = parser.parse_args() | |
| print(args) | |
| return args | |
| def main(): | |
| args = get_args() | |
| val_scores = [] | |
| if args.val_best: | |
| yamls = glob.glob('{}/*.yaml'.format(args.src_path)) | |
| yamls = [ | |
| f for f in yamls | |
| if not (os.path.basename(f).startswith('train') | |
| or os.path.basename(f).startswith('init')) | |
| ] | |
| for y in yamls: | |
| with open(y, 'r') as f: | |
| dic_yaml = yaml.load(f, Loader=yaml.BaseLoader) | |
| loss = float(dic_yaml['loss_dict']['loss']) | |
| epoch = int(dic_yaml['epoch']) | |
| step = int(dic_yaml['step']) | |
| tag = dic_yaml['tag'] | |
| val_scores += [[epoch, step, loss, tag]] | |
| sorted_val_scores = sorted(val_scores, | |
| key=lambda x: x[2], | |
| reverse=False) | |
| print("best val (epoch, step, loss, tag) = " + | |
| str(sorted_val_scores[:args.num])) | |
| path_list = [ | |
| args.src_path + '/epoch_{}_whole.pt'.format(score[0]) | |
| for score in sorted_val_scores[:args.num] | |
| ] | |
| print(path_list) | |
| avg = {} | |
| num = args.num | |
| assert num == len(path_list) | |
| for path in path_list: | |
| print('Processing {}'.format(path)) | |
| states = torch.load(path, map_location=torch.device('cpu')) | |
| for k in states.keys(): | |
| if k not in ['step', 'epoch']: | |
| if k not in avg.keys(): | |
| avg[k] = states[k].clone() | |
| else: | |
| avg[k] += states[k] | |
| # average | |
| for k in avg.keys(): | |
| if avg[k] is not None: | |
| # pytorch 1.6 use true_divide instead of /= | |
| avg[k] = torch.true_divide(avg[k], num) | |
| print('Saving to {}'.format(args.dst_model)) | |
| torch.save(avg, args.dst_model) | |
| if __name__ == '__main__': | |
| main() | |