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theblackcat102
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import re
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import yaml
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from torch.utils.data import Subset
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from sklearn.model_selection import train_test_split
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from transformers import AutoTokenizer
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re_reference_remove = re.compile(r'\[([0-9])+\]|\[([0-9])+,([0-9])+\]')
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def webgpt_return_format(row):
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if row['score_0'] >= row['score_1']:
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# remove this to prevent information leak, since we are not using reference
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return {
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'question': row['question']['full_text'],
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'pos': re_reference_remove.sub('', row['answer_0']),
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'neg': re_reference_remove.sub('', row['answer_1'])
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}
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return {
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'question': row['question']['full_text'],
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'pos': re_reference_remove.sub('', row['answer_1']),
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'neg': re_reference_remove.sub('', row['answer_0'])
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}
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def get_tokenizer(tokenizer_name):
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
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if 'galactica' in tokenizer_name:
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tokenizer.add_special_tokens({'pad_token':'<pad>', 'eos_token': '</s>' })
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return tokenizer
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def train_val_dataset(dataset, val_split=0.2):
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train_idx, val_idx = train_test_split(list(range(len(dataset))),
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test_size=val_split, random_state=666, shuffle=True)
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# [3879, 11479, 8341, 9177, 10798, 18177, 5735, 15669, 4837, 2760]
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print(val_idx[:10])
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# [13582, 5919, 11875, 7373, 19135, 13706, 8555, 15788, 15005, 15209]
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print(train_idx[:10])
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return Subset(dataset, train_idx), Subset(dataset, val_idx)
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def freeze_top_n_layers(model, target_layers):
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for name, param in model.named_parameters():
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if 'embed' in name:
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param.requires_grad = False
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elif '.layer' in name:
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tokens = name.split('.')
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idx = 0
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for token in tokens:
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if 'layer' in token:
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break
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idx += 1
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if idx >= len(tokens):
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continue
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layer_ = int(tokens[idx+1])
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if layer_ < target_layers:
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param.requires_grad = False
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return model
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def argument_parsing(parser):
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default_params = {
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'num_train_epochs': 4,
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'learning_rate': 3e-5,
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'eval_steps': 500,
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'loss': 'rank',
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'max_length': 440,
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'per_device_train_batch_size': 8,
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'gradient_accumulation_steps': 8,
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'gradient_checkpointing': False,
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'datasets': ['webgpt']
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}
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args = parser.parse_args()
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with open(args.config, 'r', encoding='utf-8') as f:
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training_conf = yaml.safe_load(f.read())
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params = { **default_params, **training_conf }
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params['gradient_accumulation_steps'] = int(params['gradient_accumulation_steps'])
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params['num_train_epochs'] = int(params['num_train_epochs'])
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params['per_device_train_batch_size'] = int(params['per_device_train_batch_size'])
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params['learning_rate'] = float(params['learning_rate'])
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return params
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