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https://github.com/hpcaitech/ColossalAI.git
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613efebc5c
* [chatgpt]fix train_rm bug with lora * [chatgpt]support colossalai strategy to train rm * fix pre-commit * fix pre-commit 2
79 lines
3.0 KiB
Python
79 lines
3.0 KiB
Python
import argparse
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import loralib as lora
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import torch
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from chatgpt.dataset import RewardDataset
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from chatgpt.nn import BLOOMRM
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from chatgpt.trainer import RewardModelTrainer
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from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
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from datasets import load_dataset
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from torch.optim import Adam
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from transformers import BloomTokenizerFast
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from colossalai.nn.optimizer import HybridAdam
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def train(args):
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# configure strategy
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if args.strategy == 'naive':
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strategy = NaiveStrategy()
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elif args.strategy == 'ddp':
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strategy = DDPStrategy()
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elif args.strategy == 'colossalai_gemini':
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strategy = ColossalAIStrategy(stage=3, placement_policy='cuda')
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elif args.strategy == 'colossalai_zero2':
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strategy = ColossalAIStrategy(stage=2, placement_policy='cuda')
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else:
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raise ValueError(f'Unsupported strategy "{args.strategy}"')
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# configure model
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tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain)
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tokenizer.pad_token = tokenizer.eos_token
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model = BLOOMRM(pretrained=args.pretrain).cuda()
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max_len = 1024
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# configure optimizer
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if args.strategy.startswith('colossalai'):
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optim = HybridAdam(model.parameters(), lr=5e-5)
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else:
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optim = Adam(model.parameters(), lr=5e-5)
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# prepare for data and dataset
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data = load_dataset(args.dataset)
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train_data = data["train"].select(range(100))
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eval_data = data['test'].select(range(5))
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train_dataset = RewardDataset(train_data, tokenizer, max_len)
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eval_dataset = RewardDataset(eval_data, tokenizer, max_len)
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# batch_size here is expected to be C(k,2), k means # response of each prompt
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# be limited with the format of dataset 'Dahoas/rm-static', we'd better use batch_size as 1
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trainer = RewardModelTrainer(model=model,
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strategy=strategy,
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optim=optim,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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batch_size=args.batch_size,
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max_epochs=args.max_epochs)
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trainer.fit(use_lora=args.lora_rank)
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if args.lora_rank > 0:
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torch.save({'model_state_dict': lora.lora_state_dict(trainer.model)}, args.save_path)
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else:
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torch.save(trainer.model, args.save_path)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--strategy',
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choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
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default='naive')
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parser.add_argument('--pretrain', type=str, default=None)
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parser.add_argument('--dataset', type=str, default='Dahoas/rm-static')
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parser.add_argument('--save_path', type=str, default='rm_ckpt.pth')
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parser.add_argument('--max_epochs', type=int, default=2)
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parser.add_argument('--batch_size', type=int, default=1)
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parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
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args = parser.parse_args()
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train(args)
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