mirror of https://github.com/hpcaitech/ColossalAI
144 lines
6.3 KiB
Python
144 lines
6.3 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 HhRlhfDataset, RmStaticDataset
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from chatgpt.models import LogSigLoss, LogExpLoss
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from chatgpt.models.base import RewardModel
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from chatgpt.models.bloom import BLOOMRM
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from chatgpt.models.gpt import GPTRM
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from chatgpt.models.opt import OPTRM
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from chatgpt.models.deberta import DebertaRM
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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 random import randint
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from torch.optim import Adam
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from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
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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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with strategy.model_init_context():
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if args.model == 'bloom':
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model = BLOOMRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
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elif args.model == 'opt':
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model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
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elif args.model == 'gpt2':
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model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
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elif args.model == 'deberta':
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model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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if args.model_path is not None:
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state_dict = torch.load(args.model_path)
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model.load_state_dict(state_dict)
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# configure tokenizer
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if args.model == 'gpt2':
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == 'bloom':
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tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m')
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elif args.model == 'opt':
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
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elif args.model == 'deberta':
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tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large')
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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max_len = args.max_len
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# configure optimizer
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if args.strategy.startswith('colossalai'):
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optim = HybridAdam(model.parameters(), lr=1.5e-5)
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else:
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optim = Adam(model.parameters(), lr=1.5e-5)
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# configure loss function
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if args.loss_fn == 'log_sig':
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loss_fn = LogSigLoss()
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elif args.loss_fn == 'log_exp':
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loss_fn = LogExpLoss()
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else:
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raise ValueError(f'Unsupported loss function "{args.loss_fn}"')
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# prepare for data and dataset
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if args.subset is not None:
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data = load_dataset(args.dataset, data_dir=args.subset)
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else:
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data = load_dataset(args.dataset)
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if args.test:
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train_data = data['train'].select(range(100))
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eval_data = data['test'].select(range(10))
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else:
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train_data = data['train']
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eval_data = data['test']
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valid_data = data['test'].select((randint(0, len(eval_data) - 1) for _ in range(len(eval_data)//10)))
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if args.dataset == 'Dahoas/rm-static':
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train_dataset = RmStaticDataset(train_data, tokenizer, max_len)
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valid_dataset = RmStaticDataset(valid_data, tokenizer, max_len)
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eval_dataset = RmStaticDataset(eval_data, tokenizer, max_len)
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elif args.dataset == 'Anthropic/hh-rlhf':
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train_dataset = HhRlhfDataset(train_data, tokenizer, max_len)
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valid_dataset = HhRlhfDataset(valid_data, tokenizer, max_len)
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eval_dataset = HhRlhfDataset(eval_data, tokenizer, max_len)
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else:
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raise ValueError(f'Unsupported dataset "{args.dataset}"')
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trainer = RewardModelTrainer(model=model,
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strategy=strategy,
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optim=optim,
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loss_fn = loss_fn,
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train_dataset=train_dataset,
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valid_dataset=valid_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()
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# save model checkpoint after fitting on only rank0
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strategy.save_model(trainer.model, args.save_path, only_rank0=True)
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# save optimizer checkpoint on all ranks
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if args.need_optim_ckpt:
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strategy.save_optimizer(trainer.optimizer, 'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()), only_rank0=False)
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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('--model', choices=['gpt2', 'bloom', 'opt', 'deberta'], default='bloom')
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parser.add_argument('--pretrain', type=str, default=None)
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parser.add_argument('--model_path', type=str, default=None)
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parser.add_argument('--need_optim_ckpt', type=bool, default=False)
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parser.add_argument('--dataset', type=str,
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choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'],
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default='Dahoas/rm-static')
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parser.add_argument('--subset', type=str, default=None)
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parser.add_argument('--save_path', type=str, default='rm_ckpt.pt')
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parser.add_argument('--max_epochs', type=int, default=1)
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parser.add_argument('--batch_size', type=int, default=1)
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parser.add_argument('--max_len', type=int, default=512)
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parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
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parser.add_argument('--loss_fn', type=str, default='log_sig', choices=['log_sig', 'log_exp'])
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parser.add_argument('--test', type=bool, default=False)
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args = parser.parse_args()
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train(args)
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