train_reward_model.py 9.61 KB
Newer Older
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
1
2
3
4
5
import argparse
from random import randint

import loralib as lora
import torch
6
import torch.distributed as dist
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
7
8
9
10
11
12
13
14
from coati.dataset import HhRlhfDataset, RmStaticDataset
from coati.models import LogExpLoss, LogSigLoss
from coati.models.base import RewardModel
from coati.models.bloom import BLOOMRM
from coati.models.deberta import DebertaRM
from coati.models.gpt import GPTRM
from coati.models.llama import LlamaRM
from coati.models.opt import OPTRM
15
from coati.models.roberta import RoBERTaRM
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
16
from coati.trainer import RewardModelTrainer
17
from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
18
19
20
from coati.utils import prepare_llama_tokenizer_and_embedding
from datasets import load_dataset
from torch.optim import Adam
21
from torch.optim.lr_scheduler import CosineAnnealingLR
22
23
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
24
from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
25
26
27
28
29
30
31
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer

from colossalai.nn.optimizer import HybridAdam


def train(args):
    # configure strategy
32
    if args.strategy == 'ddp':
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
33
34
        strategy = DDPStrategy()
    elif args.strategy == 'colossalai_gemini':
35
        strategy = GeminiStrategy(placement_policy='cuda')
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
36
    elif args.strategy == 'colossalai_zero2':
37
        strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda')
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
    else:
        raise ValueError(f'Unsupported strategy "{args.strategy}"')

    # configure model
    with strategy.model_init_context():
        if args.model == 'bloom':
            model = BLOOMRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'opt':
            model = OPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'gpt2':
            model = GPTRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'deberta':
            model = DebertaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'llama':
            model = LlamaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
53
54
        elif args.model == 'roberta':
            model = RoBERTaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
        else:
            raise ValueError(f'Unsupported model "{args.model}"')

        if args.model_path is not None:
            state_dict = torch.load(args.model_path)
            model.load_state_dict(state_dict)

    model = model.to(torch.float16)

    # configure tokenizer
    if args.model == 'gpt2':
        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
    elif args.model == 'bloom':
        tokenizer = BloomTokenizerFast.from_pretrained('bigscience/bloom-560m')
    elif args.model == 'opt':
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
    elif args.model == 'deberta':
        tokenizer = DebertaV2Tokenizer.from_pretrained('microsoft/deberta-v3-large')
    elif args.model == 'llama':
        tokenizer = LlamaTokenizer.from_pretrained(args.pretrain)
75
76
    elif args.model == 'roberta':
        tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
    else:
        raise ValueError(f'Unsupported model "{args.model}"')
    max_len = args.max_len

    if args.model == 'llama':
        tokenizer = prepare_llama_tokenizer_and_embedding(tokenizer, model)
    else:
        tokenizer.pad_token = tokenizer.eos_token

    # configure optimizer
    if args.strategy.startswith('colossalai'):
        optim = HybridAdam(model.parameters(), lr=5e-6)
    else:
        optim = Adam(model.parameters(), lr=5e-6)

    # configure loss function
    if args.loss_fn == 'log_sig':
        loss_fn = LogSigLoss()
    elif args.loss_fn == 'log_exp':
        loss_fn = LogExpLoss()
    else:
        raise ValueError(f'Unsupported loss function "{args.loss_fn}"')

    # prepare for data and dataset
    if args.subset is not None:
        data = load_dataset(args.dataset, data_dir=args.subset)
    else:
        data = load_dataset(args.dataset)

    if args.test:
        train_data = data['train'].select(range(100))
        eval_data = data['test'].select(range(10))
    else:
        train_data = data['train']
        eval_data = data['test']
    valid_data = data['test'].select((randint(0, len(eval_data) - 1) for _ in range(len(eval_data) // 5)))

    if args.dataset == 'Dahoas/rm-static':
        train_dataset = RmStaticDataset(train_data, tokenizer, max_len)
        valid_dataset = RmStaticDataset(valid_data, tokenizer, max_len)
        eval_dataset = RmStaticDataset(eval_data, tokenizer, max_len)
    elif args.dataset == 'Anthropic/hh-rlhf':
        train_dataset = HhRlhfDataset(train_data, tokenizer, max_len)
        valid_dataset = HhRlhfDataset(valid_data, tokenizer, max_len)
        eval_dataset = HhRlhfDataset(eval_data, tokenizer, max_len)
    else:
        raise ValueError(f'Unsupported dataset "{args.dataset}"')

125
    if dist.is_initialized() and dist.get_world_size() > 1:
126
127
128
129
130
        train_sampler = DistributedSampler(train_dataset,
                                           shuffle=True,
                                           seed=42,
                                           drop_last=True,
                                           rank=dist.get_rank(),
131
                                           num_replicas=dist.get_world_size())
132
133
134
135
136
        valid_sampler = DistributedSampler(valid_dataset,
                                           shuffle=True,
                                           seed=42,
                                           drop_last=True,
                                           rank=dist.get_rank(),
137
                                           num_replicas=dist.get_world_size())
138
139
140
141
142
        eval_sampler = DistributedSampler(eval_dataset,
                                          shuffle=True,
                                          seed=42,
                                          drop_last=True,
                                          rank=dist.get_rank(),
143
144
145
146
147
148
149
150
151
152
153
154
                                          num_replicas=dist.get_world_size())
    else:
        train_sampler = None
        valid_sampler = None
        eval_sampler = None

    train_dataloader = DataLoader(train_dataset,
                                  shuffle=(train_sampler is None),
                                  sampler=train_sampler,
                                  batch_size=args.batch_size,
                                  pin_memory=True)

155
156
    valid_dataloader = DataLoader(valid_dataset,
                                  shuffle=(valid_sampler is None),
157
                                  sampler=valid_sampler,
158
159
                                  batch_size=args.batch_size,
                                  pin_memory=True)
160

161
162
163
164
165
    eval_dataloader = DataLoader(eval_dataset,
                                 shuffle=(eval_sampler is None),
                                 sampler=eval_sampler,
                                 batch_size=args.batch_size,
                                 pin_memory=True)
166

167
168
169
170
171
172
173
    lr_scheduler = CosineAnnealingLR(optim, train_dataloader.__len__() // 100)
    strategy_dict = strategy.prepare(
        dict(model=model, optimizer=optim, lr_scheduler=lr_scheduler)
    )
    model = strategy_dict['model']
    optim = strategy_dict['optimizer']
    lr_scheduler = strategy_dict['lr_scheduler']
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
174
175
176
    trainer = RewardModelTrainer(model=model,
                                 strategy=strategy,
                                 optim=optim,
177
                                 lr_scheduler=lr_scheduler,
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
178
179
180
                                 loss_fn=loss_fn,
                                 max_epochs=args.max_epochs)

181
182
183
    trainer.fit(train_dataloader=train_dataloader,
                valid_dataloader=valid_dataloader,
                eval_dataloader=eval_dataloader)
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
184
    # save model checkpoint after fitting on only rank0
185
    strategy.save_model(model, args.save_path, only_rank0=True)
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
186
187
188
189
190
191
192
193
194
195
    # save optimizer checkpoint on all ranks
    if args.need_optim_ckpt:
        strategy.save_optimizer(trainer.optimizer,
                                'rm_optim_checkpoint_%d.pt' % (torch.cuda.current_device()),
                                only_rank0=False)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--strategy',
196
                        choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'],
197
                        default='colossalai_zero2')
198
    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom')
Fazzie-Maqianli's avatar
Fazzie-Maqianli committed
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
    parser.add_argument('--pretrain', type=str, default=None)
    parser.add_argument('--model_path', type=str, default=None)
    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
    parser.add_argument('--dataset',
                        type=str,
                        choices=['Anthropic/hh-rlhf', 'Dahoas/rm-static'],
                        default='Dahoas/rm-static')
    parser.add_argument('--subset', type=str, default=None)
    parser.add_argument('--save_path', type=str, default='rm_ckpt')
    parser.add_argument('--max_epochs', type=int, default=1)
    parser.add_argument('--batch_size', type=int, default=1)
    parser.add_argument('--max_len', type=int, default=512)
    parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
    parser.add_argument('--loss_fn', type=str, default='log_sig', choices=['log_sig', 'log_exp'])
    parser.add_argument('--test', type=bool, default=False)
    args = parser.parse_args()
    train(args)