train_reward_model.py

import argparse
from random import randint

import loralib as lora
import torch
import torch.distributed as dist
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
from coati.models.roberta import RoBERTaRM
from coati.trainer import RewardModelTrainer
from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
from coati.utils import prepare_llama_tokenizer_and_embedding
from datasets import load_dataset
from torch.optim import Adam
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer, BloomTokenizerFast, DebertaV2Tokenizer, LlamaTokenizer, RobertaTokenizer
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer

from colossalai.nn.optimizer import HybridAdam


def train(args):
    # configure strategy
    if args.strategy == 'ddp':
        strategy = DDPStrategy()
    elif args.strategy == 'colossalai_gemini':
        strategy = GeminiStrategy(placement_policy='cuda')
    elif args.strategy == 'colossalai_zero2':
        strategy = LowLevelZeroStrategy(stage=2, placement_policy='cuda')
    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())
        elif args.model == 'roberta':
            model = RoBERTaRM(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        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)
    elif args.model == 'roberta':
        tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
    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}"')

    if dist.is_initialized() and dist.get_world_size() > 1:
        train_sampler = DistributedSampler(train_dataset,
                                           shuffle=True,
                                           seed=42,
                                           drop_last=True,
                                           rank=dist.get_rank(),
                                           num_replicas=dist.get_world_size())
        valid_sampler = DistributedSampler(valid_dataset,
                                           shuffle=True,
                                           seed=42,
                                           drop_last=True,
                                           rank=dist.get_rank(),
                                           num_replicas=dist.get_world_size())
        eval_sampler = DistributedSampler(eval_dataset,
                                          shuffle=True,
                                          seed=42,
                                          drop_last=True,
                                          rank=dist.get_rank(),
                                          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)

    valid_dataloader = DataLoader(valid_dataset,
                                  shuffle=(valid_sampler is None),
                                  sampler=valid_sampler,
                                  batch_size=args.batch_size,
                                  pin_memory=True)

    eval_dataloader = DataLoader(eval_dataset,
                                 shuffle=(eval_sampler is None),
                                 sampler=eval_sampler,
                                 batch_size=args.batch_size,
                                 pin_memory=True)

    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']
    trainer = RewardModelTrainer(model=model,
                                 strategy=strategy,
                                 optim=optim,
                                 lr_scheduler=lr_scheduler,
                                 loss_fn=loss_fn,
                                 max_epochs=args.max_epochs)

    trainer.fit(train_dataloader=train_dataloader,
                valid_dataloader=valid_dataloader,
                eval_dataloader=eval_dataloader)
    # save model checkpoint after fitting on only rank0
    strategy.save_model(model, args.save_path, only_rank0=True)
    # 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',
                        choices=['ddp', 'colossalai_gemini', 'colossalai_zero2'],
                        default='colossalai_zero2')
    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'deberta', 'llama', 'roberta'], default='bloom')
    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)