ColossalAI/applications/Chat/examples/train_sft.py

import argparse
import math
import warnings

import torch
import torch.distributed as dist
from coati.dataset import SFTDataset, SupervisedDataset
from coati.models.bloom import BLOOMActor
from coati.models.gpt import GPTActor
from coati.models.llama import LlamaActor
from coati.models.opt import OPTActor
from coati.trainer import SFTTrainer
from coati.trainer.strategies import DDPStrategy, GeminiStrategy, LowLevelZeroStrategy
from datasets import load_dataset
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer, BloomTokenizerFast, LlamaTokenizer
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
from transformers.trainer import get_scheduler

from colossalai.logging import get_dist_logger
from colossalai.nn.optimizer import HybridAdam
from colossalai.tensor import ColoParameter


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')
    elif args.strategy == 'colossalai_zero2_cpu':
        strategy = LowLevelZeroStrategy(stage=2, placement_policy='cpu')
    else:
        raise ValueError(f'Unsupported strategy "{args.strategy}"')

    # configure model
    if args.lora_rank > 0:
        warnings.warn("Gradient checkpoint is disabled when using LoRA")
        args.grad_checkpoint = False
    with strategy.model_init_context():
        if args.model == 'bloom':
            model = BLOOMActor(pretrained=args.pretrain,
                               lora_rank=args.lora_rank,
                               checkpoint=args.grad_checkpoint)
        elif args.model == 'opt':
            model = OPTActor(pretrained=args.pretrain,
                             lora_rank=args.lora_rank,
                             checkpoint=args.grad_checkpoint)
        elif args.model == 'gpt2':
            model = GPTActor(pretrained=args.pretrain,
                             lora_rank=args.lora_rank,
                             checkpoint=args.grad_checkpoint)
        elif args.model == 'llama':
            model = LlamaActor(pretrained=args.pretrain,
                               lora_rank=args.lora_rank,
                               checkpoint=args.grad_checkpoint)
        else:
            raise ValueError(f'Unsupported model "{args.model}"')

        model.to(torch.float16).to(torch.cuda.current_device())

    # configure tokenizer
    if args.model == 'gpt2':
        tokenizer = GPT2Tokenizer.from_pretrained(
            'gpt2' if args.tokenizer is None else args.tokenizer)
        tokenizer.pad_token = tokenizer.eos_token
    elif args.model == 'bloom':
        tokenizer = BloomTokenizerFast.from_pretrained(
            'bigscience/bloom-560m' if args.tokenizer is None else args.tokenizer)
        tokenizer.pad_token = tokenizer.eos_token
    elif args.model == 'opt':
        tokenizer = AutoTokenizer.from_pretrained(
            "facebook/opt-350m" if args.tokenizer is None else args.tokenizer)
        tokenizer.pad_token = tokenizer.eos_token
    elif args.model == 'llama':
        tokenizer = LlamaTokenizer.from_pretrained(
            "hf-internal-testing/llama-tokenizer" if args.tokenizer is None else args.tokenizer)
        tokenizer.eos_token = '<\s>'
        tokenizer.pad_token = tokenizer.unk_token
    else:
        raise ValueError(f'Unsupported model "{args.model}"')

    if args.model == 'llama' and args.strategy == 'colossalai_gemini':
        # this is a hack to deal with the resized embedding
        # to make sure all parameters are ColoParameter for Colossal-AI Gemini Compatibility
        for name, param in model.named_parameters():
            if not isinstance(param, ColoParameter):
                sub_module_name = '.'.join(name.split('.')[:-1])
                weight_name = name.split('.')[-1]
                sub_module = model.get_submodule(sub_module_name)
                setattr(sub_module, weight_name, ColoParameter(param))

    # configure optimizer
    if args.strategy.startswith('colossalai'):
        optim = HybridAdam(model.parameters(), lr=args.lr, clipping_norm=1.0)
    else:
        optim = Adam(model.parameters(), lr=args.lr)

    logger = get_dist_logger()

    # configure dataset
    if args.dataset == 'yizhongw/self_instruct':
        train_data = load_dataset(args.dataset, 'super_natural_instructions', split='train')
        eval_data = load_dataset(args.dataset, 'super_natural_instructions', split='test')

        train_dataset = SFTDataset(train_data, tokenizer, args.max_len)
        eval_dataset = SFTDataset(eval_data, tokenizer, args.max_len)

    else:
        train_dataset = SupervisedDataset(tokenizer=tokenizer,
                                          data_path=args.dataset,
                                          max_datasets_size=args.max_datasets_size,
                                          max_length=args.max_len)
        eval_dataset = None

    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())
        if eval_dataset is not None:
            eval_sampler = DistributedSampler(eval_dataset,
                                              shuffle=False,
                                              seed=42,
                                              drop_last=False,
                                              rank=dist.get_rank(),
                                              num_replicas=dist.get_world_size())
    else:
        train_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)
    if eval_dataset is not None:
        eval_dataloader = DataLoader(eval_dataset,
                                     shuffle=(eval_sampler is None),
                                     sampler=eval_sampler,
                                     batch_size=args.batch_size,
                                     pin_memory=True)
    else:
        eval_dataloader = None

    num_update_steps_per_epoch = len(train_dataloader) // args.accumulation_steps
    max_steps = math.ceil(args.max_epochs * num_update_steps_per_epoch)
    lr_scheduler = get_scheduler("cosine",
                                 optim,
                                 num_warmup_steps=math.ceil(max_steps * 0.03),
                                 num_training_steps=max_steps)
    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 = SFTTrainer(model=model,
                         strategy=strategy,
                         optim=optim,
                         lr_scheduler=lr_scheduler,
                         max_epochs=args.max_epochs,
                         accumulation_steps=args.accumulation_steps)

    trainer.fit(train_dataloader=train_dataloader,
                eval_dataloader=eval_dataloader,
                logger=logger,
                use_wandb=args.use_wandb)

    # save model checkpoint after fitting on only rank0
    strategy.save_pretrained(model, path=args.save_path, only_rank0=True, tokenizer=tokenizer)
    # 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', 'colossalai_zero2_cpu'],
                        default='colossalai_zero2')
    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom')
    parser.add_argument('--tokenizer', type=str, default=None)
    parser.add_argument('--pretrain', type=str, default=None)
    parser.add_argument('--dataset', type=str, default=None)
    parser.add_argument('--max_datasets_size', type=int, default=None)
    parser.add_argument('--save_path', type=str, default='output')
    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
    parser.add_argument('--max_epochs', type=int, default=3)
    parser.add_argument('--batch_size', type=int, default=4)
    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('--log_interval', type=int, default=100, help="how many steps to log")
    parser.add_argument('--lr', type=float, default=5e-6)
    parser.add_argument('--accumulation_steps', type=int, default=8)
    parser.add_argument('--use_wandb', default=False, action='store_true')
    parser.add_argument('--grad_checkpoint', default=False, action='store_true')
    args = parser.parse_args()
    train(args)