ColossalAI/applications/Chat/examples/train_dummy.py

import argparse
from copy import deepcopy

import torch
from coati.models.base import RewardModel
from coati.models.bloom import BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTActor, GPTCritic
from coati.models.opt import OPTActor, OPTCritic
from coati.models.roberta import RoBERTaActor, RoBERTaCritic
from coati.trainer import PPOTrainer
from coati.trainer.callbacks import SaveCheckpoint
from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
from torch.optim import Adam
from transformers import AutoTokenizer, BloomTokenizerFast, RobertaTokenizer
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer

from colossalai.nn.optimizer import HybridAdam


def preprocess_batch(samples):
    input_ids = torch.stack(samples)
    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
    return {'input_ids': input_ids, 'attention_mask': attention_mask}


def main(args):
    # configure strategy
    if args.strategy == 'naive':
        strategy = NaiveStrategy()
    elif args.strategy == 'ddp':
        strategy = DDPStrategy()
    elif args.strategy == 'colossalai_gemini':
        strategy = ColossalAIStrategy(stage=3, placement_policy='cuda', initial_scale=2**5)
    elif args.strategy == 'colossalai_zero2':
        strategy = ColossalAIStrategy(stage=2, placement_policy='cuda')
    else:
        raise ValueError(f'Unsupported strategy "{args.strategy}"')

    # configure model
    with strategy.model_init_context():
        if args.model == 'gpt2':
            actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
            critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'bloom':
            actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
            critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'opt':
            actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
            critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        elif args.model == 'roberta':
            actor = RoBERTaActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
            critic = RoBERTaCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
        else:
            raise ValueError(f'Unsupported model "{args.model}"')

        initial_model = deepcopy(actor).to(torch.cuda.current_device())
        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device())

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

    # configure tokenizer
    if args.model == 'gpt2':
        tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
        tokenizer.pad_token = tokenizer.eos_token
    elif args.model == 'bloom':
        tokenizer = BloomTokenizerFast.from_pretrained(args.pretrain)
        tokenizer.pad_token = tokenizer.eos_token
    elif args.model == 'opt':
        tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
    elif args.model == 'roberta':
        tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
    else:
        raise ValueError(f'Unsupported model "{args.model}"')

    (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare(
        (actor, actor_optim), (critic, critic_optim), reward_model, initial_model)

    callbacks = []
    if args.save_ckpt_path:
        ckpt_callback = SaveCheckpoint(
            args.save_ckpt_path,
            args.save_ckpt_interval,
            strategy,
            actor,
            critic,
            actor_optim,
            critic_optim,
        )
        callbacks.append(ckpt_callback)

    # configure trainer

    trainer = PPOTrainer(strategy,
                         actor,
                         critic,
                         reward_model,
                         initial_model,
                         actor_optim,
                         critic_optim,
                         max_epochs=args.max_epochs,
                         train_batch_size=args.train_batch_size,
                         tokenizer=preprocess_batch,
                         max_length=128,
                         do_sample=True,
                         temperature=1.0,
                         top_k=50,
                         pad_token_id=tokenizer.pad_token_id,
                         eos_token_id=tokenizer.eos_token_id,
                         callbacks=callbacks)

    random_prompts = torch.randint(tokenizer.vocab_size, (1000, 64), device=torch.cuda.current_device())
    trainer.fit(random_prompts,
                num_episodes=args.num_episodes,
                max_timesteps=args.max_timesteps,
                update_timesteps=args.update_timesteps)

    # save model checkpoint after fitting
    trainer.save_model(args.save_path, only_rank0=True)
    # save optimizer checkpoint on all ranks
    if args.need_optim_ckpt:
        strategy.save_optimizer(actor_optim,
                                'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()),
                                only_rank0=False)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--strategy',
                        choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
                        default='naive')
    parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt', 'roberta'])
    parser.add_argument('--pretrain', type=str, default=None)
    parser.add_argument('--save_path', type=str, default='actor_checkpoint_dummy.pt')
    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
    parser.add_argument('--num_episodes', type=int, default=50)
    parser.add_argument('--max_timesteps', type=int, default=10)
    parser.add_argument('--update_timesteps', type=int, default=10)
    parser.add_argument('--max_epochs', type=int, default=5)
    parser.add_argument('--train_batch_size', type=int, default=8)
    parser.add_argument('--experience_batch_size', type=int, default=8)
    parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
    parser.add_argument('--save_ckpt_path',
                        type=str,
                        default=None,
                        help="path to save checkpoint, None means not to save")
    parser.add_argument('--save_ckpt_interval', type=int, default=1, help="the interval of episode to save checkpoint")
    args = parser.parse_args()
    main(args)