train_prompts.py

import argparse

import pandas as pd
import torch
import torch.distributed as dist
from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset
from coati.models.bloom import BLOOMRM, BLOOMActor, BLOOMCritic
from coati.models.gpt import GPTRM, GPTActor, GPTCritic
from coati.models.llama import LlamaActor
from coati.models.opt import OPTRM, OPTActor, OPTCritic
from coati.trainer import PPOTrainer
from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
from coati.utils import prepare_llama_tokenizer_and_embedding
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer

from colossalai.nn.optimizer import HybridAdam


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}"')

    if args.rm_path is not None:
        state_dict = torch.load(args.rm_path, map_location='cpu')

    # configure model
    if args.model == 'gpt2':
        initial_model = GPTActor(pretrained=args.pretrain)
        reward_model = GPTRM(pretrained=args.rm_pretrain)
    elif args.model == 'bloom':
        initial_model = BLOOMActor(pretrained=args.pretrain)
        reward_model = BLOOMRM(pretrained=args.rm_pretrain)
    elif args.model == 'opt':
        initial_model = OPTActor(pretrained=args.pretrain)
        reward_model = OPTRM(pretrained=args.rm_pretrain)
    elif args.model == 'llama':
        initial_model = LlamaActor(pretrained=args.pretrain)
        reward_model = BLOOMRM(pretrained=args.rm_pretrain)
    else:
        raise ValueError(f'Unsupported model "{args.model}"')
    if args.rm_path is not None:
        reward_model.load_state_dict(state_dict)

    if args.strategy != 'colossalai_gemini':
        initial_model.to(torch.float16).to(torch.cuda.current_device())
        reward_model.to(torch.float16).to(torch.cuda.current_device())

    with strategy.model_init_context():
        if args.model == 'gpt2':
            actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
            critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
        elif args.model == 'bloom':
            actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
            critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
        elif args.model == 'opt':
            actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
            critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
        elif args.model == 'llama':
            actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
            critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
        else:
            raise ValueError(f'Unsupported model "{args.model}"')
        if args.rm_path is not None:
            critic.load_state_dict(state_dict)
            del state_dict

    if args.strategy != 'colossalai_gemini':
        critic.to(torch.float16).to(torch.cuda.current_device())
        actor.to(torch.float16).to(torch.cuda.current_device())

    # configure optimizer
    if args.strategy.startswith('colossalai'):
        actor_optim = HybridAdam(actor.parameters(), lr=1e-7)
        critic_optim = HybridAdam(critic.parameters(), lr=1e-7)
    else:
        actor_optim = Adam(actor.parameters(), lr=1e-7)
        critic_optim = Adam(critic.parameters(), lr=1e-7)

    # 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 == 'llama':
        tokenizer = LlamaTokenizer.from_pretrained(args.pretrain)
        tokenizer.eos_token = '<\s>'
    else:
        raise ValueError(f'Unsupported model "{args.model}"')

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

    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)

    prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_path, max_datasets_size=16384)
    if dist.is_initialized() and dist.get_world_size() > 1:
        prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True)
    prompt_dataloader = DataLoader(prompt_dataset,
                                   shuffle=(prompt_sampler is None),
                                   sampler=prompt_sampler,
                                   batch_size=args.train_batch_size)

    pretrain_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384)
    if dist.is_initialized() and dist.get_world_size() > 1:
        pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True)
    pretrain_dataloader = DataLoader(pretrain_dataset,
                                     shuffle=(pretrain_sampler is None),
                                     sampler=pretrain_sampler,
                                     batch_size=args.ptx_batch_size,
                                     collate_fn=data_collator)

    def tokenize_fn(texts):
        # MUST padding to max length to ensure inputs of all ranks have the same length
        # Different length may lead to hang when using gemini, as different generation steps
        batch = tokenizer(texts, return_tensors='pt', max_length=96, padding='max_length', truncation=True)
        return {k: v.to(torch.cuda.current_device()) for k, v in batch.items()}

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

    # configure trainer
    trainer = PPOTrainer(
        strategy,
        actor,
        critic,
        reward_model,
        initial_model,
        actor_optim,
        critic_optim,
        kl_coef=args.kl_coef,
        ptx_coef=args.ptx_coef,
        max_epochs=args.max_epochs,
        train_batch_size=args.train_batch_size,
        experience_batch_size=args.experience_batch_size,
        tokenizer=tokenize_fn,
        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,
    )

    trainer.fit(prompt_dataloader=prompt_dataloader,
                pretrain_dataloader=pretrain_dataloader,
                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, tokenizer=tokenizer)
    # save optimizer checkpoint on all ranks
    if args.need_optim_ckpt:
        strategy.save_optimizer(actor_optim,
                                'actor_optim_checkpoint_prompts_%d.pt' % (torch.cuda.current_device()),
                                only_rank0=False)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--prompt_path', type=str, default=None, help='path to the prompt dataset')
    parser.add_argument('--pretrain_dataset', type=str, default=None, help='path to the pretrained dataset')
    parser.add_argument('--strategy',
                        choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
                        default='naive',
                        help='strategy to use')
    parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama'])
    parser.add_argument('--pretrain', type=str, default=None)
    parser.add_argument('--rm_path', type=str, default=None)
    parser.add_argument('--rm_pretrain', type=str, default=None)
    parser.add_argument('--save_path', type=str, default='actor_checkpoint_prompts')
    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
    parser.add_argument('--num_episodes', type=int, default=10)
    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('--ptx_batch_size', type=int, default=1)
    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('--kl_coef', type=float, default=0.1)
    parser.add_argument('--ptx_coef', type=float, default=0.9)
    args = parser.parse_args()
    main(args)