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, BLOOMCritic from coati.models.gpt import GPTRM, GPTActor, GPTCritic from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM 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 easy_dataset import EasyPromptsDataset, EasySupervisedDataset from easy_models import BLOOMActor from peft import PeftModel 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='cpu', initial_scale=2**5) elif args.strategy == 'colossalai_zero2': strategy = ColossalAIStrategy(stage=2, placement_policy='cpu') 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 == 'bloom': # initial_model = BLOOMActor(pretrained=args.pretrain) print('Using peft lora to load Bloom model as inital_model') initial_model = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path) print('Using peft lora to load Bloom model as initial_model (Done)') else: raise ValueError(f'Unsupported actor model "{args.model}"') if args.rm_model == None: rm_model_name = args.model else: rm_model_name = args.rm_model if rm_model_name == 'gpt2': reward_model = GPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'bloom': print("load bloom reward model ", args.rm_pretrain) reward_model = BLOOMRM(pretrained=args.rm_pretrain) elif rm_model_name == 'opt': reward_model = OPTRM(pretrained=args.rm_pretrain) elif rm_model_name == 'llama': reward_model = LlamaRM(pretrained=args.rm_pretrain) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') if args.rm_path is not None: print('Loading reward model from', args.rm_path) 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 == 'bloom': # actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) print('Using peft lora to load Bloom model as Actor') actor = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path) print('Using peft lora to load Bloom model as Actor (Done)') else: raise ValueError(f'Unsupported actor model "{args.model}"') if rm_model_name == 'gpt2': critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'bloom': print("load bloom critic ", args.rm_pretrain, " lora_rank ", args.lora_rank, " use_action_mask ", True) critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) print("load bloom critic (Done) ") elif rm_model_name == 'opt': critic = OPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) elif rm_model_name == 'llama': critic = LlamaCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') if args.rm_path is not None: print('Loading reward model from', args.rm_path) 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(args.rm_pretrain) elif args.model == 'bloom': tokenizer = BloomTokenizerFast.from_pretrained(args.rm_pretrain) elif args.model == 'opt': tokenizer = AutoTokenizer.from_pretrained(args.rm_pretrain) 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 = EasyPromptsDataset(args.prompt_path, tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True) else: prompt_sampler = None prompt_dataloader = DataLoader(prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, batch_size=args.train_batch_size) pretrain_dataset = EasySupervisedDataset(args.pretrain_dataset, tokenizer) if dist.is_initialized() and dist.get_world_size() > 1: pretrain_sampler = DistributedSampler(pretrain_dataset, shuffle=True, seed=42, drop_last=True) else: pretrain_sampler = None 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=512, 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('--sft_lora_path', type=str, default=None) parser.add_argument('--rm_model', default=None, choices=['gpt2', 'bloom', 'opt', 'llama']) 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=2) 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)