import argparse import warnings import torch import torch.distributed as dist from coati.dataset import PromptDataset, SupervisedDataset from coati.models.bloom import BLOOMRM, BLOOMActor, 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 DDPStrategy, GeminiStrategy, LowLevelZeroStrategy 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 == "ddp": strategy = DDPStrategy() elif args.strategy == "colossalai_gemini": strategy = GeminiStrategy(placement_policy="cuda", initial_scale=2**5) elif args.strategy == "colossalai_zero2": strategy = LowLevelZeroStrategy(stage=2, placement_policy="cuda") else: raise ValueError(f'Unsupported strategy "{args.strategy}"') if args.rm_path is not None: warnings.warn("LoRA weights should be merged with the model weights") state_dict = torch.load(args.rm_path, map_location="cpu") with strategy.model_init_context(): # configure model if args.model == "gpt2": initial_model = GPTActor(pretrained=args.pretrain) elif args.model == "bloom": initial_model = BLOOMActor(pretrained=args.pretrain) elif args.model == "opt": initial_model = OPTActor(pretrained=args.pretrain) elif args.model == "llama": initial_model = LlamaActor(pretrained=args.pretrain) else: raise ValueError(f'Unsupported actor model "{args.model}"') if args.rm_model is 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, lora_rank=args.lora_rank) elif rm_model_name == "bloom": reward_model = BLOOMRM(pretrained=args.rm_pretrain, lora_rank=args.lora_rank) elif rm_model_name == "opt": reward_model = OPTRM(pretrained=args.rm_pretrain, lora_rank=args.lora_rank) elif rm_model_name == "llama": reward_model = LlamaRM(pretrained=args.rm_pretrain, lora_rank=args.lora_rank) else: raise ValueError(f'Unsupported reward model "{rm_model_name}"') if args.rm_path is not None: reward_model.load_state_dict(state_dict, strict=False) initial_model.to(torch.float16).to(torch.cuda.current_device()) reward_model.to(torch.float16).to(torch.cuda.current_device()) if args.model == "gpt2": actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == "bloom": actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == "opt": actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) elif args.model == "llama": actor = LlamaActor(pretrained=args.pretrain, lora_rank=args.lora_rank) 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": critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True) 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: critic.load_state_dict(state_dict, strict=False) 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" 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}"') prompt_dataset = PromptDataset(tokenizer=tokenizer, data_path=args.prompt_dataset, 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) else: prompt_sampler = None prompt_dataloader = DataLoader( prompt_dataset, shuffle=(prompt_sampler is None), sampler=prompt_sampler, batch_size=args.experience_batch_size ) pretrain_dataset = SupervisedDataset( tokenizer=tokenizer, data_path=args.pretrain_dataset, max_datasets_size=16384, max_length=args.max_input_len ) 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 ) # NOTE: For small models like opt-1.3b, reward model and initial model are not required to be parallelized. (actor, actor_optim), (critic, critic_optim), reward_model, initial_model = strategy.prepare( (actor, actor_optim), (critic, critic_optim), reward_model, initial_model ) # 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, train_batch_size=args.train_batch_size, max_length=args.max_seq_len, use_cache=True, do_sample=True, temperature=1.0, top_k=50, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, offload_inference_models=args.strategy != "colossalai_gemini", ) trainer.fit( prompt_dataloader=prompt_dataloader, pretrain_dataloader=pretrain_dataloader, num_episodes=args.num_episodes, num_collect_steps=args.num_collect_steps, num_update_steps=args.num_update_steps, ) # save model checkpoint after fitting strategy.save_model(actor, 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_prompts_%d.pt" % (torch.cuda.current_device()), only_rank0=False ) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--prompt_dataset", 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=["ddp", "colossalai_gemini", "colossalai_zero2"], default="colossalai_zero2", help="strategy to use", ) parser.add_argument("--model", default="gpt2", choices=["gpt2", "bloom", "opt", "llama"]) parser.add_argument("--tokenizer", type=str, default=None) parser.add_argument("--pretrain", 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("--num_collect_steps", type=int, default=10) parser.add_argument("--num_update_steps", 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) parser.add_argument("--max_input_len", type=int, default=96) parser.add_argument("--max_seq_len", type=int, default=128) args = parser.parse_args() main(args)