import argparse import os import socket from functools import partial import ray import torch from coati.quant import llama_load_quant, low_resource_init from coati.ray.detached_trainer_ppo import DetachedPPOTrainer from coati.ray.experience_maker_holder import ExperienceMakerHolder from coati.ray.utils import ( get_actor_from_args, get_critic_from_args, get_receivers_per_sender, get_reward_model_from_args, get_strategy_from_args, ) from torch.utils.data import DataLoader from transformers import AutoConfig, AutoTokenizer from transformers.modeling_utils import no_init_weights def get_free_port(): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(('', 0)) return s.getsockname()[1] def get_local_ip(): with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: s.connect(('8.8.8.8', 80)) return s.getsockname()[0] def main(args): master_addr = str(get_local_ip()) # trainer_env_info trainer_port = str(get_free_port()) env_info_trainers = [{ 'local_rank': '0', 'rank': str(rank), 'world_size': str(args.num_trainers), 'master_port': trainer_port, 'master_addr': master_addr } for rank in range(args.num_trainers)] # maker_env_info maker_port = str(get_free_port()) env_info_maker = { 'local_rank': '0', 'rank': '0', 'world_size': '1', 'master_port': maker_port, 'master_addr': master_addr } # configure tokenizer tokenizer = AutoTokenizer.from_pretrained(args.pretrain) tokenizer.pad_token = tokenizer.eos_token def model_fn(): actor_cfg = AutoConfig.from_pretrained(args.pretrain) critic_cfg = AutoConfig.from_pretrained(args.critic_pretrain) actor = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() critic = get_critic_from_args(args.critic_model, config=critic_cfg).requires_grad_(False).half().cuda() reward_model = get_reward_model_from_args(args.critic_model, config=critic_cfg).requires_grad_(False).half().cuda() if args.initial_model_quant_ckpt is not None and args.model == 'llama': # quantize initial model with low_resource_init(), no_init_weights(): initial_model = get_actor_from_args(args.model, config=actor_cfg) initial_model.model = llama_load_quant(initial_model.model, args.initial_model_quant_ckpt, args.quant_bits, args.quant_group_size).cuda().requires_grad_(False) else: initial_model = get_actor_from_args(args.model, config=actor_cfg).requires_grad_(False).half().cuda() return actor, critic, reward_model, initial_model # configure Experience Maker experience_holder_ref = ExperienceMakerHolder.options(name="maker0", num_gpus=1, max_concurrency=2).remote( detached_trainer_name_list=[f'trainer{i}' for i in range(args.num_trainers)], strategy_fn=partial(get_strategy_from_args, args.maker_strategy), model_fn=model_fn, env_info=env_info_maker, kl_coef=0.1, debug=args.debug, # sync_models_from_trainers=True, # generation kwargs: 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, eval_performance=True, use_cache=True, ) def trainer_model_fn(): actor = get_actor_from_args(args.model, config=AutoConfig.from_pretrained(args.pretrain)).half().cuda() critic = get_critic_from_args(args.critic_model, config=AutoConfig.from_pretrained(args.critic_pretrain)).half().cuda() return actor, critic # configure Trainer trainer_refs = [ DetachedPPOTrainer.options(name=f"trainer{i}", num_gpus=1, max_concurrency=2).remote( experience_maker_holder_name_list=[ f'maker{x}' for x in get_receivers_per_sender(i, args.num_trainers, 1, allow_idle_sender=True) ], strategy_fn=partial(get_strategy_from_args, args.trainer_strategy), model_fn=trainer_model_fn, env_info=env_info_trainer, train_batch_size=args.train_batch_size, buffer_limit=16, eval_performance=True, debug=args.debug, ) for i, env_info_trainer in enumerate(env_info_trainers) ] dataset_size = args.experience_batch_size * 4 def data_gen_fn(): input_ids = torch.randint(tokenizer.vocab_size, (256,), device=torch.cuda.current_device()) attn_mask = torch.ones_like(input_ids) return {'input_ids': input_ids, 'attention_mask': attn_mask} def build_dataloader(size): dataset = [data_gen_fn() for _ in range(size)] dataloader = DataLoader(dataset, batch_size=args.experience_batch_size) return dataloader # uncomment this function if sync_models_from_trainers is True # ray.get([ # trainer_ref.sync_models_to_remote_makers.remote() # for trainer_ref in trainer_refs # ]) wait_tasks = [] wait_tasks.append( experience_holder_ref.workingloop.remote(partial(build_dataloader, dataset_size), num_steps=args.experience_steps)) total_steps = args.experience_batch_size * args.experience_steps // (args.num_trainers * args.train_batch_size) for trainer_ref in trainer_refs: wait_tasks.append(trainer_ref.fit.remote(total_steps, args.update_steps, args.train_epochs)) ray.get(wait_tasks) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--num_trainers', type=int, default=1) parser.add_argument('--trainer_strategy', choices=[ 'ddp', 'colossalai_gemini', 'colossalai_zero2', 'colossalai_gemini_cpu', 'colossalai_zero2_cpu' ], default='ddp') parser.add_argument('--maker_strategy', choices=['naive'], default='naive') parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--critic_model', default='gpt2', choices=['gpt2', 'bloom', 'opt', 'llama']) parser.add_argument('--pretrain', type=str, default=None) parser.add_argument('--critic_pretrain', type=str, default=None) parser.add_argument('--experience_steps', type=int, default=4) parser.add_argument('--experience_batch_size', type=int, default=8) parser.add_argument('--train_epochs', type=int, default=1) parser.add_argument('--update_steps', type=int, default=2) parser.add_argument('--train_batch_size', type=int, default=8) parser.add_argument('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank") parser.add_argument('--initial_model_quant_ckpt', type=str, default=None) parser.add_argument('--quant_bits', type=int, default=4) parser.add_argument('--quant_group_size', type=int, default=128) parser.add_argument('--debug', action='store_true') args = parser.parse_args() ray.init(namespace=os.environ["RAY_NAMESPACE"], runtime_env={"env_vars": dict(os.environ)}) main(args)