mirror of https://github.com/hpcaitech/ColossalAI
114 lines
4.7 KiB
Python
114 lines
4.7 KiB
Python
import argparse
|
|
from copy import deepcopy
|
|
|
|
import pandas as pd
|
|
from chatgpt.nn import BLOOMActor, BLOOMCritic, GPTActor, GPTCritic, OPTActor, OPTCritic, RewardModel
|
|
from chatgpt.nn.generation_utils import gpt_prepare_inputs_fn, update_model_kwargs_fn
|
|
from chatgpt.trainer import PPOTrainer
|
|
from chatgpt.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
|
|
from torch.optim import Adam
|
|
from transformers import AutoTokenizer, BloomTokenizerFast
|
|
from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
|
|
|
|
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')
|
|
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().cuda()
|
|
critic = GPTCritic().cuda()
|
|
elif args.model == 'bloom':
|
|
actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda()
|
|
critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).cuda()
|
|
elif args.model == 'opt':
|
|
actor = OPTActor(lora_rank=args.lora_rank).cuda()
|
|
critic = OPTCritic(lora_rank=args.lora_rank).cuda()
|
|
else:
|
|
raise ValueError(f'Unsupported model "{args.model}"')
|
|
|
|
initial_model = deepcopy(actor)
|
|
reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda()
|
|
|
|
# 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")
|
|
else:
|
|
raise ValueError(f'Unsupported model "{args.model}"')
|
|
|
|
dataset = pd.read_csv(args.prompt_path)['prompt']
|
|
|
|
def tokenize_fn(texts):
|
|
batch = tokenizer(texts, return_tensors='pt', max_length=96, padding=True, truncation=True)
|
|
return {k: v.cuda() for k, v in batch.items()}
|
|
|
|
# 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=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,
|
|
prepare_inputs_fn=gpt_prepare_inputs_fn,
|
|
update_model_kwargs_fn=update_model_kwargs_fn)
|
|
|
|
trainer.fit(dataset,
|
|
num_episodes=args.num_episodes,
|
|
max_timesteps=args.max_timesteps,
|
|
update_timesteps=args.update_timesteps)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('prompt_path')
|
|
parser.add_argument('--strategy',
|
|
choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
|
|
default='naive')
|
|
parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
|
|
parser.add_argument('--pretrain', type=str, default=None)
|
|
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('--lora_rank', type=int, default=0, help="low-rank adaptation matrices rank")
|
|
args = parser.parse_args()
|
|
main(args)
|