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[chat] fix train_prompts.py gemini strategy bug (#3666) 

* fix gemini strategy bug

* add comment

* add comment

* better solution
pull/3699/head
zhang-yi-chi 2023-05-06 16:46:38 +08:00 committed by GitHub
parent d556648885
commit 2da5d81dec
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1 changed files with 38 additions and 38 deletions
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76
applications/Chat/examples/train_prompts.py
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@ -36,45 +36,45 @@ def main(args):
if args.rm_path is not None: if args.rm_path is not None:
state_dict = torch.load(args.rm_path, map_location='cpu') state_dict = torch.load(args.rm_path, map_location='cpu')
# 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)
elif args.model == 'roberta':
initial_model = RoBERTaActor(pretrained=args.pretrain)
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':
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)
elif rm_model_name == 'roberta':
reward_model = RoBERTaRM(pretrained=args.rm_pretrain)
else:
raise ValueError(f'Unsupported reward model "{rm_model_name}"')
if args.rm_path is not None:
reward_model.load_state_dict(state_dict)
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(): 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)
elif args.model == 'roberta':
initial_model = RoBERTaActor(pretrained=args.pretrain)
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':
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)
elif rm_model_name == 'roberta':
reward_model = RoBERTaRM(pretrained=args.rm_pretrain)
else:
raise ValueError(f'Unsupported reward model "{rm_model_name}"')
if args.rm_path is not None:
reward_model.load_state_dict(state_dict)
initial_model.to(torch.float16).to(torch.cuda.current_device())
reward_model.to(torch.float16).to(torch.cuda.current_device())
if args.model == 'gpt2': if args.model == 'gpt2':
actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank) actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
elif args.model == 'bloom': elif args.model == 'bloom':