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

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Merged
ver217 merged 4 commits into from
May 6, 2023
Merged

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

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3f2b8a0
fix gemini strategy bug
zhang-yi-chi Apr 28, 2023
18db2e8
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zhang-yi-chi Apr 28, 2023
7aaad2c
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zhang-yi-chi Apr 28, 2023
4e2bab6
better solution
zhang-yi-chi Apr 28, 2023
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70 changes: 35 additions & 35 deletions applications/Chat/examples/train_prompts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,45 +36,45 @@ def main(args):
if args.rm_path is not None:
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}"')
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_model == None:
rm_model_name = args.model
else:
rm_model_name = args.rm_model

if args.rm_path is not None:
reward_model.load_state_dict(state_dict)
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}"')

initial_model.to(torch.float16).to(torch.cuda.current_device())
reward_model.to(torch.float16).to(torch.cuda.current_device())
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():
if args.model == 'gpt2':
actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
elif args.model == 'bloom':
Expand Down