[chatgpt]Fix examples (#3116)

* fix train_dummy

* fix train-prompts

applications/ChatGPT/examples/train_dummy.py

@@ -38,19 +38,19 @@ def main(args):
     # configure model
     with strategy.model_init_context():
         if args.model == 'gpt2':
-            actor = GPTActor().cuda()
-            critic = GPTCritic().cuda()
+            actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         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()
+            actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         elif args.model == 'opt':
-            actor = OPTActor().cuda()
-            critic = OPTCritic().cuda()
+            actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         else:
             raise ValueError(f'Unsupported model "{args.model}"')
 
-        initial_model = deepcopy(actor).cuda()
-        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda()
+        initial_model = deepcopy(actor).to(torch.cuda.current_device())
+        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device())
 
     # configure optimizer
     if args.strategy.startswith('colossalai'):
@@ -114,9 +114,10 @@ def main(args):
                 max_timesteps=args.max_timesteps,
                 update_timesteps=args.update_timesteps)
 
-    # save model checkpoint after fitting on only rank0
+    # save model checkpoint after fitting
     strategy.save_model(actor, 'actor_checkpoint_dummy.pt', only_rank0=True)
     # save optimizer checkpoint on all ranks
+    if args.need_optim_ckpt:
         strategy.save_optimizer(actor_optim,
                                 'actor_optim_checkpoint_dummy_%d.pt' % (torch.cuda.current_device()),
                                 only_rank0=False)
@@ -129,6 +130,7 @@ if __name__ == '__main__':
                         default='naive')
     parser.add_argument('--model', type=str, default='gpt2', choices=['gpt2', 'bloom', 'opt'])
     parser.add_argument('--pretrain', type=str, default=None)
+    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
     parser.add_argument('--num_episodes', type=int, default=50)
     parser.add_argument('--max_timesteps', type=int, default=10)
     parser.add_argument('--update_timesteps', type=int, default=10)

applications/ChatGPT/examples/train_prompts.py

@@ -32,19 +32,20 @@ def main(args):
     # configure model
     with strategy.model_init_context():
         if args.model == 'gpt2':
-            actor = GPTActor().cuda()
-            critic = GPTCritic().cuda()
+            actor = GPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = GPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         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()
+            actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = BLOOMCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         elif args.model == 'opt':
-            actor = OPTActor(lora_rank=args.lora_rank).cuda()
-            critic = OPTCritic(lora_rank=args.lora_rank).cuda()
+            actor = OPTActor(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
+            critic = OPTCritic(pretrained=args.pretrain, lora_rank=args.lora_rank).to(torch.cuda.current_device())
         else:
             raise ValueError(f'Unsupported model "{args.model}"')
 
         initial_model = deepcopy(actor)
-        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).cuda()
+        reward_model = RewardModel(deepcopy(critic.model), deepcopy(critic.value_head)).to(torch.cuda.current_device())
+
 
     # configure optimizer
     if args.strategy.startswith('colossalai'):
@@ -100,9 +101,10 @@ def main(args):
                 num_episodes=args.num_episodes,
                 max_timesteps=args.max_timesteps,
                 update_timesteps=args.update_timesteps)
-    # save model checkpoint after fitting on only rank0
+    # save model checkpoint after fitting 
     strategy.save_model(actor, 'actor_checkpoint_prompts.pt', 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)
@@ -116,6 +118,7 @@ if __name__ == '__main__':
                         default='naive')
     parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
     parser.add_argument('--pretrain', type=str, default=None)
+    parser.add_argument('--need_optim_ckpt', type=bool, default=False)
     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)