opt ppo model

src/rlhf/ppo.py

@@ -323,10 +323,8 @@ class RLHF(pl.LightningModule):
     def generate(
         self,
         max_seq_len,
-        *args,
         prompt,
-        num_samples = 4,  # sample 4 per prompt and select the one with highest reward
-        **kwargs
+        num_samples = 4  # sample 4 per prompt and select the one with highest reward
     ):
         ''' 未参与训练，仅推理时使用
         '''
@@ -344,10 +342,8 @@ class RLHF(pl.LightningModule):
             _
         ) = self.actor_critic.generate(
             prompt,
-            *args,
             max_seq_len = max_seq_len,
-            return_values = False,
-            **kwargs
+            return_values = False
         )
 
         rewards = self.reward_model(
@@ -468,7 +464,6 @@ class RLHF(pl.LightningModule):
             rearrange(state, 'n -> 1 n'),
             max_seq_len = self.args.ctx_len,
             eos_token = eos_token,
-            temperature = temperature,
             return_values = True
         )
         action_logits = shift(action_logits, shift = 1, dim = -2) # need to shift along sequence dimension by 1, since actions start from the last prompt (state) token