[chatgpt]add flag of action mask in critic(#3086)

applications/ChatGPT/chatgpt/models/base/actor.py

@@ -37,7 +37,7 @@ class Actor(LoRAModule):
         if pad_token_id is not None:
             attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device)
         if not return_action_mask:
-            return sequences, attention_mask
+            return sequences, attention_mask, None
         input_len = input_ids.size(1)
         eos_token_id = kwargs.get('eos_token_id', None)
         if eos_token_id is None:

applications/ChatGPT/chatgpt/models/base/critic.py

@@ -18,15 +18,19 @@ class Critic(LoRAModule):
         lora_train_bias (str): LoRA bias training mode.
     """
 
-    def __init__(self,
-                 model: nn.Module,
-                 value_head: nn.Module,
-                 lora_rank: int = 0,
-                 lora_train_bias: str = 'none') -> None:
+    def __init__(
+        self,
+        model: nn.Module,
+        value_head: nn.Module,
+        lora_rank: int = 0,
+        lora_train_bias: str = 'none',
+        use_action_mask: bool = False,
+    ) -> None:
 
         super().__init__(lora_rank=lora_rank, lora_train_bias=lora_train_bias)
         self.model = model
         self.value_head = value_head
+        self.use_action_mask = use_action_mask
         self.convert_to_lora()
 
     def forward(self,
@@ -38,7 +42,7 @@ class Critic(LoRAModule):
 
         values = self.value_head(last_hidden_states).squeeze(-1)
 
-        if action_mask is not None:
+        if action_mask is not None and self.use_action_mask:
             num_actions = action_mask.size(1)
             prompt_mask = attention_mask[:, :-num_actions]
             values = values[:, :-num_actions]
@@ -46,5 +50,5 @@ class Critic(LoRAModule):
             return value
 
         values = values[:, :-1]
-        value = values.mean(dim=1).squeeze(1)
+        value = values.mean(dim=1)
         return value

applications/ChatGPT/chatgpt/models/bloom/bloom_critic.py

@@ -24,7 +24,8 @@ class BLOOMCritic(Critic):
                  config: Optional[BloomConfig] = None,
                  checkpoint: bool = False,
                  lora_rank: int = 0,
-                 lora_train_bias: str = 'none') -> None:
+                 lora_train_bias: str = 'none',
+                 **kwargs) -> None:
         if pretrained is not None:
             model = BloomModel.from_pretrained(pretrained)
         elif config is not None:
@@ -34,4 +35,4 @@ class BLOOMCritic(Critic):
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.hidden_size, 1)
-        super().__init__(model, value_head, lora_rank, lora_train_bias)
+        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)

applications/ChatGPT/chatgpt/models/gpt/gpt_critic.py

@@ -20,7 +20,8 @@ class GPTCritic(Critic):
     def __init__(self,
                  pretrained: Optional[str] = None,
                  config: Optional[GPT2Config] = None,
-                 checkpoint: bool = False) -> None:
+                 checkpoint: bool = False,
+                 **kwargs) -> None:
         if pretrained is not None:
             model = GPT2Model.from_pretrained(pretrained)
         elif config is not None:
@@ -30,4 +31,4 @@ class GPTCritic(Critic):
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.n_embd, 1)
-        super().__init__(model, value_head)
+        super().__init__(model, value_head, **kwargs)

applications/ChatGPT/chatgpt/models/opt/opt_critic.py

@@ -24,7 +24,8 @@ class OPTCritic(Critic):
                  config: Optional[OPTConfig] = None,
                  checkpoint: bool = False,
                  lora_rank: int = 0,
-                 lora_train_bias: str = 'none') -> None:
+                 lora_train_bias: str = 'none',
+                 **kargs) -> None:
         if pretrained is not None:
             model = OPTModel.from_pretrained(pretrained)
         elif config is not None:
@@ -34,4 +35,4 @@ class OPTCritic(Critic):
         if checkpoint:
             model.gradient_checkpointing_enable()
         value_head = nn.Linear(model.config.hidden_size, 1)
-        super().__init__(model, value_head, lora_rank, lora_train_bias)
+        super().__init__(model, value_head, lora_rank, lora_train_bias, **kwargs)