fix loading valuehead

src/utils/common.py

@@ -94,7 +94,7 @@ def _init_adapter(
     if model_args.checkpoint_dir is not None:
         if finetuning_args.finetuning_type != "lora":
             assert is_mergeable and len(model_args.checkpoint_dir) == 1, "Only LoRA tuning accepts multiple checkpoints."
-            load_trainable_params(model, model_args.checkpoint_dir[0]) # load model checkpoints for non-peft methods
+            assert load_trainable_params(model, model_args.checkpoint_dir[0]), "Model checkpoint is not correctly loaded."
         else:
             assert is_mergeable or len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
 
@@ -217,18 +217,19 @@ def load_pretrained(
         model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
 
         if stage == "rm" and model_args.checkpoint_dir is not None: # load valuehead weights to evaluate reward model
-            load_valuehead_params(model, model_args.checkpoint_dir[0])
-            model.v_head.load_state_dict({
-                "summary.weight": getattr(model, "reward_head_weight"),
-                "summary.bias": getattr(model, "reward_head_bias")
-            })
+            logger.warning("Only the last checkpoint containing valuehead will be loaded as the valuehead.")
+            if load_valuehead_params(model, model_args.checkpoint_dir[-1]):
+                model.v_head.load_state_dict({
+                    "summary.weight": getattr(model, "reward_head_weight"),
+                    "summary.bias": getattr(model, "reward_head_bias")
+                })
 
         if stage == "ppo": # load reward model
             assert is_trainable, "PPO stage cannot be performed at evaluation."
             assert model_args.reward_model is not None, "Reward model is necessary for PPO training."
             logger.info("Load reward model from {}".format(model_args.reward_model))
             model.pretrained_model.load_adapter(model_args.reward_model, "reward", is_trainable=False)
-            load_valuehead_params(model, model_args.reward_model)
+            assert load_valuehead_params(model, model_args.reward_model), "Reward model is not correctly loaded."
 
     if not is_trainable:
         model.requires_grad_(False) # fix all model params

src/utils/other.py

@@ -126,21 +126,27 @@ def get_state_dict(model: torch.nn.Module) -> Dict[str, torch.Tensor]: # get sta
     return filtered_state_dict
 
 
-def load_trainable_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> None:
+def load_trainable_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool:
     weights_file = os.path.join(checkpoint_dir, WEIGHTS_NAME)
-    assert os.path.exists(weights_file), f"Provided path ({checkpoint_dir}) does not contain the pretrained weights."
+    if not os.path.exists(weights_file):
+        logger.warning("Provided path ({}) does not contain pre-trained weights.".format(checkpoint_dir))
+        return False
     model_state_dict = torch.load(weights_file, map_location="cpu")
     model.load_state_dict(model_state_dict, strict=False) # skip missing keys
+    return True
 
 
-def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> None:
+def load_valuehead_params(model: torch.nn.Module, checkpoint_dir: os.PathLike) -> bool:
     valuehead_file = os.path.join(checkpoint_dir, VALUE_HEAD_FILE_NAME)
-    assert os.path.exists(valuehead_file), f"Provided path ({checkpoint_dir}) does not contain the valuehead weights."
+    if not os.path.exists(valuehead_file):
+        logger.warning("Provided path ({}) does not contain valuehead weights.".format(checkpoint_dir))
+        return False
     valuehead_state_dict = torch.load(valuehead_file, map_location="cpu")
     model.register_buffer("reward_head_weight", valuehead_state_dict["summary.weight"])
     model.register_buffer("reward_head_bias", valuehead_state_dict["summary.bias"])
     model.register_buffer("default_head_weight", torch.zeros_like(valuehead_state_dict["summary.weight"]))
     model.register_buffer("default_head_bias", torch.zeros_like(valuehead_state_dict["summary.bias"]))
+    return True
 
 
 def smooth(scalars: List[float], weight: Optional[float] = 0.9) -> List[float]: