refactor adapter hparam

Former-commit-id: f82aece9ebd6df83a7a005cc7cbbcec07fa6e14d

src/llmtuner/model/utils.py

@@ -1,5 +1,4 @@
 import torch
-import inspect
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, Tuple
 
 from transformers.utils import cached_file
@@ -86,29 +85,26 @@ def get_modelcard_args(
     }
 
 
-def load_valuehead_params(
-    path_or_repo_id: str,
-    model_args: "ModelArguments"
-) -> Dict[str, torch.Tensor]:
+def load_valuehead_params(model_args: "ModelArguments") -> Dict[str, torch.Tensor]:
     r"""
     Loads value head parameters from Hugging Face Hub or local disk.
 
     Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
     """
+    if model_args.adapter_name_or_path is not None:
+        path_or_repo_id = model_args.adapter_name_or_path[-1]
+    else:
+        path_or_repo_id = model_args.model_name_or_path
+
     kwargs = {
         "path_or_repo_id": path_or_repo_id,
-        "cache_dir": model_args.cache_dir
+        "cache_dir": model_args.cache_dir,
+        "token": model_args.hf_hub_token
     }
 
-    if "token" in inspect.signature(cached_file).parameters:
-        kwargs["token"] = model_args.hf_hub_token
-    elif "use_auth_token" in inspect.signature(cached_file).parameters: # for transformers==4.31.0
-        kwargs["use_auth_token"] = model_args.hf_hub_token
-    else:
-        logger.warning("Ignore `hf_hub_token` since matched parameter is not found.")
-
     try:
         vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
+        logger.info("Loaded valuehead from {}".format(path_or_repo_id))
         return torch.load(vhead_file, map_location="cpu")
     except Exception as err:
         logger.info("Failed to load {}: {}".format(WEIGHTS_NAME, str(err)))
@@ -116,6 +112,7 @@ def load_valuehead_params(
     try:
         from safetensors import safe_open
         vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
+        logger.info("Loaded valuehead from {}".format(path_or_repo_id))
         with safe_open(vhead_file, framework="pt", device="cpu") as f:
             return {
                 "v_head.summary.weight": f.get_tensor("v_head.summary.weight"),