fix torch gc

src/llamafactory/extras/misc.py

@@ -212,12 +212,17 @@ def has_tokenized_data(path: os.PathLike) -> bool:
 
 def torch_gc() -> None:
     r"""
-    Collects GPU memory.
+    Collects GPU or NPU memory.
     """
     gc.collect()
-    if torch.cuda.is_available():
+    if is_torch_xpu_available():
+        torch.xpu.empty_cache()
+    elif is_torch_npu_available():
+        torch.npu.empty_cache()
+    elif is_torch_mps_available():
+        torch.mps.empty_cache()
+    elif is_torch_cuda_available():
         torch.cuda.empty_cache()
-        torch.cuda.ipc_collect()
 
 
 def try_download_model_from_ms(model_args: "ModelArguments") -> str:

src/llamafactory/model/utils/embedding.py

@@ -15,7 +15,7 @@ if TYPE_CHECKING:
 logger = get_logger(__name__)
 
 
-def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int) -> None:
+def _noisy_mean_initialization(embed_weight: "torch.Tensor", num_new_tokens: int) -> None:
     embedding_dim = embed_weight.size(1)
     avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True)
     noise_weight = torch.empty_like(embed_weight[-num_new_tokens:])

src/llamafactory/model/utils/valuehead.py

@@ -23,6 +23,7 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
     Returns: dict with keys `v_head.summary.weight` and `v_head.summary.bias`.
     """
     kwargs = {"path_or_repo_id": path_or_repo_id, "cache_dir": model_args.cache_dir, "token": model_args.hf_hub_token}
+    err_text = ""
 
     try:
         from safetensors import safe_open
@@ -31,16 +32,16 @@ def load_valuehead_params(path_or_repo_id: str, model_args: "ModelArguments") ->
         with safe_open(vhead_file, framework="pt", device="cpu") as f:
             return {key: f.get_tensor(key) for key in f.keys()}
     except Exception as err:
-        logger.info("Failed to load {}: {}".format(V_HEAD_SAFE_WEIGHTS_NAME, str(err)))
+        err_text = str(err)
 
     try:
         vhead_file = cached_file(filename=V_HEAD_WEIGHTS_NAME, **kwargs)
         return torch.load(vhead_file, map_location="cpu")
     except Exception as err:
-        logger.info("Failed to load {}: {}".format(V_HEAD_WEIGHTS_NAME, str(err)))
+        err_text = str(err)
 
-    logger.info("Provided path ({}) does not contain value head weights.".format(path_or_repo_id))
-    logger.info("Ignore these messages if you are not resuming the training of a value head model.")
+    logger.info("Provided path ({}) does not contain value head weights: {}.".format(path_or_repo_id, err_text))
+    logger.info("Ignore the above message if you are not resuming the training of a value head model.")
     return None