Set weights_only=True when using torch.load() (#12366)

Signed-off-by: Russell Bryant <rbryant@redhat.com>

vllm/assets/image.py

@@ -26,4 +26,4 @@ class ImageAsset:
         """
         image_path = get_vllm_public_assets(filename=f"{self.name}.pt",
                                             s3_prefix=VLM_IMAGES_DIR)
-        return torch.load(image_path, map_location="cpu")
+        return torch.load(image_path, map_location="cpu", weights_only=True)

vllm/lora/models.py

@@ -273,7 +273,8 @@ class LoRAModel(AdapterModel):
                 new_embeddings_tensor_path)
         elif os.path.isfile(new_embeddings_bin_file_path):
             embeddings = torch.load(new_embeddings_bin_file_path,
-                                    map_location=device)
+                                    map_location=device,
+                                    weights_only=True)
 
         return cls.from_lora_tensors(
             lora_model_id=get_lora_id()

vllm/model_executor/model_loader/weight_utils.py

@@ -93,7 +93,7 @@ def convert_bin_to_safetensor_file(
     pt_filename: str,
     sf_filename: str,
 ) -> None:
-    loaded = torch.load(pt_filename, map_location="cpu")
+    loaded = torch.load(pt_filename, map_location="cpu", weights_only=True)
     if "state_dict" in loaded:
         loaded = loaded["state_dict"]
     shared = _shared_pointers(loaded)
@@ -381,7 +381,9 @@ def np_cache_weights_iterator(
                     disable=not enable_tqdm,
                     bar_format=_BAR_FORMAT,
             ):
-                state = torch.load(bin_file, map_location="cpu")
+                state = torch.load(bin_file,
+                                   map_location="cpu",
+                                   weights_only=True)
                 for name, param in state.items():
                     param_path = os.path.join(np_folder, name)
                     with open(param_path, "wb") as f:
@@ -447,7 +449,7 @@ def pt_weights_iterator(
             disable=not enable_tqdm,
             bar_format=_BAR_FORMAT,
     ):
-        state = torch.load(bin_file, map_location="cpu")
+        state = torch.load(bin_file, map_location="cpu", weights_only=True)
         yield from state.items()
         del state
         torch.cuda.empty_cache()

vllm/prompt_adapter/utils.py

@@ -89,6 +89,7 @@ def load_peft_weights(model_id: str,
         adapters_weights = safe_load_file(filename, device=device)
     else:
         adapters_weights = torch.load(filename,
-                                      map_location=torch.device(device))
+                                      map_location=torch.device(device),
+                                      weights_only=True)
 
     return adapters_weights