Fix weights_only

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -330,10 +330,11 @@ def convert_pt_checkpoint_to_tf(
     if compare_with_pt_model:
         tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network
 
+        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
         state_dict = torch.load(
             pytorch_checkpoint_path,
             map_location="cpu",
-            weights_only=is_torch_greater_or_equal_than_1_13,
+            **weights_only_kwarg,
         )
         pt_model = pt_model_class.from_pretrained(
             pretrained_model_name_or_path=None, config=config, state_dict=state_dict

src/transformers/modeling_flax_pytorch_utils.py

@@ -74,7 +74,8 @@ def load_pytorch_checkpoint_in_flax_state_dict(
                 )
                 raise
 
-            pt_state_dict = torch.load(pt_path, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
+            weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+            pt_state_dict = torch.load(pt_path, map_location="cpu", **weights_only_kwarg)
             logger.info(f"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()):,} parameters.")
 
         flax_state_dict = convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model)
@@ -252,7 +253,8 @@ def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
     flax_state_dict = {}
     for shard_file in shard_filenames:
         # load using msgpack utils
-        pt_state_dict = torch.load(shard_file, weights_only=is_torch_greater_or_equal_than_1_13)
+        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+        pt_state_dict = torch.load(shard_file, **weights_only_kwarg)
         pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
 
         model_prefix = flax_model.base_model_prefix

src/transformers/modeling_tf_pytorch_utils.py

@@ -188,7 +188,8 @@ def load_pytorch_checkpoint_in_tf2_model(
         if pt_path.endswith(".safetensors"):
             state_dict = safe_load_file(pt_path)
         else:
-            state_dict = torch.load(pt_path, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
+            weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+            state_dict = torch.load(pt_path, map_location="cpu", **weights_only_kwarg)
 
         pt_state_dict.update(state_dict)
 

src/transformers/modeling_utils.py

@@ -482,11 +482,8 @@ def load_sharded_checkpoint(model, folder, strict=True, prefer_safe=True):
             error_message += f"\nMissing key(s): {str_unexpected_keys}."
         raise RuntimeError(error_message)
 
-    loader = (
-        safe_load_file
-        if load_safe
-        else partial(torch.load, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
-    )
+    weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
+    loader = safe_load_file if load_safe else partial(torch.load, map_location="cpu", **weights_only_kwarg)
 
     for shard_file in shard_files:
         state_dict = loader(os.path.join(folder, shard_file))
@@ -530,10 +527,11 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
             and is_zipfile(checkpoint_file)
         ):
             extra_args = {"mmap": True}
+        weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
         return torch.load(
             checkpoint_file,
             map_location=map_location,
-            weights_only=is_torch_greater_or_equal_than_1_13,
+            **weights_only_kwarg,
             **extra_args,
         )
     except Exception as e:

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1334,10 +1334,11 @@ def load_adapter(self, target_lang: str, force_load=True, **kwargs):
                     cache_dir=cache_dir,
                 )
 
+                weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
                 state_dict = torch.load(
                     weight_path,
                     map_location="cpu",
-                    weights_only=is_torch_greater_or_equal_than_1_13,
+                    **weights_only_kwarg,
                 )
 
             except EnvironmentError:

src/transformers/trainer.py

@@ -2091,6 +2091,7 @@ def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
                 )
 
         if os.path.isfile(weights_file) or os.path.isfile(safe_weights_file) or is_fsdp_ckpt:
+            weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
             # If the model is on the GPU, it still works!
             if is_sagemaker_mp_enabled():
                 if os.path.isfile(os.path.join(resume_from_checkpoint, "user_content.pt")):
@@ -2109,7 +2110,7 @@ def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
                     state_dict = torch.load(
                         weights_file,
                         map_location="cpu",
-                        weights_only=is_torch_greater_or_equal_than_1_13,
+                        **weights_only_kwarg,
                     )
                     # Required for smp to not auto-translate state_dict from hf to smp (is already smp).
                     state_dict["_smp_is_partial"] = False
@@ -2126,7 +2127,7 @@ def _load_from_checkpoint(self, resume_from_checkpoint, model=None):
                     state_dict = torch.load(
                         weights_file,
                         map_location="cpu",
-                        weights_only=is_torch_greater_or_equal_than_1_13,
+                        **weights_only_kwarg,
                     )
 
                 # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
@@ -2179,6 +2180,7 @@ def _load_best_model(self):
             or os.path.exists(best_safe_adapter_model_path)
         ):
             has_been_loaded = True
+            weights_only_kwarg = {"weights_only": True} if is_torch_greater_or_equal_than_1_13 else {}
             if is_sagemaker_mp_enabled():
                 if os.path.isfile(os.path.join(self.state.best_model_checkpoint, "user_content.pt")):
                     # If the 'user_content.pt' file exists, load with the new smp api.
@@ -2198,7 +2200,7 @@ def _load_best_model(self):
                         state_dict = torch.load(
                             best_model_path,
                             map_location="cpu",
-                            weights_only=is_torch_greater_or_equal_than_1_13,
+                            **weights_only_kwarg,
                         )
 
                     state_dict["_smp_is_partial"] = False
@@ -2231,7 +2233,7 @@ def _load_best_model(self):
                         state_dict = torch.load(
                             best_model_path,
                             map_location="cpu",
-                            weights_only=is_torch_greater_or_equal_than_1_13,
+                            **weights_only_kwarg,
                         )
 
                     # If the model is on the GPU, it still works!