Use `weights_only` only if torch >= 1.13 (#28506)

* fix

* fix

* fix

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -129,6 +129,7 @@ if is_torch_available():
         XLMWithLMHeadModel,
         XLNetLMHeadModel,
     )
+    from .pytorch_utils import is_torch_greater_or_equal_than_1_13
 
 
 logging.set_verbosity_info()
@@ -329,7 +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
 
-        state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu", weights_only=True)
+        state_dict = torch.load(
+            pytorch_checkpoint_path,
+            map_location="cpu",
+            weights_only=is_torch_greater_or_equal_than_1_13,
+        )
         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

@@ -50,6 +50,8 @@ def load_pytorch_checkpoint_in_flax_state_dict(
     """Load pytorch checkpoints in a flax model"""
     try:
         import torch  # noqa: F401
+
+        from .pytorch_utils import is_torch_greater_or_equal_than_1_13  # noqa: F401
     except (ImportError, ModuleNotFoundError):
         logger.error(
             "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
@@ -68,7 +70,7 @@ def load_pytorch_checkpoint_in_flax_state_dict(
                 for k in f.keys():
                     pt_state_dict[k] = f.get_tensor(k)
         else:
-            pt_state_dict = torch.load(pt_path, map_location="cpu", weights_only=True)
+            pt_state_dict = torch.load(pt_path, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
         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)
@@ -245,11 +247,13 @@ def convert_pytorch_state_dict_to_flax(pt_state_dict, flax_model):
 def convert_pytorch_sharded_state_dict_to_flax(shard_filenames, flax_model):
     import torch
 
+    from .pytorch_utils import is_torch_greater_or_equal_than_1_13
+
     # Load the index
     flax_state_dict = {}
     for shard_file in shard_filenames:
         # load using msgpack utils
-        pt_state_dict = torch.load(shard_file, weights_only=True)
+        pt_state_dict = torch.load(shard_file, weights_only=is_torch_greater_or_equal_than_1_13)
         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

@@ -167,6 +167,8 @@ def load_pytorch_checkpoint_in_tf2_model(
         import tensorflow as tf  # noqa: F401
         import torch  # noqa: F401
         from safetensors.torch import load_file as safe_load_file  # noqa: F401
+
+        from .pytorch_utils import is_torch_greater_or_equal_than_1_13  # noqa: F401
     except ImportError:
         logger.error(
             "Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see "
@@ -186,7 +188,7 @@ 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=True)
+            state_dict = torch.load(pt_path, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
 
         pt_state_dict.update(state_dict)
 

src/transformers/modeling_utils.py

@@ -48,6 +48,7 @@ from .pytorch_utils import (  # noqa: F401
     apply_chunking_to_forward,
     find_pruneable_heads_and_indices,
     id_tensor_storage,
+    is_torch_greater_or_equal_than_1_13,
     prune_conv1d_layer,
     prune_layer,
     prune_linear_layer,
@@ -481,7 +482,11 @@ 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=True)
+    loader = (
+        safe_load_file
+        if load_safe
+        else partial(torch.load, map_location="cpu", weights_only=is_torch_greater_or_equal_than_1_13)
+    )
 
     for shard_file in shard_files:
         state_dict = loader(os.path.join(folder, shard_file))
@@ -525,7 +530,12 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
             and is_zipfile(checkpoint_file)
         ):
             extra_args = {"mmap": True}
-        return torch.load(checkpoint_file, map_location=map_location, weights_only=True, **extra_args)
+        return torch.load(
+            checkpoint_file,
+            map_location=map_location,
+            weights_only=is_torch_greater_or_equal_than_1_13,
+            **extra_args,
+        )
     except Exception as e:
         try:
             with open(checkpoint_file) as f:

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -37,6 +37,7 @@ from ...modeling_outputs import (
     XVectorOutput,
 )
 from ...modeling_utils import PreTrainedModel
+from ...pytorch_utils import is_torch_greater_or_equal_than_1_13
 from ...utils import (
     ModelOutput,
     add_code_sample_docstrings,
@@ -1333,7 +1334,11 @@ class Wav2Vec2PreTrainedModel(PreTrainedModel):
                     cache_dir=cache_dir,
                 )
 
-                state_dict = torch.load(weight_path, map_location="cpu", weights_only=True)
+                state_dict = torch.load(
+                    weight_path,
+                    map_location="cpu",
+                    weights_only=is_torch_greater_or_equal_than_1_13,
+                )
 
             except EnvironmentError:
                 # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted

src/transformers/trainer.py

@@ -64,7 +64,7 @@ from .modelcard import TrainingSummary
 from .modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model
 from .models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
 from .optimization import Adafactor, get_scheduler
-from .pytorch_utils import ALL_LAYERNORM_LAYERS
+from .pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
 from .tokenization_utils_base import PreTrainedTokenizerBase
 from .trainer_callback import (
     CallbackHandler,
@@ -2103,7 +2103,11 @@ class Trainer:
                         logger.warning(
                             "Enabling FP16 and loading from smp < 1.10 checkpoint together is not suppported."
                         )
-                    state_dict = torch.load(weights_file, map_location="cpu", weights_only=True)
+                    state_dict = torch.load(
+                        weights_file,
+                        map_location="cpu",
+                        weights_only=is_torch_greater_or_equal_than_1_13,
+                    )
                     # Required for smp to not auto-translate state_dict from hf to smp (is already smp).
                     state_dict["_smp_is_partial"] = False
                     load_result = model.load_state_dict(state_dict, strict=True)
@@ -2116,7 +2120,11 @@ class Trainer:
                 if self.args.save_safetensors and os.path.isfile(safe_weights_file):
                     state_dict = safetensors.torch.load_file(safe_weights_file, device="cpu")
                 else:
-                    state_dict = torch.load(weights_file, map_location="cpu", weights_only=True)
+                    state_dict = torch.load(
+                        weights_file,
+                        map_location="cpu",
+                        weights_only=is_torch_greater_or_equal_than_1_13,
+                    )
 
                 # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963
                 # which takes *args instead of **kwargs
@@ -2184,7 +2192,11 @@ class Trainer:
                     if self.args.save_safetensors and os.path.isfile(best_safe_model_path):
                         state_dict = safetensors.torch.load_file(best_safe_model_path, device="cpu")
                     else:
-                        state_dict = torch.load(best_model_path, map_location="cpu", weights_only=True)
+                        state_dict = torch.load(
+                            best_model_path,
+                            map_location="cpu",
+                            weights_only=is_torch_greater_or_equal_than_1_13,
+                        )
 
                     state_dict["_smp_is_partial"] = False
                     load_result = model.load_state_dict(state_dict, strict=True)
@@ -2213,7 +2225,11 @@ class Trainer:
                     if self.args.save_safetensors and os.path.isfile(best_safe_model_path):
                         state_dict = safetensors.torch.load_file(best_safe_model_path, device="cpu")
                     else:
-                        state_dict = torch.load(best_model_path, map_location="cpu", weights_only=True)
+                        state_dict = torch.load(
+                            best_model_path,
+                            map_location="cpu",
+                            weights_only=is_torch_greater_or_equal_than_1_13,
+                        )
 
                     # If the model is on the GPU, it still works!
                     # workaround for FSDP bug https://github.com/pytorch/pytorch/issues/82963