Fix: take into account meta device

src/transformers/modeling_utils.py

@@ -361,6 +361,9 @@ def check_support_param_buffer_assignment(model_to_load, state_dict, start_prefi
 
     Note: We fully disable this if we are using `deepspeed`
     """
+    if model_to_load.device.type == "meta":
+        return False
+
     if len([key for key in state_dict if key.startswith(start_prefix)]) == 0:
         return False
 
@@ -375,7 +378,7 @@ def check_support_param_buffer_assignment(model_to_load, state_dict, start_prefi
         return False
 
     # If the model does, the incoming `state_dict` and the `model_to_load` must be the same dtype
-    first_key = list(model_to_load.state_dict().keys())[0]
+    first_key = next(iter(model_to_load.state_dict().keys()))
     if start_prefix + first_key in state_dict:
         return state_dict[start_prefix + first_key].dtype == model_to_load.state_dict()[first_key].dtype
 

tests/utils/test_modeling_utils.py

@@ -14,6 +14,7 @@
 # limitations under the License.
 import copy
 import glob
+import itertools
 import json
 import os
 import os.path
@@ -459,6 +460,19 @@ def test_model_from_config_torch_dtype_str(self):
         with self.assertRaises(ValueError):
             model = AutoModel.from_pretrained(TINY_T5, torch_dtype="int64")
 
+    @require_torch
+    def test_model_from_pretrained_meta_device(self):
+        def is_on_meta(model_id, dtype):
+            with torch.device("meta"):
+                model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype)
+                return all(value.device.type == "meta" for value in model.state_dict().values())
+
+        model_ids = ("fxmarty/tiny-llama-fast-tokenizer", "fxmarty/small-llama-testing")
+        dtypes = (None, "auto", torch.float16)
+
+        for model_id, dtype in itertools.product(model_ids, dtypes):
+            self.assertTrue(is_on_meta(model_id, dtype))
+
     def test_model_from_pretrained_torch_dtype(self):
         # test that the model can be instantiated with dtype of either
         # 1. explicit from_pretrained's torch_dtype argument