Fix mismatching loading in from_pretrained with/without accelerate (#28414)

* fix mismatching behavior in from_pretrained with/without accelerate

* meaningful refactor

* remove added space

* add test

* fix model on the hub

* comment

* use tiny model

* style

src/transformers/modeling_utils.py

@@ -756,18 +756,23 @@ def _load_state_dict_into_meta_model(
             else:
                 param = param.to(dtype)
 
-        # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model
+        # For compatibility with PyTorch load_state_dict which converts state dict dtype to existing dtype in model, and which
-        if dtype is None:
+        # uses `param.copy_(input_param)` that preserves the contiguity of the parameter in the model.
-            old_param = model
+        # Reference: https://github.com/pytorch/pytorch/blob/db79ceb110f6646523019a59bbd7b838f43d4a86/torch/nn/modules/module.py#L2040C29-L2040C29
-            splits = param_name.split(".")
+        old_param = model
-            for split in splits:
+        splits = param_name.split(".")
-                old_param = getattr(old_param, split)
+        for split in splits:
-                if old_param is None:
+            old_param = getattr(old_param, split)
-                    break
+            if old_param is None:
+                break
 
-            if old_param is not None:
+        if old_param is not None:
+            if dtype is None:
                 param = param.to(old_param.dtype)
 
+            if old_param.is_contiguous():
+                param = param.contiguous()
+
         set_module_kwargs["value"] = param
 
         if device_map is None:

tests/test_modeling_utils.py

@@ -34,6 +34,7 @@ from requests.exceptions import HTTPError
 from transformers import (
     AutoConfig,
     AutoModel,
+    OwlViTForObjectDetection,
     PretrainedConfig,
     is_torch_available,
     logging,
@@ -835,6 +836,23 @@ class ModelUtilsTest(TestCasePlus):
             outputs2 = new_model_with_offload(inputs)
             self.assertTrue(torch.allclose(outputs1[0].cpu(), outputs2[0].cpu()))
 
+    @slow
+    @require_torch
+    def test_from_pretrained_non_contiguous_checkpoint(self):
+        # See: https://github.com/huggingface/transformers/pull/28414
+        # Tiny models on the Hub have contiguous weights, contrarily to google/owlvit
+        model = OwlViTForObjectDetection.from_pretrained("fxmarty/owlvit-tiny-non-contiguous-weight")
+        self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous())
+
+        model = OwlViTForObjectDetection.from_pretrained(
+            "fxmarty/owlvit-tiny-non-contiguous-weight", device_map="auto"
+        )
+        self.assertTrue(model.owlvit.visual_projection.weight.is_contiguous())
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir, safe_serialization=False)
+            model.save_pretrained(tmp_dir, safe_serialization=True)
+
     def test_cached_files_are_used_when_internet_is_down(self):
         # A mock response for an HTTP head request to emulate server down
         response_mock = mock.Mock()