Fix disk offload for full safetensors checkpoints (#20497)

src/transformers/modeling_utils.py

@@ -597,6 +597,9 @@ def _load_state_dict_into_meta_model(
         # in int/uint/bool and not cast them.
         if dtype is not None and torch.is_floating_point(param):
             param = param.to(dtype)
+        # For compatibility with PyTorch which loads float16/bfloat16 weights in fp32
+        if is_safetensors and dtype is None and torch.is_floating_point(param):
+            param = param.to(torch.float32)
 
         if device_map is None:
             param_device = "cpu"
@@ -2452,6 +2455,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             if offload_state_dict is None:
                 offload_state_dict = True
 
+        is_sharded_safetensors = is_safetensors and sharded_metadata is not None
         # Retrieve missing & unexpected_keys
         model_state_dict = model.state_dict()
         expected_keys = list(model_state_dict.keys())
@@ -2567,12 +2571,21 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
 
         folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
         if device_map is not None and is_safetensors:
-            param_device_map = expand_device_map(device_map, sharded_metadata["all_checkpoint_keys"])
+            param_device_map = expand_device_map(device_map, original_loaded_keys)
 
-            str_dtype = str(dtype).replace("torch.", "")
+            str_dtype = str(dtype).replace("torch.", "") if dtype is not None else "float32"
+            if sharded_metadata is None:
+                archive_file = (
+                    resolved_archive_file[0]
+                    if isinstance(resolved_archive_file, (list, tuple))
+                    else resolved_archive_file
+                )
+                weight_map = {p: archive_file for p in original_loaded_keys}
+            else:
+                weight_map = {p: os.path.join(folder, f) for p, f in sharded_metadata["weight_map"].items()}
             offload_index = {
-                p: {"safetensors_file": os.path.join(folder, f), "weight_name": p, "dtype": str_dtype}
-                for p, f in sharded_metadata["weight_map"].items()
+                p: {"safetensors_file": f, "weight_name": p, "dtype": str_dtype}
+                for p, f in weight_map.items()
                 if param_device_map[p] == "disk"
             }
 
@@ -2606,7 +2619,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 state_dict_folder = None
                 state_dict_index = None
 
-            if is_safetensors:
+            if is_sharded_safetensors:
                 disk_only_shard_files = get_disk_only_shard_files(device_map, sharded_metadata=sharded_metadata)
                 disk_only_shard_files = [os.path.join(folder, f) for f in disk_only_shard_files]
             else: