BOFT bug fix when saving

src/peft/tuners/boft/layer.py

@@ -520,7 +520,7 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = N
                             f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
                         )
 
-                    self.base_layer.weight.data = orig_weight
+                    self.base_layer.weight.data = orig_weight.contiguous()
                 else:
                     butterfly_oft_mat, boft_s = self.get_delta_weight(active_adapter)
                     orig_weight = base_layer.weight.data.clone()
@@ -529,7 +529,7 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = N
                     orig_weight = torch.transpose(orig_weight, 0, 1)
                     orig_weight = orig_weight * boft_s
 
-                    self.base_layer.weight.data = orig_weight
+                    self.base_layer.weight.data = orig_weight.contiguous()
 
                 self.merged_adapters.append(active_adapter)
 
@@ -817,7 +817,7 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = N
                         self.out_features, self.in_features, base_layer.kernel_size[0], base_layer.kernel_size[0]
                     )
 
-                    self.base_layer.weight.data = orig_weight
+                    self.base_layer.weight.data = orig_weight.contiguous()
                 else:
                     butterfly_oft_mat, boft_s = self.get_delta_weight(active_adapter)
 
@@ -831,7 +831,7 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[list[str]] = N
                         self.out_features, self.in_features, base_layer.kernel_size[0], base_layer.kernel_size[0]
                     )
 
-                    self.base_layer.weight.data = orig_weight
+                    self.base_layer.weight.data = orig_weight.contiguous()
 
                 self.merged_adapters.append(active_adapter)
 

src/peft/tuners/boft/model.py

@@ -24,7 +24,12 @@
 from torch import nn
 from tqdm import tqdm
 
-from peft.tuners.tuners_utils import BaseTuner, BaseTunerLayer, check_target_module_exists
+from peft.tuners.tuners_utils import (
+    BaseTuner, 
+    BaseTunerLayer, 
+    check_target_module_exists,
+    onload_layer,
+)
 from peft.utils import (
     TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING,
     ModulesToSaveWrapper,
@@ -265,22 +270,30 @@ def _unload_and_optionally_merge(
         safe_merge: bool = False,
         adapter_names: Optional[List[str]] = None,
     ):
-        self._unloading_checks(adapter_names)
+        if merge:
+            self._check_merge_allowed()
+
         key_list = [key for key, _ in self.model.named_modules() if self.prefix not in key]
         desc = "Unloading " + ("and merging " if merge else "") + "model"
         for key in tqdm(key_list, disable=not progressbar, desc=desc):
             try:
                 parent, target, target_name = _get_submodules(self.model, key)
             except AttributeError:
                 continue
-
-            if hasattr(target, "base_layer"):
-                if merge:
-                    target.merge(safe_merge=safe_merge, adapter_names=adapter_names)
-                self._replace_module(parent, target_name, target.get_base_layer(), target)
-            elif isinstance(target, ModulesToSaveWrapper):
-                # save any additional trainable modules part of `modules_to_save`
-                setattr(parent, target_name, target.modules_to_save[target.active_adapter])
+            with onload_layer(target):
+                if hasattr(target, "base_layer"):
+                    if merge:
+                        target.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                    self._replace_module(parent, target_name, target.get_base_layer(), target)
+                elif isinstance(target, ModulesToSaveWrapper):
+                    # save any additional trainable modules part of `modules_to_save`
+                    new_module = target.modules_to_save[target.active_adapter]
+                    if hasattr(new_module, "base_layer"):
+                        # check if the module is itself a tuner layer
+                        if merge:
+                            new_module.merge(safe_merge=safe_merge, adapter_names=adapter_names)
+                        new_module = new_module.get_base_layer()
+                    setattr(parent, target_name, new_module)
 
         return self.model
 

src/peft/tuners/oft/layer.py

@@ -171,7 +171,7 @@ def merge(self, safe_merge: bool = False, adapter_names: Optional[List[str]] = N
                         f"NaNs detected in the merged weights. The adapter {active_adapter} seems to be broken"
                     )
 
-                base_layer.weight.data = new_weights
+                base_layer.weight.data = new_weights.contiguous()
                 self.merged_adapters.append(active_adapter)
 
     def unmerge(self) -> None:
@@ -215,7 +215,7 @@ def unmerge(self) -> None:
                             base_layer.kernel_size[1],
                         ]
                     )
-                base_layer.weight.data = orig_weights
+                base_layer.weight.data = orig_weights.contiguous()
 
     def get_delta_weight(self, adapter_name: str) -> torch.Tensor:
         rank = self.r[adapter_name]

tests/testing_common.py

@@ -763,6 +763,10 @@ def _test_safe_merge(self, model_id, config_cls, config_kwargs):
         # check that the logits are the same after unloading
         assert torch.allclose(logits_peft, logits_unloaded, atol=atol, rtol=rtol)
 
+        # serializing with safetensors works
+        from safetensors.torch import save_file
+        save_file(model_unloaded.state_dict(), os.path.join(tmp_dirname, "model.safetensors"))
+
     def _test_mixed_adapter_batches(self, model_id, config_cls, config_kwargs):
         # Test for mixing different adapters in a single batch by passing the adapter_names argument
         if config_cls not in (LoraConfig,):