Breakout fix of #2481 for #2477, patch release prep (#2497)

This change is a breakout of the changes in PR #2481 to form a patch release.
There are no additional tests but the HF_HUB_OFFLINE feature was merged to improve the CI experience.

* Testing common uses situational HF_HUB_OFFLINE (#2490)

Employ offline mode when the model was already accessed once from the hub in order to speed up the CI and make the process less prone to rate limiting.

The idea here is that we can mark contexts that, once they were visited once for a specific model id, we can assume that they are cached locally and can set HF_HUB_OFFLINE=1 for this context. This PR tests this concept for testing_common which is already a big chunk of the tests and probably has the biggest gain given the amount of change.

We already saw that the assumption does not always hold true: for the prompt tuning tests (_test_prepare_input_for_generation) there is a case where one time the tokenizer is not used for model X and after that time the tokenizer is used - since we're setting the hub to offline for the second time the tokenizer from_pretrained call will fail. This problem is alleviated by adding the tokenizer name to the model id as cache identifier.

(cherry picked from commit 10839648629571ef0bbcaed627e320765b871293)
(Removed delete adapter tests)

setup.py

@@ -15,7 +15,7 @@
 from setuptools import find_packages, setup
 
 
-VERSION = "0.15.1"
+VERSION = "0.15.2"
 
 extras = {}
 extras["quality"] = [

src/peft/__init__.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.15.1"
+__version__ = "0.15.2"
 
 from .auto import (
     MODEL_TYPE_TO_PEFT_MODEL_MAPPING,

src/peft/peft_model.py

@@ -953,7 +953,7 @@ class PeftModel(PushToHubMixin, torch.nn.Module):
             else:
                 self.modules_to_save.update(peft_config.modules_to_save)
             # this may add a new ModulesToSaveWrapper
-            _set_trainable(self, adapter_name, module_names=peft_config.modules_to_save)
+            _set_trainable(self, adapter_name, module_names=getattr(peft_config, "modules_to_save", None))
 
         if getattr(peft_config, "trainable_token_indices", None) is not None:
             if isinstance(peft_config.trainable_token_indices, dict):
@@ -1497,13 +1497,14 @@ class PeftModelForSequenceClassification(PeftModel):
             else:
                 peft_config.modules_to_save.extend(classifier_module_names)
 
-        for name, _ in self.base_model.named_children():
+        if self.modules_to_save:
-            if any(module_name in name for module_name in self.modules_to_save):
+            for name, _ in self.base_model.named_children():
-                self.cls_layer_name = name
+                if any(module_name in name for module_name in self.modules_to_save):
-                break
+                    self.cls_layer_name = name
+                    break
 
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
-        _set_trainable(self, adapter_name, module_names=peft_config.modules_to_save)
+        _set_trainable(self, adapter_name, module_names=getattr(peft_config, "modules_to_save", None))
 
     def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """
@@ -2288,13 +2289,14 @@ class PeftModelForTokenClassification(PeftModel):
             else:
                 peft_config.modules_to_save.extend(classifier_module_names)
 
-        for name, _ in self.base_model.named_children():
+        if self.modules_to_save is not None:
-            if any(module_name in name for module_name in self.modules_to_save):
+            for name, _ in self.base_model.named_children():
-                self.cls_layer_name = name
+                if any(module_name in name for module_name in self.modules_to_save):
-                break
+                    self.cls_layer_name = name
+                    break
 
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
-        _set_trainable(self, adapter_name, module_names=peft_config.modules_to_save)
+        _set_trainable(self, adapter_name, module_names=getattr(peft_config, "modules_to_save", None))
 
     def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """
@@ -2515,7 +2517,7 @@ class PeftModelForQuestionAnswering(PeftModel):
                 break
 
         # to make sure classifier layer is trainable; this may add a new ModulesToSaveWrapper
-        _set_trainable(self, adapter_name, module_names=peft_config.modules_to_save)
+        _set_trainable(self, adapter_name, module_names=getattr(peft_config, "modules_to_save", None))
 
     def add_adapter(self, adapter_name: str, peft_config: PeftConfig, low_cpu_mem_usage: bool = False) -> None:
         """

src/peft/utils/other.py

@@ -749,6 +749,9 @@ def _set_trainable(
     if wrapper_cls is None:
         wrapper_cls = ModulesToSaveWrapper
 
+    if module_names is None:
+        return
+
     trainable_modules = []
     found_modules = set()
     # disable removal of duplicates to support targeting tied weights