FIX Bug when targeting an nn.Parameter on the root module

src/peft/tuners/tuners_utils.py

@@ -1083,6 +1083,15 @@ def strip_base_layer_from_name(module_name):
 
         def create_and_replace_param(module_name, key, param_name):
             # helper function to avoid duplication
+            if module_name == "":
+                # nn.Parameters that are registered directly on the top-level module (i.e. the module passed to
+                # get_peft_model) cannot be targeted. Wrapping the parameter would require replacing the module that
+                # holds it with lora.ParamWrapper, but that module is its own parent, so the wrapper ends up registered
+                # as a submodule of the very module it wraps. This creates a cyclic module graph, resulting in an error.
+                raise ValueError(
+                    f"Targeting an nn.Parameter on the top-level module is not supported (parameter '{param_name}'). "
+                )
+
             parent, target, target_name = _get_submodules(model, module_name)
             unwrapped_module_name = strip_base_layer_from_name(module_name)
             unwrapped_module = model.get_submodule(unwrapped_module_name)

tests/test_target_parameters.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
+
 import pytest
 import torch
 from torch import nn
@@ -598,3 +600,20 @@ def test_target_parameter_init_does_not_warn_about_unknown_layer_type(self, recw
             warn_messages = (w.message.args[0] for w in recwarn.list)
             msg_start = "Unsupported layer type"
             assert not any(msg.startswith(msg_start) for msg in warn_messages)
+
+    def test_target_parameter_on_top_level_module_raises(self):
+        # nn.Parameters that are registered directly on the top-level module (i.e. the module passed to get_peft_model)
+        # cannot be targeted. Wrapping the parameter would require replacing the module that holds it with
+        # lora.ParamWrapper, but that module is its own parent, so the wrapper ends up registered as a submodule of the
+        # very module it wraps. This creates a cyclic module graph, resulting in an error.
+
+        class MyModule(nn.Module):
+            # module with a 2d and a 3d nn.Parameter registered directly on the top-level module
+            def __init__(self):
+                super().__init__()
+                self.param = nn.Parameter(torch.zeros(10, 10))
+
+        config = LoraConfig(target_parameters=["param"])
+        msg = re.escape("Targeting an nn.Parameter on the top-level module is not supported (parameter 'param')")
+        with pytest.raises(ValueError, match=msg):
+            get_peft_model(MyModule(), config)