FIX #2295: Warn when user reloads modified model (#2306)

When modifying a model with `get_peft_model` that was already modified
in the same way, even specifying a different config may not change
the trainable parameter count, e.g. when specifying target modules that
are only a subset of the previous target modules.

With this patch a warning will be issued with a hint to `.unload()`
when calling `get_peft_model` on an already modified model.

src/peft/mapping.py

@@ -70,7 +70,7 @@ from .tuners import (
     VeraModel,
     XLoraConfig,
 )
-from .tuners.tuners_utils import BaseTuner
+from .tuners.tuners_utils import BaseTuner, BaseTunerLayer
 from .utils import _prepare_prompt_learning_config
 from .utils.constants import PEFT_TYPE_TO_PREFIX_MAPPING
 
@@ -182,6 +182,15 @@ def get_peft_model(
     new_name = model.__dict__.get("name_or_path", None)
     peft_config.base_model_name_or_path = new_name
 
+    # Especially in notebook environments there could be a case that a user wants to experiment with different
+    # configuration values. However, it is likely that there won't be any changes for new configs on an already
+    # initialized PEFT model. The best we can do is warn the user about it.
+    if any(isinstance(module, BaseTunerLayer) for module in model.modules()):
+        warnings.warn(
+            "You are trying to modify a model with PEFT for a second time. If you want to reload the model with a "
+            "different config, make sure to call `.unload()` before."
+        )
+
     if (old_name is not None) and (old_name != new_name):
         warnings.warn(
             f"The PEFT config's `base_model_name_or_path` was renamed from '{old_name}' to '{new_name}'. "

tests/test_mapping.py

@@ -0,0 +1,55 @@
+# Copyright 2025-present the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import pytest
+import torch
+
+from peft import LoraConfig, get_peft_model
+
+
+class TestGetPeftModel:
+    RELOAD_WARNING_EXPECTED_MATCH = r"You are trying to modify a model .*"
+
+    @pytest.fixture
+    def lora_config_0(self):
+        return LoraConfig(target_modules="0")
+
+    @pytest.fixture
+    def base_model(self):
+        return torch.nn.Sequential(torch.nn.Linear(10, 2), torch.nn.Linear(2, 10))
+
+    def test_get_peft_model_warns_when_reloading_model(self, lora_config_0, base_model):
+        get_peft_model(base_model, lora_config_0)
+
+        with pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH):
+            get_peft_model(base_model, lora_config_0)
+
+    def test_get_peft_model_proposed_fix_in_warning_helps(self, lora_config_0, base_model, recwarn):
+        peft_model = get_peft_model(base_model, lora_config_0)
+        peft_model.unload()
+        get_peft_model(base_model, lora_config_0)
+
+        warning_checker = pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH)
+
+        for warning in recwarn:
+            if warning_checker.matches(warning):
+                pytest.fail("Warning raised even though model was unloaded.")
+
+    def test_get_peft_model_repeated_invocation(self, lora_config_0, base_model):
+        peft_model = get_peft_model(base_model, lora_config_0)
+
+        # use direct-addressing of the other layer to accomodate for the nested model
+        lora_config_1 = LoraConfig(target_modules="base_model.model.1")
+
+        with pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH):
+            get_peft_model(peft_model, lora_config_1)