FIX huggingface#2295: Warn when user reloads modified model

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

@@ -31,6 +31,7 @@
     PeftModelForSeq2SeqLM,
     PeftModelForSequenceClassification,
     PeftModelForTokenClassification,
+    get_layer_status,
 )
 from .tuners import (
     AdaLoraConfig,
@@ -181,6 +182,21 @@ 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.
+    try:
+        if len(get_layer_status(model)) > 0:
+            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."
+            )
+    except ValueError:
+        # not a PEFT model or no adapters in use
+        pass
+
     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 +1,39 @@
+import pytest
+import torch
+
+
+class TestGetPeftModel:
+    RELOAD_WARNING_EXPECTED_MATCH = r"You are trying to modify a model .*"
+
+    @pytest.fixture
+    def get_peft_model(self):
+        from peft import get_peft_model
+
+        return get_peft_model
+
+    @pytest.fixture
+    def lora_config(self):
+        from peft import LoraConfig
+
+        return LoraConfig(target_modules="0")
+
+    @pytest.fixture
+    def base_model(self):
+        return torch.nn.Sequential(torch.nn.Linear(10, 2))
+
+    def test_get_peft_model_warns_when_reloading_model(self, get_peft_model, lora_config, base_model):
+        get_peft_model(base_model, lora_config)
+
+        with pytest.warns(UserWarning, match=self.RELOAD_WARNING_EXPECTED_MATCH):
+            get_peft_model(base_model, lora_config)
+
+    def test_get_peft_model_proposed_fix_in_warning_help(self, get_peft_model, lora_config, base_model, recwarn):
+        peft_model = get_peft_model(base_model, lora_config)
+        peft_model.unload()
+        get_peft_model(base_model, lora_config)
+
+        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.")