PiSSA, OLoRA: Delete initial adapter after conversion instead of the active adapter (huggingface#1933)

Resolves huggingface#1860

As discussed in that issue, it's not user friendly to delete the default
adapter of a PiSSA/OLoRA model after calling save_pretrained with weight
conversion. Instead, it is much more intuitive to delete the initial
adapter instead, since it is loaded inside the method and not by the
user, so it's really an implementation detail.

Apart from this, I made the following related changes:

- Put everything in a try ... finally to ensure that the initial adapter
  does not hang around if there is an error (thus not hogging memory).
- Renamed initial_adapter to initial_adapter_name, to make it clear that
  this is the name and not the adapter itself.

Author: BenjaminBossan

src/peft/peft_model.py

@@ -271,24 +271,29 @@ def save_mutated_as_lora(peft_config, path_initial_model_for_weight_conversion,
                 str(peft_config.init_lora_weights).lower().startswith(prefix) for prefix in ["pissa", "olora", "true"]
             ):
                 warnings.warn(
-                    "`path_initial_model_for_weight_conversion` only works for converting a PiSSA or OLoRA adapter to a LoRA adapter"
+                    "`path_initial_model_for_weight_conversion` only works for converting a PiSSA or OLoRA adapter to "
+                    "a LoRA adapter"
                 )
-            initial_adapter = os.path.basename(path_initial_model_for_weight_conversion)
-            self.load_adapter(
-                os.path.dirname(path_initial_model_for_weight_conversion),
-                subfolder=initial_adapter,
-                adapter_name=initial_adapter,
-            )
-            if any(
-                str(self.peft_config[initial_adapter].init_lora_weights).lower().startswith(prefix)
-                for prefix in ["pissa", "olora"]
-            ):
-                raise ValueError(
-                    "The `init_lora_weights` parameter of the initial adapter should be set to `True`. "
-                    "Otherwise, `self.load_adapter` will subtract the decomposed values again based on the residual model."
+            initial_adapter_name = os.path.basename(path_initial_model_for_weight_conversion)
+            try:
+                self.load_adapter(
+                    os.path.dirname(path_initial_model_for_weight_conversion),
+                    subfolder=initial_adapter_name,
+                    adapter_name=initial_adapter_name,
+                )
+                is_pissa = str(self.peft_config[initial_adapter_name].init_lora_weights).lower().startswith("pissa")
+                is_olora = str(self.peft_config[initial_adapter_name].init_lora_weights).lower() == "olora"
+                if is_pissa or is_olora:
+                    raise ValueError(
+                        "The `init_lora_weights` parameter of the initial adapter should be set to `True`. "
+                        "Otherwise, `self.load_adapter` will subtract the decomposed values again based on the "
+                        "residual model."
+                    )
+                output_state_dict = self.base_model.subtract_mutated_init(
+                    output_state_dict, initial_adapter_name, kwargs
                 )
-            output_state_dict = self.base_model.subtract_mutated_init(output_state_dict, initial_adapter, kwargs)
-            self.delete_adapter(adapter_name)
+            finally:
+                self.delete_adapter(initial_adapter_name)
             return output_state_dict
 
         if is_main_process:

tests/test_initialization.py

@@ -323,9 +323,13 @@ def test_lora_pissa_conversion_same_output_after_loading(self, data, tmp_path):
         )
 
         # save the model with conversion
+        peft_config_keys_before = list(peft_model.peft_config.keys())
         peft_model.save_pretrained(
             tmp_path / "pissa-model-converted", path_initial_model_for_weight_conversion=tmp_path / "init-model"
         )
+        peft_config_keys_after = list(peft_model.peft_config.keys())
+        assert peft_config_keys_before == peft_config_keys_after
+
         model_converted = PeftModel.from_pretrained(deepcopy(model), tmp_path / "pissa-model-converted")
         output_converted = model_converted(data)[0]
 
@@ -597,9 +601,13 @@ def test_olora_conversion_same_output_after_loading(self, data, tmp_path):
         )
 
         # save the model with conversion
+        peft_config_keys_before = list(peft_model.peft_config.keys())
         peft_model.save_pretrained(
             tmp_path / "olora-model-converted", path_initial_model_for_weight_conversion=tmp_path / "init-model"
         )
+        peft_config_keys_after = list(peft_model.peft_config.keys())
+        assert peft_config_keys_before == peft_config_keys_after
+
         model_converted = PeftModel.from_pretrained(deepcopy(model), tmp_path / "olora-model-converted")
         output_converted = model_converted(data)[0]