Repository: huggingface/peft

Issue:
add_weighted_adapter() is unusable, throws error: "Invalid type <class 'list'> found in target_modules"
### System Info

- `transformers` version: 4.34.0
- Platform: Linux-6.5.6-arch2-1-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.17.3
- Safetensors version: 0.4.0
- Accelerate version: 0.23.0
- Accelerate config:    not found
- PyTorch version (GPU?): 2.1.0+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: false
- Using distributed or parallel set-up in script?: false

### Who can help?

@pacman100 @you

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder
- [X] My own task or dataset (give details below)

### Reproduction

Load the model:
```
model = AutoModelForCausalLM.from_pretrained(
        "RWKV/rwkv-4-169m-pile"
        device_map="auto"
    )
```
Convert it into a PeftModel (to make the `add_weighted_adapter()` method available):
```
model = PeftModel.from_pretrained(
        model, f"{adapter_dir}/{adapter}", adapter_name='adapter_1'
    )
```
Load the 2nd adapter:
```
model.load_adapter(
        f"{adapter_dir}/{adapter}", adapter_name='adapter_2'
    )
```
Attempt to merge the two adapters:
```
model.add_weighted_adapter(
        adapters=['adapter_1', 'adapter_2'],
        weights=[0.5, 0.5],
        adapter_name="combined",
        combination_type="svd",
    )
```
This will result in the error message:
```
Invalid type <class 'list'> found in target_modules
```

### Expected behavior

I have 2 fully trained LoRA adapters. The only configuration difference between them is the rank and alpha used for each.

I would like to merge the two adapters into a new adapter, and set that adapter to the "active" one.

However, trying to use the `add_weighted_adapter()` method always results in the following error:
```
Invalid type <class 'list'> found in target_modules
```

I would expect the `add_weighted_adapter()` method to accept a list, for the "target_modules" argument.

If you can provide any advice, I would greatly appreciate it. I suspect that this is either unsupported and/or not fully-implemented; or, it has something to do with the way I'm attaching adapters. I've tried a bunch of alternate configurations, but I'm not having luck.

Thanks in advance for any help you might provide.


Discussion:
Thanks for reporting. Could you please paste the full stacktrace?
Thanks for the followup! Here's the stack trace:
```
vtx-lab-1   | Traceback (most recent call last):
vtx-lab-1   |   File "/src/lab/aigen/aigen/aigen.py", line 246, in __init__
vtx-lab-1   |     self.model.add_weighted_adapter(
vtx-lab-1   |   File "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/model.py", line 525, in add_weighted_adapter
vtx-lab-1   |     raise TypeError(f"Invalid type {target_module_types[0]} found in target_modules")
vtx-lab-1   | TypeError: Invalid type <class 'list'> found in target_modules
```
I see, thanks. This will be fixed soon, once #1041 is merged.

Resolving patch:
diff --git a/src/peft/config.py b/src/peft/config.py
--- a/src/peft/config.py
+++ b/src/peft/config.py
@@ -130,12 +130,8 @@ def from_pretrained(cls, pretrained_model_name_or_path: str, subfolder: Optional
         else:
             config_cls = cls
 
-        config = config_cls(**class_kwargs)
-
-        for key, value in loaded_attributes.items():
-            if hasattr(config, key):
-                setattr(config, key, value)
-
+        kwargs = {**class_kwargs, **loaded_attributes}
+        config = config_cls(**kwargs)
         return config
 
     @classmethod


Test patch:
diff --git a/tests/test_config.py b/tests/test_config.py
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -20,11 +20,15 @@
 import warnings
 
 import pytest
+from parameterized import parameterized
 
 from peft import (
+    AdaLoraConfig,
     AdaptionPromptConfig,
     IA3Config,
+    LoHaConfig,
     LoraConfig,
+    MultitaskPromptTuningConfig,
     PeftConfig,
     PrefixTuningConfig,
     PromptEncoder,
@@ -35,20 +39,22 @@
 
 PEFT_MODELS_TO_TEST = [("lewtun/tiny-random-OPTForCausalLM-delta", "v1")]
 
-
-class PeftConfigTestMixin:
-    all_config_classes = (
-        LoraConfig,
-        PromptEncoderConfig,
-        PrefixTuningConfig,
-        PromptTuningConfig,
-        AdaptionPromptConfig,
-        IA3Config,
-    )
+ALL_CONFIG_CLASSES = (
+    AdaptionPromptConfig,
+    AdaLoraConfig,
+    IA3Config,
+    LoHaConfig,
+    LoraConfig,
+    MultitaskPromptTuningConfig,
+    PrefixTuningConfig,
+    PromptEncoderConfig,
+    PromptTuningConfig,
+)
 
 
-class PeftConfigTester(unittest.TestCase, PeftConfigTestMixin):
-    def test_methods(self):
+class PeftConfigTester(unittest.TestCase):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_methods(self, config_class):
         r"""
         Test if all configs have the expected methods. Here we test
         - to_dict
@@ -57,109 +63,107 @@ def test_methods(self):
         - from_json_file
         """
         # test if all configs have the expected methods
-        for config_class in self.all_config_classes:
-            config = config_class()
-            self.assertTrue(hasattr(config, "to_dict"))
-            self.assertTrue(hasattr(config, "save_pretrained"))
-            self.assertTrue(hasattr(config, "from_pretrained"))
-            self.assertTrue(hasattr(config, "from_json_file"))
-
-    def test_task_type(self):
-        for config_class in self.all_config_classes:
-            # assert this will not fail
-            _ = config_class(task_type="test")
-
-    def test_from_pretrained(self):
+        config = config_class()
+        self.assertTrue(hasattr(config, "to_dict"))
+        self.assertTrue(hasattr(config, "save_pretrained"))
+        self.assertTrue(hasattr(config, "from_pretrained"))
+        self.assertTrue(hasattr(config, "from_json_file"))
+
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_task_type(self, config_class):
+        config_class(task_type="test")
+
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_from_pretrained(self, config_class):
         r"""
         Test if the config is correctly loaded using:
         - from_pretrained
         """
-        for config_class in self.all_config_classes:
-            for model_name, revision in PEFT_MODELS_TO_TEST:
-                # Test we can load config from delta
-                _ = config_class.from_pretrained(model_name, revision=revision)
+        for model_name, revision in PEFT_MODELS_TO_TEST:
+            # Test we can load config from delta
+            config_class.from_pretrained(model_name, revision=revision)
 
-    def test_save_pretrained(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_save_pretrained(self, config_class):
         r"""
         Test if the config is correctly saved and loaded using
         - save_pretrained
         """
-        for config_class in self.all_config_classes:
-            config = config_class()
-            with tempfile.TemporaryDirectory() as tmp_dirname:
-                config.save_pretrained(tmp_dirname)
+        config = config_class()
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            config.save_pretrained(tmp_dirname)
 
-                config_from_pretrained = config_class.from_pretrained(tmp_dirname)
-                self.assertEqual(config.to_dict(), config_from_pretrained.to_dict())
+            config_from_pretrained = config_class.from_pretrained(tmp_dirname)
+            self.assertEqual(config.to_dict(), config_from_pretrained.to_dict())
 
-    def test_from_json_file(self):
-        for config_class in self.all_config_classes:
-            config = config_class()
-            with tempfile.TemporaryDirectory() as tmp_dirname:
-                config.save_pretrained(tmp_dirname)
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_from_json_file(self, config_class):
+        config = config_class()
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            config.save_pretrained(tmp_dirname)
 
-                config_from_json = config_class.from_json_file(os.path.join(tmp_dirname, "adapter_config.json"))
-                self.assertEqual(config.to_dict(), config_from_json)
+            config_from_json = config_class.from_json_file(os.path.join(tmp_dirname, "adapter_config.json"))
+            self.assertEqual(config.to_dict(), config_from_json)
 
-    def test_to_dict(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_to_dict(self, config_class):
         r"""
         Test if the config can be correctly converted to a dict using:
         - to_dict
         """
-        for config_class in self.all_config_classes:
-            config = config_class()
-            self.assertTrue(isinstance(config.to_dict(), dict))
+        config = config_class()
+        self.assertTrue(isinstance(config.to_dict(), dict))
 
-    def test_from_pretrained_cache_dir(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_from_pretrained_cache_dir(self, config_class):
         r"""
         Test if the config is correctly loaded with extra kwargs
         """
         with tempfile.TemporaryDirectory() as tmp_dirname:
-            for config_class in self.all_config_classes:
-                for model_name, revision in PEFT_MODELS_TO_TEST:
-                    # Test we can load config from delta
-                    _ = config_class.from_pretrained(model_name, revision=revision, cache_dir=tmp_dirname)
+            for model_name, revision in PEFT_MODELS_TO_TEST:
+                # Test we can load config from delta
+                config_class.from_pretrained(model_name, revision=revision, cache_dir=tmp_dirname)
 
     def test_from_pretrained_cache_dir_remote(self):
         r"""
         Test if the config is correctly loaded with a checkpoint from the hub
         """
         with tempfile.TemporaryDirectory() as tmp_dirname:
-            _ = PeftConfig.from_pretrained("ybelkada/test-st-lora", cache_dir=tmp_dirname)
+            PeftConfig.from_pretrained("ybelkada/test-st-lora", cache_dir=tmp_dirname)
             self.assertTrue("models--ybelkada--test-st-lora" in os.listdir(tmp_dirname))
 
-    def test_set_attributes(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_set_attributes(self, config_class):
         # manually set attributes and check if they are correctly written
-        for config_class in self.all_config_classes:
-            config = config_class(peft_type="test")
+        config = config_class(peft_type="test")
 
-            # save pretrained
-            with tempfile.TemporaryDirectory() as tmp_dirname:
-                config.save_pretrained(tmp_dirname)
+        # save pretrained
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            config.save_pretrained(tmp_dirname)
 
-                config_from_pretrained = config_class.from_pretrained(tmp_dirname)
-                self.assertEqual(config.to_dict(), config_from_pretrained.to_dict())
+            config_from_pretrained = config_class.from_pretrained(tmp_dirname)
+            self.assertEqual(config.to_dict(), config_from_pretrained.to_dict())
 
-    def test_config_copy(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_config_copy(self, config_class):
         # see https://github.com/huggingface/peft/issues/424
-        for config_class in self.all_config_classes:
-            config = config_class()
-            copied = copy.copy(config)
-            self.assertEqual(config.to_dict(), copied.to_dict())
+        config = config_class()
+        copied = copy.copy(config)
+        self.assertEqual(config.to_dict(), copied.to_dict())
 
-    def test_config_deepcopy(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_config_deepcopy(self, config_class):
         # see https://github.com/huggingface/peft/issues/424
-        for config_class in self.all_config_classes:
-            config = config_class()
-            copied = copy.deepcopy(config)
-            self.assertEqual(config.to_dict(), copied.to_dict())
+        config = config_class()
+        copied = copy.deepcopy(config)
+        self.assertEqual(config.to_dict(), copied.to_dict())
 
-    def test_config_pickle_roundtrip(self):
+    @parameterized.expand(ALL_CONFIG_CLASSES)
+    def test_config_pickle_roundtrip(self, config_class):
         # see https://github.com/huggingface/peft/issues/424
-        for config_class in self.all_config_classes:
-            config = config_class()
-            copied = pickle.loads(pickle.dumps(config))
-            self.assertEqual(config.to_dict(), copied.to_dict())
+        config = config_class()
+        copied = pickle.loads(pickle.dumps(config))
+        self.assertEqual(config.to_dict(), copied.to_dict())
 
     def test_prompt_encoder_warning_num_layers(self):
         # This test checks that if a prompt encoder config is created with an argument that is ignored, there should be
@@ -182,3 +186,15 @@ def test_prompt_encoder_warning_num_layers(self):
             PromptEncoder(config)
         expected_msg = "for MLP, the argument `encoder_num_layers` is ignored. Exactly 2 MLP layers are used."
         assert str(record.list[0].message) == expected_msg
+
+    @parameterized.expand([LoHaConfig, LoraConfig, IA3Config])
+    def test_save_pretrained_with_target_modules(self, config_class):
+        # See #1041, #1045
+        config = config_class(target_modules=["a", "list"])
+        with tempfile.TemporaryDirectory() as tmp_dirname:
+            config.save_pretrained(tmp_dirname)
+
+            config_from_pretrained = config_class.from_pretrained(tmp_dirname)
+            self.assertEqual(config.to_dict(), config_from_pretrained.to_dict())
+            # explicit test that target_modules should be converted to set
+            self.assertTrue(isinstance(config_from_pretrained.target_modules, set))