FIX Call super().__post_init__() in CPTConfig and RandLoraConfig (#3365)

CPTConfig and RandLoraConfig override __post_init__ without calling
super().__post_init__(), so PeftConfig.__post_init__ never runs for them.
As a result the peft_version field is left as None (instead of being
auto-filled) and the task_type validation in the base class is skipped.
Every other tuner config that overrides __post_init__ calls super();
add the call (first, matching the convention) so these two behave the
same and record peft_version in their saved adapter_config.json.

Follow-up to adding the missing configs to ALL_CONFIG_CLASSES:
- CPTConfig requires task_type=CAUSAL_LM to construct and only supports that
  task type, so pin it in mandatory_kwargs and skip it in the generic
  valid/invalid task_type tests (it's still covered by the round-trip tests,
  which is what would have caught the original __post_init__ omission).
- OFTConfig's from_pretrained back-compat guard fires before the generic load
  path, so skip it in the four from_pretrained-based tests.
All other matrix tests now exercise both configs.

Author: DaoyuanLi2816

src/peft/tuners/cpt/config.py

@@ -72,6 +72,7 @@ def __post_init__(self):
         """
         Post-initialization hook to set additional attributes after the config is initialized.
         """
+        super().__post_init__()
         # CPT-specific static attributes
         self.is_prompt_learning = True  # Indicates that CPT is a prompt-learning method.
         self.num_layers = None  # Number of layers (optional, not always required).

src/peft/tuners/randlora/config.py

@@ -186,6 +186,7 @@ class RandLoraConfig(PeftConfig):
     )
 
     def __post_init__(self):
+        super().__post_init__()
         self.peft_type = PeftType.RANDLORA
         self.target_modules = (
             set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules

tests/test_config.py

@@ -29,6 +29,7 @@
     C3AConfig,
     CartridgeConfig,
     CPTConfig,
+    DeloraConfig,
     FourierFTConfig,
     FrodConfig,
     GraloraConfig,
@@ -53,13 +54,16 @@
     PromptEncoderConfig,
     PromptTuningConfig,
     PsoftConfig,
+    PveraConfig,
+    RandLoraConfig,
     RoadConfig,
     ShiraConfig,
     TaskType,
     TinyLoraConfig,
     TrainableTokensConfig,
     VBLoRAConfig,
     VeraConfig,
+    WaveFTConfig,
     XLoraConfig,
 )
 
@@ -106,6 +110,12 @@ class TestingCommitHashError(Exception):
     (VeraConfig, {}),
     (VBLoRAConfig, {}),
     (XLoraConfig, {"hidden_size": 32, "adapters": {}}),
+    (CPTConfig, {"task_type": "CAUSAL_LM"}),
+    (RandLoraConfig, {}),
+    (DeloraConfig, {}),
+    (OFTConfig, {}),
+    (PveraConfig, {}),
+    (WaveFTConfig, {}),
 )
 
 
@@ -132,13 +142,17 @@ def test_valid_task_type(self, config_class, mandatory_kwargs, valid_task_type):
         r"""
         Test if all configs work correctly for all valid task types
         """
+        if config_class is CPTConfig:
+            pytest.skip("CPTConfig only supports the CAUSAL_LM task type (validated in its __post_init__)")
         config_class(task_type=valid_task_type, **mandatory_kwargs)
 
     @pytest.mark.parametrize("config_class, mandatory_kwargs", ALL_CONFIG_CLASSES)
     def test_invalid_task_type(self, config_class, mandatory_kwargs):
         r"""
         Test if all configs correctly raise the defined error message for invalid task types.
         """
+        if config_class is CPTConfig:
+            pytest.skip("CPTConfig validates task_type with a config-specific message in its __post_init__")
         invalid_task_type = "invalid-task-type"
         with pytest.raises(
             ValueError,
@@ -171,6 +185,8 @@ def test_from_pretrained(self, config_class, mandatory_kwargs):
         Test if the config is correctly loaded using:
         - from_pretrained
         """
+        if config_class is OFTConfig:
+            pytest.skip("OFT's from_pretrained back-compat guard fires before the generic load path tested here")
         for model_name, revision in PEFT_MODELS_TO_TEST:
             # Test we can load config from delta
             config_class.from_pretrained(model_name, revision=revision)
@@ -221,6 +237,8 @@ def test_from_pretrained_cache_dir(self, config_class, mandatory_kwargs):
         r"""
         Test if the config is correctly loaded with extra kwargs
         """
+        if config_class is OFTConfig:
+            pytest.skip("OFT's from_pretrained back-compat guard fires before the generic load path tested here")
         with tempfile.TemporaryDirectory() as tmp_dirname:
             for model_name, revision in PEFT_MODELS_TO_TEST:
                 # Test we can load config from delta
@@ -239,6 +257,8 @@ def test_save_pretrained_with_runtime_config(self, config_class, mandatory_kwarg
         r"""
         Test if the config correctly removes runtime config when saving
         """
+        if config_class is OFTConfig:
+            pytest.skip("OFT's from_pretrained back-compat guard fires before the generic load path tested here")
         with tempfile.TemporaryDirectory() as tmp_dirname:
             for model_name, revision in PEFT_MODELS_TO_TEST:
                 cfg = config_class.from_pretrained(model_name, revision=revision)
@@ -482,6 +502,8 @@ def test_from_pretrained_sanity_check(self, config_class, mandatory_kwargs, tmp_
         """Following up on the previous test about forward compatibility, we *don't* want any random json to be accepted as
         a PEFT config. There should be a minimum set of required keys.
         """
+        if config_class is OFTConfig:
+            pytest.skip("OFT's from_pretrained back-compat guard fires before the generic load path tested here")
         non_peft_json = {"foo": "bar", "baz": 123}
         with open(tmp_path / "adapter_config.json", "w") as f:
             json.dump(non_peft_json, f)