rename adapter_types -> adapter_methods

src/adapters/interface.py

@@ -12,6 +12,14 @@
 class AdapterMethod:
     """
     Enum of all supported adapter method types.
+
+    Attributes:
+        bottleneck: Adapter methods using bottleneck layers.
+        prefix_tuning: Adapters methods based on Prefix Tuning.
+        lora: Adapter methods based on low-rank adaptation.
+        prompt_tuning: Adapter methods based on Prompt Tuning.
+        reft: Adapters methods based on Representation Fine-Tuning.
+        invertible: Adapter methods using invertible modules.
     """
 
     bottleneck = "bottleneck"
@@ -51,7 +59,7 @@ class AdapterModelInterface:
     This interface translates generic accessor names to model-specific attribute names.
 
     Args:
-        adapter_types (List[str]): List of adapter types that are supported by the model.
+        adapter_methods (List[str]): List of adapter types that are supported by the model.
         model_embeddings (str): Name of the model's embedding layer.
         model_layers (str): Name of the model's layer list.
         layer_self_attn (str): Name of the self-attention layer in a transformer layer.
@@ -69,7 +77,7 @@ class AdapterModelInterface:
         layer_ln_2 (Optional[str]): Layer norm *after* the feed forward layer. Used for extended bottleneck adapter support.
     """
 
-    adapter_types: List[str]
+    adapter_methods: List[str]
 
     model_embeddings: str
     model_layers: str

src/adapters/model_mixin.py

@@ -456,7 +456,7 @@ def init_adapters(self, model_config, adapters_config):
 
         # Initialize adapter types defined in interface
         if getattr(self.base_model, "adapter_interface", None) is not None:
-            for adapter_type in self.base_model.adapter_interface.adapter_types:
+            for adapter_type in self.base_model.adapter_interface.adapter_methods:
                 init_func = METHOD_INIT_MAPPING[adapter_type]
                 init_func(self)
         else:
@@ -499,7 +499,7 @@ def supports_adapter(self, type_or_config: Union[str, AdapterConfig]) -> bool:
         supported = []
         for _type in types:
             if getattr(self.base_model, "adapter_interface", None) is not None:
-                supported.append(_type in self.base_model.adapter_interface.adapter_types)
+                supported.append(_type in self.base_model.adapter_interface.adapter_methods)
             elif _type == AdapterMethod.prompt_tuning:
                 supported.append(self.base_model.support_prompt_tuning)
             elif _type == AdapterMethod.invertible:

tests/test_methods/test_on_custom_interface.py

@@ -27,7 +27,7 @@ class CustomInterfaceModelTestBase(TextAdapterTestBase):
     )
     tokenizer_name = "yujiepan/gemma-2-tiny-random"
     adapter_interface = AdapterModelInterface(
-        adapter_types=["bottleneck", "lora", "reft", "invertible"],
+        adapter_methods=["bottleneck", "lora", "reft", "invertible"],
         model_embeddings="embed_tokens",
         model_layers="layers",
         layer_self_attn="self_attn",

tests/test_misc/test_custom_interface_compat.py

@@ -34,7 +34,7 @@ class CustomInterfaceCompatTest(unittest.TestCase):
         pad_token_id=0,
     )
     llama_interface = AdapterModelInterface(
-        adapter_types=["bottleneck", "lora", "reft", "invertible"],
+        adapter_methods=["bottleneck", "lora", "reft", "invertible"],
         model_embeddings="embed_tokens",
         model_layers="layers",
         layer_self_attn="self_attn",
@@ -52,7 +52,7 @@ class CustomInterfaceCompatTest(unittest.TestCase):
         layer_ln_2=None,
     )
     bert_interface = AdapterModelInterface(
-        adapter_types=["bottleneck", "lora", "reft", "prompt_tuning", "invertible"],
+        adapter_methods=["bottleneck", "lora", "reft", "prompt_tuning", "invertible"],
         model_embeddings="embeddings",
         model_layers="encoder.layer",
         layer_self_attn="attention",