Rename AdapterConfigBase to AdapterConfig

docs/classes/adapter_config.rst

@@ -6,7 +6,7 @@ Classes representing the architectures of adapter modules and fusion layers.
 Single (bottleneck) adapters
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: adapters.AdapterConfigBase
+.. autoclass:: adapters.AdapterConfig
     :members:
 
 .. autoclass:: adapters.BnConfig

docs/contributing/adding_adapter_methods.md

@@ -18,14 +18,14 @@ Therefore, the described steps might not be applicable to each implementation.
 These module classes then have to be inserted into the correct locations within the Transformer model implementation.
 Thus, each adapter method implementation at least should provide two classes:
 
-- a configuration class deriving from `AdapterConfigBase` that provides attributes for all configuration options of the method
+- a configuration class deriving from `AdapterConfig` that provides attributes for all configuration options of the method
 - a module class deriving from the abstract `AdapterLayerBase` that provides the method parameters and a set of standard adapter management functions
     - modules supporting [adapter composition](https://docs.adapterhub.ml/adapter_composition.html) should instead derive from `ComposableAdapterLayerBase`
 
 ### Configuration
 
 All configuration classes reside in `src/adapters/configuration/adapter_config.py`.
-- To add a new configuration class for a new method, create a new subclass of [`AdapterConfigBase`](adapters.AdapterConfigBase).
+- To add a new configuration class for a new method, create a new subclass of [`AdapterConfig`](adapters.AdapterConfig).
     Make sure to set the `architecture` attribute in your class.
 - Finally, also make sure the config class is added to the `__init__.py` files in `src/adapters`.
 

docs/overview.md

@@ -64,7 +64,7 @@ Identifiers and configuration classes are explained in more detail in the [next
 ## Configuration
 
 All supported adapter methods can be added, trained, saved and shared using the same set of model class functions (see [class documentation](adapters.ModelAdaptersMixin)).
-Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfigBase`](adapters.AdapterConfigBase) class.
+Each method is specified and configured using a specific configuration class, all of which derive from the common [`AdapterConfig`](adapters.AdapterConfig) class.
 E.g., adding one of the supported adapter methods to an existing model instance follows this scheme:
 ```python
 model.add_adapter("name", config=<ADAPTER_CONFIG>)

docs/training.md

@@ -59,7 +59,7 @@ Compared to fine-tuning the entire model, we have to make only one significant a
 # task adapter - only add if not existing
 if task_name not in model.adapters_config:
     # resolve the adapter config
-    adapter_config = AdapterConfigBase.load(adapter_args.adapter_config)
+    adapter_config = AdapterConfig.load(adapter_args.adapter_config)
     # add a new adapter
     model.add_adapter(task_name, config=adapter_config)
 # Enable adapter training

docs/transitioning.md

@@ -50,6 +50,12 @@ The `adapters` library supports the configuration of adapters using [config stri
 For a complete list of config strings and classes see [here](https://docs.adapterhub.ml/overview.html). We strongly recommend using the new config strings, but we will continue to support the old config strings for the time being to make the transition easier.
 Note that with the config strings the coresponding adapter config classes have changed, e.g. `PfeifferConfig` -> `SeqBnConfig`.
 
+Another consequence of this that the `AdapterConfig` class is now not only for the bottleneck adapters anymore, but the base class of all the configurations (previously `AdapterConfigBase`). Hence the function this class serves has changed. However, you can still load adapter configs with:
+```
+adapter_config = AdapterConfig.load("lora")
+```
+
+
 ## Features that are not supported by `adapters`
 
 Compared to `adapter-transformers`, there are a few features that are no longer supported by the `adapters` library: 

examples/pytorch/dependency-parsing/run_udp.py

@@ -13,7 +13,7 @@
 
 import adapters
 import adapters.composition as ac
-from adapters import AdapterArguments, AdapterConfigBase, AutoAdapterModel, setup_adapter_training
+from adapters import AdapterArguments, AdapterConfig, AutoAdapterModel, setup_adapter_training
 from preprocessing import preprocess_dataset
 from transformers import AutoConfig, AutoTokenizer, HfArgumentParser, set_seed
 from utils_udp import UD_HEAD_LABELS, DependencyParsingAdapterTrainer, DependencyParsingTrainer, UDTrainingArguments
@@ -252,7 +252,7 @@ def main():
             logger.info("Loading best model for predictions.")
 
             if adapter_args.train_adapter:
-                adapter_config = AdapterConfigBase.load(adapter_args.adapter_config, **adapter_config_kwargs)
+                adapter_config = AdapterConfig.load(adapter_args.adapter_config, **adapter_config_kwargs)
                 model.load_adapter(
                     os.path.join(training_args.output_dir, "best_model", task_name)
                     if training_args.do_train
@@ -262,9 +262,7 @@ def main():
                     **adapter_load_kwargs,
                 )
                 if adapter_args.load_lang_adapter:
-                    lang_adapter_config = AdapterConfigBase.load(
-                        adapter_args.lang_adapter_config, **adapter_config_kwargs
-                    )
+                    lang_adapter_config = AdapterConfig.load(adapter_args.lang_adapter_config, **adapter_config_kwargs)
                     lang_adapter_name = model.load_adapter(
                         os.path.join(training_args.output_dir, "best_model", lang_adapter_name)
                         if training_args.do_train