address PR feedback

docs/source/developer_guides/model_merging.md

@@ -141,11 +141,17 @@ print(tokenizer.decode(outputs[0]))
 
 
 ## Merging (IA)³ Models
-(IA)³ models support linear model merging. To merge (IA)³ models, you can use the [`~IA3Model.add_weighted_adapter`] method. This method is similar to the [`~LoraModel.add_weighted_adapter`] method, but it doesn't accept the `combination_type` parameter. Assuming we have a PEFT model and three (IA)³ adapters, we can merge them as follows:
+The (IA)³ models facilitate linear merging of adapters. To merge adapters in an (IA)³ model, utilize the `add_weighted_adapter` method from the `IA3Model` class. This method is analogous to the `add_weighted_adapter` method used in `LoraModel`, with the key difference being the absence of the `combination_type` parameter. For example, to merge three (IA)³ adapters into a PEFT model, you would proceed as follows:
 
 ```py
 adapters = ["adapter1", "adapter2", "adapter3"]
 weights = [0.4, 0.3, 0.3]
 adapter_name = "merge"
 model.add_weighted_adapter(adapters, weights, adapter_name)
 ```
+
+It is recommended that the weights sum to 1.0 to preserve the scale of the model. The merged model can then be set as the active model using the `set_adapter` method:
+
+```py
+model.set_adapter("merge")
+```

tests/testing_common.py

@@ -1198,11 +1198,7 @@ def _test_weighted_combination_of_adapters_lora(self, model, config, adapter_lis
 
         # test ties re-weighting with multiple adapters
         model.add_weighted_adapter(
-            adapter_list[:2],
-            weight_list[:2],
-            "multi_adapter_ties_reweighting",
-            combination_type="ties",
-            density=0.5,
+            adapter_list[:2], weight_list[:2], "multi_adapter_ties_reweighting", combination_type="ties", density=0.5
         )
 
         # test dare_linear re-weighting with multiple adapters
@@ -1367,20 +1363,17 @@ def _test_weighted_combination_of_adapters(self, model_id, config_cls, config_kw
             **config_kwargs,
         )
 
-        # Define a dictionary to map config types to their respective test functions
-        test_functions = {
-            LoraConfig: self._test_weighted_combination_of_adapters_lora,
-            IA3Config: self._test_weighted_combination_of_adapters_ia3,
-        }
+        if not isinstance(config, (LoraConfig, IA3Config)):
+            # This test is only applicable for Lora and IA3 configs
+            return pytest.skip(f"Test not applicable for {config}")
 
-        # Get the test function based on the config type
-        test_function = test_functions.get(type(config))
+        model = self.transformers_class.from_pretrained(model_id)
+        model = get_peft_model(model, config, adapter_list[0])
 
-        if test_function:
-            # Only instantiate the model if a valid config is provided
-            model = self.transformers_class.from_pretrained(model_id)
-            model = get_peft_model(model, config, adapter_list[0])
-            test_function(model, config, adapter_list, weight_list)
+        if isinstance(config, LoraConfig):
+            self._test_weighted_combination_of_adapters_lora(model, config, adapter_list, weight_list)
+        elif isinstance(config, IA3Config):
+            self._test_weighted_combination_of_adapters_ia3(model, config, adapter_list, weight_list)
         else:
             pytest.skip(f"Test not applicable for {config}")