Utilize existing model from GS in BestPointMixin._get_hypervolume (#3285

)

Summary:
Pull Request resolved: #3285

This was previously re-constructing the model using `get_model_from_generator_run`, which is a helper that I want to deprecate. Since the GS is readily available, we can utilize the model from the GS rather than re-constructing & fitting it from scratch.

Reviewed By: esantorella

Differential Revision: D68836172

fbshipit-source-id: af8300f6a80f6802977d6bdb482eab2dc982421e

ax/service/utils/best_point_mixin.py

@@ -32,7 +32,7 @@
     predicted_hypervolume,
     validate_and_apply_final_transform,
 )
-from ax.modelbridge.registry import get_model_from_generator_run, ModelRegistryBase
+from ax.modelbridge.registry import ModelRegistryBase
 from ax.modelbridge.torch import TorchModelBridge
 from ax.modelbridge.transforms.derelativize import Derelativize
 from ax.models.torch.botorch_moo_defaults import (
@@ -387,35 +387,15 @@ def _get_hypervolume(
         )
 
         if use_model_predictions:
-            current_model = generation_strategy._curr.model_spec_to_gen_from.model_enum
-            # Cover for the case where source of `self._curr.model` was not a `Models`
-            # enum but a factory function, in which case we cannot do
-            # `get_model_from_generator_run` (since we don't have model type and inputs
-            # recorded on the generator run.
-            models_enum = (
-                current_model.__class__
-                if isinstance(current_model, ModelRegistryBase)
-                else None
-            )
-
-            if models_enum is None:
-                raise ValueError(
-                    f"Model {current_model} is not in the ModelRegistry, cannot "
-                    "calculate predicted hypervolume."
-                )
-
-            model = get_model_from_generator_run(
-                generator_run=none_throws(generation_strategy.last_generator_run),
-                experiment=experiment,
-                data=experiment.fetch_data(trial_indices=trial_indices),
-                models_enum=models_enum,
-            )
+            # Make sure that the model is fitted. If model is fitted already,
+            # this should be a no-op.
+            generation_strategy._fit_current_model(data=None)
+            model = generation_strategy.model
             if not isinstance(model, TorchModelBridge):
                 raise ValueError(
-                    f"Model {current_model} is not of type TorchModelBridge, cannot "
+                    f"Model {model} is not of type TorchModelBridge, cannot "
                     "calculate predicted hypervolume."
                 )
-
             return predicted_hypervolume(
                 modelbridge=model, optimization_config=optimization_config
             )