MBM Surrogate: Clean up model fitting behavior and **kwargs usage (fa…

…cebook#1882)

Summary:

Kwargs: These were originally added to support passing around additional kwargs in subclasses that no-longer exist. They later silently took on the role of carrying the kwargs that gets passed down to model input constructors. The argument name has been updated with added docstring explaining what these do. These are now used to update `Surrogate.model_options` and passed to the input constructors from there.

Model fitting clean up: We had a bunch of duplicate logic for constructing the models, left over from the merger of `Surrogate` & `ListSurrogate`, which led to several bugs in the past and made the code much harder to maintain and review. This diff deduplicates and simplifies the model fitting logic.

Differential Revision: D49707895

ax/modelbridge/tests/test_registry.py

@@ -47,7 +47,7 @@
 from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
 from botorch.models.gp_regression import FixedNoiseGP
 from botorch.models.model_list_gp_regression import ModelListGP
-from botorch.models.multitask import FixedNoiseMultiTaskGP
+from botorch.models.multitask import MultiTaskGP
 from botorch.utils.types import DEFAULT
 from gpytorch.kernels.matern_kernel import MaternKernel
 from gpytorch.kernels.scale_kernel import ScaleKernel
@@ -475,7 +475,7 @@ def test_ST_MTGP(self, use_saas: bool = False) -> None:
                 if use_saas
                 else [
                     Surrogate(
-                        botorch_model_class=FixedNoiseMultiTaskGP,
+                        botorch_model_class=MultiTaskGP,
                         mll_class=ExactMarginalLogLikelihood,
                         covar_module_class=ScaleMaternKernel,
                         covar_module_options={
@@ -514,9 +514,7 @@ def test_ST_MTGP(self, use_saas: bool = False) -> None:
                 for i in range(len(models)):
                     self.assertIsInstance(
                         models[i],
-                        SaasFullyBayesianMultiTaskGP
-                        if use_saas
-                        else FixedNoiseMultiTaskGP,
+                        SaasFullyBayesianMultiTaskGP if use_saas else MultiTaskGP,
                     )
                     if use_saas is False:
                         self.assertIsInstance(models[i].covar_module, ScaleKernel)

ax/models/torch/botorch_modular/model.py

@@ -24,7 +24,6 @@
 from ax.models.torch.botorch_modular.utils import (
     choose_botorch_acqf_class,
     construct_acquisition_and_optimizer_options,
-    convert_to_block_design,
 )
 from ax.models.torch.utils import _to_inequality_constraints
 from ax.models.torch_base import TorchGenResults, TorchModel, TorchOptConfig
@@ -33,7 +32,6 @@
 from ax.utils.common.docutils import copy_doc
 from ax.utils.common.typeutils import checked_cast
 from botorch.acquisition.acquisition import AcquisitionFunction
-from botorch.models import ModelList
 from botorch.models.deterministic import FixedSingleSampleModel
 from botorch.models.model import Model
 from botorch.models.transforms.input import InputTransform
@@ -247,7 +245,7 @@ def fit(
         # state dict by surrogate label
         state_dicts: Optional[Mapping[str, Dict[str, Tensor]]] = None,
         refit: bool = True,
-        **kwargs: Any,
+        **additional_model_inputs: Any,
     ) -> None:
         """Fit model to m outcomes.
 
@@ -264,6 +262,8 @@ def fit(
                 surrogate_specs. If using a single, pre-instantiated model use
                 `Keys.ONLY_SURROGATE.
             refit: Whether to re-optimize model parameters.
+            additional_model_inputs: Additional kwargs to pass to the
+                model input constructor in ``Surrogate.fit``.
         """
 
         if len(datasets) != len(metric_names):
@@ -288,7 +288,7 @@ def fit(
                 if state_dicts
                 else None,
                 refit=refit,
-                **kwargs,
+                additional_model_inputs=additional_model_inputs,
             )
             return
 
@@ -340,20 +340,6 @@ def fit(
                 datasets_by_metric_name[metric_name]
                 for metric_name in subset_metric_names
             ]
-            if (
-                len(subset_datasets) > 1
-                # if Surrogate's model is none a ModelList will be autoset
-                and surrogate._model is not None
-                and not isinstance(surrogate.model, ModelList)
-            ):
-                # Note: If the datasets do not confirm to a block design then this
-                # will filter the data and drop observations to make sure that it does.
-                # This can happen e.g. if only some metrics are observed at some points
-                subset_datasets, metric_names = convert_to_block_design(
-                    datasets=subset_datasets,
-                    metric_names=metric_names,
-                    force=True,
-                )
 
             surrogate.fit(
                 datasets=subset_datasets,
@@ -362,7 +348,7 @@ def fit(
                 candidate_metadata=candidate_metadata,
                 state_dict=(state_dicts or {}).get(label),
                 refit=refit,
-                **kwargs,
+                additional_model_inputs=additional_model_inputs,
             )
 
     @copy_doc(TorchModel.update)
@@ -372,7 +358,7 @@ def update(
         metric_names: List[str],
         search_space_digest: SearchSpaceDigest,
         candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
-        **kwargs: Any,
+        **additional_model_inputs: Any,
     ) -> None:
         if len(self.surrogates) == 0:
             raise UnsupportedError("Cannot update model that has not been fitted.")
@@ -417,7 +403,7 @@ def update(
                 candidate_metadata=candidate_metadata,
                 state_dict=state_dict,
                 refit=self.refit_on_update,
-                **kwargs,
+                additional_model_inputs=additional_model_inputs,
             )
 
     @single_surrogate_only
@@ -536,7 +522,7 @@ def cross_validate(
         metric_names: List[str],
         X_test: Tensor,
         search_space_digest: SearchSpaceDigest,
-        **kwargs: Any,
+        **additional_model_inputs: Any,
     ) -> Tuple[Tensor, Tensor]:
         # Will fail if metric_names exist across multiple models
         surrogate_labels = (
@@ -589,7 +575,7 @@ def cross_validate(
                 search_space_digest=search_space_digest,
                 state_dicts=state_dicts,
                 refit=self.refit_on_cv,
-                **kwargs,
+                **additional_model_inputs,
             )
             X_test_prediction = self.predict_from_surrogate(
                 surrogate_label=surrogate_label, X=X_test