Support mixing SAAS & SingleTaskGP models in ModelListGP

Summary: Adds support for broadcasting MVNs produced by the underlying models to enable mixing together SAAS & SingleTaskGP models within a ModelListGP.

Differential Revision: D68503063

botorch/models/gpytorch.py

@@ -719,6 +719,7 @@ def posterior(
                     interleaved=False,
                 )
             else:
+                mvns = self._broadcast_mvns(mvns=mvns)
                 mvn = (
                     mvns[0]
                     if len(mvns) == 1
@@ -738,6 +739,38 @@ def posterior(
     def condition_on_observations(self, X: Tensor, Y: Tensor, **kwargs: Any) -> Model:
         raise NotImplementedError()
 
+    def _broadcast_mvns(self, mvns: list[MultivariateNormal]) -> MultivariateNormal:
+        """Broadcasts the batch shapes of the given MultivariateNormals.
+
+        The MVNs will have a batch shape of `input_batch_shape x model_batch_shape`.
+        If the model batch shapes are broadcastable, we will broadcast the mvns to
+        a batch shape of `input_batch_shape x self.batch_shape`.
+
+        Args:
+            mvns: A list of MultivariateNormals.
+
+        Returns:
+            A list of MultivariateNormals with broadcasted batch shapes.
+        """
+        mvn_batch_shapes = {mvn.batch_shape for mvn in mvns}
+        if len(mvn_batch_shapes) == 1:
+            # All MVNs have the same batch shape. We can return as is.
+            return mvns
+        # This call will error out of they're not broadcastable.
+        # If they're broadcastable, it'll log a warning.
+        target_model_shape = self.batch_shape
+        max_batch = max(mvn_batch_shapes, key=len)
+        max_len = len(max_batch)
+        input_batch_len = max_len - len(target_model_shape)
+        for i in range(len(mvns)):  # Loop over index since we modify contents.
+            while len(mvns[i].batch_shape) < max_len:
+                # MVN is missing batch dimensions. Unsqueeze as needed.
+                mvns[i] = mvns[i].unsqueeze(input_batch_len)
+            if mvns[i].batch_shape != max_batch:
+                # Expand to match the batch shapes.
+                mvns[i] = mvns[i].expand(max_batch)
+        return mvns
+
 
 class MultiTaskGPyTorchModel(GPyTorchModel, ABC):
     r"""Abstract base class for multi-task models based on GPyTorch models.

test/models/test_model_list_gp_regression.py

@@ -12,7 +12,8 @@
 from botorch.acquisition.objective import ScalarizedPosteriorTransform
 from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
 from botorch.exceptions.warnings import OptimizationWarning
-from botorch.fit import fit_gpytorch_mll
+from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
+from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
 from botorch.models.gp_regression import SingleTaskGP
 from botorch.models.model_list_gp_regression import ModelListGP
 from botorch.models.multitask import MultiTaskGP
@@ -733,3 +734,29 @@ def test_fantasize_with_outcome_transform_fixed_noise(self) -> None:
                             self.assertTrue(
                                 torch.equal(fm_i.train_inputs[0][0][-1], X[1 - i])
                             )
+
+    def test_with_different_batch_shapes(self) -> None:
+        # Tests that we can mix single task and SAAS models together.
+        tkwargs = {"device": self.device, "dtype": torch.double}
+        m1 = SaasFullyBayesianSingleTaskGP(
+            train_X=torch.rand(10, 2, **tkwargs), train_Y=torch.rand(10, 1, **tkwargs)
+        )
+        fit_fully_bayesian_model_nuts(m1, warmup_steps=0, num_samples=8, thinning=1)
+        m2 = SingleTaskGP(
+            train_X=torch.rand(10, 2, **tkwargs), train_Y=torch.rand(10, 1, **tkwargs)
+        )
+        m = ModelListGP(m1, m2)
+        with self.assertWarnsRegex(UserWarning, "Component models of"):
+            self.assertEqual(m.batch_shape, torch.Size([8]))
+        # Non-batched evaluation.
+        with self.assertWarnsRegex(UserWarning, "Component models of"):
+            post = m.posterior(torch.rand(1, 2))
+        self.assertEqual(post.batch_shape, torch.Size([8]))
+        self.assertEqual(post.rsample(torch.Size([2])).shape, torch.Size([2, 8, 1, 2]))
+        # Batched evaluation.
+        with self.assertWarnsRegex(UserWarning, "Component models of"):
+            post = m.posterior(torch.rand(5, 1, 2))
+        self.assertEqual(post.batch_shape, torch.Size([5, 8]))
+        self.assertEqual(
+            post.rsample(torch.Size([2])).shape, torch.Size([2, 5, 8, 1, 2])
+        )