Merge remote-tracking branch 'upstream/main'

botorch/acquisition/logei.py

@@ -468,9 +468,13 @@ def _get_samples_and_objectives(self, X: Tensor) -> tuple[Tensor, Tensor]:
         if not self._cache_root:
             samples_full = super().get_posterior_samples(posterior)
             obj_full = self.objective(samples_full, X=X_full)
+            # Calculate the positive index for splitting the samples & objective values.
+            split_dim = len(obj_full.shape) - 1
             # assigning baseline buffers so `best_f` can be computed in _sample_forward
-            self.baseline_samples, samples = samples_full.split([n_baseline, q], dim=-2)
-            self.baseline_obj, obj = obj_full.split([n_baseline, q], dim=-1)
+            self.baseline_samples, samples = samples_full.split(
+                [n_baseline, q], dim=split_dim
+            )
+            self.baseline_obj, obj = obj_full.split([n_baseline, q], dim=split_dim)
             return samples, obj
 
         # handle one-to-many input transforms

botorch/acquisition/multi_objective/utils.py

@@ -60,7 +60,7 @@ def get_default_partitioning_alpha(num_objectives: int) -> float:
             BotorchWarning,
             stacklevel=3,
         )
-    return 10 ** (-8 + num_objectives)
+    return 10 ** (-2 if num_objectives >= 6 else -3)
 
 
 def prune_inferior_points_multi_objective(

botorch/models/gpytorch.py

@@ -402,6 +402,8 @@ def _apply_noise(
             mvn = self.likelihood(mvn, X)
         return mvn
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter
+    # `Keywords(typing.Any)` in overriding signature.
     def posterior(
         self,
         X: Tensor,
@@ -470,6 +472,7 @@ def posterior(
             return posterior_transform(posterior)
         return posterior
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter `noise`.
     def condition_on_observations(
         self, X: Tensor, Y: Tensor, **kwargs: Any
     ) -> BatchedMultiOutputGPyTorchModel:
@@ -632,7 +635,7 @@ def batch_shape(self) -> torch.Size:
                 raise NotImplementedError(msg + " that are not broadcastble.")
         return next(iter(batch_shapes))
 
-    # pyre-fixme[15]: Inconsistent override in return types
+    # pyre-fixme[14]: Inconsistent override in return types
     def posterior(
         self,
         X: Tensor,
@@ -838,6 +841,8 @@ def _apply_noise(
             )
         return self.likelihood(mvn, X)
 
+    # pyre-ignore[14]: Inconsistent override. Could not find parameter
+    # `Keywords(typing.Any)` in overriding signature.
     def posterior(
         self,
         X: Tensor,