condition_on_observations does not apply input transforms

botorch/models/gpytorch.py

@@ -242,8 +242,8 @@ def condition_on_observations(
             >>> new_Y = torch.sin(new_X[:, :1]) + torch.cos(new_X[:, 1:])
             >>> model = model.condition_on_observations(X=new_X, Y=new_Y)
         """
+        X = self.transform_inputs(X)
         Yvar = noise
-
         if hasattr(self, "outcome_transform"):
             # pass the transformed data to get_fantasy_model below
             # (unless we've already trasnformed if BatchedMultiOutputGPyTorchModel)

botorch/models/model.py

@@ -379,7 +379,7 @@ def fantasize(
             if observation_noise is not None:
                 kwargs["noise"] = observation_noise.expand(Y.shape[1:])
             return self.condition_on_observations(
-                X=self.transform_inputs(X),
+                X=X,
                 Y=Y,
                 **kwargs,
             )
@@ -395,9 +395,7 @@ def fantasize(
             Y_fantasized = sampler(post_X)  # num_fantasies x batch_shape x n' x m
             if observation_noise is not None:
                 kwargs["noise"] = observation_noise.expand(Y_fantasized.shape[1:])
-            return self.condition_on_observations(
-                X=self.transform_inputs(X), Y=Y_fantasized, **kwargs
-            )
+            return self.condition_on_observations(X=X, Y=Y_fantasized, **kwargs)
 
 
 class ModelList(Model):

test/models/test_gpytorch.py

@@ -690,3 +690,47 @@ def test_condition_on_observations_model_list(self):
                 model.condition_on_observations(
                     X=torch.rand(2, 1, **tkwargs), Y=torch.rand(2, 2, **tkwargs)
                 )
+
+    def test_condition_on_observations_input_transform_consistency(self):
+        """Test that input transforms are applied consistently in
+         condition_on_observations.
+
+        This addresses https://github.com/pytorch/botorch/issues/2533:
+        inputs should be transformed when conditioning.
+        """
+        for dtype in (torch.float, torch.double):
+            tkwargs = {"device": self.device, "dtype": dtype}
+
+            # Create model with input transform
+            train_X = torch.tensor([[0.0], [0.5], [1.0]], **tkwargs)
+            train_Y = torch.tensor([[1.0], [2.0], [3.0]], **tkwargs)
+
+            input_transform = SimpleInputTransform(transform_on_train=True)
+            model = SimpleGPyTorchModel(
+                train_X, train_Y, input_transform=input_transform
+            )
+
+            # Condition on new observations
+            new_X = torch.tensor([[0.25], [0.75]], **tkwargs)
+            new_Y = torch.tensor([[1.5], [2.5]], **tkwargs)
+
+            # Get original train_inputs for comparison
+            model.eval()  # Put in eval mode to see transformed inputs
+            original_transformed_inputs = model.train_inputs[0].clone()
+            _ = model.posterior(train_X)
+            # Condition on observations
+            conditioned_model = model.condition_on_observations(new_X, new_Y)
+            conditioned_model.eval()  # Put in eval mode to see transformed inputs
+
+            # Check that new inputs were transformed before being added
+            expected_transformed_new_X = input_transform(new_X)
+            expected_combined_inputs = torch.cat(
+                [original_transformed_inputs, expected_transformed_new_X], dim=0
+            )
+
+            # NOTE This would not have passed before - the last two inputs
+            # (corresponding to new_X) would not have been transformed.
+            self.assertAllClose(
+                conditioned_model.train_inputs[0],
+                expected_combined_inputs,
+            )