Affine input transforms should error with data of incorrect dimension…

…, even in eval mode (#2510)

Summary:
Pull Request resolved: #2510

Context: #2509 gives a clear overview

This PR:
* Checks the shape of the `X` provided to an `AffineInputTransform` when it transforms the data, regardless of whether it is updating the coefficients.

Makes some unrelated changes:
* Fixes the example in the docstring for `batched_multi_output_to_single_output`
* fixes an incorrect shape in `test_approximate_gp`
* Makes data and transform batch shapes match in `TestConverters`, since those usages will now (appropriately) error

Reviewed By: saitcakmak

Differential Revision: D62318530

botorch/models/converter.py

@@ -388,8 +388,8 @@ def batched_multi_output_to_single_output(
     Example:
         >>> train_X = torch.rand(5, 2)
         >>> train_Y = torch.rand(5, 2)
-        >>> batch_mo_gp = SingleTaskGP(train_X, train_Y)
-        >>> batch_so_gp = batched_multioutput_to_single_output(batch_gp)
+        >>> batch_mo_gp = SingleTaskGP(train_X, train_Y, outcome_transform=None)
+        >>> batch_so_gp = batched_multi_output_to_single_output(batch_mo_gp)
     """
     warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning, stacklevel=2)
     was_training = batch_mo_model.training

botorch/models/transforms/input.py

@@ -412,8 +412,8 @@ def _transform(self, X: Tensor) -> Tensor:
         Returns:
             A `batch_shape x n x d`-dim tensor of transformed inputs.
         """
+        self._check_shape(X)
         if self.learn_coefficients and self.training:
-            self._check_shape(X)
             self._update_coefficients(X)
         self._to(X)
         return (X - self.offset) / self.coefficient

test/acquisition/test_proximal.py

@@ -78,7 +78,7 @@ def test_proximal(self):
                     proximal_test_X = test_X.clone()
                     if transformed_weighting:
                         if input_transform is not None:
-                            last_X = input_transform(train_X[-1])
+                            last_X = input_transform(train_X[-1].unsqueeze(0))
                             proximal_test_X = input_transform(test_X)
 
                     mv_normal = MultivariateNormal(last_X, torch.diag(proximal_weights))
@@ -105,7 +105,7 @@ def test_proximal(self):
                     proximal_test_X = test_X.clone()
                     if transformed_weighting:
                         if input_transform is not None:
-                            last_X = input_transform(train_X[-1])
+                            last_X = input_transform(train_X[-1].unsqueeze(0))
                             proximal_test_X = input_transform(test_X)
 
                     mv_normal = MultivariateNormal(last_X, torch.diag(proximal_weights))
@@ -122,7 +122,7 @@ def test_proximal(self):
                     proximal_test_X = test_X.clone()
                     if transformed_weighting:
                         if input_transform is not None:
-                            last_X = input_transform(train_X[-1])
+                            last_X = input_transform(train_X[-1].unsqueeze(0))
                             proximal_test_X = input_transform(test_X)
 
                     ei = EI(test_X)
@@ -143,7 +143,7 @@ def test_proximal(self):
                     proximal_test_X = test_X.clone()
                     if transformed_weighting:
                         if input_transform is not None:
-                            last_X = input_transform(train_X[-1])
+                            last_X = input_transform(train_X[-1].unsqueeze(0))
                             proximal_test_X = input_transform(test_X)
 
                     qEI_prox = ProximalAcquisitionFunction(

test/models/test_approximate_gp.py

@@ -327,5 +327,5 @@ def test_input_transform(self) -> None:
                     model.likelihood, model.model, num_data=train_X.shape[-2]
                 )
                 fit_gpytorch_mll(mll)
-                post = model.posterior(torch.tensor([train_X.mean()]))
+                post = model.posterior(torch.tensor([[train_X.mean()]]))
                 self.assertAllClose(post.mean[0][0], y.mean(), atol=1e-3, rtol=1e-3)

test/models/test_converter.py

@@ -278,13 +278,21 @@ def test_model_list_to_batched(self):
                 batch_shape=torch.Size([3]),
             )
             gp1_ = SingleTaskGP(
-                train_X, train_Y1, input_transform=input_tf2, outcome_transform=None
+                train_X=train_X.unsqueeze(0),
+                train_Y=train_Y1.unsqueeze(0),
+                input_transform=input_tf2,
+                outcome_transform=None,
             )
             gp2_ = SingleTaskGP(
-                train_X, train_Y2, input_transform=input_tf2, outcome_transform=None
+                train_X=train_X.unsqueeze(0),
+                train_Y=train_Y2.unsqueeze(0),
+                input_transform=input_tf2,
+                outcome_transform=None,
             )
             list_gp = ModelListGP(gp1_, gp2_)
-            with self.assertRaises(UnsupportedError):
+            with self.assertRaisesRegex(
+                UnsupportedError, "Batched input_transforms are not supported."
+            ):
                 model_list_to_batched(list_gp)
 
             # test outcome transform
@@ -457,7 +465,6 @@ def test_batched_multi_output_to_single_output(self):
                 bounds=torch.tensor(
                     [[-1.0, -1.0], [1.0, 1.0]], device=self.device, dtype=dtype
                 ),
-                batch_shape=torch.Size([2]),
             )
             batched_mo_model = SingleTaskGP(
                 train_X, train_Y, input_transform=input_tf, outcome_transform=None

test/models/transforms/test_input.py

@@ -228,7 +228,10 @@ def test_normalize(self) -> None:
             self.assertTrue(nlz.mins.dtype == other_dtype)
             # test incompatible dimensions of specified bounds
             bounds = torch.zeros(2, 3, device=self.device, dtype=dtype)
-            with self.assertRaises(BotorchTensorDimensionError):
+            with self.assertRaisesRegex(
+                BotorchTensorDimensionError,
+                "Dimensions of provided `bounds` are incompatible",
+            ):
                 Normalize(d=2, bounds=bounds)
 
             # test jitter
@@ -266,7 +269,12 @@ def test_normalize(self) -> None:
                 # test errors on wrong shape
                 nlz = Normalize(d=2, batch_shape=batch_shape)
                 X = torch.randn(*batch_shape, 2, 1, device=self.device, dtype=dtype)
-                with self.assertRaises(BotorchTensorDimensionError):
+                expected_msg = "Wrong input dimension. Received 1, expected 2."
+                with self.assertRaisesRegex(BotorchTensorDimensionError, expected_msg):
+                    nlz(X)
+                # Same error in eval mode
+                nlz.eval()
+                with self.assertRaisesRegex(BotorchTensorDimensionError, expected_msg):
                     nlz(X)
 
                 # fixed bounds
@@ -328,9 +336,8 @@ def test_normalize(self) -> None:
                     [X.min(dim=-2, keepdim=True)[0], X.max(dim=-2, keepdim=True)[0]],
                     dim=-2,
                 )[..., indices]
-                self.assertTrue(
-                    torch.allclose(nlz.bounds, expected_bounds, atol=1e-4, rtol=1e-4)
-                )
+                self.assertAllClose(nlz.bounds, expected_bounds, atol=1e-4, rtol=1e-4)
+
                 # test errors on wrong shape
                 nlz = Normalize(d=2, batch_shape=batch_shape)
                 X = torch.randn(*batch_shape, 2, 1, device=self.device, dtype=dtype)