Fix cuda tests (#1357)

Summary:
Pull Request resolved: #1357

These slipped through.

Reviewed By: saitcakmak

Differential Revision: D38896631

fbshipit-source-id: afe1e4e7d394113bd5ab6de235c78256b40caf24

Author: Balandat

botorch/acquisition/multi_objective/multi_output_risk_measures.py

@@ -557,7 +557,7 @@ def __init__(
                 apply feasibility-weighting to samples.
         """
         super().__init__(alpha=alpha, n_w=n_w)
-        self.chebyshev_weights = chebyshev_weights
+        self.chebyshev_weights = torch.as_tensor(chebyshev_weights)
         self.baseline_Y = baseline_Y
         self.register_buffer(
             "ref_point", torch.as_tensor(ref_point) if ref_point is not None else None
@@ -661,7 +661,7 @@ def chebyshev_obj(Y: Tensor, X: Optional[Tensor] = None) -> Tensor:
             Y = normalize(Y, bounds=Y_bounds)
             if ref_point is not None:
                 Y = Y - ref_point
-            product = torch.einsum("...m,m->...m", Y, self.chebyshev_weights)
+            product = torch.einsum("...m,m->...m", Y, self.chebyshev_weights.to(Y))
             return product.min(dim=-1).values
 
         self._chebyshev_objective = chebyshev_obj

test/acquisition/multi_objective/test_multi_output_risk_measures.py

@@ -614,4 +614,4 @@ def test_end_to_end(self):
             mars_vals = mars(samples)
             self.assertEqual(mars_vals.shape, torch.Size([5, 3]))
             self.assertEqual(mars_vals.dtype, dtype)
-            self.assertEqual(mars_vals.device, self.device)
+            self.assertEqual(mars_vals.device.type, self.device.type)

test/acquisition/test_proximal.py

@@ -131,7 +131,7 @@ def test_proximal(self):
                     self.assertTrue(
                         torch.allclose(qei_prox, qei * test_prox_weight.flatten())
                     )
-                    self.assertTrue(qei_prox.shape == torch.Size([4]))
+                    self.assertEqual(qei_prox.shape, torch.Size([4]))
 
             # test gradient
             test_X = torch.rand(
@@ -185,14 +185,12 @@ def test_proximal(self):
 
             multi_output_model = SingleTaskGP(train_X, train_Y).to(device=self.device)
             ptransform = ScalarizedPosteriorTransform(
-                weights=torch.ones(2, dtype=dtype)
+                weights=torch.ones(2, dtype=dtype, device=self.device)
             )
-            acq = ProximalAcquisitionFunction(
-                ExpectedImprovement(
-                    multi_output_model, 0.0, posterior_transform=ptransform
-                ),
-                proximal_weights,
+            ei = ExpectedImprovement(
+                multi_output_model, 0.0, posterior_transform=ptransform
             )
+            acq = ProximalAcquisitionFunction(ei, proximal_weights)
             acq(test_X)
 
     def test_proximal_model_list(self):

test/generation/test_gen.py

@@ -238,7 +238,7 @@ def test_gen_candidates_scipy_warns_opt_no_res(self):
         with mock.patch(
             "botorch.generation.gen.minimize"
         ) as mock_minimize, warnings.catch_warnings(record=True) as ws:
-            mock_minimize.return_value = OptimizeResult(x=test_ics.numpy())
+            mock_minimize.return_value = OptimizeResult(x=test_ics.cpu().numpy())
 
             gen_candidates_scipy(
                 initial_conditions=test_ics,

test/models/test_model_list_gp_regression.py

@@ -390,7 +390,7 @@ def test_fantasize_with_outcome_transform_fixed_noise(self) -> None:
                     FixedNoiseGP(X, Y, yvar, outcome_transform=Standardize(m=1))
                 )
 
-                model.posterior(torch.zeros((1, 1)))
+                model.posterior(torch.zeros((1, 1), **tkwargs))
 
                 fant = model.fantasize(
                     X, sampler=IIDNormalSampler(n_fants, seed=0), noise=yvar