Robustify prune_inferior_points tests against sorting order (#2548)

Summary:
Our nightly CI started failing, likely due to a sorting order change introduced in pytorch/pytorch#127936

This change robustifies the tests against the point order (and also fixes a torch deprecation warning)

Pull Request resolved: #2548

Differential Revision: D63260870

Pulled By: Balandat

botorch/acquisition/utils.py

@@ -335,15 +335,16 @@ def prune_inferior_points(
         marginalize_dim=marginalize_dim,
     )
     if infeas.any():
-        # set infeasible points to worse than worst objective
-        # across all samples
+        # set infeasible points to worse than worst objective across all samples
+        # Use clone() here to avoid deprecated `index_put_` on an expanded tensor
+        obj_vals = obj_vals.clone()
         obj_vals[infeas] = obj_vals.min() - 1
 
     is_best = torch.argmax(obj_vals, dim=-1)
     idcs, counts = torch.unique(is_best, return_counts=True)
 
     if len(idcs) > max_points:
-        counts, order_idcs = torch.sort(counts, descending=True)
+        counts, order_idcs = torch.sort(counts, stable=True, descending=True)
         idcs = order_idcs[:max_points]
 
     return X[idcs]

test/acquisition/multi_objective/test_utils.py

@@ -130,13 +130,14 @@ def test_prune_inferior_points_multi_objective(self):
                 X_pruned = prune_inferior_points_multi_objective(
                     model=mm, X=X, ref_point=ref_point, max_frac=2 / 3
                 )
-            if self.device.type == "cuda":
-                # sorting has different order on cuda
-                self.assertTrue(
-                    torch.equal(X_pruned, X[[2, 1]]) or torch.equal(X_pruned, X[[1, 2]])
+            # sorting has different order on cuda
+            X_expected = X[1:3] if self.device.type == "cuda" else X[:2]
+            self.assertTrue(
+                torch.equal(
+                    torch.sort(X_pruned, stable=True).values,
+                    torch.sort(X_expected, stable=True).values,
                 )
-            else:
-                self.assertTrue(torch.equal(X_pruned, X[:2]))
+            )
             # test that zero-probability is in fact pruned
             samples[2, 0, 0] = 10
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):

test/acquisition/test_utils.py

@@ -270,11 +270,14 @@ def test_prune_inferior_points(self):
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):
                 mm = MockModel(MockPosterior(samples=samples))
                 X_pruned = prune_inferior_points(model=mm, X=X, max_frac=2 / 3)
-            if self.device.type == "cuda":
-                # sorting has different order on cuda
-                self.assertTrue(torch.equal(X_pruned, torch.stack([X[2], X[1]], dim=0)))
-            else:
-                self.assertTrue(torch.equal(X_pruned, X[:2]))
+            # sorting has different order on cuda
+            X_expected = X[1:3] if self.device.type == "cuda" else X[:2]
+            self.assertTrue(
+                torch.equal(
+                    torch.sort(X_pruned, stable=True).values,
+                    torch.sort(X_expected, stable=True).values,
+                )
+            )
             # test that zero-probability is in fact pruned
             samples[2, 0, 0] = 10
             with mock.patch.object(MockPosterior, "rsample", return_value=samples):
@@ -289,11 +292,7 @@ def test_prune_inferior_points(self):
                 device=self.device,
                 dtype=dtype,
             )
-            mm = MockModel(
-                MockPosterior(
-                    samples=samples,
-                )
-            )
+            mm = MockModel(MockPosterior(samples=samples))
             X_pruned = prune_inferior_points(
                 model=mm,
                 X=X,