fea: add test that checks that we don't call get_batch_initial_condit…

…ions if doing L0 norm

ax/models/torch/botorch_modular/sebo.py

@@ -37,7 +37,7 @@
     optimize_acqf_homotopy,
 )
 from botorch.utils.datasets import SupervisedDataset
-from pyre_extensions import none_throws
+from pyre_extensions import assert_is_instance, none_throws
 from torch import Tensor
 
 CLAMP_TOL = 1e-2
@@ -296,14 +296,14 @@ def _optimize_with_homotopy(
             ],
         )
 
-        if "batch_initial_conditions" not in optimizer_options:
-            optimizer_options["batch_initial_conditions"] = (
+        if "batch_initial_conditions" not in optimizer_options_with_defaults:
+            optimizer_options_with_defaults["batch_initial_conditions"] = (
                 get_batch_initial_conditions(
                     acq_function=self.acqf,
                     raw_samples=optimizer_options_with_defaults["raw_samples"],
                     inequality_constraints=inequality_constraints,
                     fixed_features=fixed_features,
-                    X_pareto=self.acqf.X_baseline,
+                    X_pareto=assert_is_instance(self.acqf.X_baseline, Tensor),
                     target_point=self.target_point,
                     bounds=bounds,
                     num_restarts=optimizer_options_with_defaults["num_restarts"],

ax/models/torch/tests/test_sebo.py

@@ -380,3 +380,43 @@ def test_get_batch_initial_conditions(
         self.assertEqual(batch_initial_conditions.shape, torch.Size([3, 1, 3]))
         self.assertTrue(torch.all(batch_initial_conditions[:1] != 0.5))
         self.assertTrue(torch.all(batch_initial_conditions[1:, :, 1] == 0.5))
+
+    @mock.patch(f"{SEBOACQUISITION_PATH}.optimize_acqf_homotopy")
+    @mock.patch(
+        f"{SEBOACQUISITION_PATH}.get_batch_initial_conditions",
+        wraps=get_batch_initial_conditions,
+    )
+    def test_optimize_with_provided_batch_initial_conditions(
+        self, mock_get_batch_initial_conditions: Mock, mock_optimize_acqf_homotopy: Mock
+    ) -> None:
+        mock_optimize_acqf_homotopy.return_value = (
+            torch.tensor([[0.1, 0.1, 0.1]], dtype=torch.double),
+            torch.tensor([1.0], dtype=torch.double),
+        )
+
+        # Create batch initial conditions
+        batch_ics = torch.rand(3, 1, 3, dtype=torch.double)
+
+        acquisition = self.get_acquisition_function(
+            options={
+                "target_point": self.target_point,
+                "penalty": "L0_norm",
+            },
+        )
+
+        acquisition.optimize(
+            n=1,
+            search_space_digest=self.search_space_digest,
+            optimizer_options={
+                "batch_initial_conditions": batch_ics,
+                Keys.NUM_RESTARTS: 3,
+                Keys.RAW_SAMPLES: 32,
+            },
+        )
+
+        # Verify get_batch_initial_conditions was not called
+        mock_get_batch_initial_conditions.assert_not_called()
+
+        # Verify the batch_initial_conditions were passed to optimize_acqf_homotopy
+        call_kwargs = mock_optimize_acqf_homotopy.call_args[1]
+        self.assertTrue(torch.equal(call_kwargs["batch_initial_conditions"], batch_ics))