Add optimize_acqf_mixed_alternating to `fast_botorch_optimize_conte…

…xt_manager`

Summary: The previous diff adds mixed optimizer to MBM. This diff adds it to fast optimizer mocks.

Differential Revision: D65067691

ax/utils/testing/mock.py

@@ -17,6 +17,7 @@
     gen_batch_initial_conditions,
     gen_one_shot_kg_initial_conditions,
 )
+from botorch.optim.optimize_acqf_mixed import optimize_acqf_mixed_alternating
 from scipy.optimize import OptimizeResult
 from torch import Tensor
 
@@ -57,6 +58,19 @@ def minimal_gen_os_ics(*args: Any, **kwargs: Any) -> Tensor | None:
     def minimal_fit_fully_bayesian(*args: Any, **kwargs: Any) -> None:
         fit_fully_bayesian_model_nuts(*args, **_get_minimal_mcmc_kwargs(**kwargs))
 
+    def minimal_mixed_optimizer(*args: Any, **kwargs: Any) -> tuple[Tensor, Tensor]:
+        kwargs["raw_samples"] = 2
+        kwargs["num_restarts"] = 1
+        kwargs["options"].update(
+            {
+                "maxiter_alternating": 1,
+                "maxiter_continuous": 1,
+                "maxiter_init": 1,
+                "maxiter_discrete": 1,
+            }
+        )
+        return optimize_acqf_mixed_alternating(*args, **kwargs)
+
     with ExitStack() as es:
         mock_generation = es.enter_context(
             mock.patch(
@@ -93,6 +107,14 @@ def minimal_fit_fully_bayesian(*args: Any, **kwargs: Any) -> None:
             )
         )
 
+        mock_mixed_optimizer = es.enter_context(
+            mock.patch(
+                "ax.models.torch.botorch_modular.acquisition."
+                "optimize_acqf_mixed_alternating",
+                wraps=minimal_mixed_optimizer,
+            )
+        )
+
         yield
 
     if (not force) and all(
@@ -103,6 +125,7 @@ def minimal_fit_fully_bayesian(*args: Any, **kwargs: Any) -> None:
             mock_gen_ics,
             mock_gen_os_ics,
             mock_mcmc_mbm,
+            mock_mixed_optimizer,
         ]
     ):
         raise AssertionError(