Ignore sequential argument when using optimize_acqf_mixed (#2545)

Summary:
Pull Request resolved: #2545

Passing `sequential` has been raising a deprecation warning, and stopping passing it enables  pytorch/botorch#2390 .

Currently, the `sequential` argument is always provided by Ax and can be overridden by the user. This solution silently ignores the argument whenever the mixed optimizer is used. A nicer solution would be for Ax to only construct the `sequential` argument when it is needed and for there to be an exception when the user passes `sequential=False` and the mixed optimizer is used. If BoTorch plans to eventually enable `sequential=True` with `optimize_acqf_mixed`, then the exception should be raised by BoTorch so that Ax doesn't have to stay in sync with BoTorch's current capabilities. However, I think this code could use a thorough cleanup, so I went with the simple solution rather than add more `if` statements.

Reviewed By: Balandat

Differential Revision: D59057005

fbshipit-source-id: c545f030672b7e30c4405c734b7e7c6605d8a1f8

ax/models/torch/botorch_modular/acquisition.py

@@ -543,6 +543,8 @@ def optimize(
             return candidates, acqf_values, arm_weights
 
         # 2b. Handle mixed search spaces that have discrete and continuous features.
+        # Only sequential optimization is supported for `optimize_acqf_mixed`.
+        optimizer_options_with_defaults.pop("sequential")
         candidates, acqf_values = optimize_acqf_mixed(
             acq_function=self.acqf,
             bounds=bounds,

ax/models/torch/tests/test_acquisition.py

@@ -510,9 +510,7 @@ def test_optimize_acqf_discrete_local_search(
         tkwargs = {"dtype": self.X.dtype, "device": self.X.device}
         ssd = SearchSpaceDigest(
             feature_names=["a", "b", "c"],
-            # pyre-fixme[6]: For 2nd param expected `List[Tuple[Union[float, int],
-            #  Union[float, int]]]` but got `List[Tuple[int, int, int]]`.
-            bounds=[(0, 0, 0), (1, 1, 1)],
+            bounds=[(0, 1) for _ in range(3)],
             categorical_features=[0, 1, 2],
             discrete_choices={  # 30 * 60 * 90 > 100,000
                 k: np.linspace(0, 1, 30 * (k + 1)).tolist() for k in range(3)
@@ -568,7 +566,6 @@ def test_optimize_mixed(self, mock_optimize_acqf_mixed: Mock) -> None:
         )
         mock_optimize_acqf_mixed.assert_called_with(
             acq_function=acquisition.acqf,
-            sequential=True,
             bounds=mock.ANY,
             q=3,
             options={"init_batch_limit": 32, "batch_limit": 5},