Add error message for one shot acqf in optimize_acqf_discrete (#939)

Summary:
<!--
Thank you for sending the PR! We appreciate you spending the time to make BoTorch better.

Help us understand your motivation by explaining why you decided to make this change.

You can learn more about contributing to BoTorch here: https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md
-->

## Motivation

As noted in #938, use of one-shot acquisition functions in optimize_acqf_discrete leads to errors down the line that are hard to interpret. This PR adds an explicit error message noting that this is not supported.

### Have you read the [Contributing Guidelines on pull requests](https://github.com/pytorch/botorch/blob/main/CONTRIBUTING.md#pull-requests)?

Yes

Pull Request resolved: #939

Test Plan: Units.

Reviewed By: danielrjiang

Differential Revision: D35750109

Pulled By: saitcakmak

fbshipit-source-id: c1fc51ea5398613f44554de18b339d5c7d64524f

Author: saitcakmak

botorch/optim/optimize.py

@@ -18,6 +18,7 @@
     OneShotAcquisitionFunction,
 )
 from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
+from botorch.exceptions import UnsupportedError
 from botorch.generation.gen import gen_candidates_scipy
 from botorch.logging import logger
 from botorch.optim.initializers import (
@@ -594,6 +595,11 @@ def optimize_acqf_discrete(
         - a `q x d`-dim tensor of generated candidates.
         - an associated acquisition value.
     """
+    if isinstance(acq_function, OneShotAcquisitionFunction):
+        raise UnsupportedError(
+            "Discrete optimization is not supported for"
+            "one-shot acquisition functions."
+        )
     choices_batched = choices.unsqueeze(-2)
     if q > 1:
         candidate_list, acq_value_list = [], []

test/optim/test_optimize.py

@@ -13,6 +13,7 @@
     AcquisitionFunction,
     OneShotAcquisitionFunction,
 )
+from botorch.exceptions import UnsupportedError
 from botorch.optim.optimize import (
     _filter_infeasible,
     _filter_invalid,
@@ -928,6 +929,14 @@ def test_optimize_acqf_discrete(self):
             self.assertTrue(torch.allclose(acq_value, expected_acq_value))
             self.assertTrue(torch.allclose(candidates, expected_candidates))
 
+        with self.assertRaises(UnsupportedError):
+            acqf = MockOneShotAcquisitionFunction()
+            optimize_acqf_discrete(
+                acq_function=acqf,
+                q=1,
+                choices=torch.tensor([[0.5], [0.2]]),
+            )
+
     def test_optimize_acqf_discrete_local_search(self):
         for q, dtype in itertools.product((1, 2), (torch.float, torch.double)):
             tkwargs = {"device": self.device, "dtype": dtype}