Warn if inoperable keyword arguments are passed to optimizers (pytorc…

…h#1421)

Summary:
X-link: facebook/Ax#1421

Pull Request resolved: pytorch#1677

The previous diff discusses issues with kwargs in BoTorch optimizers and resolves some of them. This diff warns if keyword arguments that do nothing are passed to an optimizer, and stops Ax (botorch-modular) from doing that.

Reviewed By: lena-kashtelyan

Differential Revision: D43277102

fbshipit-source-id: f22b4cd1c4b6227a5210f27d1501c3667209a1d7

botorch/optim/optimize.py

@@ -156,6 +156,24 @@ def ic_gen(self) -> TGenInitialConditions:
         return gen_batch_initial_conditions
 
 
+def _raise_deprecation_warning_if_kwargs(fn_name: str, kwargs: Dict[str, Any]) -> None:
+    """
+    Raise a warning if kwargs are provided.
+
+    Some functions used to support **kwargs. The applicable parameters have now been
+    refactored to be named arguments, so no warning will be raised for users passing
+    the expected arguments. However, if a user had been passing an inapplicable
+    keyword argument, this will now raise a warning whereas in the past it did
+    nothing.
+    """
+    if len(kwargs) > 0:
+        warnings.warn(
+            f"`{fn_name}` does not support arguments {list(kwargs.keys())}. In "
+            "the future, this will become an error.",
+            DeprecationWarning,
+        )
+
+
 def _optimize_acqf_all_features_fixed(
     *,
     bounds: Tensor,
@@ -830,6 +848,7 @@ def optimize_acqf_mixed(
                 "are currently not supported when `q > 1`. This is needed to "
                 "compute the joint acquisition value."
             )
+    _raise_deprecation_warning_if_kwargs("optimize_acqf_mixed", kwargs)
 
     if q == 1:
         ff_candidate_list, ff_acq_value_list = [], []
@@ -932,6 +951,7 @@ def optimize_acqf_discrete(
         )
     if choices.numel() == 0:
         raise InputDataError("`choices` must be non-emtpy.")
+    _raise_deprecation_warning_if_kwargs("optimize_acqf_discrete", kwargs)
     choices_batched = choices.unsqueeze(-2)
     if q > 1:
         candidate_list, acq_value_list = [], []
@@ -1091,6 +1111,7 @@ def optimize_acqf_discrete_local_search(
         - a `q x d`-dim tensor of generated candidates.
         - an associated acquisition value.
     """
+    _raise_deprecation_warning_if_kwargs("optimize_acqf_discrete_local_search", kwargs)
     candidate_list = []
     base_X_pending = acq_function.X_pending if q > 1 else None
     base_X_avoid = X_avoid

test/optim/test_optimize.py

@@ -1394,7 +1394,6 @@ def test_optimize_acqf_discrete(self):
 
             mock_acq_function = SquaredAcquisitionFunction()
             mock_acq_function.set_X_pending(None)
-
             # ensure proper raising of errors if no choices
             with self.assertRaisesRegex(InputDataError, "`choices` must be non-emtpy."):
                 optimize_acqf_discrete(
@@ -1404,6 +1403,17 @@ def test_optimize_acqf_discrete(self):
                 )
 
             choices = torch.rand(5, 2, **tkwargs)
+
+            # warning for unsupported keyword arguments
+            with self.assertWarnsRegex(
+                DeprecationWarning,
+                r"`optimize_acqf_discrete` does not support arguments "
+                r"\['num_restarts'\]. In the future, this will become an error.",
+            ):
+                optimize_acqf_discrete(
+                    acq_function=mock_acq_function, q=q, choices=choices, num_restarts=8
+                )
+
             exp_acq_vals = mock_acq_function(choices)
 
             # test unique