Clean up support for legacy format non-linear constraints

Summary: Legacy non-linear inequality constraint format has been deprecated since pytorch#1793.

Differential Revision: D66003879

botorch/generation/gen.py

@@ -22,10 +22,7 @@
 from botorch.acquisition import AcquisitionFunction
 from botorch.exceptions.errors import OptimizationGradientError
 from botorch.exceptions.warnings import OptimizationWarning
-from botorch.generation.utils import (
-    _convert_nonlinear_inequality_constraints,
-    _remove_fixed_features_from_optimization,
-)
+from botorch.generation.utils import _remove_fixed_features_from_optimization
 from botorch.logging import logger
 from botorch.optim.parameter_constraints import (
     _arrayify,
@@ -136,16 +133,6 @@ def gen_candidates_scipy(
         else:
             reduced_domain = None not in fixed_features.values()
 
-    if nonlinear_inequality_constraints:
-        if not isinstance(nonlinear_inequality_constraints, list):
-            raise ValueError(
-                "`nonlinear_inequality_constraints` must be a list of tuples, "
-                f"got {type(nonlinear_inequality_constraints)}."
-            )
-        nonlinear_inequality_constraints = _convert_nonlinear_inequality_constraints(
-            nonlinear_inequality_constraints
-        )
-
     if reduced_domain:
         _no_fixed_features = _remove_fixed_features_from_optimization(
             fixed_features=fixed_features,

botorch/generation/utils.py

@@ -6,12 +6,10 @@
 
 from __future__ import annotations
 
-import warnings
 from collections.abc import Callable
 from dataclasses import dataclass
 
 import torch
-
 from botorch.acquisition import AcquisitionFunction, FixedFeatureAcquisitionFunction
 from botorch.optim.parameter_constraints import (
     _generate_unfixed_lin_constraints,
@@ -20,34 +18,6 @@
 from torch import Tensor
 
 
-def _convert_nonlinear_inequality_constraints(
-    nonlinear_inequality_constraints: list[Callable | tuple[Callable, bool]],
-) -> list[tuple[Callable, bool]]:
-    """Convert legacy defintions of nonlinear inequality constraints into the new
-    format. Assumes intra-point constraints.
-    """
-    nlcs = []
-    legacy = False
-    # return nonlinear_inequality_constraints
-    for nlc in nonlinear_inequality_constraints:
-        if callable(nlc):
-            # old style --> convert
-            nlcs.append((nlc, True))
-            legacy = True
-        else:
-            nlcs.append(nlc)
-    if legacy:
-        warnings.warn(
-            "The `nonlinear_inequality_constraints` argument is expected "
-            "take a list of tuples. Passing a list of callables "
-            "will result in an error in future versions.",
-            DeprecationWarning,
-            stacklevel=3,
-        )
-
-    return nlcs
-
-
 def _flip_sub_unique(x: Tensor, k: int) -> Tensor:
     """Get the first k unique elements of a single-dimensional tensor, traversing the
     tensor from the back.

test/generation/test_utils.py

@@ -12,35 +12,13 @@
 
 from botorch.acquisition import FixedFeatureAcquisitionFunction
 from botorch.generation.utils import (
-    _convert_nonlinear_inequality_constraints,
     _flip_sub_unique,
     _remove_fixed_features_from_optimization,
 )
 from botorch.utils.testing import BotorchTestCase, MockAcquisitionFunction
 
 
 class TestGenerationUtils(BotorchTestCase):
-    def test_convert_nonlinear_inequality_constraints(self):
-        def nlc(x):
-            return x[..., 2]
-
-        def nlc2(x):
-            return x[..., 3]
-
-        nlcs = [nlc]
-        with self.assertWarns(DeprecationWarning):
-            new_nlcs = _convert_nonlinear_inequality_constraints(nlcs)
-        self.assertEqual(new_nlcs, [(nlc, True)])
-
-        nlcs = [(nlc, False)]
-        new_nlcs = _convert_nonlinear_inequality_constraints(nlcs)
-        self.assertEqual(new_nlcs, [(nlc, False)])
-
-        nlcs = [(nlc, False), nlc2]
-        with self.assertWarns(DeprecationWarning):
-            new_nlcs = _convert_nonlinear_inequality_constraints(nlcs)
-        self.assertEqual(new_nlcs, [(nlc, False), (nlc2, True)])
-
     def test_flip_sub_unique(self):
         for dtype in (torch.float, torch.double):
             tkwargs = {"device": self.device, "dtype": dtype}