Delete MultiObjectiveLegacyBoTorchGenerator

ax/adapter/tests/test_torch_moo_adapter.py

@@ -32,7 +32,6 @@
 )
 from ax.core.parameter_constraint import ParameterConstraint
 from ax.generators.torch.botorch_modular.generator import BoTorchGenerator
-from ax.generators.torch.botorch_moo import MultiObjectiveLegacyBoTorchGenerator
 from ax.generators.torch.botorch_moo_defaults import (
     infer_objective_thresholds,
     pareto_frontier_evaluator,
@@ -104,7 +103,7 @@ def helper_test_pareto_frontier(
         )
         adapter = TorchAdapter(
             experiment=exp,
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
         )
         with patch(
             PARETO_FRONTIER_EVALUATOR_PATH, wraps=pareto_frontier_evaluator
@@ -272,7 +271,7 @@ def test_get_pareto_frontier_and_configs_input_validation(self) -> None:
             experiment=exp,
             search_space=exp.search_space,
             data=exp.fetch_data(),
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
             transforms=[],
         )
         observation_features = [
@@ -351,7 +350,7 @@ def test_hypervolume(self, _, cuda: bool = False) -> None:
             )
             adapter = TorchAdapter(
                 search_space=exp.search_space,
-                generator=MultiObjectiveLegacyBoTorchGenerator(),
+                generator=BoTorchGenerator(),
                 optimization_config=optimization_config,
                 transforms=[],
                 experiment=exp,
@@ -437,7 +436,7 @@ def test_infer_objective_thresholds(self, _, cuda: bool = False) -> None:
         data = exp.fetch_data()
         adapter = TorchAdapter(
             search_space=exp.search_space,
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
             optimization_config=exp.optimization_config,
             transforms=Cont_X_trans + Y_trans,
             torch_device=torch.device("cuda" if cuda else "cpu"),
@@ -569,7 +568,7 @@ def test_infer_objective_thresholds(self, _, cuda: bool = False) -> None:
         set_rng_seed(0)  # make model fitting deterministic
         adapter = TorchAdapter(
             search_space=exp.search_space,
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
             optimization_config=exp.optimization_config,
             transforms=ST_MTGP_trans,
             experiment=exp,
@@ -633,7 +632,7 @@ def test_infer_objective_thresholds(self, _, cuda: bool = False) -> None:
         exp._trials = get_hss_trials_with_fixed_parameter(exp=exp)
         adapter = TorchAdapter(
             search_space=hss,
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
             optimization_config=exp.optimization_config,
             transforms=Cont_X_trans + Y_trans,
             torch_device=torch.device("cuda" if cuda else "cpu"),
@@ -716,7 +715,7 @@ def test_best_point(self) -> None:
         )
         adapter = TorchAdapter(
             search_space=exp.search_space,
-            generator=MultiObjectiveLegacyBoTorchGenerator(),
+            generator=BoTorchGenerator(),
             optimization_config=exp.optimization_config,
             transforms=[],
             experiment=exp,

ax/adapter/torch.py

@@ -68,7 +68,6 @@
 from ax.exceptions.generation_strategy import OptimizationConfigRequired
 from ax.generators.torch.botorch import LegacyBoTorchGenerator
 from ax.generators.torch.botorch_modular.generator import BoTorchGenerator
-from ax.generators.torch.botorch_moo import MultiObjectiveLegacyBoTorchGenerator
 from ax.generators.torch.botorch_moo_defaults import infer_objective_thresholds
 from ax.generators.torch_base import TorchGenerator, TorchOptConfig
 from ax.generators.types import TConfig
@@ -226,17 +225,13 @@ def infer_objective_thresholds(
                 "`infer_objective_thresholds` does not support risk measures."
             )
         # Infer objective thresholds.
-        if isinstance(self.generator, MultiObjectiveLegacyBoTorchGenerator):
-            model = self.generator.model
-            Xs = self.generator.Xs
-        elif isinstance(self.generator, BoTorchGenerator):
+        if isinstance(self.generator, BoTorchGenerator):
             model = self.generator.surrogate.model
             Xs = self.generator.surrogate.Xs
         else:
             raise UnsupportedError(
-                "Generator must be a MultiObjectiveLegacyBoTorchGenerator or an "
-                "appropriate Modular Botorch Generator to infer_objective_thresholds. "
-                f"Found {type(self.generator)}."
+                "Generator must be a Modular Botorch Generator to "
+                f"infer_objective_thresholds. Found {type(self.generator)}."
             )
 
         obj_thresholds = infer_objective_thresholds(

ax/generators/tests/test_botorch_moo_defaults.py

@@ -16,7 +16,6 @@
 from ax.core.search_space import SearchSpaceDigest
 from ax.generators.torch.botorch_defaults import NO_OBSERVED_POINTS_MESSAGE
 from ax.generators.torch.botorch_modular.generator import BoTorchGenerator
-from ax.generators.torch.botorch_moo import MultiObjectiveLegacyBoTorchGenerator
 from ax.generators.torch.botorch_moo_defaults import (
     get_outcome_constraint_transforms,
     get_qLogEHVI,
@@ -31,12 +30,10 @@
 from ax.utils.common.testutils import TestCase
 from ax.utils.testing.mock import mock_botorch_optimize_context_manager
 from botorch.models.gp_regression import SingleTaskGP
-from botorch.models.model import Model
 from botorch.utils.datasets import SupervisedDataset
 from botorch.utils.multi_objective.hypervolume import infer_reference_point
 from botorch.utils.testing import MockModel, MockPosterior
 from gpytorch.utils.warnings import NumericalWarning
-from torch._tensor import Tensor
 
 
 MOO_DEFAULTS_PATH: str = "ax.generators.torch.botorch_moo_defaults"
@@ -89,7 +86,7 @@ def setUp(self) -> None:
     def test_pareto_frontier_raise_error_when_missing_data(self) -> None:
         with self.assertRaises(ValueError):
             pareto_frontier_evaluator(
-                model=MultiObjectiveLegacyBoTorchGenerator(),
+                model=BoTorchGenerator(),
                 objective_thresholds=self.objective_thresholds,
                 objective_weights=self.objective_weights,
                 Yvar=self.Yvar,
@@ -151,17 +148,7 @@ def test_pareto_frontier_evaluator_raw(self) -> None:
         self.assertTrue(torch.equal(torch.tensor([], dtype=torch.long), indx))
 
     def test_pareto_frontier_evaluator_predict(self) -> None:
-        def dummy_predict(
-            model: Model,
-            X: Tensor,
-            use_posterior_predictive: bool = False,
-        ) -> tuple[Tensor, Tensor]:
-            # Add column to X that is a product of previous elements.
-            mean = torch.cat([X, torch.prod(X, dim=1).reshape(-1, 1)], dim=1)
-            cov = torch.zeros(mean.shape[0], mean.shape[1], mean.shape[1])
-            return mean, cov
-
-        model = MultiObjectiveLegacyBoTorchGenerator(model_predictor=dummy_predict)
+        model = BoTorchGenerator()
         _fit_model(model=model, X=self.X, Y=self.Y, Yvar=self.Yvar)
 
         Y, _, indx = pareto_frontier_evaluator(
@@ -171,11 +158,11 @@ def dummy_predict(
             X=self.X,
         )
         pred = self.Y[2:4]
-        self.assertAllClose(Y, pred)
+        self.assertAllClose(Y, pred, rtol=1e-3)
         self.assertTrue(torch.equal(torch.arange(2, 4), indx))
 
     def test_pareto_frontier_evaluator_with_outcome_constraints(self) -> None:
-        model = MultiObjectiveLegacyBoTorchGenerator()
+        model = BoTorchGenerator()
         Y, _, indx = pareto_frontier_evaluator(
             model=model,
             objective_weights=self.objective_weights,