Don't allow unused **kwargs in input_constructors except for a define…

…d set of exceptions (facebook#1772)

Summary:
Pull Request resolved: facebook#1772

X-link: pytorch/botorch#1872

[x] Remove unused arguments from input constructors and related functions. The idea is especially not to let unused keyword arguments disappear into `**kwargs` and be silently ignored
[x] add arguments to some input constructors so they don't need any `**kwargs`
[x] Add a decorator that ensures that each input constructor can accept a certain set of keyword arguments, even if those are not used are the constructor, while still erroring on
[ ] Prevent arguments from having different defaults in the input constructors as in acquisition functions

Differential Revision: https://internalfb.com/D46519588

fbshipit-source-id: 4954b0cf76003492639b98419675a17dc67db49b

ax/benchmark/methods/modular_botorch.py

@@ -34,8 +34,6 @@ def get_sobol_botorch_modular_fixed_noise_gp_qnei(
             },
             Keys.ACQF_KWARGS: {
                 "prune_baseline": True,
-                "qmc": True,
-                "mc_samples": 512,
             },
         }
     }

ax/benchmark/tests/test_benchmark.py

@@ -14,8 +14,14 @@
 from ax.benchmark.benchmark_method import BenchmarkMethod
 from ax.benchmark.benchmark_problem import SingleObjectiveBenchmarkProblem
 from ax.benchmark.benchmark_result import BenchmarkResult
-from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
-from ax.modelbridge.registry import Models
+from ax.benchmark.methods.gpei_and_moo import get_gpei_default
+from ax.benchmark.methods.modular_botorch import (
+    get_sobol_botorch_modular_default,
+    get_sobol_botorch_modular_fixed_noise_gp_qnehvi,
+    get_sobol_botorch_modular_fixed_noise_gp_qnei,
+    get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnehvi,
+    get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnei,
+)
 from ax.service.utils.scheduler_options import SchedulerOptions
 from ax.storage.json_store.load import load_experiment
 from ax.storage.json_store.save import save_experiment
@@ -25,7 +31,6 @@
     get_multi_objective_benchmark_problem,
     get_single_objective_benchmark_problem,
     get_sobol_benchmark_method,
-    get_sobol_gpei_benchmark_method,
 )
 from ax.utils.testing.core_stubs import get_experiment
 from ax.utils.testing.mock import fast_botorch_optimize
@@ -84,12 +89,11 @@ def test_benchmark_result_invalid_inputs(self) -> None:
                 gen_time=0.0,
             )
 
-    def test_replication_synthetic(self) -> None:
+    def test_replication_synthetic_sobol(self) -> None:
         problem = get_single_objective_benchmark_problem()
+        method = get_sobol_benchmark_method()
 
-        res = benchmark_replication(
-            problem=problem, method=get_sobol_benchmark_method(), seed=0
-        )
+        res = benchmark_replication(problem=problem, method=method, seed=0)
 
         self.assertEqual(
             problem.num_trials,
@@ -98,7 +102,51 @@ def test_replication_synthetic(self) -> None:
 
         self.assertTrue(np.all(res.score_trace <= 100))
 
-    def test_replication_moo(self) -> None:
+    @fast_botorch_optimize
+    def test_replication_mbm(self) -> None:
+        for method, problem in [
+            (
+                get_sobol_botorch_modular_fixed_noise_gp_qnei(),
+                get_single_objective_benchmark_problem(infer_noise=False, num_trials=6),
+            ),
+            (
+                get_sobol_botorch_modular_fixed_noise_gp_qnehvi(),
+                get_multi_objective_benchmark_problem(infer_noise=False, num_trials=6),
+            ),
+            (
+                get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnei(),
+                get_single_objective_benchmark_problem(num_trials=6),
+            ),
+            (
+                get_sobol_botorch_modular_saas_fully_bayesian_single_task_gp_qnehvi(),
+                get_multi_objective_benchmark_problem(num_trials=6),
+            ),
+        ]:
+            with self.subTest(method=method, problem=problem):
+                res = benchmark_replication(problem=problem, method=method, seed=0)
+                self.assertEqual(
+                    problem.num_trials,
+                    len(not_none(res.experiment).trials),
+                )
+                self.assertTrue(np.all(res.score_trace <= 100))
+
+    @fast_botorch_optimize
+    def test_replication_mbm_default(self) -> None:
+        method = get_sobol_botorch_modular_default()
+        for problem in [
+            get_single_objective_benchmark_problem(infer_noise=False, num_trials=6),
+            get_multi_objective_benchmark_problem(infer_noise=False, num_trials=6),
+            get_single_objective_benchmark_problem(num_trials=6),
+            get_multi_objective_benchmark_problem(num_trials=6),
+        ]:
+            with self.subTest(problem=problem):
+                res = benchmark_replication(problem=problem, method=method, seed=0)
+                self.assertEqual(
+                    problem.num_trials,
+                    len(not_none(res.experiment).trials),
+                )
+
+    def test_replication_moo_sobol(self) -> None:
         problem = get_multi_objective_benchmark_problem()
 
         res = benchmark_replication(
@@ -139,13 +187,12 @@ def test_benchmark_one_method_problem(self) -> None:
     @fast_botorch_optimize
     def test_benchmark_multiple_problems_methods(self) -> None:
         aggs = benchmark_multiple_problems_methods(
-            problems=[get_single_objective_benchmark_problem()],
-            methods=[get_sobol_benchmark_method(), get_sobol_gpei_benchmark_method()],
+            problems=[get_single_objective_benchmark_problem(num_trials=6)],
+            methods=[get_sobol_benchmark_method(), get_gpei_default()],
             seeds=(0, 1),
         )
 
         self.assertEqual(len(aggs), 2)
-
         for agg in aggs:
             for col in ["mean", "P25", "P50", "P75"]:
                 self.assertTrue((agg.score_trace[col] <= 100).all())
@@ -157,17 +204,7 @@ def test_timeout(self) -> None:
             num_trials=1000,  # Unachievable num_trials
         )
 
-        generation_strategy = GenerationStrategy(
-            name="SOBOL+GPEI::default",
-            steps=[
-                GenerationStep(model=Models.SOBOL, num_trials=5, min_trials_observed=5),
-                GenerationStep(
-                    model=Models.GPEI,
-                    num_trials=-1,
-                    max_parallelism=1,
-                ),
-            ],
-        )
+        generation_strategy = get_gpei_default().generation_strategy
 
         method = BenchmarkMethod(
             name=generation_strategy.name,

ax/core/generator_run.py

@@ -146,7 +146,10 @@ def __init__(
         if weights is None:
             weights = [1.0 for i in range(len(arms))]
         if len(arms) != len(weights):
-            raise ValueError("Weights and arms must have the same length.")
+            raise ValueError(
+                "Weights and arms must have the same length. Arms have length "
+                f"{len(arms)}, weights have length {len(weights)}."
+            )
         if bridge_kwargs is not None or model_kwargs is not None:
             if model_key is None:
                 raise ValueError(

ax/models/torch/botorch_modular/acquisition.py

@@ -193,7 +193,7 @@ def __init__(
         ]
 
         # Subset model only to the outcomes we need for the optimization.
-        if self.options.get(Keys.SUBSET_MODEL, True):
+        if self.options.pop(Keys.SUBSET_MODEL, True):
             subset_model_results = subset_model(
                 model=primary_surrogate.model,
                 objective_weights=torch_opt_config.objective_weights,

ax/models/torch/botorch_modular/sebo.py

@@ -61,8 +61,8 @@ def __init__(
 
         tkwargs = {"dtype": surrogate.dtype, "device": surrogate.device}
         options = options or {}
-        self.penalty_name: str = options.get("penalty", "L0_norm")
-        self.target_point: Tensor = options.get("target_point", None)
+        self.penalty_name: str = options.pop("penalty", "L0_norm")
+        self.target_point: Tensor = options.pop("target_point", None)
         if self.target_point is None:
             raise ValueError("please provide target point.")
         self.target_point.to(**tkwargs)  # pyre-ignore
@@ -93,6 +93,8 @@ def __init__(
         )
 
         # instantiate botorch_acqf_class
+        if not issubclass(botorch_acqf_class, qExpectedHypervolumeImprovement):
+            raise ValueError("botorch_acqf_class must be qEHVI to use SEBO")
         super().__init__(
             surrogates={"sebo": surrogate_f},
             search_space_digest=search_space_digest,

ax/models/torch/tests/test_acquisition.py

@@ -20,7 +20,11 @@
 from ax.exceptions.core import AxWarning, SearchSpaceExhausted
 from ax.models.torch.botorch_modular.acquisition import Acquisition
 from ax.models.torch.botorch_modular.surrogate import Surrogate
-from ax.models.torch.utils import SubsetModelData
+from ax.models.torch.utils import (
+    _get_X_pending_and_observed,
+    subset_model,
+    SubsetModelData,
+)
 from ax.models.torch_base import TorchOptConfig
 from ax.utils.common.constants import Keys
 from ax.utils.common.testutils import TestCase
@@ -124,7 +128,7 @@ def setUp(self) -> None:
         )
         self.linear_constraints = None
         self.fixed_features = {1: 2.0}
-        self.options = {"best_f": 0.0, "cache_root": False, "prune_baseline": False}
+        self.options = {"cache_root": False, "prune_baseline": False}
         self.inequality_constraints = [
             (torch.tensor([0, 1], **tkwargs), torch.tensor([-1.0, 1.0], **tkwargs), 1)
         ]
@@ -159,41 +163,25 @@ def tearDown(self) -> None:
         # Avoid polluting the registry for other tests.
         ACQF_INPUT_CONSTRUCTOR_REGISTRY.pop(DummyAcquisitionFunction)
 
-    @mock.patch(f"{ACQUISITION_PATH}._get_X_pending_and_observed")
-    @mock.patch(
-        f"{ACQUISITION_PATH}.subset_model",
-        # pyre-fixme[6]: For 1st param expected `Model` but got `None`.
-        # pyre-fixme[6]: For 5th param expected `Tensor` but got `None`.
-        return_value=SubsetModelData(None, torch.ones(1), None, None, None),
-    )
-    @mock.patch(f"{ACQUISITION_PATH}.get_botorch_objective_and_transform")
-    @mock.patch(
-        f"{CURRENT_PATH}.Acquisition.compute_model_dependencies",
-        return_value={"current_value": 1.2},
-    )
-    @mock.patch(
-        f"{DummyAcquisitionFunction.__module__}.DummyAcquisitionFunction.__init__",
-        return_value=None,
-    )
-    def test_init(
-        self,
-        mock_botorch_acqf_class: Mock,
-        mock_compute_model_deps: Mock,
-        mock_get_objective_and_transform: Mock,
-        mock_subset_model: Mock,
-        mock_get_X: Mock,
-    ) -> None:
+    def test_init_raises_when_missing_acqf_cls(self) -> None:
         with self.assertRaisesRegex(TypeError, ".* missing .* 'botorch_acqf_class'"):
-            # pyre-fixme[20]: Argument `botorch_acqf_class` expected.
+            # pyre-ignore[20]: Argument `botorch_acqf_class` expected.
             Acquisition(
                 surrogates={"surrogate": self.surrogate},
                 search_space_digest=self.search_space_digest,
                 torch_opt_config=self.torch_opt_config,
             )
 
-        botorch_objective = LinearMCObjective(weights=torch.tensor([1.0]))
-        mock_get_objective_and_transform.return_value = (botorch_objective, None)
-        mock_get_X.return_value = (self.pending_observations[0], self.X[:1])
+    @mock.patch(
+        f"{ACQUISITION_PATH}._get_X_pending_and_observed",
+        wraps=_get_X_pending_and_observed,
+    )
+    @mock.patch(f"{ACQUISITION_PATH}.subset_model", wraps=subset_model)
+    def test_init(
+        self,
+        mock_subset_model: Mock,
+        mock_get_X: Mock,
+    ) -> None:
         acquisition = Acquisition(
             surrogates={"surrogate": self.surrogate},
             search_space_digest=self.search_space_digest,
@@ -224,10 +212,36 @@ def test_init(
             outcome_constraints=self.outcome_constraints,
             objective_thresholds=self.objective_thresholds,
         )
-        mock_subset_model.reset_mock()
-        mock_get_objective_and_transform.reset_mock()
-        self.mock_input_constructor.reset_mock()
-        mock_botorch_acqf_class.reset_mock()
+
+    @mock.patch(f"{ACQUISITION_PATH}._get_X_pending_and_observed")
+    @mock.patch(
+        f"{ACQUISITION_PATH}.subset_model",
+        # pyre-fixme[6]: For 1st param expected `Model` but got `None`.
+        # pyre-fixme[6]: For 5th param expected `Tensor` but got `None`.
+        return_value=SubsetModelData(None, torch.ones(1), None, None, None),
+    )
+    @mock.patch(
+        f"{ACQUISITION_PATH}.get_botorch_objective_and_transform",
+    )
+    @mock.patch(
+        f"{CURRENT_PATH}.Acquisition.compute_model_dependencies",
+        return_value={"eta": 0.1},
+    )
+    @mock.patch(
+        f"{DummyAcquisitionFunction.__module__}.DummyAcquisitionFunction.__init__",
+        return_value=None,
+    )
+    def test_init_with_subset_model_false(
+        self,
+        mock_botorch_acqf_class: Mock,
+        mock_compute_model_deps: Mock,
+        mock_get_objective_and_transform: Mock,
+        mock_subset_model: Mock,
+        mock_get_X: Mock,
+    ) -> None:
+        botorch_objective = LinearMCObjective(weights=torch.tensor([1.0]))
+        mock_get_objective_and_transform.return_value = (botorch_objective, None)
+        mock_get_X.return_value = (self.pending_observations[0], self.X[:1])
         self.options[Keys.SUBSET_MODEL] = False
         with mock.patch(
             f"{ACQUISITION_PATH}.get_outcome_constraint_transforms",
@@ -249,7 +263,7 @@ def test_init(
             self.assertIs(ckwargs["outcome_constraints"], self.outcome_constraints)
             self.assertTrue(torch.equal(ckwargs["X_observed"], self.X[:1]))
             # Check final `acqf` creation
-            model_deps = {Keys.CURRENT_VALUE: 1.2}
+            model_deps = {"eta": 0.1}
             self.mock_input_constructor.assert_called_once()
             mock_botorch_acqf_class.assert_called_once()
             _, ckwargs = self.mock_input_constructor.call_args

ax/utils/testing/benchmark_stubs.py

@@ -30,15 +30,26 @@ def get_benchmark_problem() -> BenchmarkProblem:
     )
 
 
-def get_single_objective_benchmark_problem() -> SingleObjectiveBenchmarkProblem:
+def get_single_objective_benchmark_problem(
+    infer_noise: bool = True,
+    num_trials: int = 4,
+) -> SingleObjectiveBenchmarkProblem:
     return SingleObjectiveBenchmarkProblem.from_botorch_synthetic(
-        test_problem_class=Branin, test_problem_kwargs={}, num_trials=4
+        test_problem_class=Branin,
+        test_problem_kwargs={},
+        num_trials=num_trials,
+        infer_noise=infer_noise,
     )
 
 
-def get_multi_objective_benchmark_problem() -> MultiObjectiveBenchmarkProblem:
+def get_multi_objective_benchmark_problem(
+    infer_noise: bool = True, num_trials: int = 4
+) -> MultiObjectiveBenchmarkProblem:
     return MultiObjectiveBenchmarkProblem.from_botorch_multi_objective(
-        test_problem_class=BraninCurrin, test_problem_kwargs={}, num_trials=4
+        test_problem_class=BraninCurrin,
+        test_problem_kwargs={},
+        num_trials=num_trials,
+        infer_noise=infer_noise,
     )
 
 
@@ -67,6 +78,8 @@ def get_sobol_gpei_benchmark_method() -> BenchmarkMethod:
                     num_trials=-1,
                     model_kwargs={
                         "surrogate": Surrogate(SingleTaskGP),
+                        # TODO: tests should better reflect defaults and not
+                        # re-implement this logic.
                         "botorch_acqf_class": qNoisyExpectedImprovement,
                     },
                     model_gen_kwargs={
@@ -77,8 +90,6 @@ def get_sobol_gpei_benchmark_method() -> BenchmarkMethod:
                             },
                             Keys.ACQF_KWARGS: {
                                 "prune_baseline": True,
-                                "qmc": True,
-                                "mc_samples": 512,
                             },
                         }
                     },