Remove BenchmarkProblemWithKnownOptimum type annotation (#2602)

Summary:
Pull Request resolved: #2602

These are no longer necessary. See previous PR for context. This change reaps them and updates type annotations.

Reviewed By: saitcakmak

Differential Revision: D60146081

ax/benchmark/benchmark.py

@@ -27,10 +27,7 @@
 import numpy as np
 
 from ax.benchmark.benchmark_method import BenchmarkMethod
-from ax.benchmark.benchmark_problem import (
-    BenchmarkProblemProtocol,
-    BenchmarkProblemWithKnownOptimum,
-)
+from ax.benchmark.benchmark_problem import BenchmarkProblemProtocol
 from ax.benchmark.benchmark_result import AggregatedBenchmarkResult, BenchmarkResult
 from ax.benchmark.metrics.base import BenchmarkMetricBase, GroundTruthMetricMixin
 from ax.core.experiment import Experiment
@@ -60,9 +57,7 @@ def compute_score_trace(
     # Use the first GenerationStep's best found point as baseline. Sometimes (ex. in
     # a timeout) the first GenerationStep will not have not completed and we will not
     # have enough trials; in this case we do not score.
-    if (len(optimization_trace) <= num_baseline_trials) or not isinstance(
-        problem, BenchmarkProblemWithKnownOptimum
-    ):
+    if len(optimization_trace) <= num_baseline_trials:
         return np.full(len(optimization_trace), np.nan)
     optimum = problem.optimal_value
     baseline = optimization_trace[num_baseline_trials - 1]

ax/benchmark/benchmark_problem.py

@@ -10,7 +10,6 @@
 # `BenchmarkProblem` as return type annotation, used for serialization and rendering
 # in the UI.
 
-import abc
 from dataclasses import dataclass, field
 from typing import (
     Any,
@@ -92,14 +91,7 @@ class BenchmarkProblemProtocol(Protocol):
         bool, Dict[str, bool]
     ]  # Whether we observe the observation noise level
     has_ground_truth: bool  # if True, evals (w/o synthetic noise) are determinstic
-
-    @abc.abstractproperty
-    def runner(self) -> Runner:
-        pass  # pragma: no cover
-
-
-@runtime_checkable
-class BenchmarkProblemWithKnownOptimum(Protocol):
+    runner: Runner
     optimal_value: float
 
 
@@ -109,7 +101,8 @@ class BenchmarkProblem(Base):
     Problem against which diffrent methods can be benchmarked.
 
     Defines how data is generated, the objective (via the OptimizationConfig),
-    and the SearchSpace.
+    and the SearchSpace. Does not define the runner, which must be handled by
+    subclasses.
 
     Args:
         name: Can be generated programmatically with `_get_name`.