Make simulator fm invocation more explicit

src/ert/run_models/everest_run_model.py

@@ -501,14 +501,15 @@ def _create_optimizer(self, simulator: Simulator) -> BasicOptimizer:
         )
 
         ropt_output_folder = Path(self.everest_config.optimization_output_dir)
+        ropt_evaluator_fn = simulator.create_forward_model_evaluator_function()
 
         # Initialize the optimizer with output tables. `min_header_len` is set
         # to ensure that all tables have the same number of header lines,
         # simplifying code that reads them as fixed width tables. `maximize` is
         # set because ropt reports minimization results, while everest wants
         # maximization results, necessitating a conversion step.
         optimizer = (
-            BasicOptimizer(enopt_config=self.ropt_config, evaluator=simulator)
+            BasicOptimizer(enopt_config=self.ropt_config, evaluator=ropt_evaluator_fn)
             .add_table(
                 columns=RESULT_COLUMNS,
                 path=ropt_output_folder / "results.txt",

src/everest/simulator/simulator.py

@@ -7,7 +7,7 @@
 import numpy as np
 from numpy import float64
 from numpy._typing import NDArray
-from ropt.evaluator import EvaluatorContext, EvaluatorResult
+from ropt.evaluator import Evaluator, EvaluatorContext, EvaluatorResult
 
 from ert import BatchSimulator, WorkflowRunner
 from ert.config import ErtConfig, HookRuntime
@@ -90,7 +90,18 @@ def _get_aliases(self, ever_config: EverestConfig) -> Dict[str, str]:
                 aliases[f"{constraint.name}:upper"] = constraint.name
         return aliases
 
-    def __call__(
+    def create_forward_model_evaluator_function(
+        self,
+    ) -> Evaluator:
+        def run_forward_model(
+            control_values: NDArray[np.float64], metadata: EvaluatorContext
+        ) -> EvaluatorResult:
+            nonlocal self
+            return self._run_forward_model(control_values, metadata)
+
+        return run_forward_model
+
+    def _run_forward_model(
         self, control_values: NDArray[np.float64], metadata: EvaluatorContext
     ) -> EvaluatorResult:
         active = (

tests/everest/test_simulator_cache.py

@@ -12,15 +12,15 @@
 
 def test_simulator_cache(monkeypatch, copy_math_func_test_data_to_tmp):
     n_evals = 0
-    original_call = Simulator.__call__
+    original_call = Simulator._run_forward_model
 
     def new_call(*args):
         nonlocal n_evals
         result = original_call(*args)
         n_evals += (result.evaluation_ids >= 0).sum()
         return result
 
-    monkeypatch.setattr(Simulator, "__call__", new_call)
+    monkeypatch.setattr(Simulator, "_run_forward_model", new_call)
 
     config = EverestConfig.load_file(CONFIG_FILE)
     config.simulator = SimulatorConfig(enable_cache=True)
@@ -33,7 +33,10 @@ def new_call(*args):
 
         # Run once, populating the cache of the simulator:
         variables1 = (
-            BasicOptimizer(enopt_config=ropt_config, evaluator=simulator)
+            BasicOptimizer(
+                enopt_config=ropt_config,
+                evaluator=simulator.create_forward_model_evaluator_function(),
+            )
             .run()
             .variables
         )
@@ -44,7 +47,10 @@ def new_call(*args):
         # Run again with the same simulator:
         n_evals = 0
         variables2 = (
-            BasicOptimizer(enopt_config=ropt_config, evaluator=simulator)
+            BasicOptimizer(
+                enopt_config=ropt_config,
+                evaluator=simulator.create_forward_model_evaluator_function(),
+            )
             .run()
             .variables
         )