Create Scheduler.generate_candidates() function (facebook#2640)

Summary:
Pull Request resolved: facebook#2640

Add `Scheduler.generate_candidates()` method which calls
- poll and fetch
- get next trial
- eventaully gen report
- save new trials

Differential Revision: D59606488

ax/exceptions/generation_strategy.py

@@ -74,3 +74,10 @@ def __init__(self, error_info: Optional[str]) -> None:
             + "check the documentation, and adjust the configuration accordingly. "
             + f"{error_info}"
         )
+
+
+class OptimizationConfigRequired(ValueError):
+    """Error indicating that an candidate generation cannot be completed
+    because an optimization config was not provided."""
+
+    pass

ax/modelbridge/torch.py

@@ -38,6 +38,7 @@
 from ax.core.search_space import SearchSpace
 from ax.core.types import TCandidateMetadata, TModelPredictArm
 from ax.exceptions.core import DataRequiredError, UnsupportedError
+from ax.exceptions.generation_strategy import OptimizationConfigRequired
 from ax.modelbridge.base import gen_arms, GenResults, ModelBridge
 from ax.modelbridge.modelbridge_utils import (
     array_to_observation_data,
@@ -804,7 +805,7 @@ def _get_transformed_model_gen_args(
             search_space=search_space, param_names=self.parameters
         )
         if optimization_config is None:
-            raise ValueError(
+            raise OptimizationConfigRequired(
                 f"{self.__class__.__name__} requires an OptimizationConfig "
                 "to be specified"
             )

ax/service/scheduler.py

@@ -55,6 +55,7 @@
 from ax.exceptions.generation_strategy import (
     AxGenerationException,
     MaxParallelismReachedException,
+    OptimizationConfigRequired,
 )
 from ax.modelbridge.base import ModelBridge
 from ax.modelbridge.generation_strategy import GenerationStrategy
@@ -1708,6 +1709,15 @@ def _get_next_trials(self, num_trials: int = 1, n: int = 1) -> List[BaseTrial]:
                 )
             self.logger.debug(f"Message from generation strategy: {err}")
             return []
+        except OptimizationConfigRequired as err:
+            if self._log_next_no_trials_reason:
+                self.logger.info(
+                    "Generated all trials that can be generated currently. "
+                    "`generation_strategy` requires an optimization config "
+                    "to be set before generating more trials."
+                )
+            self.logger.debug(f"Message from generation strategy: {err}")
+            return []
 
         if self.options.trial_type == TrialType.TRIAL and any(
             len(generator_run_list[0].arms) > 1 or len(generator_run_list) > 1
@@ -1737,6 +1747,39 @@ def _get_next_trials(self, num_trials: int = 1, n: int = 1) -> List[BaseTrial]:
             trials.append(trial)
         return trials
 
+    def generate_candidates(
+        self,
+        num_trials: int = 1,
+        reduce_state_generator_runs: bool = False,
+    ) -> List[BaseTrial]:
+        """Fetch the latest data and generate new candidate trials.
+
+        Args:
+            num_trials: Number of candidate trials to generate.
+            reduce_state_generator_runs: Flag to determine
+                whether to save model state for every generator run (default)
+                or to only save model state on the final generator run of each
+                batch.
+
+        Returns:
+            List of trials, empty if generation is not possible.
+        """
+        self.poll_and_process_results()
+        new_trials = self._get_next_trials(
+            num_trials=num_trials,
+            n=self.options.batch_size or 1,
+        )
+        if len(new_trials) > 0:
+            new_generator_runs = [gr for t in new_trials for gr in t.generator_runs]
+            self._save_or_update_trials_and_generation_strategy_if_possible(
+                experiment=self.experiment,
+                trials=new_trials,
+                generation_strategy=self.generation_strategy,
+                new_generator_runs=new_generator_runs,
+                reduce_state_generator_runs=reduce_state_generator_runs,
+            )
+        return new_trials
+
     def _gen_new_trials_from_generation_strategy(
         self,
         num_trials: int,