Adding ensemble support to optuna

model_analyzer/analyzer.py

@@ -431,6 +431,7 @@ def _populate_search_parameters(self, client, gpus):
                 config=self._config,
                 model=model_profile_spec,
                 is_bls_model=bool(self._config.bls_composing_models),
+                is_ensemble_model=model_profile_spec.is_ensemble(),
             )
 
     def _populate_composing_search_parameters(self, client, gpus):

model_analyzer/config/generate/optuna_run_config_generator.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 
 import logging
+from sys import maxsize
 from typing import Any, Dict, Generator, List, Optional, TypeAlias, Union
 
 import optuna
@@ -183,7 +184,7 @@ def get_configs(self) -> Generator[RunConfig, None, None]:
             yield run_config
 
             score = self._calculate_score()
-            self._set_best_measurement(run_config, score)
+            self._set_best_measurement(run_config, score, trial_number)
 
             if logging.DEBUG:
                 self._print_debug_score_info(run_config, score)
@@ -429,15 +430,18 @@ def _create_objective_based_run_config(
     ) -> RunConfig:
         run_config = RunConfig(self._triton_env)
 
-        # TODO: TMA-1927: Add support for multi-model
-        model_config_variant = self._create_model_config_variant(
-            self._models[0], trial_objectives
-        )
-
         composing_model_config_variants = self._create_composing_model_config_variants(
             composing_trial_objectives
         )
 
+        # TODO: TMA-1927: Add support for multi-model
+        model_config_variant = self._create_model_config_variant(
+            model=self._models[0],
+            trial_objectives=trial_objectives,
+            composing_trial_objectives=composing_trial_objectives,
+            composing_model_config_variants=composing_model_config_variants,
+        )
+
         # TODO: TMA-1927: Add support for multi-model
         model_run_config = self._create_model_run_config(
             model=self._models[0],
@@ -451,6 +455,44 @@ def _create_objective_based_run_config(
         return run_config
 
     def _create_parameter_combo(
+        self,
+        model: ModelProfileSpec,
+        trial_objectives: TrialObjectives,
+        composing_trial_objectives: ComposingTrialObjectives,
+    ) -> ParameterCombo:
+        if model.is_ensemble():
+            param_combo = self._create_ensemble_parameter_combo(
+                composing_trial_objectives
+            )
+        else:
+            param_combo = self._create_non_ensemble_parameter_combo(trial_objectives)
+
+        return param_combo
+
+    def _create_ensemble_parameter_combo(
+        self,
+        composing_trial_objectives: ComposingTrialObjectives,
+    ) -> ParameterCombo:
+        """
+        For the ensemble model the only parameter we need to set
+        is the max batch size; which will be the minimum batch size
+        found in the composing_model max batch sizes
+        """
+
+        min_val_of_max_batch_size = maxsize
+        for composing_trial_objective in composing_trial_objectives.values():
+            min_val_of_max_batch_size = int(
+                min(
+                    composing_trial_objective.get("max_batch_size", 1),
+                    min_val_of_max_batch_size,
+                )
+            )
+
+        param_combo = {"max_batch_size": min_val_of_max_batch_size}
+
+        return param_combo
+
+    def _create_non_ensemble_parameter_combo(
         self, trial_objectives: TrialObjectives
     ) -> ParameterCombo:
         param_combo: ParameterCombo = {}
@@ -481,17 +523,32 @@ def _create_parameter_combo(
         return param_combo
 
     def _create_model_config_variant(
-        self, model: ModelProfileSpec, trial_objectives: TrialObjectives
+        self,
+        model: ModelProfileSpec,
+        trial_objectives: TrialObjectives,
+        composing_trial_objectives: ComposingTrialObjectives = {},
+        composing_model_config_variants: List[ModelConfigVariant] = [],
     ) -> ModelConfigVariant:
-        param_combo = self._create_parameter_combo(trial_objectives)
-
-        model_config_variant = BaseModelConfigGenerator.make_model_config_variant(
-            param_combo=param_combo,
-            model=model,
-            model_variant_name_manager=self._model_variant_name_manager,
-            c_api_mode=self._c_api_mode,
+        param_combo = self._create_parameter_combo(
+            model, trial_objectives, composing_trial_objectives
         )
 
+        if model.is_ensemble():
+            model_config_variant = BaseModelConfigGenerator.make_ensemble_model_config_variant(
+                model=model,
+                ensemble_composing_model_config_variants=composing_model_config_variants,
+                model_variant_name_manager=self._model_variant_name_manager,
+                param_combo=param_combo,
+                c_api_mode=self._c_api_mode,
+            )
+        else:
+            model_config_variant = BaseModelConfigGenerator.make_model_config_variant(
+                param_combo=param_combo,
+                model=model,
+                model_variant_name_manager=self._model_variant_name_manager,
+                c_api_mode=self._c_api_mode,
+            )
+
         return model_config_variant
 
     def _create_composing_model_config_variants(
@@ -500,8 +557,10 @@ def _create_composing_model_config_variants(
         composing_model_config_variants = []
         for composing_model in self._composing_models:
             composing_model_config_variant = self._create_model_config_variant(
-                composing_model,
-                composing_trial_objectives[composing_model.model_name()],
+                model=composing_model,
+                trial_objectives=composing_trial_objectives[
+                    composing_model.model_name()
+                ],
             )
             composing_model_config_variants.append(composing_model_config_variant)
 

model_analyzer/config/generate/run_config_generator_factory.py

@@ -95,6 +95,15 @@ def create_run_config_generator(
             new_models, command_config, client, gpus
         )
 
+        for composing_model in composing_models:
+            composing_search_parameters[
+                composing_model.model_name()
+            ] = SearchParameters(
+                config=command_config,
+                model=composing_model,
+                is_composing_model=True,
+            )
+
         if command_config.run_config_search_mode == "optuna":
             return RunConfigGeneratorFactory._create_optuna_plus_concurrency_sweep_run_config_generator(
                 command_config=command_config,

model_analyzer/config/generate/search_parameters.py

@@ -46,13 +46,15 @@ def __init__(
         model: ModelProfileSpec,
         config: ConfigCommandProfile = ConfigCommandProfile(),
         is_bls_model: bool = False,
+        is_ensemble_model: bool = False,
         is_composing_model: bool = False,
     ):
         self._config = config
         self._parameters = model.parameters()
         self._model_config_parameters = model.model_config_parameters()
         self._supports_max_batch_size = model.supports_batching()
         self._search_parameters: Dict[str, SearchParameter] = {}
+        self._is_ensemble_model = is_ensemble_model
         self._is_bls_model = is_bls_model
         self._is_composing_model = is_composing_model
 
@@ -196,7 +198,7 @@ def _populate_instance_group(self) -> None:
                 parameter_list=parameter_list,
                 parameter_category=ParameterCategory.INT_LIST,
             )
-        else:
+        elif not self._is_ensemble_model:
             # Need to populate instance_group based on RCS min/max values
             # when no model config parameters are present
             self._populate_rcs_parameter(