Add more hyperparams for EvolutionOptimizer

nncf/config/schemata/algo/filter_pruning.py

@@ -27,7 +27,11 @@
 from nncf.config.schemata.defaults import PRUNING_INTERLAYER_RANKING_TYPE
 from nncf.config.schemata.defaults import PRUNING_LEGR_GENERATIONS
 from nncf.config.schemata.defaults import PRUNING_LEGR_MAX_PRUNING
+from nncf.config.schemata.defaults import PRUNING_LEGR_MUTATE_PERCENT
+from nncf.config.schemata.defaults import PRUNING_LEGR_NUM_SAMPLES
+from nncf.config.schemata.defaults import PRUNING_LEGR_POPULATION_SIZE
 from nncf.config.schemata.defaults import PRUNING_LEGR_RANDOM_SEED
+from nncf.config.schemata.defaults import PRUNING_LEGR_SIGMA_SCALE
 from nncf.config.schemata.defaults import PRUNING_LEGR_TRAIN_STEPS
 from nncf.config.schemata.defaults import PRUNING_NUM_INIT_STEPS
 from nncf.config.schemata.defaults import PRUNING_SCHEDULE
@@ -162,6 +166,26 @@
                             description="Random seed for LeGR coefficients generation.",
                             default=PRUNING_LEGR_RANDOM_SEED,
                         ),
+                        "population_size": with_attributes(
+                            NUMBER,
+                            description="Size of population for the evolution algorithm.",
+                            default=PRUNING_LEGR_POPULATION_SIZE,
+                        ),
+                        "num_samples": with_attributes(
+                            NUMBER,
+                            description="Number of samples for the evolution algorithm.",
+                            default=PRUNING_LEGR_NUM_SAMPLES,
+                        ),
+                        "mutate_percent": with_attributes(
+                            NUMBER,
+                            description="Percent of mutate for the evolution algorithm.",
+                            default=PRUNING_LEGR_MUTATE_PERCENT,
+                        ),
+                        "scale_sigma": with_attributes(
+                            NUMBER,
+                            description="Scale sigma for the evolution algorithm.",
+                            default=PRUNING_LEGR_SIGMA_SCALE,
+                        ),
                     },
                     "additionalProperties": False,
                 },

nncf/config/schemata/defaults.py

@@ -59,6 +59,10 @@
 PRUNING_LEGR_TRAIN_STEPS = 200
 PRUNING_LEGR_MAX_PRUNING = 0.8
 PRUNING_LEGR_RANDOM_SEED = 42
+PRUNING_LEGR_POPULATION_SIZE = 64
+PRUNING_LEGR_NUM_SAMPLES = 16
+PRUNING_LEGR_MUTATE_PERCENT = 0.1
+PRUNING_LEGR_SIGMA_SCALE = 1
 
 SPARSITY_INIT = 0.0
 MAGNITUDE_SPARSITY_WEIGHT_IMPORTANCE = "normed_abs"

nncf/torch/pruning/filter_pruning/global_ranking/evolutionary_optimization.py

@@ -20,6 +20,11 @@
 from torch import optim
 
 from nncf.config.config import NNCFConfig
+from nncf.config.schemata.defaults import PRUNING_LEGR_GENERATIONS
+from nncf.config.schemata.defaults import PRUNING_LEGR_MUTATE_PERCENT
+from nncf.config.schemata.defaults import PRUNING_LEGR_NUM_SAMPLES
+from nncf.config.schemata.defaults import PRUNING_LEGR_POPULATION_SIZE
+from nncf.config.schemata.defaults import PRUNING_LEGR_SIGMA_SCALE
 from nncf.torch.utils import get_filters_num
 
 
@@ -48,11 +53,11 @@ def __init__(self, initial_filter_norms: Dict, hparams: Dict, random_seed: int):
         """
         self.random_seed = random_seed
         # Optimizer hyper-params
-        self.population_size = hparams.get("population_size", 64)
-        self.num_generations = hparams.get("num_generations", 400)
-        self.num_samples = hparams.get("num_samples", 16)
-        self.mutate_percent = hparams.get("mutate_percent", 0.1)
-        self.scale_sigma = hparams.get("sigma_scale", 1)
+        self.population_size = hparams.get("population_size", PRUNING_LEGR_POPULATION_SIZE)
+        self.num_generations = hparams.get("num_generations", PRUNING_LEGR_GENERATIONS)
+        self.num_samples = hparams.get("num_samples", PRUNING_LEGR_NUM_SAMPLES)
+        self.mutate_percent = hparams.get("mutate_percent", PRUNING_LEGR_MUTATE_PERCENT)
+        self.scale_sigma = hparams.get("sigma_scale", PRUNING_LEGR_SIGMA_SCALE)
         self.max_reward = -np.inf
         self.mean_rewards = []
 

nncf/torch/pruning/filter_pruning/global_ranking/legr.py

@@ -15,7 +15,11 @@
 from nncf.common.logging import nncf_logger
 from nncf.config.schemata.defaults import PRUNING_LEGR_GENERATIONS
 from nncf.config.schemata.defaults import PRUNING_LEGR_MAX_PRUNING
+from nncf.config.schemata.defaults import PRUNING_LEGR_MUTATE_PERCENT
+from nncf.config.schemata.defaults import PRUNING_LEGR_NUM_SAMPLES
+from nncf.config.schemata.defaults import PRUNING_LEGR_POPULATION_SIZE
 from nncf.config.schemata.defaults import PRUNING_LEGR_RANDOM_SEED
+from nncf.config.schemata.defaults import PRUNING_LEGR_SIGMA_SCALE
 from nncf.config.schemata.defaults import PRUNING_LEGR_TRAIN_STEPS
 from nncf.torch.pruning.filter_pruning.global_ranking.evolutionary_optimization import EvolutionOptimizer
 from nncf.torch.pruning.filter_pruning.global_ranking.evolutionary_optimization import LeGREvolutionEnv
@@ -38,6 +42,10 @@ def __init__(
         generations: int = PRUNING_LEGR_GENERATIONS,
         max_pruning: float = PRUNING_LEGR_MAX_PRUNING,
         random_seed: int = PRUNING_LEGR_RANDOM_SEED,
+        population_size: int = PRUNING_LEGR_POPULATION_SIZE,
+        num_samples: int = PRUNING_LEGR_NUM_SAMPLES,
+        mutate_percent: float = PRUNING_LEGR_MUTATE_PERCENT,
+        scale_sigma: float = PRUNING_LEGR_SIGMA_SCALE,
     ):
         """
         Initializing all necessary structures for optimization- LeGREvolutionEnv environment and EvolutionOptimizer
@@ -53,10 +61,20 @@ def __init__(
         self.num_generations = generations
         self.max_pruning = max_pruning
         self.train_steps = train_steps
+        self.population_size = population_size
+        self.num_samples = num_samples
+        self.mutate_percent = mutate_percent
+        self.scale_sigma = scale_sigma
 
         self.pruner = LeGRPruner(pruning_ctrl, target_model)
         init_filter_norms = self.pruner.init_filter_norms
-        agent_hparams = {"num_generations": self.num_generations}
+        agent_hparams = {
+            "num_generations": self.num_generations,
+            "population_size": self.population_size,
+            "num_samples": self.num_samples,
+            "mutate_percent": self.mutate_percent,
+            "sigma_scale": self.scale_sigma,
+        }
         self.agent = EvolutionOptimizer(init_filter_norms, agent_hparams, random_seed)
         self.env = LeGREvolutionEnv(
             self.pruner,