Feature score eviction frontend support

torchrec/distributed/batched_embedding_kernel.py

@@ -84,6 +84,7 @@
     CountTimestampMixedEvictionPolicy,
     data_type_to_sparse_type,
     FeatureL2NormBasedEvictionPolicy,
+    FeatureScoreBasedEvictionPolicy,
     NoEvictionPolicy,
     pooling_type_to_pooling_mode,
     TimestampBasedEvictionPolicy,
@@ -235,6 +236,9 @@ def _populate_zero_collision_tbe_params(
         counter_thresholds = [0] * len(config.embedding_tables)
         ttls_in_mins = [0] * len(config.embedding_tables)
         counter_decay_rates = [0.0] * len(config.embedding_tables)
+        feature_score_counter_decay_rates = [0.0] * len(config.embedding_tables)
+        max_training_id_num_per_table = [0] * len(config.embedding_tables)
+        target_eviction_percent_per_table = [0.0] * len(config.embedding_tables)
         l2_weight_thresholds = [0.0] * len(config.embedding_tables)
         eviction_strategy = -1
         table_names = [table.name for table in config.embedding_tables]
@@ -251,6 +255,20 @@ def _populate_zero_collision_tbe_params(
                         raise ValueError(
                             f"Do not support multiple eviction strategy in one tbe {eviction_strategy} and 1 for tables {table_names}"
                         )
+                elif isinstance(policy_t, FeatureScoreBasedEvictionPolicy):
+                    feature_score_counter_decay_rates[i] = policy_t.decay_rate
+                    max_training_id_num_per_table[i] = (
+                        policy_t.max_training_id_num_per_rank
+                    )
+                    target_eviction_percent_per_table[i] = (
+                        policy_t.target_eviction_percent
+                    )
+                    if eviction_strategy == -1 or eviction_strategy == 5:
+                        eviction_strategy = 5
+                    else:
+                        raise ValueError(
+                            f"Do not support multiple eviction strategy in one tbe {eviction_strategy} and 5 for tables {table_names}"
+                        )
                 elif isinstance(policy_t, TimestampBasedEvictionPolicy):
                     ttls_in_mins[i] = policy_t.eviction_ttl_mins
                     if eviction_strategy == -1 or eviction_strategy == 0:
@@ -288,6 +306,9 @@ def _populate_zero_collision_tbe_params(
             counter_thresholds=counter_thresholds,
             ttls_in_mins=ttls_in_mins,
             counter_decay_rates=counter_decay_rates,
+            feature_score_counter_decay_rates=feature_score_counter_decay_rates,
+            max_training_id_num_per_table=max_training_id_num_per_table,
+            target_eviction_percent_per_table=target_eviction_percent_per_table,
             l2_weight_thresholds=l2_weight_thresholds,
             meta_header_lens=meta_header_lens,
         )

torchrec/modules/embedding_configs.py

@@ -203,6 +203,17 @@ def __post_init__(self) -> None:
             self.inference_eviction_threshold = self.eviction_threshold
 
 
+@dataclass
+class FeatureScoreBasedEvictionPolicy(VirtualTableEvictionPolicy):
+    """
+    Feature score based eviction policy for virtual table.
+    """
+
+    decay_rate: float = 0.99  # default decay by default #TODO: Change to real value
+    max_training_id_num_per_rank: int = 0  # max number of training ids per rank
+    target_eviction_percent: float = 0.0  # target eviction percent
+
+
 @dataclass
 class TimestampBasedEvictionPolicy(VirtualTableEvictionPolicy):
     """