support config changes from MVAI down to fbgemm (pytorch#2259)

Summary:
Pull Request resolved: pytorch#2259

integrate the new rocksdb config into mvai model authoring chain so that we could tune the model config and affect the rocksdb changes

Differential Revision: D59785241

fbshipit-source-id: f689a86e3743b7fb7e939708af0267aaed60370e

torchrec/distributed/types.py

@@ -26,6 +26,7 @@
     Union,
 )
 
+from fbgemm_gpu.runtime_monitor import TBEStatsReporterConfig
 from fbgemm_gpu.split_table_batched_embeddings_ops_common import (
     BoundsCheckMode,
     CacheAlgorithm,
@@ -588,16 +589,31 @@ class KeyValueParams:
         ps_hosts (Optional[Tuple[Tuple[str, int]]]): List of PS host ip addresses
             and ports. Example: (("::1", 2000), ("::1", 2001), ("::1", 2002)).
             Reason for using tuple is we want it hashable.
+        ssd_rocksdb_write_buffer_size: Optional[int]: rocksdb write buffer size per tbe,
+            relavant to rocksdb compaction frequency
+        ssd_rocksdb_shards: Optional[int]: rocksdb shards number
+        gather_ssd_cache_stats: bool: whether enable ssd stats collection, std reporter and ods reporter
+        report_interval: int: report interval in train iteration if gather_ssd_cache_stats is enabled
+        ods_prefix: str: ods prefix for ods reporting
     """
 
     ssd_storage_directory: Optional[str] = None
     ps_hosts: Optional[Tuple[Tuple[str, int], ...]] = None
+    ssd_rocksdb_write_buffer_size: Optional[int] = None
+    ssd_rocksdb_shards: Optional[int] = None
+    gather_ssd_cache_stats: Optional[bool] = None
+    stats_reporter_config: Optional[TBEStatsReporterConfig] = None
+    use_passed_in_path: bool = True
 
     def __hash__(self) -> int:
         return hash(
             (
                 self.ssd_storage_directory,
                 self.ps_hosts,
+                self.ssd_rocksdb_write_buffer_size,
+                self.ssd_rocksdb_shards,
+                self.gather_ssd_cache_stats,
+                self.stats_reporter_config,
             )
         )
 

torchrec/distributed/utils.py

@@ -410,10 +410,15 @@ def add_params_from_parameter_sharding(
         parameter_sharding.compute_kernel in {EmbeddingComputeKernel.KEY_VALUE.value}
         and parameter_sharding.key_value_params is not None
     ):
-        key_value_params_dict = asdict(parameter_sharding.key_value_params)
+        kv_params = parameter_sharding.key_value_params
+        key_value_params_dict = asdict(kv_params)
         key_value_params_dict = {
             k: v for k, v in key_value_params_dict.items() if v is not None
         }
+        if kv_params.stats_reporter_config:
+            key_value_params_dict["stats_reporter_config"] = (
+                kv_params.stats_reporter_config
+            )
         fused_params.update(key_value_params_dict)
 
     # print warning if sharding_type is data_parallel or kernel is dense