Update batched_embedding_kernel (#2702)

Summary:

After this diff stack:

EmbeddingKernelConfig now supports adding embedding_table_int32_index_type and embedding_table_int32_offset_type to the fused_params.

These are used downstream to determine whether the indices and offsets types for split_table_batched_embeddings_ops_training.py

Differential Revision: D66919716

Author: basilwong

torchrec/distributed/batched_embedding_kernel.py

@@ -759,6 +759,13 @@ def __init__(
         self._feature_table_map: List[int] = []
         self.table_name_to_count: Dict[str, int] = {}
         self._param_per_table: Dict[str, TableBatchedEmbeddingSlice] = {}
+        self._fused_params: Dict[str, Any] = config.fused_params or {}
+        self._embedding_table_index_type: torch.dtype = self._fused_params.get(
+            "embedding_table_index_type", torch.int64
+        )
+        self._embedding_table_offset_type: torch.dtype = self._fused_params.get(
+            "embedding_table_offset_type", torch.int64
+        )
 
         # pyre-fixme[9]: config has type `GroupedEmbeddingConfig`; used as
         #  `ShardedEmbeddingTable`.
@@ -800,8 +807,16 @@ def init_parameters(self) -> None:
 
     def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
         return self.emb_module(
-            indices=features.values().long(),
-            offsets=features.offsets().long(),
+            indices=(
+                features.values()
+                if self._embedding_table_index_type == torch.int32
+                else features.values().long()
+            ),
+            offsets=(
+                features.offsets().type(dtype=features.values().dtype)
+                if self._embedding_table_offset_type == torch.int32
+                else features.offsets().long()
+            ),
         )
 
     # pyre-fixme[14]: `state_dict` overrides method defined in `Module` inconsistently.
@@ -1213,6 +1228,13 @@ def __init__(
         self._lengths_per_emb: List[int] = []
         self.table_name_to_count: Dict[str, int] = {}
         self._param_per_table: Dict[str, TableBatchedEmbeddingSlice] = {}
+        self._fused_params: Dict[str, Any] = config.fused_params or {}
+        self._embedding_table_index_type: torch.dtype = self._fused_params.get(
+            "embedding_table_index_type", torch.int64
+        )
+        self._embedding_table_offset_type: torch.dtype = self._fused_params.get(
+            "embedding_table_offset_type", torch.int64
+        )
 
         # pyre-fixme[9]: config has type `GroupedEmbeddingConfig`; used as
         #  `ShardedEmbeddingTable`.
@@ -1265,15 +1287,31 @@ def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
             ),
         ):
             return self.emb_module(
-                indices=features.values().long(),
-                offsets=features.offsets().long(),
+                indices=(
+                    features.values()
+                    if self._embedding_table_index_type == torch.int32
+                    else features.values().long()
+                ),
+                offsets=(
+                    features.offsets().type(dtype=features.values().dtype)
+                    if self._embedding_table_offset_type == torch.int32
+                    else features.offsets().long()
+                ),
                 per_sample_weights=weights,
                 batch_size_per_feature_per_rank=features.stride_per_key_per_rank(),
             )
         else:
             return self.emb_module(
-                indices=features.values().long(),
-                offsets=features.offsets().long(),
+                indices=(
+                    features.values()
+                    if self._embedding_table_index_type == torch.int32
+                    else features.values().long()
+                ),
+                offsets=(
+                    features.offsets().type(dtype=features.values().dtype)
+                    if self._embedding_table_offset_type == torch.int32
+                    else features.offsets().long()
+                ),
                 per_sample_weights=weights,
             )