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Make EmbeddingBagCollection torch scriptable #2355

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Make EmbeddingBagCollection torch scriptable #2355

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15 changes: 12 additions & 3 deletions torchrec/quant/embedding_modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,6 +259,8 @@ def _fx_trec_unwrap_kjt(


class EmbeddingBagCollection(EmbeddingBagCollectionInterface, ModuleNoCopyMixin):
_emb_modules: List[IntNBitTableBatchedEmbeddingBagsCodegen]
_key_to_tables: Dict[Tuple[PoolingType, DataType], List[EmbeddingBagConfig]]
"""
EmbeddingBagCollection represents a collection of pooled embeddings (EmbeddingBags).
This EmbeddingBagCollection is quantized for lower precision. It relies on fbgemm quantized ops and provides
Expand Down Expand Up @@ -342,13 +344,17 @@ def __init__(
self._embedding_bag_configs: List[EmbeddingBagConfig] = tables
self._key_to_tables: Dict[
Tuple[PoolingType, DataType], List[EmbeddingBagConfig]
] = defaultdict(list)
] = torch.jit.annotate(
Dict[Tuple[PoolingType, DataType], List[EmbeddingBagConfig]], {}
)
self._feature_names: List[str] = []
self._feature_splits: List[int] = []
self._length_per_key: List[int] = []
# Registering in a List instead of ModuleList because we want don't want them to be auto-registered.
# Their states will be modified via self.embedding_bags
self._emb_modules: List[nn.Module] = []
self._emb_modules: List[IntNBitTableBatchedEmbeddingBagsCodegen] = (
torch.jit.annotate(List[IntNBitTableBatchedEmbeddingBagsCodegen], [])
)
self._output_dtype = output_dtype
self._device: torch.device = device
self._table_name_to_quantized_weights: Optional[
Expand All @@ -363,7 +369,10 @@ def __init__(
raise ValueError(f"Duplicate table name {table.name}")
table_names.add(table.name)
key = (table.pooling, table.data_type)
self._key_to_tables[key].append(table)
if key not in self._key_to_tables:
self._key_to_tables[key] = [table]
else:
self._key_to_tables[key].append(table)

location = (
EmbeddingLocation.HOST if device.type == "cpu" else EmbeddingLocation.DEVICE
Expand Down