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Cache all_optimizer_states to speed up model sharding #2747

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Cache all_optimizer_states to speed up model sharding #2747

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6 changes: 5 additions & 1 deletion torchrec/distributed/batched_embedding_kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,6 +216,7 @@ def __init__( # noqa C901
create_for_table: Optional[str] = None,
param_weight_for_table: Optional[nn.Parameter] = None,
embedding_weights_by_table: Optional[List[torch.Tensor]] = None,
all_optimizer_states: Optional[List[Dict[str, torch.Tensor]]] = None,
) -> None:
"""
Implementation of a FusedOptimizer. Designed as a base class Embedding kernels
Expand Down Expand Up @@ -395,7 +396,7 @@ def get_optimizer_pointwise_shard_metadata_and_global_metadata(
embedding_weights_by_table or emb_module.split_embedding_weights()
)

all_optimizer_states = emb_module.get_optimizer_state()
all_optimizer_states = all_optimizer_states or emb_module.get_optimizer_state()
optimizer_states_keys_by_table: Dict[str, List[torch.Tensor]] = {}
for (
table_config,
Expand Down Expand Up @@ -678,6 +679,8 @@ def _gen_named_parameters_by_table_fused(
# TODO: move logic to FBGEMM to avoid accessing fbgemm internals
# Cache embedding_weights_by_table
embedding_weights_by_table = emb_module.split_embedding_weights()
# Cache all_optimizer_states
all_optimizer_states = emb_module.get_optimizer_state()
for t_idx, (rows, dim, location, _) in enumerate(emb_module.embedding_specs):
table_name = config.embedding_tables[t_idx].name
if table_name not in table_name_to_count:
Expand Down Expand Up @@ -714,6 +717,7 @@ def _gen_named_parameters_by_table_fused(
create_for_table=table_name,
param_weight_for_table=weight,
embedding_weights_by_table=embedding_weights_by_table,
all_optimizer_states=all_optimizer_states,
)
]
yield (table_name, weight)
Expand Down
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