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split_table_batched_embeddings_ops_training int32 support behind jk #4449

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split_table_batched_embeddings_ops_training int32 support behind jk #4449

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17 changes: 12 additions & 5 deletions fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@

# @manual=//deeplearning/fbgemm/fbgemm_gpu/codegen:split_embedding_codegen_lookup_invokers
import fbgemm_gpu.split_embedding_codegen_lookup_invokers as invokers
import pyjk as justknobs

from fbgemm_gpu.config import FeatureGate, FeatureGateName
from fbgemm_gpu.runtime_monitor import (
Expand Down Expand Up @@ -3600,16 +3601,22 @@ def prepare_inputs(
# NOTE: Force offsets to have the same dtype as indices since the
# kernels assume same dtype. We might need to revisit the assumption
# of same dtypes in the future.
if self.embedding_table_index_type == torch.int32:
if (
self.embedding_table_index_type == torch.int32
and self.embedding_table_offset_type == torch.int32
and justknobs.check("pytorch/torchrec:int32_rollout_killswitch")
):
self.log(
"Casting indices to int32 based on embedding_table_index_type input."
"Casting indices and offsets to int32 based on embedding_table_index_type and embedding_table_offset_type inputs."
)
indices = indices.to(torch.int32)
if self.embedding_table_index_type != self.embedding_table_offset_type:
offsets = offsets.to(torch.int32)
else:
self.log(
f"Force casting offsets to {self.embedding_table_index_type} so that it is the same as the indices type."
"Casting indices and offsets to int64 as either embedding_table_index_type or embedding_table_offset_type is not int32."
)
offsets = offsets.to(dtype=indices.dtype)
indices = indices.to(torch.int64)
offsets = offsets.to(torch.int64)

# Force casting per_sample_weights to float
if per_sample_weights is not None:
Expand Down
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