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batched_embedding_kernel int32 support behind jk #3164

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batched_embedding_kernel int32 support behind jk #3164

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90 changes: 79 additions & 11 deletions torchrec/distributed/batched_embedding_kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@
PartiallyMaterializedTensor,
)
from torch import nn

from torch.distributed._tensor import DTensor, Replicate, Shard as DTensorShard
from torchrec.distributed.comm import get_local_rank, get_node_group_size
from torchrec.distributed.composable.table_batched_embedding_slice import (
Expand Down Expand Up @@ -1071,6 +1072,13 @@ 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
)

for idx, table_config in enumerate(self._config.embedding_tables):
self._local_rows.append(table_config.local_rows)
Expand Down Expand Up @@ -1113,6 +1121,25 @@ def init_parameters(self) -> None:
)

def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
if torch._utils_internal.justknobs_check(
"pytorch/torchrec:int32_rollout_killswitch"
):
indices_dtype = (
torch.int32
if self._embedding_table_index_type == torch.int32
and self._embedding_table_offset_type == torch.int32
else torch.int64
)
offsets_dtype = (
torch.int32
if self._embedding_table_index_type == torch.int32
and self._embedding_table_offset_type == torch.int32
else torch.int64
)
return self.emb_module(
indices=features.values().type(dtype=indices_dtype),
offsets=features.offsets().type(dtype=offsets_dtype),
)
return self.emb_module(
indices=features.values().long(),
offsets=features.offsets().long(),
Expand Down Expand Up @@ -1857,6 +1884,13 @@ 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
)

for idx, table_config in enumerate(self._config.embedding_tables):
self._local_rows.append(table_config.local_rows)
Expand Down Expand Up @@ -1902,6 +1936,20 @@ def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
weights = features.weights_or_none()
if weights is not None and not torch.is_floating_point(weights):
weights = None

indices_dtype = (
torch.int32
if self._embedding_table_index_type == torch.int32
and self._embedding_table_offset_type == torch.int32
else torch.int64
)
offsets_dtype = (
torch.int32
if self._embedding_table_index_type == torch.int32
and self._embedding_table_offset_type == torch.int32
else torch.int64
)

if features.variable_stride_per_key() and isinstance(
self.emb_module,
(
Expand All @@ -1910,18 +1958,38 @@ def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
SSDTableBatchedEmbeddingBags,
),
):
return self.emb_module(
indices=features.values().long(),
offsets=features.offsets().long(),
per_sample_weights=weights,
batch_size_per_feature_per_rank=features.stride_per_key_per_rank(),
)

if torch._utils_internal.justknobs_check(
"pytorch/torchrec:int32_rollout_killswitch"
):
return self.emb_module(
indices=features.values().type(dtype=indices_dtype),
offsets=features.offsets().type(dtype=offsets_dtype),
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(),
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(),
per_sample_weights=weights,
)
if torch._utils_internal.justknobs_check(
"pytorch/torchrec:int32_rollout_killswitch"
):
return self.emb_module(
indices=features.values().type(dtype=indices_dtype),
offsets=features.offsets().type(dtype=offsets_dtype),
per_sample_weights=weights,
)
else:
return self.emb_module(
indices=features.values().long(),
offsets=features.offsets().long(),
per_sample_weights=weights,
)

# pyre-fixme[14]: `state_dict` overrides method defined in `Module` inconsistently.
def state_dict(
Expand Down
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