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fix to handle output dist when only some ranks have tables sharded onto them #3518

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fix to handle output dist when only some ranks have tables sharded onto them #3518

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4 changes: 3 additions & 1 deletion torchrec/distributed/dist_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1240,7 +1240,9 @@ def forward(self, tensors: List[torch.Tensor]) -> List[torch.Tensor]:
Awaitable[torch.Tensor]: awaitable of the merged pooled embeddings.
"""

assert len(tensors) == self._world_size
assert (
len(tensors) == self._world_size
), f"length of input tensor {len(tensors)} must match with world size {self._world_size}"
return torch.ops.fbgemm.all_to_one_device(
tensors,
self._device,
Expand Down
68 changes: 40 additions & 28 deletions torchrec/distributed/quant_embedding.py
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Expand Up @@ -305,37 +305,49 @@ def _construct_jagged_tensors_tw(
storage_device_type: str,
) -> Dict[str, JaggedTensor]:
ret: Dict[str, JaggedTensor] = {}
index = 0
for i in range(len(embedding_names_per_rank)):
embeddings_i = embeddings[i]
features_i: KeyedJaggedTensor = features[i]
if storage_device_type in ["ssd", "cpu"]:
embeddings_i = _get_batching_hinted_output(
_fx_trec_get_feature_length(features_i, embedding_names_per_rank[i]),
embeddings_i,
)

lengths = features_i.lengths().view(-1, features_i.stride())
values = features_i.values()
embeddings_list = _fx_split_embeddings_per_feature_length(
embeddings_i, features_i
)
stride = features_i.stride()
lengths_tuple = torch.unbind(lengths.view(-1, stride), dim=0)
if need_indices:
values_list = _fx_split_embeddings_per_feature_length(values, features_i)
for j, key in enumerate(embedding_names_per_rank[i]):
ret[key] = JaggedTensor(
lengths=lengths_tuple[j],
values=embeddings_list[j],
weights=values_list[j],
if len(embedding_names_per_rank[i]) > 0:
embeddings_i = embeddings[index]
features_i: KeyedJaggedTensor = features[i]
if storage_device_type in ["ssd", "cpu"]:
embeddings_i = _get_batching_hinted_output(
_fx_trec_get_feature_length(
features_i, embedding_names_per_rank[index]
),
embeddings_i,
)
else:
for j, key in enumerate(embedding_names_per_rank[i]):
ret[key] = JaggedTensor(
lengths=lengths_tuple[j],
values=embeddings_list[j],
weights=None,

lengths = features_i.lengths().view(-1, features_i.stride())
values = features_i.values()
embeddings_list = _fx_split_embeddings_per_feature_length(
embeddings_i, features_i
)
stride = features_i.stride()
lengths_tuple = torch.unbind(lengths.view(-1, stride), dim=0)
if need_indices:
values_list = _fx_split_embeddings_per_feature_length(
values, features_i
)
for j, key in enumerate(embedding_names_per_rank[i]):
ret[key] = JaggedTensor(
lengths=lengths_tuple[j],
values=embeddings_list[j],
weights=values_list[j],
)
else:
for j, key in enumerate(embedding_names_per_rank[i]):
ret[key] = JaggedTensor(
lengths=lengths_tuple[j],
values=embeddings_list[j],
weights=None,
)
index += 1
# for cuda storage device, empty embeddding per rank is already skipped
# as part of tw_sequence_sharding output dist before executing
# SeqEmbeddingsAllToOne (for cpu / ssd SeqEmbeddingsAllToOne is not required)
elif storage_device_type in ["cpu", "ssd"]:
index += 1
return ret


Expand Down
14 changes: 13 additions & 1 deletion torchrec/distributed/sharding/tw_sequence_sharding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,12 +175,21 @@ def __init__(
device: torch.device,
world_size: int,
storage_device_type_from_sharding_infos: Optional[str] = None,
embedding_names_per_rank: Optional[List[List[str]]] = None,
) -> None:
super().__init__()
self._dist: SeqEmbeddingsAllToOne = SeqEmbeddingsAllToOne(device, world_size)
self._adjusted_world_size: int = (
world_size
if embedding_names_per_rank is None
else sum(1 for sublist in embedding_names_per_rank if len(sublist) > 0)
)
self._dist: SeqEmbeddingsAllToOne = SeqEmbeddingsAllToOne(
device, self._adjusted_world_size
)
self._storage_device_type_from_sharding_infos: Optional[str] = (
storage_device_type_from_sharding_infos
)
self._embedding_names_per_rank = embedding_names_per_rank

def forward(
self,
Expand Down Expand Up @@ -216,6 +225,8 @@ def forward(
local_emb,
)
for i, local_emb in enumerate(local_embs)
if self._embedding_names_per_rank is not None
and len(self._embedding_names_per_rank[i]) > 0
]
return self._dist(local_embs)
else:
Expand Down Expand Up @@ -269,4 +280,5 @@ def create_output_dist(
device,
self._world_size,
self._storage_device_type_from_sharding_infos,
self.embedding_names_per_rank(),
)
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