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col-wise ads config #6

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46 changes: 33 additions & 13 deletions distributed/embedding_lookup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,7 +492,7 @@ def to_pooling_mode(pooling_type: PoolingType) -> PoolingMode:
self._weight_init_mins: List[float] = []
self._weight_init_maxs: List[float] = []
self._num_embeddings: List[int] = []
self._embedding_dims: List[int] = []
self._local_cols: List[int] = []
self._feature_table_map: List[int] = []
self._emb_names: List[str] = []
self._lengths_per_emb: List[int] = []
Expand All @@ -503,7 +503,7 @@ def to_pooling_mode(pooling_type: PoolingType) -> PoolingMode:
self._weight_init_mins.append(config.get_weight_init_min())
self._weight_init_maxs.append(config.get_weight_init_max())
self._num_embeddings.append(config.num_embeddings)
self._embedding_dims.append(config.local_cols)
self._local_cols.append(config.local_cols)
self._feature_table_map.extend([idx] * config.num_features())
for feature_name in config.feature_names:
if feature_name not in shared_feature:
Expand All @@ -526,7 +526,7 @@ def init_parameters(self) -> None:
)
for (rows, emb_dim, weight_init_min, weight_init_max, param) in zip(
self._local_rows,
self._embedding_dims,
self._local_cols,
self._weight_init_mins,
self._weight_init_maxs,
self.emb_module.split_embedding_weights(),
Expand Down Expand Up @@ -616,13 +616,28 @@ def __init__(
def to_rowwise_sharded_metadata(
local_metadata: ShardMetadata,
global_metadata: ShardedTensorMetadata,
sharding_dim: int,
) -> Tuple[ShardMetadata, ShardedTensorMetadata]:
rw_shards: List[ShardMetadata] = []
rw_local_shard: ShardMetadata = local_metadata
for shard in global_metadata.shards_metadata:
shards_metadata = global_metadata.shards_metadata
# column-wise sharding
# sort the metadata based on column offset and
# we construct the momentum tensor in row-wise sharded way
if sharding_dim == 1:
shards_metadata = sorted(
shards_metadata, key=lambda shard: shard.shard_offsets[1]
)

for idx, shard in enumerate(shards_metadata):
offset = shard.shard_offsets[0]
# for column-wise sharding, we still create row-wise sharded metadata for optimizer
# manually create a row-wise offset
if sharding_dim == 1:
offset = idx * shard.shard_lengths[0]
rw_shard = ShardMetadata(
shard_lengths=[shard.shard_lengths[0]],
shard_offsets=[shard.shard_offsets[0]],
shard_offsets=[offset],
placement=shard.placement,
)

Expand All @@ -638,10 +653,10 @@ def to_rowwise_sharded_metadata(
memory_format=global_metadata.tensor_properties.memory_format,
pin_memory=global_metadata.tensor_properties.pin_memory,
)

len_rw_shards = len(shards_metadata) if sharding_dim == 1 else 1
rw_metadata = ShardedTensorMetadata(
shards_metadata=rw_shards,
size=torch.Size([global_metadata.size[0]]),
size=torch.Size([global_metadata.size[0] * len_rw_shards]),
tensor_properties=tensor_properties,
)
return rw_local_shard, rw_metadata
Expand Down Expand Up @@ -673,10 +688,15 @@ def to_rowwise_sharded_metadata(
state[weight] = {}
# momentum1
assert table_config.local_rows == optimizer_states[0].size(0)
sharding_dim = (
1 if table_config.local_cols != table_config.embedding_dim else 0
)
momentum1_key = f"{table_config.name}.momentum1"
if optimizer_states[0].dim() == 1:
(local_metadata, sharded_tensor_metadata) = to_rowwise_sharded_metadata(
table_config.local_metadata, table_config.global_metadata
table_config.local_metadata,
table_config.global_metadata,
sharding_dim,
)
else:
(local_metadata, sharded_tensor_metadata) = (
Expand All @@ -699,7 +719,9 @@ def to_rowwise_sharded_metadata(
local_metadata,
sharded_tensor_metadata,
) = to_rowwise_sharded_metadata(
table_config.local_metadata, table_config.global_metadata
table_config.local_metadata,
table_config.global_metadata,
sharding_dim,
)
else:
(local_metadata, sharded_tensor_metadata) = (
Expand Down Expand Up @@ -769,9 +791,7 @@ def to_embedding_location(
self._emb_module: SplitTableBatchedEmbeddingBagsCodegen = (
SplitTableBatchedEmbeddingBagsCodegen(
embedding_specs=list(
zip(
self._local_rows, self._embedding_dims, managed, compute_devices
)
zip(self._local_rows, self._local_cols, managed, compute_devices)
),
feature_table_map=self._feature_table_map,
pooling_mode=self._pooling,
Expand Down Expand Up @@ -822,7 +842,7 @@ def __init__(

self._emb_module: DenseTableBatchedEmbeddingBagsCodegen = (
DenseTableBatchedEmbeddingBagsCodegen(
list(zip(self._local_rows, self._embedding_dims)),
list(zip(self._local_rows, self._local_cols)),
feature_table_map=self._feature_table_map,
pooling_mode=self._pooling,
use_cpu=device is None or device.type == "cpu",
Expand Down
6 changes: 1 addition & 5 deletions distributed/planner/embedding_planner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@
deallocate_param,
param_sort_key,
to_plan,
MIN_DIM,
)
from torchrec.distributed.types import (
ShardingPlan,
Expand Down Expand Up @@ -398,13 +397,10 @@ def _get_num_col_wise_shards(
col_wise_shard_dim = (
col_wise_shard_dim_hint
if col_wise_shard_dim_hint is not None
else MIN_DIM
else param.shape[1]
)
# column-wise shard the weights
num_col_wise_shards, residual = divmod(param.shape[1], col_wise_shard_dim)
assert (
num_col_wise_shards > 0
), f"the table {name} cannot be column-wise sharded into shards of {col_wise_shard_dim} dimensions"
if residual > 0:
num_col_wise_shards += 1
elif sharding_type == ShardingType.TABLE_WISE.value:
Expand Down
3 changes: 3 additions & 0 deletions distributed/planner/tests/test_embedding_planner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -551,6 +551,7 @@ def test_allocation_planner_cw_balanced(self) -> None:
hints={
"table_0": ParameterHints(
sharding_types=[ShardingType.COLUMN_WISE.value],
col_wise_shard_dim=32,
),
},
)
Expand Down Expand Up @@ -653,9 +654,11 @@ def test_allocation_planner_cw_two_big_rest_small_with_residual(self) -> None:
hints={
"table_0": ParameterHints(
sharding_types=[ShardingType.COLUMN_WISE.value],
col_wise_shard_dim=32,
),
"table_1": ParameterHints(
sharding_types=[ShardingType.COLUMN_WISE.value],
col_wise_shard_dim=32,
),
},
)
Expand Down