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[DistPart] Fix corner case in dist partition which always led to an assertion error being triggered. #7395

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merged 3 commits into from
May 14, 2024
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[DistPart] Fix corner case in dist partition which always led to an assertion error being triggered. #7395

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e982161
Fix corner case which always led to an assertion error being triggered.
thvasilo May 10, 2024
a602c17
Address review comments.
thvasilo May 13, 2024
6085eee
Apply lintrunner changes
thvasilo May 14, 2024
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16 changes: 12 additions & 4 deletions tools/distpartitioning/data_shuffle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -402,7 +402,6 @@ def exchange_feature(
"""
# type_ids for this feature subset on the current rank
gids_feat = np.arange(gid_start, gid_end)
tids_feat = np.arange(type_id_start, type_id_end)
local_idx = np.arange(0, type_id_end - type_id_start)

feats_per_rank = []
Expand Down Expand Up @@ -473,12 +472,19 @@ def exchange_feature(
)

# exchange actual data here.
if featdata_key != None:
logging.debug(f"Rank: {rank} {featdata_key.shape=}")
if featdata_key is not None:
feat_dims_dtype = list(featdata_key.shape)
# When a feature is 2-dim, the shape should match the feature dimension.
if len(featdata_key.shape) == 2:
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feature_dimension = feat_dims_dtype[1]
else:
feature_dimension = 0
feat_dims_dtype.append(DATA_TYPE_ID[featdata_key.dtype])
else:
feat_dims_dtype = list(np.zeros((rank0_shape_len), dtype=np.int64))
feat_dims_dtype.append(DATA_TYPE_ID[torch.float32])
feature_dimension = 0

logging.debug(f"Sending the feature shape information - {feat_dims_dtype}")
all_dims_dtype = allgather_sizes(
Expand All @@ -488,13 +494,15 @@ def exchange_feature(
for idx in range(world_size):
cond = partid_slice == (idx + local_part_id * world_size)
gids_per_partid = gids_feat[cond]
tids_per_partid = tids_feat[cond]
local_idx_partid = local_idx[cond]

if gids_per_partid.shape[0] == 0:
assert len(all_dims_dtype) % world_size == 0
dim_len = int(len(all_dims_dtype) / world_size)
rank0_shape = tuple(list(np.zeros((dim_len - 1), dtype=np.int32)))
rank0_shape = list(np.zeros((dim_len - 1), dtype=np.int32))
# When a feature is 2-dim, the shape[1] (number of columns) should match the feature dimension.
if len(rank0_shape) == 2:
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rank0_shape[1] = feature_dimension
rank0_dtype = REV_DATA_TYPE_ID[
all_dims_dtype[(dim_len - 1) : (dim_len)][0]
]
Expand Down
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