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Add sparse relation-aware Graph Transformer signals #652

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ed818c2
potential fix
mkolodner-sc May 19, 2026
abb8e56
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mkolodner-sc May 19, 2026
a0e84fa
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mkolodner-sc May 19, 2026
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mkolodner-sc May 19, 2026
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readout mode
yliu2-sc May 19, 2026
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yliu2-sc May 20, 2026
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Add sparse relation-aware graph transformer signals
yliu2-sc May 27, 2026
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21 changes: 8 additions & 13 deletions gigl/distributed/dist_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -499,16 +499,11 @@ def _initialize_node_ids(
)
else:
train_nodes, val_nodes, test_nodes = splits
self._num_train = (
train_nodes.numel() # ty: ignore[unresolved-attribute]
)
self._num_val = val_nodes.numel() # ty: ignore[unresolved-attribute]
self._num_test = test_nodes.numel() # ty: ignore[unresolved-attribute]
self._num_train = train_nodes.numel()
self._num_val = val_nodes.numel()
self._num_test = test_nodes.numel()
self._node_ids = _append_non_split_node_ids(
train_nodes, # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
val_nodes, # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
test_nodes, # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
node_ids_on_machine,
train_nodes, val_nodes, test_nodes, node_ids_on_machine
)
else:
logger.info(
Expand Down Expand Up @@ -642,8 +637,8 @@ def _initialize_node_features(
# if it is not an edge type, since it must be one of the two.
assert not isinstance(node_type, EdgeType)
self._node_feature_info[node_type] = FeatureInfo(
dim=node_features_per_node_type.size(1), # ty: ignore[unresolved-attribute] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
dtype=node_features_per_node_type.dtype, # ty: ignore[unresolved-attribute] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
dim=node_features_per_node_type.size(1),
dtype=node_features_per_node_type.dtype,
)
logger.info(
f"Initialized node features for heterogeneous graph to dataset with node types: {node_features.keys()}"
Expand Down Expand Up @@ -725,8 +720,8 @@ def _initialize_edge_features(
for edge_type, edge_features_per_edge_type in edge_features.items():
assert isinstance(edge_type, EdgeType)
self._edge_feature_info[edge_type] = FeatureInfo(
dim=edge_features_per_edge_type.size(1), # ty: ignore[unresolved-attribute] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
dtype=edge_features_per_edge_type.dtype, # ty: ignore[unresolved-attribute] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
dim=edge_features_per_edge_type.size(1),
dtype=edge_features_per_edge_type.dtype,
)
logger.info(
f"Initialized edge features for heterogeneous graph to dataset with edge types: {edge_features.keys()}"
Expand Down
10 changes: 5 additions & 5 deletions gigl/distributed/dist_ppr_sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -537,7 +537,7 @@ async def _sample_from_nodes(
seed_types = list(nodes_to_sample.keys())
ppr_results = await asyncio.gather(
*[
self._compute_ppr_scores(nodes_to_sample[seed_type], seed_type) # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
self._compute_ppr_scores(nodes_to_sample[seed_type], seed_type)
for seed_type in seed_types
]
)
Expand All @@ -556,20 +556,20 @@ async def _sample_from_nodes(

for ntype, flat_ids in ntype_to_flat_ids.items():
ppr_edge_type: EdgeType = (seed_type, "ppr", ntype)
valid_counts = ntype_to_valid_counts[ntype] # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
valid_counts = ntype_to_valid_counts[ntype]
ppr_edge_type_to_flat_weights[ppr_edge_type] = (
ntype_to_flat_weights[ntype] # ty: ignore[invalid-argument-type] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
ntype_to_flat_weights[ntype]
)

# Skip empty pairs; induce_next handles deduplication across
# seed types so a neighbor reachable from multiple seed types
# gets one consistent local index in node_dict[ntype].
if flat_ids.numel() > 0: # ty: ignore[unresolved-attribute] TODO(ty-torch-keyed-access): fix ty false positives for torch-backed keyed container access.
if flat_ids.numel() > 0:
nbr_dict[ppr_edge_type] = [
src_dict[seed_type],
flat_ids,
valid_counts,
] # ty: ignore[invalid-assignment] TODO(ty-torch-container-shapes): fix ty false positives for torch container and return shapes.
]

# induce_next processes all PPR edge types in nbr_dict in one
# pass, assigning local indices to neighbors not yet registered and
Expand Down
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