pylibcugraphops.operators.agg_weighted_n2n_fwd

pylibcugraphops.operators.agg_weighted_n2n_fwd = <nanobind.nb_func object>

Computes the forward pass for a weighted aggregation (agg_weighted) using node features in an

node-to-node reduction (n2n). The reduction operates on a graph and each incoming message is weighted according to its edge weight.

agg_weighted_n2n_fwd(
    output_embedding: device array, input_embedding: device array,
    edge_weight: device array,
    graph: Union[pylibcugraphops.csc_int[32|64], pylibcugraphops.bipartite_csc_int[32|64]],
    aggregation_operation: pylibcugraphops.operators.AggOp = pylibcugraphops.operators.AggOp.Sum,
    output_extrema_location: Optional[device array] = None, node_degree: Optional[device array],
    stream_id: int = 0
) -> None

Parameters:

output_embeddingdevice array type

Device array containing the output node embeddings. Shape: (graph.n_dst_nodes, dim).

input_embeddingdevice array type

Device array containing the input node embeddings. Shape: (graph.n_dst_nodes, dim).

edge_weight: device array type

Device array containing the edge weights. Shape: (n_edges, ).

graphopaque graph type

The graph used for the operation.

aggregation_operationAggOp, default=AggOp.Sum

The kind of aggregation operation.

output_extrema_locationoptional device array type

Device array containing the location of the min/max embeddings. This is required for min/max aggregation only, and can be None otherwise. Shape: (graph.n_dst_nodes, dim) if set.

node_degreeoptional device array type

Device array containing the in-node degree, i.e. the sum of incoming weights. This is required for mean aggregation only, and can be None otherwise. Shape: (graph.n_dst_nodes, ) if set.

stream_idint, default=0

CUDA stream pointer as a python int.