pylibcugraphops.operators.agg_simple_e2n_fwd#

pylibcugraphops.operators.agg_simple_e2n_fwd = <nanobind.nb_func object>#

Computes the forward pass for simple aggregation (agg_simple) using edge features in an: edge-to-node reduction (e2n) on a graph.

agg_simple_e2n_fwd(
    output_embedding: device array, input_embedding: 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, 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 edge embeddings. Shape: (n_edges, dim).
graphopaque graph type: The graph used for the operation.
aggregation_operationAggOp, default=AggOp.Sum: The kind of aggregation operation.
output_extrema_locationdevice array type | None: 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.
stream_idint, default=0: CUDA stream pointer as a python int.