pylibcugraphops.operators.agg_hg_basis_n2n_post_fwd#
- pylibcugraphops.operators.agg_hg_basis_n2n_post_fwd = <nanobind.nb_func object>#
- Computes the forward pass for node-to-node full-graph RGCN-like basis regularized
aggregation, with features being transformed after (post) this aggregation.
agg_hg_basis_n2n_post_fwd( output_embedding: device array, input_embedding: device array, weights_combination: Optional[device array], graph: pylibcugraphops.csc_hg_int[32|64], concat_own: bool = False, norm_by_out_degree: bool = False, stream_id: int = 0 )
- Parameters:
- output_embeddingdevice array type
Device array containing the output embeddings. Shape for
concat_own=False
:(graph.n_dst_nodes, dim_out)
; Shape forconcat_own=True
:(graph.n_dst_nodes, dim_out + dim_in)
, withdim_out = dim_in * n_bases
ifweights_combination
is set,dim_out = dim_in * n_edge_types
otherwise.- input_embeddingdevice array type
Device array containing the input embeddings. Shape:
(graph.n_dst_nodes, dim_in)
.- weights_combinationdevice array type | None
Device array containing the combination weights. If
None
, no weights are used and features of different edge types are aggregated into separate output dimensions. Shape:(n_edge_types, n_bases)
if set.- graphopaque graph type
The graph used for the operation.
- concat_ownbool, default=False
Concatenate output node embeddings in the aggregation.
- norm_by_out_degreebool, default=False
If set, output embeddings are normed by the degree of the output node.
- stream_idint, default=0
CUDA stream pointer as a python int.