pylibcugraphops.operators.mha_gat_n2n_fwd_fp32_fp32#
- pylibcugraphops.operators.mha_gat_n2n_fwd_fp32_fp32 = <nanobind.nb_func object>#
- Computes the forward pass for a multi-head attention layer (GAT-like)
without using cudnn (mha_gat) operating on bipartite graphs in a node-to-node reduction (n2n).
mha_gat_n2n_fwd( output_embedding: device array, softmax_scores: device array, src_node_embedding: device array, dst_node_embedding: device array, attention_weights: device array, graph: pylibcugraphops.bipartite_csc_int[64|32], params: pylibcugraphops.operators.mha_params, stream_id: int = 0 ) -> None
- Parameters:
- output_embeddingdevice array type
Device array containing the output node embeddings. Shape:
(graph.n_dst_nodes, dim_out)
, withdim_out = dim_node
whenparams.concat_heads
isTrue
;dim_out = dim_node / params.num_heads
otherwise.- softmax_scoresdevice array type
Device array containing the pre- and post-softmax-scores (for backward). Shape:
(2, params.num_heads, graph.n_indices)
.- src_node_embeddingdevice array type
Device array containing the source node embeddings. Shape:
(graph.n_src_nodes, dim_node)
.- dst_node_embeddingdevice array type
Device array containing the destination node embeddings. Shape:
(graph.n_dst_nodes, dim_node)
.- attention_weights: device array type
Device array containing the (learnable) attention weights. Shape:
(2 * dim_node, )
.- graphopaque graph type
graph used for the operation.
- paramsopaque mha_params type
Structure summarizing hyperparameters of the primitive like
num_heads
,concat_heads
or the used activation function.- stream_idint, default=0
CUDA stream pointer as a python int.