Baunsgaard commented on code in PR #1941: URL: https://github.com/apache/systemds/pull/1941#discussion_r1385587706
########## scripts/nn/layers/graph_conv.dml: ########## @@ -0,0 +1,262 @@ +#------------------------------------------------------------- +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +#------------------------------------------------------------- + +/* + * A graph convolutional layer as presented in 'Semi-Supervised Classification with Graph Convolutional Networks' + * by Kipf and Welling + */ + +forward = function(matrix[double] X, matrix[double] edge_index, matrix[double] edge_weight, + matrix[double] W, matrix[double] b, boolean add_self_loops) + return (matrix[double] X_out) +{ + /* Forward pass of the Graph Convolutional Layer. It transforms the node feature matrix + * with linear weights W and then executes the message passing according to the edges. + * The message passing is normalized by spectral normalization, i.e. for edge (v, w) the + * normalization factor is 1 / sqrt(degree(v) * degree(w)). + * + * n: number of nodes. + * m: number of edges. + * f_in: number of input features per node. + * f_out: number of output features per node. + * + * Inputs: + * - X: node features, matrix of shape (n, f_in). + * - edge_index: directed edge list specifying the out-node (first column) and the + * in-node (second column) of each edge, matrix of shape (m, 2). + * - edge_weight: weights of edges in edge_index, matrix of shape (m, 1). + * This should be all 1s if there should be no edge weights. + * - W: linear weights, matrix of shape (f_in, f_out). + * - b: bias, matrix of shape (1, f_out). + * - add_self_loops: boolean that specifies whether self loops should be added. + * If TRUE new self loops will be added only for nodes that do + * not yet have a self loop. Added self loops will have weight 1. + * + * Outputs: + * - X_out: convolved and transformed node features, matrix of shape (n, f_out). + */ + n = nrow(X) + m = nrow(edge_index) + + # transform + X_hat = X %*% W Review Comment: You can try out the sequence of matrix multiplications the sparse representations in SystemDS are usually much faster than the other frameworks. I understand why you designed it the way you did because it does not intuitively make much sense to use the slower matrix multiplications. That said, the use case you currently have that runs for 220 seconds sounds a bit large because of the execution time. Can it be because the computer you use has few cores, or is the use-case you are running disproportionately large? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: dev-unsubscr...@systemds.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
