dgl.distributed.edge_split(edges, partition_book=None, etype='_E', rank=None, force_even=True, edge_trainer_ids=None)[source]ΒΆ

Split edges and return a subset for the local rank.

This function splits the input edges based on the partition book and returns a subset of edges for the local rank. This method is used for dividing workloads for distributed training.

The input edges can be stored as a vector of masks. The length of the vector is the same as the number of edges in a graph; 1 indicates that the edge in the corresponding location exists.

There are two strategies to split the edges. By default, it splits the edges in a way to maximize data locality. That is, all edges that belong to a process are returned. If force_even is set to true, the edges are split evenly so that each process gets almost the same number of edges.

When force_even is True, the data locality is still preserved if a graph is partitioned with Metis and the node/edge IDs are shuffled. In this case, majority of the nodes returned for a process are the ones that belong to the process. If node/edge IDs are not shuffled, data locality is not guaranteed.

  • edges (1D tensor or DistTensor) – A boolean mask vector that indicates input edges.

  • partition_book (GraphPartitionBook, optional) – The graph partition book

  • etype (str or (str, str, str), optional) – The edge type of the input edges.

  • rank (int, optional) – The rank of a process. If not given, the rank of the current process is used.

  • force_even (bool, optional) – Force the edges are split evenly.

  • edge_trainer_ids (1D tensor or DistTensor, optional) – If not None, split the edges to the trainers on the same machine according to trainer IDs assigned to each edge. Otherwise, split randomly.


The vector of edge IDs that belong to the rank.

Return type