pip install

pylibcugraphops

pylibcugraphops is a wrapper around cugraph-ops and provides framework-agnostic and lightweight Python-bindings for the custom graph operators of cugraph-ops. For more information on the implemented functionality, check the corresponding documentations.

Structure

In terms of structure, the bindings somewhat follow the guidelines of cugraph-ops. The main difference is that pylibcugraphops is more focused on providing reasonable submodules and thus the overall structure is rather flat. The core functionality is placed under pylibcugraphops.operators similar to the C++ API with the <cugraph-ops/operators> namespace. This includes basic aggregation operators (e.g. for GraphSAGE), specialized operators (e.g. for RGCN), and operators performing pooling-like operations (e.g. readout operations aggregating from a graph-to-scalar level). While the C++ API has a further distinction into operators for message flow graphs and other kinds of graphs which can be represented through CSC, the pylibcugraphops.operators is kept as a flat submodule with the function names making the disctiontion.

Naming Convention

The name of exposed operators and corresponding files should be self-explanatory to provide an easy overview of all relevant charateristics. Therefore, we adopt the following naming convention for operators.

<operator family>_<graph type>_<"direction">_(<operator specification>_)<[fwd|bwd|bwd_rev|...]

Examples - agg_simple_csc_bwd - pool_csc_n2s_bwd - agg_hg_basis_mfg_n2n_post_bwd

Support for different datatypes is achieved through overloading and thus usually not explicitly part of operator names.

Explanation of Individual Parts

• <operator family> denotes the underlying operator, we currently have
  • agg_simple: basic aggregator performing a mean/sum/min/max reduction of the specified set
  • agg_concat: like agg_simple but additionally concatenates the self representation of each set member
  • agg_dmpnn: implmements the DMPNN edge-to-edge aggregation
  • pool: sum/mean/min/max pooling, e.g. node-to-scalar readout
  • hg_basis: heterogenous aggregation using different edge types and potentially using a basis decomposition
  • mha_gat: multi-headed attention aggregation, in this case GAT as specific variant
• <graph type> which graph type the operator is intended for
  • csc: simple csc representation of a graph
  • bipartite: simple csc representation of a bipartite graph
  • mfg: message flow graph
• <"direction">: for easier overview of which operators we already have, multiple directions are combined and separated through underscores (e.g. e2n_n2n)
  • n2n: node-to-node (basic aggregation)
  • e2n: edge-to-node
  • e2e: edge-to-edge
  • n2s: node-to-scalar
• <operator specification>: some operators define different kinds of "flavors", e.g. the "pre" and the "post" variant of the hg_basis operators

Function Signatures

The following pattern should be used

func(output_vector_arg_0, ..., output_vector_arg_n,
     input_vector_arg_0, ..., input_vector_arg_n,
     graph_structure_arg_0, ..., graph_structure_arg_n,
     *args,
     **kwargs,
     cuda_stream=None)

• *args: arguments necessary for func but neither a buffer nor a graph structure
• **kwargs: optional arguments or arguments with default values
• cuda_stream should be the last argument in a binding if applicable and defaulted to the null stream

Example for RGCN (hgbasispre):

def agg_hg_basis_mfg_n2n_pre_bwd(output_gradient,
                                 input_gradient,
                                 input_embedding,
                                 message_flow_graph_hg_csc_int32 graph,
                                 output_weight_gradient=None,
                                 weights_combination=None,
                                 concat_own=False,
                                 norm_by_degree=False,
                                 cuda_stream=None):