Try to construct DGLGraph in C++

Questions
#1

Hello,

I hope this message finds you well. I am currently attempting to construct a heterograph in C++ to accelerate my data processing pipeline. Could you please advise me on how to pass the generated DGLGraph back to Python? Below is my current attempt:

struct MetaGraph{
    dgl::HeteroGraphRef hgidx;
    py::object get_hgidx(){
        return py::cast(hgidx);
    }
};

dgl::HeteroGraphRef transformToMetaGraph(Graph& g){
    
    std::vector<int> from_vec{1, 1, 1, 1, 1, 2, 2, 2, 2, 2};
    std::vector<int> to_vec{0, 1, 2, 1, 2, 1, 2, 3, 1, 2};

    int agn;
    int bn;

    auto from = dgl::IdArray::FromVector(from_vec);
    auto to = dgl::IdArray::FromVector(to_vec);
    auto metagraph = dgl::GraphRef(dgl::ImmutableGraph::CreateFromCOO(4, from, to));

    std::vector<dgl::HeteroGraphRef> rel_graphs;
    dgl::dgl_format_code_t code = 1;

    auto row = dgl::IdArray::FromVector(g.ba.first);
    auto col = dgl::IdArray::FromVector(g.ba.second);
    auto hgptr = dgl::CreateFromCOO(
          2, bn, agn, row, col, false, false, code);
    rel_graphs.emplace_back(dgl::HeteroGraphRef(hgptr));    
    // ... 

    std::vector<dgl::HeteroGraphPtr> rel_ptrs;
    rel_ptrs.reserve(rel_graphs.size());
    for (const auto& ref : rel_graphs) {
      rel_ptrs.push_back(ref.sptr());
    }
    auto hgptr_f = dgl::CreateHeteroGraph(
        metagraph.sptr(), rel_ptrs, {agn, bn, sn, spn});
    return dgl::HeteroGraphRef(hgptr_f);
}

PYBIND11_MODULE(GraphExtract, m) {

    m.def("extractFeature", &extractFeature, "A func");
    py::class_<MetaGraph, std::shared_ptr<MetaGraph>>(m, "MetaGraph")
        .def(py::init<>())
        .def("get_hgidx", &MetaGraph::get_hgidx);
}

When using get_hgidx in Python, the following prompt occurs:

g.get_hgidx()
TypeError: Unregistered type : dgl::HeteroGraphRef

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<string>", line 1, in <module>
TypeError: Unable to convert function return value to a Python type! The signature was
	(self: GraphExtract.MetaGraph) -> object

How can I conveniently read the DGLGraph constructed in C++ in Python? Alternatively, could you tell me any methods to accelerate the construction of heterographs?

Thank you very much for your assistance.

Best regards.

#2

The main DGL code currently does not support PyBind11. But starting from DGL 2.0 we have shipped GraphBolt which supports LibTorch-like Python bindings:
dgl/python/dgl/graphbolt at master · dmlc/dgl · GitHub (Python)
dgl/graphbolt at master · dmlc/dgl · GitHub (C++)

#3

That’s helpful. Thank you for your reply!

#4

Hello, I apologize for bothering you again. I have read through the code and documentation of Graphbolt. Are you suggesting creating a FusedCSCSamplingGraph in C++ and then passing it to Python via binding?
If so, I have another question. My current task is graph-level, so I don’t intend to sample nodes and edges. I noticed that the provided examples are all for tasks involving nodes and edges, and they also involve sampling. Is there a way to convert FusedCSCSamplingGraph to a DGLGraph directly?

Besides, an error was thrown when running the example provided in dgl/notebooks/graphbolt/walkthrough.ipynb:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[6], line 14
     11 eid = torch.tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
     12                     14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])
     13 edge_attributes = {gb.ORIGINAL_EDGE_ID: eid}
---> 14 graph = gb.from_fused_csc(indptr, indices, None, None, edge_attributes, None)
     16 num_nodes = 10
     17 num_edges = 25

AttributeError: module 'dgl.graphbolt' has no attribute 'from_fused_csc'

Thank you for your generous assistance.

#5

The tutorial may need to be updated. The actual API name is fused_csc_sampling_graph and its documentation is here:
https://docs.dgl.ai/en/latest/generated/dgl.graphbolt.fused_csc_sampling_graph.html#dgl.graphbolt.fused_csc_sampling_graph

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