kernel.cc

/*!
 *  Copyright (c) 2020 by Contributors
 * \file array/kernel.cc
 * \brief New kernels
 */
#include <dgl/packed_func_ext.h>
#include <dgl/base_heterograph.h>

#ifdef USE_TVM
#include <featgraph.h>
#endif  // USE_TVM

#include "kernel_decl.h"
#include "../c_api_common.h"
#include "./check.h"

using namespace dgl::runtime;

namespace dgl {
namespace aten {
namespace {

}  // namespace

/*! \brief Generalized Sparse Matrix-Matrix Multiplication. */
void SpMM(const std::string& op, const std::string& reduce,
          HeteroGraphPtr graph,
          NDArray ufeat,
          NDArray efeat,
          NDArray out,
          std::vector<NDArray> out_aux) {
  // TODO(zihao): format tuning
  SparseFormat format = graph->SelectFormat(0, CSC_CODE);
  const auto& bcast = CalcBcastOff(op, ufeat, efeat);

  ATEN_XPU_SWITCH_CUDA(graph->Context().device_type, XPU, "SpMM", {
    ATEN_ID_TYPE_SWITCH(graph->DataType(), IdType, {
      ATEN_FLOAT_BITS_SWITCH(out->dtype, bits, "Feature data", {
        if (format == SparseFormat::kCSC) {
          SpMMCsr<XPU, IdType, bits>(
              op, reduce, bcast, graph->GetCSCMatrix(0),
              ufeat, efeat, out, out_aux);
        } else if (format == SparseFormat::kCOO) {
          SpMMCoo<XPU, IdType, bits>(
              op, reduce, bcast, graph->GetCOOMatrix(0),
              ufeat, efeat, out, out_aux);
        } else {
          LOG(FATAL) << "SpMM only supports CSC and COO formats";
        }
      });
    });
  });
}

/*! \brief Generalized Sparse Matrix-Matrix Multiplication with hetero-graph support. */
void SpMMHetero(const std::string& op, const std::string& reduce,
          HeteroGraphPtr graph,
          std::vector<NDArray> ufeat_vec,
          std::vector<NDArray> efeat_vec,
          std::vector<NDArray> out,
          std::vector<NDArray> out_aux) {
  SparseFormat format = graph->SelectFormat(0, CSC_CODE);

  std::vector<CSRMatrix> vec_graph;
  std::vector<dgl_type_t> ufeat_eid;
  std::vector<dgl_type_t> efeat_eid;
  std::vector<dgl_type_t> out_eid;
  for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
    vec_graph.push_back(graph->GetCSCMatrix(etype));
    auto pair = graph->meta_graph()->FindEdge(etype);
    ufeat_eid.push_back(pair.first);
    efeat_eid.push_back(etype);
    out_eid.push_back(pair.second);
  }
  NDArray efeat = (efeat_vec.size() == 0) ? NullArray() : efeat_vec[efeat_eid[0]];
  NDArray ufeat = (ufeat_vec.size() == 0) ? NullArray() : ufeat_vec[ufeat_eid[0]];
  const auto& bcast = CalcBcastOff(op, ufeat, efeat);

  ATEN_XPU_SWITCH_CUDA(graph->Context().device_type, XPU, "SpMM", {
    ATEN_ID_TYPE_SWITCH(graph->DataType(), IdType, {
      ATEN_FLOAT_BITS_SWITCH(out[out_eid[0]]->dtype, bits, "Feature data", {
        if (format == SparseFormat::kCSC) {
          SpMMCsrHetero<XPU, IdType, bits>(
              op, reduce, bcast, vec_graph,
              ufeat_vec, efeat_vec, out, out_aux,
              ufeat_eid, out_eid);
        } else {
          // TODO(Israt): Add support for COO format
          LOG(FATAL) << "SpMM only supports CSC format for graphs with number "
                     << "of relation types > 1";
        }
      });
    });
  });
}


/*! \brief Generalized Sampled Dense-Dense Matrix Multiplication. */
void SDDMM(const std::string& op,
           HeteroGraphPtr graph,
           NDArray lhs,
           NDArray rhs,
           NDArray out,
           int lhs_target,
           int rhs_target) {
  // TODO(zihao): format tuning
  SparseFormat format = graph->SelectFormat(0, COO_CODE);
  const auto &bcast = CalcBcastOff(op, lhs, rhs);

  ATEN_XPU_SWITCH_CUDA(graph->Context().device_type, XPU, "SDDMM", {
    ATEN_ID_TYPE_SWITCH(graph->DataType(), IdType, {
      ATEN_FLOAT_BITS_SWITCH(out->dtype, bits, "Feature data", {
        if (format == SparseFormat::kCSR) {
          SDDMMCsr<XPU, IdType, bits>(
              op, bcast, graph->GetCSRMatrix(0),
              lhs, rhs, out, lhs_target, rhs_target);
        } else if (format == SparseFormat::kCOO) {
          SDDMMCoo<XPU, IdType, bits>(
              op, bcast, graph->GetCOOMatrix(0),
              lhs, rhs, out, lhs_target, rhs_target);
        } else {
          LOG(FATAL) << "SDDMM only supports CSR and COO formats";
        }
      });
    });
  });
}


/*!
 * \brief Find the src/dst/etype id based on the target 'u', 'v' or 'e'.
 *
 * \param graph The input graph.
 * \param target 'u', 'v' or 'e'. The target of the lhs or rhs data of an etype.
 * \param etype Relation type of the input graph.
 */
int get_typeid_by_target(HeteroGraphPtr graph, int target, dgl_type_t etype) {
  auto pair = graph->meta_graph()->FindEdge(etype);
  if (target == 0)
    return pair.first;
  if (target == 2)
    return pair.second;
  return etype;
}


/*! \brief Generalized Sampled Dense-Dense Matrix Multiplication. */
void SDDMMHetero(const std::string& op,
           HeteroGraphPtr graph,
           std::vector<NDArray> lhs,
           std::vector<NDArray> rhs,
           std::vector<NDArray> out,
           int lhs_target,
           int rhs_target) {
  // TODO(Israt): change it to COO_CODE
  SparseFormat format = graph->SelectFormat(0, CSR_CODE);

  std::vector<CSRMatrix> vec_csr;
  std::vector<dgl_type_t> lhs_eid;
  std::vector<dgl_type_t> rhs_eid;
  for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
    vec_csr.push_back(graph->GetCSRMatrix(etype));
    lhs_eid.push_back(get_typeid_by_target(graph, lhs_target, etype));
    rhs_eid.push_back(get_typeid_by_target(graph, rhs_target, etype));
  }
  const auto &bcast = CalcBcastOff(op, lhs[lhs_eid[0]], rhs[rhs_eid[0]]);

  ATEN_XPU_SWITCH_CUDA(graph->Context().device_type, XPU, "SDDMM", {
    ATEN_ID_TYPE_SWITCH(graph->DataType(), IdType, {
      ATEN_FLOAT_BITS_SWITCH(out[rhs_eid[0]]->dtype, bits, "Feature data", {
        if (format == SparseFormat::kCSR) {
          SDDMMCsrHetero<XPU, IdType, bits>(
              op, bcast, vec_csr,
              lhs, rhs, out, lhs_target, rhs_target,
              lhs_eid, rhs_eid);
        } else {
          // TODO(Israt): Add support for COO format
          LOG(FATAL) << "SDDMM only supports CSC format for graphs with number "
                     << "of relation types > 1";
        }
      });
    });
  });
}

NDArray GetEdgeMapping(HeteroGraphRef graph) {
  SparseFormat format = graph->SelectFormat(0, CSC_CODE);
  if (format == SparseFormat::kCSC) {
    return graph.sptr()->GetCSCMatrix(0).data;
  } else {
    return NullArray();
  }
}

/*! \brief Segment reduce dispatch function. */
void SegmentReduceDispatch(const std::string& op,
                           NDArray feat,
                           NDArray offsets,
                           NDArray out,
                           NDArray arg) {
  ATEN_XPU_SWITCH_CUDA(feat->ctx.device_type, XPU, "SegmentReduce", {
    ATEN_ID_TYPE_SWITCH(offsets->dtype, IdType, {
      ATEN_FLOAT_BITS_SWITCH(feat->dtype, bits, "Feature data", {
          SegmentReduce<XPU, IdType, bits>(op, feat, offsets, out, arg);
      });
    });
  });
}

/*! \brief Scatter Add (on first dimension) dispatch function. */
void ScatterAddDispatch(NDArray feat, NDArray idx, NDArray out) {
  ATEN_XPU_SWITCH_CUDA(feat->ctx.device_type, XPU, "ScatterAdd", {
    ATEN_ID_TYPE_SWITCH(idx->dtype, IdType, {
      ATEN_FLOAT_BITS_SWITCH(feat->dtype, bits, "Feature data", {
        ScatterAdd<XPU, IdType, bits>(feat, idx, out);
      });
    });
  });
}

/*! \brief Backward segment cmp dispatch function.*/
void BackwardSegmentCmpDispatch(NDArray feat, NDArray arg, NDArray out) {
  ATEN_XPU_SWITCH_CUDA(feat->ctx.device_type, XPU, "BackwardSegmentCmp", {
    ATEN_ID_TYPE_SWITCH(arg->dtype, IdType, {
      ATEN_FLOAT_BITS_SWITCH(feat->dtype, bits, "Feature data", {
        BackwardSegmentCmp<XPU, IdType, bits>(feat, arg, out);
      });
    });
  });
}

std::pair<CSRMatrix, NDArray> CSRMM(
    CSRMatrix A,
    NDArray A_weights,
    CSRMatrix B,
    NDArray B_weights) {
  CHECK_EQ(A.num_cols, B.num_rows) <<
    "The number of nodes of destination node type of the first graph must be the "
    "same as the number of nodes of source node type of the second graph.";
  CheckCtx(
      A.indptr->ctx,
      {A_weights, B_weights},
      {"A's edge weights", "B's edge weights"});
  CHECK_EQ(A.indptr->ctx, B.indptr->ctx) << "Device of two graphs must match.";
  CHECK_EQ(A.indptr->dtype, B.indptr->dtype) << "ID types of two graphs must match.";
  CHECK_EQ(A_weights->dtype, B_weights->dtype) << "Data types of two edge weights must match.";

  std::pair<CSRMatrix, NDArray> ret;
  ATEN_XPU_SWITCH_CUDA(A.indptr->ctx.device_type, XPU, "CSRMM", {
    ATEN_ID_TYPE_SWITCH(A.indptr->dtype, IdType, {
      ATEN_FLOAT_TYPE_SWITCH(A_weights->dtype, DType, "Edge weights", {
        ret = CSRMM<XPU, IdType, DType>(A, A_weights, B, B_weights);
      });
    });
  });
  return ret;
}

std::pair<CSRMatrix, NDArray> CSRSum(
    const std::vector<CSRMatrix>& A,
    const std::vector<NDArray>& A_weights) {
  CHECK(A.size() > 0) << "The list of graphs must not be empty.";
  CHECK_EQ(A.size(), A_weights.size()) <<
    "The list of edge weights must have the same length as the list of graphs.";
  const auto ctx = A[0].indptr->ctx;
  const auto idtype = A[0].indptr->dtype;
  const auto dtype = A_weights[0]->dtype;
  const auto num_rows = A[0].num_rows;
  const auto num_cols = A[0].num_cols;
  for (size_t i = 0; i < A.size(); ++i) {
    CHECK_EQ(A[i].indptr->ctx, ctx) << "The devices of all graphs must be equal.";
    CHECK_EQ(A[i].indptr->dtype, idtype) << "The ID types of all graphs must be equal.";
    CHECK_EQ(A[i].indices->shape[0], A_weights[i]->shape[0]) <<
      "Shape of edge weights does not match the number of edges.";
    CHECK_EQ(A_weights[i]->ctx, ctx) <<
      "The devices of edge weights must be the same as that of the graphs.";
    CHECK_EQ(A_weights[i]->dtype, dtype) <<
      "The data types of all edge weights must be equal.";
    CHECK_EQ(A[i].num_rows, num_rows) << "Graphs must have the same number of nodes.";
    CHECK_EQ(A[i].num_cols, num_cols) << "Graphs must have the same number of nodes.";
  }

  std::pair<CSRMatrix, NDArray> ret;
  ATEN_XPU_SWITCH_CUDA(ctx.device_type, XPU, "CSRSum", {
    ATEN_ID_TYPE_SWITCH(idtype, IdType, {
      ATEN_FLOAT_TYPE_SWITCH(dtype, DType, "Edge weights", {
        ret = CSRSum<XPU, IdType, DType>(A, A_weights);
      });
    });
  });
  return ret;
}

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelSpMM")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    HeteroGraphRef graph = args[0];
    const std::string op = args[1];
    const std::string reduce_op = args[2];
    NDArray U = args[3];
    NDArray E = args[4];
    NDArray V = args[5];
    NDArray ArgU = args[6];
    NDArray ArgE = args[7];
    CheckCtx(graph->Context(), {U, E, V, ArgU, ArgE},
        {"U_data", "E_data", "out", "Arg_U", "Arg_E"});
    CheckContiguous({U, E, V, ArgU, ArgE},
        {"U_data", "E_data", "out", "Arg_U", "Arg_E"});
    CHECK_EQ(graph->NumEdgeTypes(), 1);
    auto pair = graph->meta_graph()->FindEdge(0);  // only one etype in the graph.
    const dgl_type_t src_vtype = pair.first;
    const dgl_type_t dst_vtype = pair.second;
    CheckShape(
        {graph->NumVertices(src_vtype), graph->NumEdges(0), graph->NumVertices(dst_vtype)},
        {0, 1, 2, 2, 2},
        {U, E, V, ArgU, ArgE},
        {"U_data", "E_data", "out", "Arg_U", "Arg_E"});
    SpMM(op, reduce_op, graph.sptr(), U, E, V, {ArgU, ArgE});
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelSpMMHetero")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    HeteroGraphRef graph = args[0];
    const std::string op = args[1];
    const std::string reduce_op = args[2];
    List<Value> list_U = args[3];
    List<Value> list_E = args[4];
    List<Value> list_V = args[5];
    NDArray ArgU = args[6];
    NDArray ArgE = args[7];
    std::vector<NDArray> U_vec;
    std::vector<NDArray> V_vec;
    std::vector<NDArray> E_vec;
    U_vec.reserve(list_U.size());
    V_vec.reserve(list_V.size());
    E_vec.reserve(list_E.size());
    for (Value val : list_U) {
      U_vec.push_back(val->data);
    }
    for (Value val : list_V) {
      V_vec.push_back(val->data);
    }
    for (Value val : list_E) {
      E_vec.push_back(val->data);
    }
    for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
      auto pair = graph->meta_graph()->FindEdge(etype);
      const dgl_id_t src_id = pair.first;
      const dgl_id_t dst_id = pair.second;
      NDArray U = (U_vec.size() == 0) ? NullArray() : U_vec[src_id];
      NDArray E = (E_vec.size() == 0) ? NullArray() : E_vec[etype];
      CheckCtx(graph->Context(), {U, E, V_vec[dst_id], ArgU, ArgE},
          {"U_data", "E_data", "out", "Arg_U", "Arg_E"});
      CheckContiguous({U, E, V_vec[dst_id], ArgU, ArgE},
          {"U_data", "E_data", "out", "Arg_U", "Arg_E"});
    }
    SpMMHetero(op, reduce_op, graph.sptr(), U_vec, E_vec, V_vec, {ArgU, ArgE});
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelSDDMM")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    HeteroGraphRef graph = args[0];
    const std::string op = args[1];
    NDArray lhs = args[2];
    NDArray rhs = args[3];
    NDArray out = args[4];
    int lhs_target = args[5];
    int rhs_target = args[6];
    CheckCtx(graph->Context(), {lhs, rhs, out}, {"lhs", "rhs", "out"});
    CheckContiguous({lhs, rhs, out}, {"lhs", "rhs", "out"});
    CHECK_EQ(graph->NumEdgeTypes(), 1);
    auto pair = graph->meta_graph()->FindEdge(0);  // only one etype in the graph.
    const dgl_type_t src_vtype = pair.first;
    const dgl_type_t dst_vtype = pair.second;

    CheckShape(
        {graph->NumVertices(src_vtype), graph->NumEdges(0), graph->NumVertices(dst_vtype)},
        {lhs_target, rhs_target, 1},
        {lhs, rhs, out},
        {"U_data", "E_data", "V_data"});
    SDDMM(op, graph.sptr(), lhs, rhs, out, lhs_target, rhs_target);
  });


DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelSDDMMHetero")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    HeteroGraphRef graph = args[0];
    const std::string op = args[1];
    List<Value> list_lhs = args[2];
    List<Value> list_rhs = args[3];
    List<Value> list_out = args[4];
    int lhs_target = args[5];
    int rhs_target = args[6];
    std::vector<NDArray> vec_lhs;
    std::vector<NDArray> vec_rhs;
    std::vector<NDArray> vec_out;

    vec_lhs.reserve(list_lhs.size());
    vec_rhs.reserve(list_rhs.size());
    vec_out.reserve(list_out.size());

    for (Value val : list_lhs) {
      vec_lhs.push_back(val->data);
    }
    for (Value val : list_rhs) {
      vec_rhs.push_back(val->data);
    }
    for (Value val : list_out) {
      vec_out.push_back(val->data);
    }
    SDDMMHetero(op, graph.sptr(), vec_lhs, vec_rhs, vec_out, lhs_target, rhs_target);
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelSegmentReduce")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    const std::string op = args[0];
    NDArray feat = args[1];
    NDArray offsets = args[2];
    NDArray out = args[3];
    NDArray arg = args[4];
    CheckCtx(feat->ctx, {feat, offsets, out}, {"feat", "offsets", "out"});
    CheckContiguous({feat, offsets, out}, {"feat", "offsets", "out"});
    SegmentReduceDispatch(op, feat, offsets, out, arg);
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelScatterAdd")
.set_body([](DGLArgs args, DGLRetValue *rv) {
    NDArray feat = args[0];
    NDArray idx = args[1];
    NDArray out = args[2];
    CheckCtx(feat->ctx, {feat, idx, out}, {"feat", "idx", "out"});
    CheckContiguous({feat, idx, out}, {"feat", "idx", "out"});
    ScatterAddDispatch(feat, idx, out);
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelBwdSegmentCmp")
.set_body([](DGLArgs args, DGLRetValue *rv) {
    NDArray feat = args[0];
    NDArray arg = args[1];
    NDArray out = args[2];
    CheckCtx(feat->ctx, {feat, arg, out}, {"feat", "arg", "out"});
    CheckContiguous({feat, arg, out}, {"feat", "arg", "out"});
    BackwardSegmentCmpDispatch(feat, arg, out);
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelGetEdgeMapping")
.set_body([](DGLArgs args, DGLRetValue *rv) {
    HeteroGraphRef graph = args[0];
    *rv = GetEdgeMapping(graph);
  });

/*!
 * \brief Sparse matrix multiplication with graph interface.
 *
 * \param A_ref The left operand.
 * \param A_weights The edge weights of graph A.
 * \param B_ref The right operand.
 * \param B_weights The edge weights of graph B.
 * \param num_vtypes The number of vertex types of the graph to be returned.
 * \return A pair consisting of the new graph as well as its edge weights.
 */
DGL_REGISTER_GLOBAL("sparse._CAPI_DGLCSRMM")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    const HeteroGraphRef A_ref = args[0];
    NDArray A_weights = args[1];
    const HeteroGraphRef B_ref = args[2];
    NDArray B_weights = args[3];
    int num_vtypes = args[4];

    const HeteroGraphPtr A = A_ref.sptr();
    const HeteroGraphPtr B = B_ref.sptr();
    CHECK_EQ(A->NumEdgeTypes(), 1) << "The first graph must have only one edge type.";
    CHECK_EQ(B->NumEdgeTypes(), 1) << "The second graph must have only one edge type.";
    const auto A_csr = A->GetCSRMatrix(0);
    const auto B_csr = B->GetCSRMatrix(0);
    auto result = CSRMM(A_csr, A_weights, B_csr, B_weights);

    List<ObjectRef> ret;
    ret.push_back(HeteroGraphRef(CreateFromCSR(num_vtypes, result.first, ALL_CODE)));
    ret.push_back(Value(MakeValue(result.second)));
    *rv = ret;
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLCSRSum")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    List<HeteroGraphRef> A_refs = args[0];
    List<Value> A_weights = args[1];

    std::vector<NDArray> weights = ListValueToVector<NDArray>(A_weights);
    std::vector<CSRMatrix> mats;
    mats.reserve(A_refs.size());
    int num_vtypes = 0;
    for (auto A_ref : A_refs) {
      const HeteroGraphPtr A = A_ref.sptr();
      CHECK_EQ(A->NumEdgeTypes(), 1) << "Graphs must have only one edge type.";
      mats.push_back(A->GetCSRMatrix(0));
      if (num_vtypes == 0)
        num_vtypes = A->NumVertexTypes();
    }
    auto result = CSRSum(mats, weights);

    List<ObjectRef> ret;
    ret.push_back(HeteroGraphRef(CreateFromCSR(num_vtypes, result.first, ALL_CODE)));
    ret.push_back(Value(MakeValue(result.second)));
    *rv = ret;
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_DGLCSRMask")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    const HeteroGraphRef A_ref = args[0];
    NDArray A_weights = args[1];
    const HeteroGraphRef B_ref = args[2];

    const HeteroGraphPtr A = A_ref.sptr();
    const HeteroGraphPtr B = B_ref.sptr();
    CHECK_EQ(A->NumEdgeTypes(), 1) << "Both graphs must have only one edge type.";
    CHECK_EQ(B->NumEdgeTypes(), 1) << "Both graphs must have only one edge type.";
    const CSRMatrix& A_csr = A->GetCSRMatrix(0);
    const COOMatrix& B_coo = B->GetCOOMatrix(0);
    CHECK_EQ(A_csr.num_rows, B_coo.num_rows) <<
      "Both graphs must have the same number of nodes.";
    CHECK_EQ(A_csr.num_cols, B_coo.num_cols) <<
      "Both graphs must have the same number of nodes.";

    NDArray result;
    ATEN_FLOAT_TYPE_SWITCH(A_weights->dtype, DType, "Edge weights", {
      result = aten::CSRGetData<DType>(A_csr, B_coo.row, B_coo.col, A_weights, 0.);
    });
    *rv = result;
  });

#ifdef USE_TVM
DGL_REGISTER_GLOBAL("sparse._CAPI_FG_LoadModule")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    const std::string path = args[0];
    dgl::featgraph::LoadFeatGraphModule(path);
  });

DGL_REGISTER_GLOBAL("sparse._CAPI_FG_SDDMMTreeReduction")
.set_body([] (DGLArgs args, DGLRetValue* rv) {
    HeteroGraphRef graph = args[0];
    NDArray lhs = args[1];
    NDArray rhs = args[2];
    NDArray out = args[3];
    CheckCtx(graph->Context(), {lhs, rhs, out}, {"lhs", "rhs", "out"});
    CheckContiguous({lhs, rhs, out}, {"lhs", "rhs", "out"});
    CHECK_EQ(graph->NumEdgeTypes(), 1);
    // auto pair = graph->meta_graph()->FindEdge(0);  // only one etype in the graph.
    // const dgl_type_t src_vtype = pair.first;
    // const dgl_type_t dst_vtype = pair.second;
    // CheckShape(
    //     {graph->NumVertices(src_vtype), graph->NumEdges(0), graph->NumVertices(dst_vtype)},
    //     {lhs_target, rhs_target, 1},
    //     {lhs, rhs, out},
    //     {"U_data", "E_data", "V_data"});
    COOMatrix coo = graph.sptr()->GetCOOMatrix(0);
    dgl::featgraph::SDDMMTreeReduction(coo.row.ToDLPack(), coo.col.ToDLPack(),
                                       lhs.ToDLPack(), rhs.ToDLPack(), out.ToDLPack());
  });
#endif  // USE_TVM


}  // namespace aten
}  // namespace dgl