[Misc] Replace /*! with /**. (#4823)

* replace

* blabla

* balbla

* blabla
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

src/array/filter.h

-/*!
+/**
  *  Copyright (c) 2021 by Contributors
  * @file array/filter.h
  * @brief Object for selecting items in a set, or selecting items not in a set.

src/array/kernel.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file array/kernel.cc
  * @brief New kernels
@@ -23,7 +23,7 @@ namespace {
 
 }  // namespace
 
-/*! @brief Generalized Sparse Matrix-Matrix Multiplication. */
+/** @brief Generalized Sparse Matrix-Matrix Multiplication. */
 void SpMM(const std::string& op, const std::string& reduce,
           HeteroGraphPtr graph,
           NDArray ufeat,
@@ -54,7 +54,7 @@ void SpMM(const std::string& op, const std::string& reduce,
 }
 
 
-/*! @brief Generalized segmented dense Matrix-Matrix Multiplication. */
+/** @brief Generalized segmented dense Matrix-Matrix Multiplication. */
 void SegmentMM(const NDArray A,
                const NDArray B,
                NDArray C,
@@ -102,7 +102,7 @@ void SegmentMMBackwardB(const NDArray A,
 }
 
 
-/*! @brief Generalized Dense Matrix-Matrix Multiplication according to relation types. */
+/** @brief Generalized Dense Matrix-Matrix Multiplication according to relation types. */
 void GatherMM(const NDArray A,
               const NDArray B,
               NDArray C,
@@ -139,7 +139,7 @@ void GatherMM(const NDArray A,
 }
 
 
-/*! @brief Generalized Dense Matrix-Matrix Multiplication according to relation types. */
+/** @brief Generalized Dense Matrix-Matrix Multiplication according to relation types. */
 void GatherMMScatter(const NDArray A,
                      const NDArray B,
                      NDArray C,
@@ -179,7 +179,7 @@ void GatherMMScatter(const NDArray A,
 }
 
 
-/*! @brief Generalized Sparse Matrix-Matrix Multiplication with hetero-graph support. */
+/** @brief Generalized Sparse Matrix-Matrix Multiplication with hetero-graph support. */
 void SpMMHetero(const std::string& op, const std::string& reduce,
           HeteroGraphPtr graph,
           const std::vector<NDArray>& ufeat_vec,
@@ -227,7 +227,7 @@ void SpMMHetero(const std::string& op, const std::string& reduce,
 }
 
 
-/*! @brief Generalized Sampled Dense-Dense Matrix Multiplication. */
+/** @brief Generalized Sampled Dense-Dense Matrix Multiplication. */
 void SDDMM(const std::string& op,
            HeteroGraphPtr graph,
            NDArray lhs,
@@ -258,7 +258,7 @@ void SDDMM(const std::string& op,
   });
 }
 
-/*!
+/**
  * @brief Find the src/dst/etype id based on the target 'u', 'v' or 'e'.
  *
  * @param graph The input graph.
@@ -274,7 +274,7 @@ int get_typeid_by_target(HeteroGraphPtr graph, int target, dgl_type_t etype) {
   return etype;
 }
 
-/*! @brief Generalized Sampled Dense-Dense Matrix Multiplication. */
+/** @brief Generalized Sampled Dense-Dense Matrix Multiplication. */
 void SDDMMHetero(const std::string& op,
            HeteroGraphPtr graph,
            std::vector<NDArray> lhs,
@@ -322,7 +322,7 @@ void SDDMMHetero(const std::string& op,
 }
 
 
-/*! @brief Generalized Edge_softmax op for forward */
+/** @brief Generalized Edge_softmax op for forward */
 void Edge_softmax_forward(const std::string& op,
           HeteroGraphPtr graph,
           NDArray ufeat,
@@ -342,7 +342,7 @@ void Edge_softmax_forward(const std::string& op,
 }
 
 
-/*! @brief Generalized Edge_softmax op for backward */
+/** @brief Generalized Edge_softmax op for backward */
 void Edge_softmax_backward(const std::string& op,
           HeteroGraphPtr graph,
           NDArray out,
@@ -372,7 +372,7 @@ NDArray GetEdgeMapping(HeteroGraphRef graph) {
   }
 }
 
-/*! @brief Segment reduce dispatch function. */
+/** @brief Segment reduce dispatch function. */
 void SegmentReduceDispatch(const std::string& op,
                            NDArray feat,
                            NDArray offsets,
@@ -387,7 +387,7 @@ void SegmentReduceDispatch(const std::string& op,
   });
 }
 
-/*! @brief Scatter Add (on first dimension) dispatch function. */
+/** @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, {
@@ -398,7 +398,7 @@ void ScatterAddDispatch(NDArray feat, NDArray idx, NDArray out) {
   });
 }
 
-/*! @brief Update gradients (reduce op max/min) dispatch function on heterogeneous graph. */
+/** @brief Update gradients (reduce op max/min) dispatch function on heterogeneous graph. */
 void UpdateGradMinMaxDispatchHetero(const HeteroGraphPtr& graph,
                         const std::string& op,
                         const std::vector<NDArray>& feat,
@@ -416,7 +416,7 @@ void UpdateGradMinMaxDispatchHetero(const HeteroGraphPtr& graph,
   });
 }
 
-/*! @brief Backward segment cmp dispatch function.*/
+/** @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, {
@@ -723,7 +723,7 @@ DGL_REGISTER_GLOBAL("sparse._CAPI_DGLKernelGetEdgeMapping")
     *rv = GetEdgeMapping(graph);
   });
 
-/*!
+/**
  * @brief Sparse matrix multiplication with graph interface.
  *
  * @param A_ref The left operand.

src/array/kernel_decl.h

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file array/kernel_decl.h
  * @brief Sparse matrix format-specific operator declarations.
@@ -17,7 +17,7 @@
 namespace dgl {
 namespace aten {
 
-/*!
+/**
  * @brief Generalized Sparse Matrix Dense Matrix Multiplication on Csr format.
  */
 template <int XPU, typename IdType, typename DType>
@@ -29,7 +29,7 @@ void SpMMCsr(const std::string& op, const std::string& reduce,
              NDArray out,
              std::vector<NDArray> out_aux);
 
-/*!
+/**
  * @brief Generalized Sparse Matrix Dense Matrix Multiplication on Csr format
  with heterograph support.
  */
@@ -43,7 +43,7 @@ void SpMMCsrHetero(const std::string& op, const std::string& reduce,
              std::vector<std::vector<NDArray>>* out_aux,
              const std::vector<dgl_type_t>& ufeat_eid,
              const std::vector<dgl_type_t>& out_eid);
-/*!
+/**
  * @brief Generalized Sparse Matrix Dense Matrix Multiplication on Coo format.
  */
 template <int XPU, typename IdType, typename DType>
@@ -55,7 +55,7 @@ void SpMMCoo(const std::string& op, const std::string& reduce,
              NDArray out,
              std::vector<NDArray> out_aux);
 
-/*!
+/**
  * @brief Generalized Sampled Dense-Dense Matrix Multiplication on Csr format.
  */
 template <int XPU, typename IdType, typename DType>
@@ -67,7 +67,7 @@ void SDDMMCsr(const std::string& op,
               NDArray out,
               int lhs_target,
               int rhs_target);
-/*!
+/**
  * @brief Generalized Sampled Dense-Dense Matrix Multiplication on Csr
  format with heterograph support.
   */
@@ -83,7 +83,7 @@ void SDDMMCsrHetero(const std::string& op,
               const std::vector<dgl_type_t>& ufeat_eid,
               const std::vector<dgl_type_t>& out_eid);
 
-/*!
+/**
  * @brief Generalized Sampled Dense-Dense Matrix Multiplication on Coo format.
  */
 template <int XPU, typename IdType, typename DType>
@@ -96,7 +96,7 @@ void SDDMMCoo(const std::string& op,
               int lhs_target,
               int rhs_target);
 
-/*!
+/**
  * @brief Generalized Sampled Dense-Dense Matrix Multiplication on Coo
  format with heterograph support.
   */
@@ -112,7 +112,7 @@ void SDDMMCooHetero(const std::string& op,
               const std::vector<dgl_type_t>& lhs_eid,
               const std::vector<dgl_type_t>& rhs_eid);
 
-/*!
+/**
  * @brief Generalized Dense Matrix-Matrix Multiplication according to relation types.
  */
 template <int XPU, typename IdType, typename DType>
@@ -122,7 +122,7 @@ void GatherMM(const NDArray A,
               const NDArray idx_a,
               const NDArray idx_b);
 
-/*!
+/**
  * @brief Generalized Dense Matrix-Matrix Multiplication according to relation types.
  */
 template <int XPU, typename IdType, typename DType>
@@ -133,7 +133,7 @@ void GatherMMScatter(const NDArray A,
           const NDArray idx_b,
           const NDArray idx_c);
 
-/*!
+/**
  * @brief Generalized segmented dense Matrix-Matrix Multiplication.
  */
 template <int XPU, typename IdType, typename DType>
@@ -149,7 +149,7 @@ void SegmentMMBackwardB(const NDArray A,
                         NDArray dB,
                         const NDArray seglen);
 
-/*!
+/**
  * @brief Segment reduce.
  */
 template <int XPU, typename IdType, typename DType>
@@ -159,7 +159,7 @@ void SegmentReduce(const std::string& op,
                    NDArray out,
                    NDArray arg);
 
-/*!
+/**
  * @brief Scatter Add on first dimension.
  */
 template <int XPU, typename IdType, typename DType>
@@ -167,7 +167,7 @@ void ScatterAdd(NDArray feat,
                 NDArray idx,
                 NDArray out);
 
-/*!
+/**
  * @brief Update gradients for reduce operator max and min on first dimension.
  */
 template <int XPU, typename IdType, typename DType>
@@ -178,7 +178,7 @@ void UpdateGradMinMax_hetero(const HeteroGraphPtr& g,
                 const std::vector<NDArray>& idx_etype,
                 std::vector<NDArray>* out);
 
-/*!
+/**
  * @brief Backward function of segment cmp.
  */
 template <int XPU, typename IdType, typename DType>
@@ -186,7 +186,7 @@ void BackwardSegmentCmp(NDArray feat,
                         NDArray arg,
                         NDArray out);
 
-/*!
+/**
  * @brief Sparse-sparse matrix multiplication
  *
  * @param A The left operand.
@@ -205,7 +205,7 @@ std::pair<CSRMatrix, NDArray> CSRMM(
     const CSRMatrix& B,
     NDArray B_weights);
 
-/*!
+/**
  * @brief Sparse-sparse matrix summation.
  *
  * @param A The sparse matrices with the same size.
@@ -220,7 +220,7 @@ std::pair<CSRMatrix, NDArray> CSRSum(
     const std::vector<CSRMatrix>& A,
     const std::vector<NDArray>& A_weights);
 
-/*!
+/**
  * @brief Edge_softmax_csr forward function on Csr format.
  */
 template <int XPU, typename IdType, typename DType>
@@ -230,7 +230,7 @@ void Edge_softmax_csr_forward(const std::string& op,
              NDArray ufeat,
              NDArray efeat,
              NDArray out);
-/*!
+/**
  * @brief Edge_softmax_csr backward function on Csr format.
  */
 template <int XPU, typename IdType, typename DType>

src/array/libra_partition.cc

-/*
+/**
 Copyright (c) 2021 Intel Corporation
  \file distgnn/partition/main_Libra.py
  \brief Libra - Vertex-cut based graph partitioner for distirbuted training
@@ -42,7 +42,7 @@ int32_t Ver2partition(IdType in_val, int64_t *node_map, int32_t num_parts) {
   LOG(FATAL) << "Error: Unexpected output in Ver2partition!";
 }
 
-/*!
+/**
  * @brief Identifies the lead loaded partition/community for a given edge
  * assignment.
  */
@@ -65,7 +65,7 @@ int32_t LeastLoad(int64_t *community_edges, int32_t nc) {
   return loc[r];
 }
 
-/*!
+/**
  * @brief Libra - vertexcut based graph partitioning.
  * It takes list of edges from input DGL graph and distributed them among nc
  * partitions During edge distribution, Libra assign a given edge to a partition
@@ -324,7 +324,7 @@ DGL_REGISTER_GLOBAL("sparse._CAPI_DGLLibraVertexCut")
       });
     });
 
-/*!
+/**
  * @brief
  * 1. Builds dictionary (ldt) for assigning local node IDs to nodes in the
  *    partitions
@@ -456,7 +456,7 @@ DGL_REGISTER_GLOBAL("sparse._CAPI_DGLLibra2dglBuildDict")
       *rv = ret;
     });
 
-/*!
+/**
  * @brief sets up the 1-level tree among the clones of the split-nodes.
  * @param[in] gdt_key global dict for assigning consecutive node IDs to nodes
  *            across all the partitions
@@ -506,7 +506,7 @@ DGL_REGISTER_GLOBAL("sparse._CAPI_DGLLibra2dglSetLR")
       Libra2dglSetLR(gdt_key, gdt_value, lrtensor, nc, Nn);
     });
 
-/*!
+/**
  * @brief For each node in a partition, it creates a list of remote clone IDs;
  *        also, for each node in a partition, it gathers the data (feats, label,
  *        trian, test) from input graph.

src/array/selector.h

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file array/selector.h
  * @brief Selector functions to select among src/edge/dst attributes.
@@ -22,7 +22,7 @@ namespace {
 
 }  // namespace
 
-/*!
+/**
  * @brief Select among src/edge/dst feature/idx.
  * @note the integer argument target specifies which target
  *       to choose, 0: src, 1: edge, 2: dst.

src/array/union_partition.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file array/cpu/coo_union_partition.cc
  * @brief COO union and partition

src/array/uvm_array.cc

-/*!
+/**
  *  Copyright (c) 2019-2022 by Contributors
  * @file array/uvm_array.cc
  * @brief DGL array utilities implementation

src/array/uvm_array_op.h

-/*!
+/**
  *  Copyright (c) 2019-2022 by Contributors
  * @file array/uvm_array_op.h
  * @brief Array operator templates

src/bcast.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file kernel/bcast.h
  * @brief Broadcast related function implementations.
@@ -11,7 +11,7 @@
 namespace dgl {
 
 namespace {
-/*!
+/**
  * @brief Determine whether use broadcasting or not, given the operator
  *        type, lhs array and rhs array.
  */
@@ -27,7 +27,7 @@ bool UseBcast(const std::string& op, NDArray lhs, NDArray rhs) {
 
 }  // namespace
 
-/*!
+/**
  * @brief: Compute broadcast and auxiliary information given operator
  *         and operands for kernel computation.
  * @note: Expect lhs, rhs to have ndim >= 2 and the shape of lhs/rhs

src/c_api_common.cc

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file c_runtime_api.cc
  * @brief DGL C API common implementations

src/c_api_common.h

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file c_api_common.h
  * @brief DGL C API common util functions
@@ -25,13 +25,13 @@ typedef void* CommunicatorHandle;
 // KVstore message handler type
 typedef void* KVMsgHandle;
 
-/*!
+/**
  * @brief Convert a vector of NDArray to PackedFunc.
  */
 dgl::runtime::PackedFunc ConvertNDArrayVectorToPackedFunc(
     const std::vector<dgl::runtime::NDArray>& vec);
 
-/*!
+/**
  * @brief Copy a vector to an NDArray.
  *
  * The data type of the NDArray will be IdType, which must be an integer type.

src/geometry/cpu/geometry_op_impl.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file array/cpu/geometry_op_impl.cc
  * @brief Geometry operator CPU implementation
@@ -16,7 +16,7 @@ using runtime::NDArray;
 namespace geometry {
 namespace impl {
 
-/*! @brief Knuth shuffle algorithm */
+/** @brief Knuth shuffle algorithm */
 template <typename IdType>
 void IndexShuffle(IdType *idxs, int64_t num_elems) {
   for (int64_t i = num_elems - 1; i > 0; --i) {
@@ -27,7 +27,7 @@ void IndexShuffle(IdType *idxs, int64_t num_elems) {
 template void IndexShuffle<int32_t>(int32_t *idxs, int64_t num_elems);
 template void IndexShuffle<int64_t>(int64_t *idxs, int64_t num_elems);
 
-/*! @brief Groupwise index shuffle algorithm. This function will perform shuffle
+/** @brief Groupwise index shuffle algorithm. This function will perform shuffle
  * in subarrays indicated by group index. The group index is similar to indptr
  * in CSRMatrix.
  *
@@ -76,7 +76,7 @@ IdArray GroupRandomPerm(
   return perm;
 }
 
-/*!
+/**
  * @brief Farthest Point Sampler without the need to compute all pairs of
  * distance.
  *

src/geometry/cuda/edge_coarsening_impl.cu

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file geometry/cuda/edge_coarsening_impl.cu
  * @brief Edge coarsening CUDA implementation
@@ -109,7 +109,7 @@ __global__ void weighted_respond_kernel(
   }
 }
 
-/*! @brief The colorize procedure. This procedure randomly marks unmarked
+/** @brief The colorize procedure. This procedure randomly marks unmarked
  * nodes with BLUE(-1) and RED(-2) and checks whether the node matching
  * process has finished.
  */
@@ -137,7 +137,7 @@ bool Colorize(IdType *result_data, int64_t num_nodes, float *const prop) {
   return done_h;
 }
 
-/*! @brief Weighted neighbor matching procedure (GPU version).
+/** @brief Weighted neighbor matching procedure (GPU version).
  * This implementation is from `A GPU Algorithm for Greedy Graph Matching
  * <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__
  *
@@ -198,7 +198,7 @@ template void WeightedNeighborMatching<kDGLCUDA, double, int32_t>(
 template void WeightedNeighborMatching<kDGLCUDA, double, int64_t>(
     const aten::CSRMatrix &csr, const NDArray weight, IdArray result);
 
-/*! @brief Unweighted neighbor matching procedure (GPU version).
+/** @brief Unweighted neighbor matching procedure (GPU version).
  * Instead of directly sample neighbors, we assign each neighbor
  * with a random weight. We use random weight for 2 reasons:
  *  1. Random sample for each node in GPU is expensive. Although

src/geometry/cuda/geometry_op_impl.cu

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file geometry/cuda/geometry_op_impl.cc
  * @brief Geometry operator CUDA implementation
@@ -15,7 +15,7 @@ namespace dgl {
 namespace geometry {
 namespace impl {
 
-/*!
+/**
  * @brief Farthest Point Sampler without the need to compute all pairs of
  * distance.
  *

src/geometry/geometry.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file geometry/geometry.cc
  * @brief DGL geometry utilities implementation

src/geometry/geometry_op.h

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file geometry/geometry_op.h
  * @brief Geometry operator templates
@@ -17,7 +17,7 @@ void FarthestPointSampler(
     NDArray array, int64_t batch_size, int64_t sample_points, NDArray dist,
     IdArray start_idx, IdArray result);
 
-/*! @brief Implementation of weighted neighbor matching process of edge
+/** @brief Implementation of weighted neighbor matching process of edge
  * coarsening used in Metis and Graclus for homogeneous graph coarsening. This
  * procedure keeps picking an unmarked vertex and matching it with one its
  * unmarked neighbors (that maximizes its edge weight) until no match can be
@@ -27,7 +27,7 @@ template <DGLDeviceType XPU, typename FloatType, typename IdType>
 void WeightedNeighborMatching(
     const aten::CSRMatrix &csr, const NDArray weight, IdArray result);
 
-/*! @brief Implementation of neighbor matching process of edge coarsening used
+/** @brief Implementation of neighbor matching process of edge coarsening used
  * in Metis and Graclus for homogeneous graph coarsening. This procedure keeps
  * picking an unmarked vertex and matching it with one its unmarked neighbors
  * (that maximizes its edge weight) until no match can be done.

src/graph/creators.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/creators.cc
  * @brief Functions for constructing graphs.

src/graph/gk_ops.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/gk_ops.cc
  * @brief Graph operation implemented in GKlib
@@ -14,7 +14,7 @@ namespace dgl {
 
 #if !defined(_WIN32)
 
-/*!
+/**
  * Convert DGL CSR to GKLib CSR.
  * GKLib CSR actually stores a CSR object and a CSC object of a graph.
  * @param mat the DGL CSR matrix.
@@ -60,7 +60,7 @@ gk_csr_t *Convert2GKCsr(const aten::CSRMatrix mat, bool is_row) {
   return gk_csr;
 }
 
-/*!
+/**
  * Convert GKLib CSR to DGL CSR.
  * GKLib CSR actually stores a CSR object and a CSC object of a graph.
  * @param gk_csr the GKLib CSR.

src/graph/graph.cc

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file graph/graph.cc
  * @brief DGL graph index implementation

src/graph/graph_apis.cc

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file graph/graph.cc
  * @brief DGL graph index APIs