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

* replace

* blabla

* balbla

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

src/graph/transform/compact.cc

-/*!
+/**
  *  Copyright 2019-2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/compact.h

-/*!
+/**
  *  Copyright 2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/cpu/kdtree_ndarray_adapter.h

-/*!
+/**
  *  Copyright (c) 2021 by Contributors
  * @file graph/transform/cpu/kdtree_ndarray_adapter.h
  * @brief NDArray adapter for nanoflann, without
@@ -18,7 +18,7 @@ namespace dgl {
 namespace transform {
 namespace knn_utils {
 
-/*!
+/**
  * @brief A simple 2D NDArray adapter for nanoflann, without duplicating the
  *        storage.
  *
@@ -65,7 +65,7 @@ class KDTreeNDArrayAdapter {
 
   index_type* GetIndex() { return index_; }
 
-  /*!
+  /**
    * @brief Query for the \a num_closest points to a given point
    *  Note that this is a short-cut method for GetIndex()->findNeighbors().
    */
@@ -77,19 +77,19 @@ class KDTreeNDArrayAdapter {
     index_->findNeighbors(resultSet, query_pt, nanoflann::SearchParams());
   }
 
-  /*! @brief Interface expected by KDTreeSingleIndexAdaptor */
+  /** @brief Interface expected by KDTreeSingleIndexAdaptor */
   const self_type& derived() const { return *this; }
 
-  /*! @brief Interface expected by KDTreeSingleIndexAdaptor */
+  /** @brief Interface expected by KDTreeSingleIndexAdaptor */
   self_type& derived() { return *this; }
 
-  /*!
+  /**
    * @brief Interface expected by KDTreeSingleIndexAdaptor,
    *  return the number of data points
    */
   size_t kdtree_get_point_count() const { return data_->shape[0]; }
 
-  /*!
+  /**
    * @brief Interface expected by KDTreeSingleIndexAdaptor,
    *  return the dim'th component of the idx'th point
    */
@@ -97,7 +97,7 @@ class KDTreeNDArrayAdapter {
     return data_.Ptr<FloatType>()[idx * data_->shape[1] + dim];
   }
 
-  /*!
+  /**
    * @brief Interface expected by KDTreeSingleIndexAdaptor.
    *  Optional bounding-box computation: return false to
    *  default to a standard bbox computation loop.

src/graph/transform/cpu/knn.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file graph/transform/cpu/knn.cc
  * @brief k-nearest-neighbor (KNN) implementation
@@ -27,7 +27,7 @@ namespace impl {
 // This value is directly from pynndescent
 static constexpr int NN_DESCENT_BLOCK_SIZE = 16384;
 
-/*!
+/**
  * @brief Compute Euclidean distance between two vectors, return positive
  *  infinite value if the intermediate distance is greater than the worst
  *  distance.
@@ -54,7 +54,7 @@ FloatType EuclideanDistWithCheck(
   }
 }
 
-/*! @brief Compute Euclidean distance between two vectors */
+/** @brief Compute Euclidean distance between two vectors */
 template <typename FloatType, typename IdType>
 FloatType EuclideanDist(
     const FloatType* vec1, const FloatType* vec2, int64_t dim) {
@@ -67,7 +67,7 @@ FloatType EuclideanDist(
   return dist;
 }
 
-/*! @brief Insert a new element into a heap */
+/** @brief Insert a new element into a heap */
 template <typename FloatType, typename IdType>
 void HeapInsert(
     IdType* out, FloatType* dist, IdType new_id, FloatType new_dist, int k,
@@ -104,7 +104,7 @@ void HeapInsert(
   }
 }
 
-/*! @brief Insert a new element and its flag into heap, return 1 if insert
+/** @brief Insert a new element and its flag into heap, return 1 if insert
  * successfully */
 template <typename FloatType, typename IdType>
 int FlaggedHeapInsert(
@@ -144,7 +144,7 @@ int FlaggedHeapInsert(
   return 1;
 }
 
-/*! @brief Build heap for each point. Used by NN-descent */
+/** @brief Build heap for each point. Used by NN-descent */
 template <typename FloatType, typename IdType>
 void BuildHeap(IdType* index, FloatType* dist, int k) {
   for (int i = k / 2 - 1; i >= 0; --i) {
@@ -170,7 +170,7 @@ void BuildHeap(IdType* index, FloatType* dist, int k) {
   }
 }
 
-/*!
+/**
  * @brief Neighbor update process in NN-descent. The distance between
  *  two points are computed. If this new distance is less than any worst
  *  distance of these two points, we update the neighborhood of that point.
@@ -208,7 +208,7 @@ int UpdateNeighbors(
   return num_updates;
 }
 
-/*! @brief The kd-tree implementation of K-Nearest Neighbors */
+/** @brief The kd-tree implementation of K-Nearest Neighbors */
 template <typename FloatType, typename IdType>
 void KdTreeKNN(
     const NDArray& data_points, const IdArray& data_offsets,

src/graph/transform/cuda/cuda_compact_graph.cu

-/*!
+/**
  *  Copyright 2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/cuda/cuda_map_edges.cuh

-/*!
+/**
  *  Copyright 2020-2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/cuda/cuda_to_block.cu

-/*!
+/**
  *  Copyright 2020-2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/cuda/knn.cu

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/transform/cuda/knn.cu
  * @brief k-nearest-neighbor (KNN) implementation (cuda)
@@ -22,7 +22,7 @@
 namespace dgl {
 namespace transform {
 namespace impl {
-/*!
+/**
  * @brief Utility class used to avoid linker errors with extern
  *  unsized shared memory arrays with templated type
  */
@@ -54,7 +54,7 @@ struct SharedMemory<double> {
   }
 };
 
-/*! @brief Compute Euclidean distance between two vectors in a cuda kernel */
+/** @brief Compute Euclidean distance between two vectors in a cuda kernel */
 template <typename FloatType, typename IdType>
 __device__ FloatType
 EuclideanDist(const FloatType* vec1, const FloatType* vec2, const int64_t dim) {
@@ -77,7 +77,7 @@ EuclideanDist(const FloatType* vec1, const FloatType* vec2, const int64_t dim) {
   return dist;
 }
 
-/*!
+/**
  * @brief Compute Euclidean distance between two vectors in a cuda kernel,
  *  return positive infinite value if the intermediate distance is greater
  *  than the worst distance.
@@ -238,7 +238,7 @@ __device__ bool FlaggedHeapInsert(
   return true;
 }
 
-/*!
+/**
  * @brief Brute force kNN kernel. Compute distance for each pair of input points
  * and get the result directly (without a distance matrix).
  */
@@ -278,7 +278,7 @@ __global__ void BruteforceKnnKernel(
   }
 }
 
-/*!
+/**
  * @brief Same as BruteforceKnnKernel, but use shared memory as buffer.
  *  This kernel divides query points and data points into blocks. For each
  *  query block, it will make a loop over all data blocks and compute distances.
@@ -400,7 +400,7 @@ __global__ void BruteforceKnnShareKernel(
   }
 }
 
-/*! @brief determine the number of blocks for each segment */
+/** @brief determine the number of blocks for each segment */
 template <typename IdType>
 __global__ void GetNumBlockPerSegment(
     const IdType* offsets, IdType* out, const int64_t batch_size,
@@ -411,7 +411,7 @@ __global__ void GetNumBlockPerSegment(
   }
 }
 
-/*! @brief Get the batch index and local index in segment for each block */
+/** @brief Get the batch index and local index in segment for each block */
 template <typename IdType>
 __global__ void GetBlockInfo(
     const IdType* num_block_prefixsum, IdType* block_batch_id,
@@ -429,7 +429,7 @@ __global__ void GetBlockInfo(
   }
 }
 
-/*!
+/**
  * @brief Brute force kNN. Compute distance for each pair of input points and
  * get the result directly (without a distance matrix).
  *
@@ -472,7 +472,7 @@ void BruteForceKNNCuda(
   device->FreeWorkspace(ctx, dists);
 }
 
-/*!
+/**
  * @brief Brute force kNN with shared memory.
  *  This function divides query points and data points into blocks. For each
  *  query block, it will make a loop over all data blocks and compute distances.
@@ -575,7 +575,7 @@ void BruteForceKNNSharedCuda(
   device->FreeWorkspace(ctx, block_batch_id);
 }
 
-/*! @brief Setup rng state for nn-descent */
+/** @brief Setup rng state for nn-descent */
 __global__ void SetupRngKernel(
     curandState* states, const uint64_t seed, const size_t n) {
   size_t id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -584,7 +584,7 @@ __global__ void SetupRngKernel(
   }
 }
 
-/*!
+/**
  * @brief Randomly initialize neighbors (sampling without replacement)
  * for each nodes
  */
@@ -636,7 +636,7 @@ __global__ void RandomInitNeighborsKernel(
   BuildHeap<FloatType, IdType>(neighbors + point_idx * k, current_dists, k);
 }
 
-/*!
+/**
  * @brief Randomly select candidates from current knn and reverse-knn graph for
  *        nn-descent.
  */
@@ -735,7 +735,7 @@ __global__ void FindCandidatesKernel(
   }
 }
 
-/*! @brief Update knn graph according to selected candidates for nn-descent */
+/** @brief Update knn graph according to selected candidates for nn-descent */
 template <typename FloatType, typename IdType>
 __global__ void UpdateNeighborsKernel(
     const FloatType* points, const IdType* offsets, IdType* neighbors,

src/graph/transform/knn.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file graph/transform/knn.cc
  * @brief k-nearest-neighbor (KNN) interface

src/graph/transform/knn.h

-/*!
+/**
  *  Copyright (c) 2021 by Contributors
  * @file graph/transform/knn.h
  * @brief k-nearest-neighbor (KNN) implementation
@@ -14,7 +14,7 @@
 namespace dgl {
 namespace transform {
 
-/*!
+/**
  * @brief For each point in each segment in \a query_points, find \a k nearest
  *        points in the same segment in \a data_points. \a data_offsets and \a
  *        query_offsets determine the start index of each segment in \a
@@ -35,7 +35,7 @@ void KNN(
     const NDArray& query_points, const IdArray& query_offsets, const int k,
     IdArray result, const std::string& algorithm);
 
-/*!
+/**
  * @brief For each input point, find \a k approximate nearest points in the same
  *        segment using NN-descent algorithm.
  *

src/graph/transform/line_graph.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/transform/line_graph.cc
  * @brief Line graph implementation
@@ -22,7 +22,7 @@ using namespace dgl::aten;
 
 namespace transform {
 
-/*!
+/**
  * @brief Create Line Graph.
  * @param hg Graph.
  * @param backtracking whether the pair of (v, u) (u, v) edges are treated as

src/graph/transform/metis_partition_hetero.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/metis_partition.cc
  * @brief Call Metis partitioning

src/graph/transform/partition_hetero.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/metis_partition.cc
  * @brief Call Metis partitioning

src/graph/transform/remove_edges.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file graph/transform/remove_edges.cc
  * @brief Remove edges.

src/graph/transform/to_bipartite.cc

-/*!
+/**
  *  Copyright 2019-2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/to_bipartite.h

-/*!
+/**
  *  Copyright 2021 Contributors
  *
  *  Licensed under the Apache License, Version 2.0 (the "License");

src/graph/transform/to_simple.cc

-/*!
+/**
  *  Copyright (c) 2019 by Contributors
  * @file graph/transform/to_simple.cc
  * @brief Convert multigraphs to simple graphs

src/graph/transform/union_partition.cc

-/*!
+/**
  *  Copyright (c) 2020 by Contributors
  * @file graph/transform/union_partition.cc
  * @brief Functions for partition, union multiple graphs.

src/graph/traversal.cc

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file graph/traversal.cc
  * @brief Graph traversal implementation
@@ -88,7 +88,7 @@ IdArray ComputeMergedSections(const std::vector<std::vector<DType>>& traces) {
 
 }  // namespace
 
-/*!
+/**
  * @brief Class for representing frontiers.
  *
  * Each frontier is a list of nodes/edges (specified by their ids).
@@ -96,13 +96,13 @@ IdArray ComputeMergedSections(const std::vector<std::vector<DType>>& traces) {
  * value).
  */
 struct Frontiers {
-  /*!\brief a vector store for the nodes/edges in all the frontiers */
+  /** @brief a vector store for the nodes/edges in all the frontiers */
   std::vector<dgl_id_t> ids;
 
-  /*!\brief a vector store for node/edge tags. Empty if no tags are requested */
+  /** @brief a vector store for node/edge tags. Empty if no tags are requested */
   std::vector<int64_t> tags;
 
-  /*!\brief a section vector to indicate each frontier */
+  /** @brief a section vector to indicate each frontier */
   std::vector<int64_t> sections;
 };
 

src/graph/traversal.h

-/*!
+/**
  *  Copyright (c) 2018 by Contributors
  * @file graph/traversal.h
  * @brief Graph traversal routines.
@@ -20,7 +20,7 @@
 namespace dgl {
 namespace traverse {
 
-/*!
+/**
  * @brief Traverse the graph in a breadth-first-search (BFS) order.
  *
  * The queue object must suffice following interface:
@@ -81,7 +81,7 @@ void BFSNodes(
   }
 }
 
-/*!
+/**
  * @brief Traverse the graph in a breadth-first-search (BFS) order, returning
  *        the edges of the BFS tree.
  *
@@ -145,7 +145,7 @@ void BFSEdges(
   }
 }
 
-/*!
+/**
  * @brief Traverse the graph in topological order.
  *
  * The queue object must suffice following interface:
@@ -212,13 +212,13 @@ void TopologicalNodes(
   }
 }
 
-/*!\brief Tags for ``DFSEdges``. */
+/** @brief Tags for ``DFSEdges``. */
 enum DFSEdgeTag {
   kForward = 0,
   kReverse,
   kNonTree,
 };
-/*!
+/**
  * @brief Traverse the graph in a depth-first-search (DFS) order.
  *
  * The traversal visit edges in its DFS order. Edges have three tags: