support get_edge_feature

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -820,6 +820,12 @@ void bindGraphData(py::module *m) {
            THROW_IF_ERROR(g.GetNodeFeature(node_list, feature_types, &out));
            return out.getRow();
          })
+    .def("get_edge_feature",
+         [](gnn::Graph &g, std::shared_ptr<Tensor> edge_list, std::vector<gnn::FeatureType> feature_types) {
+           TensorRow out;
+           THROW_IF_ERROR(g.GetEdgeFeature(edge_list, feature_types, &out));
+           return out.getRow();
+         })
     .def("graph_info",
          [](gnn::Graph &g) {
            py::dict out;

mindspore/ccsrc/dataset/engine/gnn/graph.cc

@@ -125,13 +125,8 @@ Status Graph::GetNodesFromEdges(const std::vector<EdgeIdType> &edge_list, std::s
 
 Status Graph::GetAllNeighbors(const std::vector<NodeIdType> &node_list, NodeType neighbor_type,
                               std::shared_ptr<Tensor> *out) {
-  if (node_list.empty()) {
-    RETURN_STATUS_UNEXPECTED("Input node_list is empty.");
-  }
-  if (node_type_map_.find(neighbor_type) == node_type_map_.end()) {
-    std::string err_msg = "Invalid neighbor type:" + std::to_string(neighbor_type);
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
+  RETURN_IF_NOT_OK(CheckNeighborType(neighbor_type));
 
   std::vector<std::vector<NodeIdType>> neighbors;
   size_t max_neighbor_num = 0;
@@ -161,6 +156,14 @@ Status Graph::CheckSamplesNum(NodeIdType samples_num) {
   return Status::OK();
 }
 
+Status Graph::CheckNeighborType(NodeType neighbor_type) {
+  if (node_type_map_.find(neighbor_type) == node_type_map_.end()) {
+    std::string err_msg = "Invalid neighbor type:" + std::to_string(neighbor_type);
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  }
+  return Status::OK();
+}
+
 Status Graph::GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
                                   const std::vector<NodeIdType> &neighbor_nums,
                                   const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) {
@@ -171,10 +174,7 @@ Status Graph::GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
     RETURN_IF_NOT_OK(CheckSamplesNum(num));
   }
   for (const auto &type : neighbor_types) {
-    if (node_type_map_.find(type) == node_type_map_.end()) {
-      std::string err_msg = "Invalid neighbor type:" + std::to_string(type);
-      RETURN_STATUS_UNEXPECTED(err_msg);
-    }
+    RETURN_IF_NOT_OK(CheckNeighborType(type));
   }
   std::vector<std::vector<NodeIdType>> neighbors_vec(node_list.size());
   for (size_t node_idx = 0; node_idx < node_list.size(); ++node_idx) {
@@ -228,44 +228,36 @@ Status Graph::GetNegSampledNeighbors(const std::vector<NodeIdType> &node_list, N
                                      NodeType neg_neighbor_type, std::shared_ptr<Tensor> *out) {
   CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
   RETURN_IF_NOT_OK(CheckSamplesNum(samples_num));
-  if (node_type_map_.find(neg_neighbor_type) == node_type_map_.end()) {
-    std::string err_msg = "Invalid neighbor type:" + std::to_string(neg_neighbor_type);
-    RETURN_STATUS_UNEXPECTED(err_msg);
-  }
+  RETURN_IF_NOT_OK(CheckNeighborType(neg_neighbor_type));
 
-  std::vector<std::vector<NodeIdType>> neighbors_vec;
-  neighbors_vec.resize(node_list.size());
+  std::vector<std::vector<NodeIdType>> neg_neighbors_vec;
+  neg_neighbors_vec.resize(node_list.size());
   for (size_t node_idx = 0; node_idx < node_list.size(); ++node_idx) {
     std::shared_ptr<Node> node;
     RETURN_IF_NOT_OK(GetNodeByNodeId(node_list[node_idx], &node));
     std::vector<NodeIdType> neighbors;
     RETURN_IF_NOT_OK(node->GetAllNeighbors(neg_neighbor_type, &neighbors));
-    std::unordered_set<NodeIdType> exclude_node;
+    std::unordered_set<NodeIdType> exclude_nodes;
     std::transform(neighbors.begin(), neighbors.end(),
-                   std::insert_iterator<std::unordered_set<NodeIdType>>(exclude_node, exclude_node.begin()),
+                   std::insert_iterator<std::unordered_set<NodeIdType>>(exclude_nodes, exclude_nodes.begin()),
                    [](const NodeIdType node) { return node; });
-    auto itr = node_type_map_.find(neg_neighbor_type);
-    if (itr == node_type_map_.end()) {
-      std::string err_msg = "Invalid node type:" + std::to_string(neg_neighbor_type);
-      RETURN_STATUS_UNEXPECTED(err_msg);
+    const std::vector<NodeIdType> &all_nodes = node_type_map_[neg_neighbor_type];
+    neg_neighbors_vec[node_idx].emplace_back(node->id());
+    if (all_nodes.size() > exclude_nodes.size()) {
+      while (neg_neighbors_vec[node_idx].size() < samples_num + 1) {
+        RETURN_IF_NOT_OK(NegativeSample(all_nodes, exclude_nodes, samples_num - neg_neighbors_vec[node_idx].size(),
+                                        &neg_neighbors_vec[node_idx]));
+      }
     } else {
-      neighbors_vec[node_idx].emplace_back(node->id());
-      if (itr->second.size() > exclude_node.size()) {
-        while (neighbors_vec[node_idx].size() < samples_num + 1) {
-          RETURN_IF_NOT_OK(NegativeSample(itr->second, exclude_node, samples_num - neighbors_vec[node_idx].size(),
-                                          &neighbors_vec[node_idx]));
-        }
-      } else {
-        MS_LOG(DEBUG) << "There are no negative neighbors. node_id:" << node->id()
-                      << " neg_neighbor_type:" << neg_neighbor_type;
-        // If there are no negative neighbors, they are filled with kDefaultNodeId
-        for (int32_t i = 0; i < samples_num; ++i) {
-          neighbors_vec[node_idx].emplace_back(kDefaultNodeId);
-        }
+      MS_LOG(DEBUG) << "There are no negative neighbors. node_id:" << node->id()
+                    << " neg_neighbor_type:" << neg_neighbor_type;
+      // If there are no negative neighbors, they are filled with kDefaultNodeId
+      for (int32_t i = 0; i < samples_num; ++i) {
+        neg_neighbors_vec[node_idx].emplace_back(kDefaultNodeId);
       }
     }
   }
-  RETURN_IF_NOT_OK(CreateTensorByVector<NodeIdType>(neighbors_vec, DataType(DataType::DE_INT32), out));
+  RETURN_IF_NOT_OK(CreateTensorByVector<NodeIdType>(neg_neighbors_vec, DataType(DataType::DE_INT32), out));
   return Status::OK();
 }
 
@@ -280,8 +272,19 @@ Status Graph::RandomWalk(const std::vector<NodeIdType> &node_list, const std::ve
 }
 
 Status Graph::GetNodeDefaultFeature(FeatureType feature_type, std::shared_ptr<Feature> *out_feature) {
-  auto itr = default_feature_map_.find(feature_type);
-  if (itr == default_feature_map_.end()) {
+  auto itr = default_node_feature_map_.find(feature_type);
+  if (itr == default_node_feature_map_.end()) {
+    std::string err_msg = "Invalid feature type:" + std::to_string(feature_type);
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  } else {
+    *out_feature = itr->second;
+  }
+  return Status::OK();
+}
+
+Status Graph::GetEdgeDefaultFeature(FeatureType feature_type, std::shared_ptr<Feature> *out_feature) {
+  auto itr = default_edge_feature_map_.find(feature_type);
+  if (itr == default_edge_feature_map_.end()) {
     std::string err_msg = "Invalid feature type:" + std::to_string(feature_type);
     RETURN_STATUS_UNEXPECTED(err_msg);
   } else {
@@ -295,7 +298,7 @@ Status Graph::GetNodeFeature(const std::shared_ptr<Tensor> &nodes, const std::ve
   if (!nodes || nodes->Size() == 0) {
     RETURN_STATUS_UNEXPECTED("Input nodes is empty");
   }
-  CHECK_FAIL_RETURN_UNEXPECTED(!feature_types.empty(), "Inpude feature_types is empty");
+  CHECK_FAIL_RETURN_UNEXPECTED(!feature_types.empty(), "Input feature_types is empty");
   TensorRow tensors;
   for (const auto &f_type : feature_types) {
     std::shared_ptr<Feature> default_feature;
@@ -340,6 +343,45 @@ Status Graph::GetNodeFeature(const std::shared_ptr<Tensor> &nodes, const std::ve
 
 Status Graph::GetEdgeFeature(const std::shared_ptr<Tensor> &edges, const std::vector<FeatureType> &feature_types,
                              TensorRow *out) {
+  if (!edges || edges->Size() == 0) {
+    RETURN_STATUS_UNEXPECTED("Input edges is empty");
+  }
+  CHECK_FAIL_RETURN_UNEXPECTED(!feature_types.empty(), "Input feature_types is empty");
+  TensorRow tensors;
+  for (const auto &f_type : feature_types) {
+    std::shared_ptr<Feature> default_feature;
+    // If no feature can be obtained, fill in the default value
+    RETURN_IF_NOT_OK(GetEdgeDefaultFeature(f_type, &default_feature));
+
+    TensorShape shape(default_feature->Value()->shape());
+    auto shape_vec = edges->shape().AsVector();
+    dsize_t size = std::accumulate(shape_vec.begin(), shape_vec.end(), 1, std::multiplies<dsize_t>());
+    shape = shape.PrependDim(size);
+    std::shared_ptr<Tensor> fea_tensor;
+    RETURN_IF_NOT_OK(
+      Tensor::CreateTensor(&fea_tensor, TensorImpl::kFlexible, shape, default_feature->Value()->type(), nullptr));
+
+    dsize_t index = 0;
+    for (auto edge_itr = edges->begin<EdgeIdType>(); edge_itr != edges->end<EdgeIdType>(); ++edge_itr) {
+      std::shared_ptr<Edge> edge;
+      RETURN_IF_NOT_OK(GetEdgeByEdgeId(*edge_itr, &edge));
+      std::shared_ptr<Feature> feature;
+      if (!edge->GetFeatures(f_type, &feature).IsOk()) {
+        feature = default_feature;
+      }
+      RETURN_IF_NOT_OK(fea_tensor->InsertTensor({index}, feature->Value()));
+      index++;
+    }
+
+    TensorShape reshape(edges->shape());
+    for (auto s : default_feature->Value()->shape().AsVector()) {
+      reshape = reshape.AppendDim(s);
+    }
+    RETURN_IF_NOT_OK(fea_tensor->Reshape(reshape));
+    fea_tensor->Squeeze();
+    tensors.push_back(fea_tensor);
+  }
+  *out = std::move(tensors);
   return Status::OK();
 }
 
@@ -405,7 +447,8 @@ Status Graph::LoadNodeAndEdge() {
   RETURN_IF_NOT_OK(gl.InitAndLoad());
   // get all maps
   RETURN_IF_NOT_OK(gl.GetNodesAndEdges(&node_id_map_, &edge_id_map_, &node_type_map_, &edge_type_map_,
-                                       &node_feature_map_, &edge_feature_map_, &default_feature_map_));
+                                       &node_feature_map_, &edge_feature_map_, &default_node_feature_map_,
+                                       &default_edge_feature_map_));
   return Status::OK();
 }
 
@@ -420,18 +463,33 @@ Status Graph::GetNodeByNodeId(NodeIdType id, std::shared_ptr<Node> *node) {
   return Status::OK();
 }
 
+Status Graph::GetEdgeByEdgeId(EdgeIdType id, std::shared_ptr<Edge> *edge) {
+  auto itr = edge_id_map_.find(id);
+  if (itr == edge_id_map_.end()) {
+    std::string err_msg = "Invalid edge id:" + std::to_string(id);
+    RETURN_STATUS_UNEXPECTED(err_msg);
+  } else {
+    *edge = itr->second;
+  }
+  return Status::OK();
+}
+
 Graph::RandomWalkBase::RandomWalkBase(Graph *graph)
     : graph_(graph), step_home_param_(1.0), step_away_param_(1.0), default_node_(-1), num_walks_(1), num_workers_(1) {}
 
 Status Graph::RandomWalkBase::Build(const std::vector<NodeIdType> &node_list, const std::vector<NodeType> &meta_path,
                                     float step_home_param, float step_away_param, const NodeIdType default_node,
                                     int32_t num_walks, int32_t num_workers) {
+  CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
   node_list_ = node_list;
   if (meta_path.empty() || meta_path.size() > kMaxNumWalks) {
     std::string err_msg = "Failed, meta path required between 1 and " + std::to_string(kMaxNumWalks) +
                           ". The size of input path is " + std::to_string(meta_path.size());
     RETURN_STATUS_UNEXPECTED(err_msg);
   }
+  for (const auto &type : meta_path) {
+    RETURN_IF_NOT_OK(graph_->CheckNeighborType(type));
+  }
   meta_path_ = meta_path;
   if (step_home_param < kGnnEpsilon || step_away_param < kGnnEpsilon) {
     std::string err_msg = "Failed, step_home_param and step_away_param required greater than " +
@@ -500,15 +558,10 @@ Status Graph::RandomWalkBase::Node2vecWalk(const NodeIdType &start_node, std::ve
 }
 
 Status Graph::RandomWalkBase::SimulateWalk(std::vector<std::vector<NodeIdType>> *walks) {
-  // Repeatedly simulate random walks from each node
-  std::vector<uint32_t> permutation(node_list_.size());
-  std::iota(permutation.begin(), permutation.end(), 0);
   for (int32_t i = 0; i < num_walks_; i++) {
-    unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();
-    std::shuffle(permutation.begin(), permutation.end(), std::default_random_engine(seed));
-    for (const auto &i_perm : permutation) {
+    for (const auto &node : node_list_) {
       std::vector<NodeIdType> walk;
-      RETURN_IF_NOT_OK(Node2vecWalk(node_list_[i_perm], &walk));
+      RETURN_IF_NOT_OK(Node2vecWalk(node, &walk));
       walks->push_back(walk);
     }
   }

mindspore/ccsrc/dataset/engine/gnn/graph.h

@@ -211,12 +211,24 @@ class Graph {
   // @return Status - The error code return
   Status GetNodeDefaultFeature(FeatureType feature_type, std::shared_ptr<Feature> *out_feature);
 
+  // Get the default feature of a edge
+  // @param FeatureType feature_type -
+  // @param std::shared_ptr<Feature> *out_feature - Returned feature
+  // @return Status - The error code return
+  Status GetEdgeDefaultFeature(FeatureType feature_type, std::shared_ptr<Feature> *out_feature);
+
   // Find node object using node id
   // @param NodeIdType id -
   // @param std::shared_ptr<Node> *node - Returned node object
   // @return Status - The error code return
   Status GetNodeByNodeId(NodeIdType id, std::shared_ptr<Node> *node);
 
+  // Find edge object using edge id
+  // @param EdgeIdType id -
+  // @param std::shared_ptr<Node> *edge - Returned edge object
+  // @return Status - The error code return
+  Status GetEdgeByEdgeId(EdgeIdType id, std::shared_ptr<Edge> *edge);
+
   // Negative sampling
   // @param std::vector<NodeIdType> &input_data - The data set to be sampled
   // @param std::unordered_set<NodeIdType> &exclude_data - Data to be excluded
@@ -228,6 +240,8 @@ class Graph {
 
   Status CheckSamplesNum(NodeIdType samples_num);
 
+  Status CheckNeighborType(NodeType neighbor_type);
+
   std::string dataset_file_;
   int32_t num_workers_;  // The number of worker threads
   std::mt19937 rnd_;
@@ -242,7 +256,8 @@ class Graph {
   std::unordered_map<NodeType, std::unordered_set<FeatureType>> node_feature_map_;
   std::unordered_map<EdgeType, std::unordered_set<FeatureType>> edge_feature_map_;
 
-  std::unordered_map<FeatureType, std::shared_ptr<Feature>> default_feature_map_;
+  std::unordered_map<FeatureType, std::shared_ptr<Feature>> default_node_feature_map_;
+  std::unordered_map<FeatureType, std::shared_ptr<Feature>> default_edge_feature_map_;
 };
 }  // namespace gnn
 }  // namespace dataset

mindspore/ccsrc/dataset/engine/gnn/graph_loader.cc

@@ -41,7 +41,8 @@ GraphLoader::GraphLoader(std::string mr_filepath, int32_t num_workers)
 
 Status GraphLoader::GetNodesAndEdges(NodeIdMap *n_id_map, EdgeIdMap *e_id_map, NodeTypeMap *n_type_map,
                                      EdgeTypeMap *e_type_map, NodeFeatureMap *n_feature_map,
-                                     EdgeFeatureMap *e_feature_map, DefaultFeatureMap *default_feature_map) {
+                                     EdgeFeatureMap *e_feature_map, DefaultNodeFeatureMap *default_node_feature_map,
+                                     DefaultEdgeFeatureMap *default_edge_feature_map) {
   for (std::deque<std::shared_ptr<Node>> &dq : n_deques_) {
     while (dq.empty() == false) {
       std::shared_ptr<Node> node_ptr = dq.front();
@@ -70,7 +71,7 @@ Status GraphLoader::GetNodesAndEdges(NodeIdMap *n_id_map, EdgeIdMap *e_id_map, N
   for (auto &itr : *n_type_map) itr.second.shrink_to_fit();
   for (auto &itr : *e_type_map) itr.second.shrink_to_fit();
 
-  MergeFeatureMaps(n_feature_map, e_feature_map, default_feature_map);
+  MergeFeatureMaps(n_feature_map, e_feature_map, default_node_feature_map, default_edge_feature_map);
   return Status::OK();
 }
 
@@ -81,7 +82,8 @@ Status GraphLoader::InitAndLoad() {
   e_deques_.resize(num_workers_);
   n_feature_maps_.resize(num_workers_);
   e_feature_maps_.resize(num_workers_);
-  default_feature_maps_.resize(num_workers_);
+  default_node_feature_maps_.resize(num_workers_);
+  default_edge_feature_maps_.resize(num_workers_);
   TaskGroup vg;
 
   shard_reader_ = std::make_unique<ShardReader>();
@@ -109,7 +111,7 @@ Status GraphLoader::InitAndLoad() {
 
 Status GraphLoader::LoadNode(const std::vector<uint8_t> &col_blob, const mindrecord::json &col_jsn,
                              std::shared_ptr<Node> *node, NodeFeatureMap *feature_map,
-                             DefaultFeatureMap *default_feature) {
+                             DefaultNodeFeatureMap *default_feature) {
   NodeIdType node_id = col_jsn["first_id"];
   NodeType node_type = static_cast<NodeType>(col_jsn["type"]);
   (*node) = std::make_shared<LocalNode>(node_id, node_type);
@@ -133,7 +135,7 @@ Status GraphLoader::LoadNode(const std::vector<uint8_t> &col_blob, const mindrec
 
 Status GraphLoader::LoadEdge(const std::vector<uint8_t> &col_blob, const mindrecord::json &col_jsn,
                              std::shared_ptr<Edge> *edge, EdgeFeatureMap *feature_map,
-                             DefaultFeatureMap *default_feature) {
+                             DefaultEdgeFeatureMap *default_feature) {
   EdgeIdType edge_id = col_jsn["first_id"];
   EdgeType edge_type = static_cast<EdgeType>(col_jsn["type"]);
   NodeIdType src_id = col_jsn["second_id"], dst_id = col_jsn["third_id"];
@@ -214,13 +216,13 @@ Status GraphLoader::WorkerEntry(int32_t worker_id) {
       std::string attr = col_jsn["attribute"];
       if (attr == "n") {
         std::shared_ptr<Node> node_ptr;
-        RETURN_IF_NOT_OK(
-          LoadNode(col_blob, col_jsn, &node_ptr, &(n_feature_maps_[worker_id]), &default_feature_maps_[worker_id]));
+        RETURN_IF_NOT_OK(LoadNode(col_blob, col_jsn, &node_ptr, &(n_feature_maps_[worker_id]),
+                                  &default_node_feature_maps_[worker_id]));
         n_deques_[worker_id].emplace_back(node_ptr);
       } else if (attr == "e") {
         std::shared_ptr<Edge> edge_ptr;
-        RETURN_IF_NOT_OK(
-          LoadEdge(col_blob, col_jsn, &edge_ptr, &(e_feature_maps_[worker_id]), &default_feature_maps_[worker_id]));
+        RETURN_IF_NOT_OK(LoadEdge(col_blob, col_jsn, &edge_ptr, &(e_feature_maps_[worker_id]),
+                                  &default_edge_feature_maps_[worker_id]));
         e_deques_[worker_id].emplace_back(edge_ptr);
       } else {
         MS_LOG(WARNING) << "attribute:" << attr << " is neither edge nor node.";
@@ -233,7 +235,8 @@ Status GraphLoader::WorkerEntry(int32_t worker_id) {
 }
 
 void GraphLoader::MergeFeatureMaps(NodeFeatureMap *n_feature_map, EdgeFeatureMap *e_feature_map,
-                                   DefaultFeatureMap *default_feature_map) {
+                                   DefaultNodeFeatureMap *default_node_feature_map,
+                                   DefaultEdgeFeatureMap *default_edge_feature_map) {
   for (int wkr_id = 0; wkr_id < num_workers_; wkr_id++) {
     for (auto &m : n_feature_maps_[wkr_id]) {
       for (auto &n : m.second) (*n_feature_map)[m.first].insert(n);
@@ -241,8 +244,11 @@ void GraphLoader::MergeFeatureMaps(NodeFeatureMap *n_feature_map, EdgeFeatureMap
     for (auto &m : e_feature_maps_[wkr_id]) {
       for (auto &n : m.second) (*e_feature_map)[m.first].insert(n);
     }
-    for (auto &m : default_feature_maps_[wkr_id]) {
-      (*default_feature_map)[m.first] = m.second;
+    for (auto &m : default_node_feature_maps_[wkr_id]) {
+      (*default_node_feature_map)[m.first] = m.second;
+    }
+    for (auto &m : default_edge_feature_maps_[wkr_id]) {
+      (*default_edge_feature_map)[m.first] = m.second;
     }
   }
   n_feature_maps_.clear();

mindspore/ccsrc/dataset/engine/gnn/graph_loader.h

@@ -43,7 +43,8 @@ using NodeTypeMap = std::unordered_map<NodeType, std::vector<NodeIdType>>;
 using EdgeTypeMap = std::unordered_map<EdgeType, std::vector<EdgeIdType>>;
 using NodeFeatureMap = std::unordered_map<NodeType, std::unordered_set<FeatureType>>;
 using EdgeFeatureMap = std::unordered_map<EdgeType, std::unordered_set<FeatureType>>;
-using DefaultFeatureMap = std::unordered_map<FeatureType, std::shared_ptr<Feature>>;
+using DefaultNodeFeatureMap = std::unordered_map<FeatureType, std::shared_ptr<Feature>>;
+using DefaultEdgeFeatureMap = std::unordered_map<FeatureType, std::shared_ptr<Feature>>;
 
 // this class interfaces with the underlying storage format (mindrecord)
 // it returns raw nodes and edges via GetNodesAndEdges
@@ -63,7 +64,7 @@ class GraphLoader {
   // random order. src_node and dst_node in Edge are node_id only with -1 as type.
   // features attached to each node and edge are expected to be filled correctly
   Status GetNodesAndEdges(NodeIdMap *, EdgeIdMap *, NodeTypeMap *, EdgeTypeMap *, NodeFeatureMap *, EdgeFeatureMap *,
-                          DefaultFeatureMap *);
+                          DefaultNodeFeatureMap *, DefaultEdgeFeatureMap *);
 
  private:
   //
@@ -77,19 +78,19 @@ class GraphLoader {
   // @param mindrecord::json &jsn - contains raw data
   // @param std::shared_ptr<Node> *node - return value
   // @param NodeFeatureMap *feature_map -
-  // @param DefaultFeatureMap *default_feature -
+  // @param DefaultNodeFeatureMap *default_feature -
   // @return Status - the status code
   Status LoadNode(const std::vector<uint8_t> &blob, const mindrecord::json &jsn, std::shared_ptr<Node> *node,
-                  NodeFeatureMap *feature_map, DefaultFeatureMap *default_feature);
+                  NodeFeatureMap *feature_map, DefaultNodeFeatureMap *default_feature);
 
   // @param std::vector<uint8_t> &blob - contains data in blob field in mindrecord
   // @param mindrecord::json &jsn - contains raw data
   // @param std::shared_ptr<Edge> *edge - return value, the edge ptr, edge is not yet connected
   // @param FeatureMap *feature_map
-  // @param DefaultFeatureMap *default_feature -
+  // @param DefaultEdgeFeatureMap *default_feature -
   // @return Status - the status code
   Status LoadEdge(const std::vector<uint8_t> &blob, const mindrecord::json &jsn, std::shared_ptr<Edge> *edge,
-                  EdgeFeatureMap *feature_map, DefaultFeatureMap *default_feature);
+                  EdgeFeatureMap *feature_map, DefaultEdgeFeatureMap *default_feature);
 
   // @param std::string key - column name
   // @param std::vector<uint8_t> &blob - contains data in blob field in mindrecord
@@ -108,7 +109,7 @@ class GraphLoader {
                            std::shared_ptr<Tensor> *tensor);
 
   // merge NodeFeatureMap and EdgeFeatureMap of each worker into 1
-  void MergeFeatureMaps(NodeFeatureMap *, EdgeFeatureMap *, DefaultFeatureMap *);
+  void MergeFeatureMaps(NodeFeatureMap *, EdgeFeatureMap *, DefaultNodeFeatureMap *, DefaultEdgeFeatureMap *);
 
   const int32_t num_workers_;
   std::atomic_int row_id_;
@@ -118,7 +119,8 @@ class GraphLoader {
   std::vector<std::deque<std::shared_ptr<Edge>>> e_deques_;
   std::vector<NodeFeatureMap> n_feature_maps_;
   std::vector<EdgeFeatureMap> e_feature_maps_;
-  std::vector<DefaultFeatureMap> default_feature_maps_;
+  std::vector<DefaultNodeFeatureMap> default_node_feature_maps_;
+  std::vector<DefaultEdgeFeatureMap> default_edge_feature_maps_;
   const std::vector<std::string> keys_;
 };
 }  // namespace gnn

mindspore/dataset/engine/graphdata.py

@@ -22,7 +22,8 @@ from mindspore._c_dataengine import Tensor
 
 from .validators import check_gnn_graphdata, check_gnn_get_all_nodes, check_gnn_get_all_edges, \
     check_gnn_get_nodes_from_edges, check_gnn_get_all_neighbors, check_gnn_get_sampled_neighbors, \
-    check_gnn_get_neg_sampled_neighbors, check_gnn_get_node_feature, check_gnn_random_walk
+    check_gnn_get_neg_sampled_neighbors, check_gnn_get_node_feature, check_gnn_get_edge_feature, \
+    check_gnn_random_walk
 
 
 class GraphData:
@@ -127,7 +128,13 @@ class GraphData:
     @check_gnn_get_sampled_neighbors
     def get_sampled_neighbors(self, node_list, neighbor_nums, neighbor_types):
         """
-        Get sampled neighbor information, maximum support 6-hop sampling.
+        Get sampled neighbor information.
+
+        The api supports multi-hop neighbor sampling. That is, the previous sampling result is used as the input of
+        next-hop sampling. A maximum of 6-hop are allowed.
+
+        The sampling result is tiled into a list in the format of [input node, 1-hop sampling result,
+        2-hop samling result ...]
 
         Args:
             node_list (list or numpy.ndarray): The given list of nodes.
@@ -207,6 +214,35 @@ class GraphData:
                 Tensor(node_list),
                 feature_types)]
 
+    @check_gnn_get_edge_feature
+    def get_edge_feature(self, edge_list, feature_types):
+        """
+        Get `feature_types` feature of the edges in `edge_list`.
+
+        Args:
+            edge_list (list or numpy.ndarray): The given list of edges.
+            feature_types (list or ndarray): The given list of feature types.
+
+        Returns:
+            numpy.ndarray: array of features.
+
+        Examples:
+            >>> import mindspore.dataset as ds
+            >>> data_graph = ds.GraphData('dataset_file', 2)
+            >>> edges = data_graph.get_all_edges(0)
+            >>> features = data_graph.get_edge_feature(edges, [1])
+
+        Raises:
+            TypeError: If `edge_list` is not list or ndarray.
+            TypeError: If `feature_types` is not list or ndarray.
+        """
+        if isinstance(edge_list, list):
+            edge_list = np.array(edge_list, dtype=np.int32)
+        return [
+            t.as_array() for t in self._graph.get_edge_feature(
+                Tensor(edge_list),
+                feature_types)]
+
     def graph_info(self):
         """
         Get the meta information of the graph, including the number of nodes, the type of nodes,

mindspore/dataset/engine/validators.py

@@ -797,7 +797,7 @@ def check_gnn_graphdata(method):
         check_file(dataset_file)
 
         if num_parallel_workers is not None:
-            type_check(num_parallel_workers, (int,), "num_parallel_workers")
+            check_num_parallel_workers(num_parallel_workers)
         return method(self, *args, **kwargs)
 
     return new_method
@@ -970,6 +970,28 @@ def check_gnn_get_node_feature(method):
     return new_method
 
 
+def check_gnn_get_edge_feature(method):
+    """A wrapper that wrap a parameter checker to the GNN `get_edge_feature` function."""
+
+    @wraps(method)
+    def new_method(self, *args, **kwargs):
+        [edge_list, feature_types], _ = parse_user_args(method, *args, **kwargs)
+
+        type_check(edge_list, (list, np.ndarray), "edge_list")
+        if isinstance(edge_list, list):
+            check_aligned_list(edge_list, 'edge_list', int)
+        elif isinstance(edge_list, np.ndarray):
+            if not edge_list.dtype == np.int32:
+                raise TypeError("Each member in {0} should be of type int32. Got {1}.".format(
+                    edge_list, edge_list.dtype))
+
+        check_gnn_list_or_ndarray(feature_types, 'feature_types')
+
+        return method(self, *args, **kwargs)
+
+    return new_method
+
+
 def check_numpyslicesdataset(method):
     """A wrapper that wrap a parameter checker to the original Dataset(NumpySlicesDataset)."""
 

tests/ut/cpp/dataset/gnn_graph_test.cc

@@ -49,9 +49,10 @@ TEST_F(MindDataTestGNNGraph, TestGraphLoader) {
   EdgeTypeMap e_type_map;
   NodeFeatureMap n_feature_map;
   EdgeFeatureMap e_feature_map;
-  DefaultFeatureMap default_feature_map;
+  DefaultNodeFeatureMap default_node_feature_map;
+  DefaultEdgeFeatureMap default_edge_feature_map;
   EXPECT_TRUE(gl.GetNodesAndEdges(&n_id_map, &e_id_map, &n_type_map, &e_type_map, &n_feature_map, &e_feature_map,
-                                  &default_feature_map)
+                                  &default_node_feature_map, &default_edge_feature_map)
                 .IsOk());
   EXPECT_EQ(n_id_map.size(), 20);
   EXPECT_EQ(e_id_map.size(), 40);
@@ -119,6 +120,17 @@ TEST_F(MindDataTestGNNGraph, TestGetSampledNeighbors) {
   std::transform(edges->begin<EdgeIdType>(), edges->end<EdgeIdType>(), edge_list.begin(),
                  [](const EdgeIdType edge) { return edge; });
 
+  TensorRow edge_features;
+  s = graph.GetEdgeFeature(edges, meta_info.edge_feature_type, &edge_features);
+  EXPECT_TRUE(s.IsOk());
+  EXPECT_TRUE(edge_features[0]->ToString() ==
+              "Tensor (shape: <40>, Type: int32)\n"
+              "[0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0]");
+  EXPECT_TRUE(edge_features[1]->ToString() ==
+              "Tensor (shape: <40>, Type: float32)\n"
+              "[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2."
+              "7,2.8,2.9,3,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4]");
+
   std::shared_ptr<Tensor> nodes;
   s = graph.GetNodesFromEdges(edge_list, &nodes);
   EXPECT_TRUE(s.IsOk());

tests/ut/python/dataset/test_graphdata.py

@@ -125,7 +125,7 @@ def test_graphdata_graphinfo():
     assert graph_info['node_num'] == {1: 10, 2: 10}
     assert graph_info['edge_num'] == {0: 40}
     assert graph_info['node_feature_type'] == [1, 2, 3, 4]
-    assert graph_info['edge_feature_type'] == []
+    assert graph_info['edge_feature_type'] == [1, 2]
 
 
 class RandomBatchedSampler(ds.Sampler):
@@ -204,7 +204,6 @@ def test_graphdata_randomwalkdefault():
     logger.info('test randomwalk with default parameters.\n')
     g = ds.GraphData(SOCIAL_DATA_FILE, 1)
     nodes = g.get_all_nodes(1)
-    print(len(nodes))
     assert len(nodes) == 33
 
     meta_path = [1 for _ in range(39)]
@@ -219,7 +218,6 @@ def test_graphdata_randomwalk():
     logger.info('test random walk with given parameters.\n')
     g = ds.GraphData(SOCIAL_DATA_FILE, 1)
     nodes = g.get_all_nodes(1)
-    print(len(nodes))
     assert len(nodes) == 33
 
     meta_path = [1 for _ in range(39)]
@@ -227,6 +225,18 @@ def test_graphdata_randomwalk():
     assert walks.shape == (33, 40)
 
 
+def test_graphdata_getedgefeature():
+    """
+    Test get edge feature
+    """
+    logger.info('test get_edge_feature.\n')
+    g = ds.GraphData(DATASET_FILE)
+    edges = g.get_all_edges(0)
+    features = g.get_edge_feature(edges, [1, 2])
+    assert features[0].shape == (40,)
+    assert features[1].shape == (40,)
+
+
 if __name__ == '__main__':
     test_graphdata_getfullneighbor()
     test_graphdata_getnodefeature_input_check()
@@ -236,3 +246,4 @@ if __name__ == '__main__':
     test_graphdata_generatordataset()
     test_graphdata_randomwalkdefault()
     test_graphdata_randomwalk()
+    test_graphdata_getedgefeature()