diff --git a/README.md b/README.md
index 876b9473..f0d8a32f 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,8 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
+- The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in [this link](./cogdl/datasets/rd2cd_data.py). 🎉
+
 - The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 🎉
 
 - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
diff --git a/README_CN.md b/README_CN.md
index 39b398ea..6a6aa14a 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -21,6 +21,8 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
+- 最新的 **v0.5.1 release** 添加了一些高效的算子，包括cpu版本的SpMM和cuda版本的scatter_max。这个版本同时增加了很多用于节点分类的[数据集](./cogdl/datasets/rd2cd_data.py)。 🎉
+
 - 最新的 **v0.5.0 release** 为图神经网络的训练设计了一套统一的流程. 这个版本去除了原先的`Task`类，引入了`DataWrapper`来准备training/validation/test过程中所需的数据，引入了`ModelWrapper`来定义模型training/validation/test的步骤. 🎉
 
 - 最新的 **v0.4.1 release** 增加了深层GNN的实现和推荐任务。这个版本同时提供了新的一些pipeline用于直接获取图表示和搭建推荐应用。欢迎大家参加我们在KDD 2021上的tutorial，时间是8月14号上午10:30 - 12:00（北京时间）。 更多的内容可以查看 https://kdd2021graph.github.io/. 🎉
diff --git a/cogdl/__init__.py b/cogdl/__init__.py
index 1407ccee..bc830ca0 100644
--- a/cogdl/__init__.py
+++ b/cogdl/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.5.0"
+__version__ = "0.5.1"
 
 from .experiments import experiment
 from .oag import oagbert
diff --git a/cogdl/configs.py b/cogdl/configs.py
index 7b5a4a61..9033d3ef 100644
--- a/cogdl/configs.py
+++ b/cogdl/configs.py
@@ -1,505 +1,483 @@
 BEST_CONFIGS = {
-    "node_classification": {
-        "chebyshev": {"general": {}},
-        "dropedge_gcn": {"general": {}},
-        "gat": {
-            "general": {"lr": 0.005, "epochs": 1000},
-            "citeseer": {"weight_decay": 0.001},
-            "pubmed": {"weight_decay": 0.001},
-            "ppi-large": {
-                "nhead": 4,
-                "hidden_size": 256,
-                "lr": 0.005,
-                "residual": True,
-                "num_layers": 3,
-                "weight_decay": 0.0,
-                "attn_drop": 0,
-                "dropout": 0,
-            },
-        },
-        "gcn": {
-            "general": {},
-            "ppi-large": {
-                "lr": 0.005,
-                "hidden_size": 2048,
-                "num_layers": 2,
-                "dropout": 0.3,
-                "epochs": 7000,
-                # 78.04
-            },
-            "flickr": {
-                "lr": 0.01,
-                "dropout": 0.5,
-                "weight_decay": 0.0,
-                "hidden_size": 256,
-            },
-        },
-        "sage": {
-            "general": {},
-        },
-        "gcnii": {
-            "general": {
-                "epochs": 1000,
-                "dropout": 0.5,
-                "wd1": 0.001,
-                "wd2": 5e-4,
-            },
-            "cora": {
-                "num_layers": 64,
-                "hidden_size": 64,
-                "dropout": 0.6,
-            },
-            "citeseer": {
-                "num_layers": 32,
-                "hidden_size": 256,
-                "lr": 0.001,
-                "patience": 200,
-                "epochs": 2000,
-                "lmbda": 0.6,
-                "dropout": 0.7,
-            },
-            "pubmed": {
-                "num_layers": 16,
-                "hidden_size": 256,
-                "lmbda": 0.4,
-                "dropout": 0.5,
-                "wd1": 5e-4,
-            },
-            "reddit": {
-                "num_layers": 3,
-                "hidden_size": 256,
-                "alpha": 0.2,
-                "wd1": 0,
-                "wd2": 0,
-                "lr": 0.001,
-                "residual": True,
-                "epochs": 1000,
-                "lmbda": 1.0,
-                # 96.42
-            },
-            "flickr": {
-                "lr": 0.002,
-                "num_layers": 4,
-                "residual": True,
-                "wd1": 0.0005,
-                "wd2": 0.0005,
-                "lmbda": 1.0,
-                "alpha": 0.1,
-            }
-            # 52.54
-        },
-        "gdc_gcn": {
-            "general": {"hidden_size": 16},
-        },
-        "grand": {
-            "general": {
-                "epochs": 1000,
-            },
-            "cora": {
-                "order": 8,
-                "sample": 4,
-                "lam": 1.0,
-                "tem": 0.5,
-                "alpha": 0.5,
-                "patience": 200,
-                "input_dropout": 0.5,
-                "hidden_dropout": 0.5,
-            },
-            "citeseer": {
-                "order": 2,
-                "sample": 2,
-                "lam": 0.7,
-                "tem": 0.3,
-                "alpha": 0.5,
-                "input_dropout": 0.0,
-                "hidden_dropout": 0.2,
-                "patience": 200,
-            },
-            "pubmed": {
-                "order": 5,
-                "sample": 4,
-                "lam": 1.0,
-                "tem": 0.2,
-                "alpha": 0.5,
-                "lr": 0.2,
-                "bn": True,
-                "input_dropout": 0.6,
-                "hidden_dropout": 0.8,
-            },
-        },
-        "graphsage": {
-            "general": {},
-        },
-        "sgc": {
-            "general": {
-                "hidden_size": 16,
-                "dropout": 0.5,
-            },
-        },
-        "sgcpn": {
-            "general": {
-                "lr": 0.005,
-                "epochs": 1000,
-                "patience": 1000,
-                "norm_mode": "PN",
-                "norm_scale": 10,
-                "dropout": 0.6,
-            },
-        },
-        "sign": {
-            "general": {
-                "lr": 0.00005,
-                "hidden_size": 2048,
-                "dropout": 0.5,
-                "dropedge_rate": 0.2,
-            },
-        },
-        "srgcn": {
-            "general": {
-                "lr": 0.005,
-                "epochs": 1000,
-            },
-            "cora": {"dropout": 0.6},
-            "citeseer": {"dropout": 0.6},
-        },
-        "unet": {
-            "general": {
-                "epochs": 1000,
-                "n_dropout": 0.90,
-                "adj_dropout": 0.05,
-                "hidden_size": 128,
-                "aug_adj": False,
-                "improved": False,
-                "n_pool": 4,
-                "pool_rate": [0.7, 0.5, 0.5, 0.4],
-            },
-        },
-        "ppnp": {
-            "general": {},
-            "flickr": {
-                "lr": 0.005,
-                "weight_decay": 0.001,
-                "dropout": 0.3,
-                "num_layers": 2,
-                "hidden_size": 256,
-                "epochs": 1000,
-                "num_iterations": 4,
-                "alpha": 0.5,
-                # 0.5227
-            },
-            "cora": {
-                "lr": 0.01,
-                "weight_decay": 0.000887,
-                "alpha": 0.1087,
-                "num_iterations": 7,
-                "num_layers": 2,
-                "epochs": 1000,
-                "hidden_size": 32,
-                "dropout": 0.8185,
-                #
-            },
-            "citeseer": {
-                "num_iterations": 4,
-                "hidden_size": 128,
-                "lr": 0.01,
-                "dropout": 0.3095,
-                "alpha": 0.171,
-                "weight_decay": 0.0004117,
-                "num_layers": 2,
-                "epochs": 1000,
-                # 0.7199
-            },
-            "pubmed": {
-                "num_iterations": 10,
-                "hidden_size": 256,
-                "dropout": 0.3766,
-                "alpha": 0.1201,
-                "weight_decay": 0.0002754,
-                "epochs": 1000,
-                "lr": 0.001,
-            },
-            "reddit": {
-                "lr": 0.005,
-                "hidden_size": 256,
-                "dropout": 0.3751,
-                "alpha": 0.1995,
-                "num_layers": 3,
-                "weight_decay": 5.918e-6,
-                "num_iterations": 3,
-                # 96.26
-            },
-        },
-        "correct_smooth_mlp": {
-            "general": {},
-            "ogbn_arxiv": {
-                "correct_norm": "row",
-                "smooth_norm": "col",
-                "correct_alpha": 0.9791632871592579,
-                "smooth_alpha": 0.7564990804200602,
-                "num_correct_prop": 50,
-                "num_smooth_prop": 50,
-                "autoscale": True,
-                "norm": "batchnorm",
-            },
-            "ogbn_products": {
-                "correct_norm": "sym",
-                "smooth_norm": "row",
-                "correct_alpha": 1.0,
-                "smooth_alpha": 0.8,
-                "num_correct_prop": 50,
-                "num_smooth_prop": 50,
-                "autoscale": False,
-                "scale": 10.0,
-                "norm": "batchnorm",
-                "act_first": True,
-            },
-        },
-        "sagn": {
-            "general": {
-                "data_gpu": True,
-                "lr": 0.001,
-                "hidden-size": 512,
-                "attn-drop": 0.0,
-                "dropout": 0.7,
-            },
-            "flickr": {
-                "threshold": 0.5,
-                "label-hop": 2,
-                "weight-decay": 3e-6,
-                "nstage": [50, 50, 50],
-                "nhop": 2,
-                "batch-size": 256,
-            },
-            "reddit": {
-                "threshold": 0.9,
-                "lr": 0.0001,
-                "batch-size": 1000,
-                "nhop": 2,
-                "label-nhop": 4,
-                "weight-decay": 0.0,
-                "nstage": [500, 500, 500],
-            },
-        },
-    },
-    "unsupervised_node_classification": {
-        "deepwalk": {
-            "general": {},
-        },
-        "dngr": {
-            "general": {
-                "hidden_size": 128,
-                "lr": 0.001,
-                "epochs": 500,
-                "hidden_size1": 1000,
-                "hidden_size2": 128,
-                "noise": 0.2,
-                "alpha": 0.1,
-                "step": 10,
-            },
-        },
-        "grarep": {
-            "general": {},
-        },
-        "hope": {
-            "general": {},
-        },
-        "line": {
-            "general": {},
-            "blogcatalog": {"walk_num": 40},
-        },
-        "netmf": {
-            "general": {},
-            "ppi-ne": {"window_size": 10, "is_large": True},
-            "blogcatalog": {"window_size": 10, "is_large": True},
-            "wikipedia": {"window_size": 1},
-        },
-        "netsmf": {
-            "general": {"window_size": 10, "num_round": 1000},
-            "wikipedia": {"window_size": 1},
-            "blogcatalog": {"num_round": 10000},
-        },
-        "node2vec": {
-            "general": {},
-        },
-        "prone": {
-            "general": {"step": 10},
-            "ppi-ne": {"mu": 0.0},
-            "wikipedia": {"mu": -4.0},
-            "dblp-ne": {"mu": -1.2, "theta": 2.0},
-        },
-        "sdne": {
-            "general": {},
-        },
-        "spectral": {
-            "general": {},
-        },
-        "dgi": {
-            "general": {"weight_decay": 0},
-        },
-        "gcc": {
-            "general": {},
-        },
-        "grace": {
-            "general": {
-                "weight_decay": 0,
-                "epochs": 1000,
-                "patience": 20,
-            },
-            "cora": {
-                "lr": 0.0005,
-                "weight_decay": 0.00001,
-                "tau": 0.4,
-                "drop_feature_rates": [0.3, 0.4],
-                "drop_edge_rates": [0.2, 0.4],
-                "epochs": 200,
-                "hidden_size": 128,
-                "proj_hidden_size": 128,
-            },
-            "citeseer": {
-                "hidden_size": 256,
-                "proj_hidden_size": 256,
-                "drop_feature_rates": [0.3, 0.2],
-                "drop_edge_rates": [0.2, 0.0],
-                "lr": 0.001,
-                "_weight_decay": 0.00001,
-                "tau": 0.9,
-                "activation": "prelu",
-            },
-            "pubmed": {
-                "hidden_size": 256,
-                "proj_hidden_size": 256,
-                "drop_edge_rates": [0.4, 0.1],
-                "drop_feature_rates": [0.0, 0.2],
-                "tau": 0.7,
-                "lr": 0.001,
-                "weight_decay": 0.00001,
-            },
-        },
-        "unsup_graphsage": {
+    "chebyshev": {"general": {}},
+    "dropedge_gcn": {"general": {}},
+    "gat": {
+        "general": {"lr": 0.005, "epochs": 1000},
+        "citeseer": {"weight_decay": 0.001},
+        "pubmed": {"weight_decay": 0.001},
+        "ppi-large": {
+            "nhead": 4,
+            "hidden_size": 256,
+            "lr": 0.005,
+            "residual": True,
+            "num_layers": 3,
+            "weight_decay": 0.0,
+            "attn_drop": 0,
+            "dropout": 0,
+        },
+    },
+    "gcn": {
+        "general": {},
+        "ppi-large": {
+            "lr": 0.005,
+            "hidden_size": 2048,
+            "num_layers": 2,
+            "dropout": 0.3,
+            "epochs": 7000,
+            # 78.04
+        },
+        "flickr": {
+            "lr": 0.01,
+            "dropout": 0.5,
+            "weight_decay": 0.0,
+            "hidden_size": 256,
+        },
+    },
+    "sage": {
+        "general": {},
+    },
+    "gcnii": {
+        "general": {
+            "epochs": 1000,
+            "dropout": 0.5,
+            "wd1": 0.001,
+            "wd2": 5e-4,
+        },
+        "cora": {
+            "num_layers": 64,
+            "hidden_size": 64,
+            "dropout": 0.6,
+        },
+        "citeseer": {
+            "num_layers": 32,
+            "hidden_size": 256,
             "lr": 0.001,
-            "weight_decay": 0,
-            "epochs": 3000,
-        },
-        "revgcn": {
-            "general": {},
-            "cora": {
-                "hidden_size": 128,
-                "lr": 0.001,
-                "dropout": 0.4706458854,
-                "weight_decay": 0.0008907,
-                "norm": "layernorm",
-                "num_layers": 10,
-                # 81.40
-            },
-        },
-    },
-    "graph_classification": {
-        "gin": {
-            "general": {"lr": 0.001},
-            "imdb-b": {"degree_feature": True},
-            "imdb-m": {"degree_feature": True},
-            "collab": {"degree_feature": True},
-            "proteins": {
-                "num_layers": 5,
-                "dropout": 0.0,
-            },
-            "nci1": {
-                "num_layers": 5,
-                "dropout": 0.3,
-                "hidden_size": 64,
-            },
-        },
-        "infograph": {
-            "general": {
-                "lr": 0.0001,
-                "weight_decay": 5e-4,
-                "sup": False,
-            },
-            "mutag": {
-                "num_layers": 1,
-                "epochs": 20,
-            },
-            "imdb-b": {"degree_feature": True},
-            "imdb-m": {"degree_feature": True},
-            "collab": {"degree_feature": True},
-            "nci1": {"num_layers": 3},
-        },
-        "sortpool": {
-            "nci1": {
-                "dropout": 0.3,
-                "hidden_size": 64,
-                "num_layers": 5,
-            },
-        },
-        "patchy_san": {
-            "general": {
-                "lr": 0.001,
-                "hidden_size": 32,
-                "gamma": 0.5,
-                "dropout": 0.5,
-            },
-            "imdb-b": {"degree_feature": True},
-            "imdb-m": {"degree_feature": True},
-            "collab": {"degree_feature": True},
-        },
-    },
-    "unsupervised_graph_classification": {
-        "graph2vec": {
-            "general": {},
-            "nci1": {
-                "lr": 0.001,
-                "window_size": 8,
-                "epochs": 10,
-                "iteration": 4,
-            },
-            "reddit-b": {
-                "lr": 0.01,
-                "degree_feature": True,
-                "hidden_size": 128,
-            },
+            "patience": 200,
+            "epochs": 2000,
+            "lmbda": 0.6,
+            "dropout": 0.7,
+        },
+        "pubmed": {
+            "num_layers": 16,
+            "hidden_size": 256,
+            "lmbda": 0.4,
+            "dropout": 0.5,
+            "wd1": 5e-4,
+        },
+        "reddit": {
+            "num_layers": 3,
+            "hidden_size": 256,
+            "alpha": 0.2,
+            "wd1": 0,
+            "wd2": 0,
+            "lr": 0.001,
+            "residual": True,
+            "epochs": 1000,
+            "lmbda": 1.0,
+            # 96.42
+        },
+        "flickr": {
+            "lr": 0.002,
+            "num_layers": 4,
+            "residual": True,
+            "wd1": 0.0005,
+            "wd2": 0.0005,
+            "lmbda": 1.0,
+            "alpha": 0.1,
         }
+        # 52.54
     },
-    "link_prediction": {},
-    "multiplex_link_prediction": {
-        "gatne": {
-            "general": {},
-            "twitter": {"eval_type": "1"},
-        }
+    "gdc_gcn": {
+        "general": {"hidden_size": 16},
+    },
+    "grand": {
+        "general": {
+            "epochs": 1000,
+        },
+        "cora": {
+            "order": 8,
+            "sample": 4,
+            "lam": 1.0,
+            "tem": 0.5,
+            "alpha": 0.5,
+            "patience": 200,
+            "input_dropout": 0.5,
+            "hidden_dropout": 0.5,
+        },
+        "citeseer": {
+            "order": 2,
+            "sample": 2,
+            "lam": 0.7,
+            "tem": 0.3,
+            "alpha": 0.5,
+            "input_dropout": 0.0,
+            "hidden_dropout": 0.2,
+            "patience": 200,
+        },
+        "pubmed": {
+            "order": 5,
+            "sample": 4,
+            "lam": 1.0,
+            "tem": 0.2,
+            "alpha": 0.5,
+            "lr": 0.2,
+            "bn": True,
+            "input_dropout": 0.6,
+            "hidden_dropout": 0.8,
+        },
+    },
+    "graphsage": {
+        "general": {},
+    },
+    "sgc": {
+        "general": {
+            "hidden_size": 16,
+            "dropout": 0.5,
+        },
+    },
+    "sgcpn": {
+        "general": {
+            "lr": 0.005,
+            "epochs": 1000,
+            "patience": 1000,
+            "norm_mode": "PN",
+            "norm_scale": 10,
+            "dropout": 0.6,
+        },
+    },
+    "sign": {
+        "general": {
+            "lr": 0.00005,
+            "hidden_size": 2048,
+            "dropout": 0.5,
+            "dropedge_rate": 0.2,
+        },
     },
-    "multiplex_node_classification": {
-        "hin2vec": {
-            "general": {
-                "lr": 0.025,
-            },
+    "srgcn": {
+        "general": {
+            "lr": 0.005,
+            "epochs": 1000,
         },
-        "metapath2vec": {
-            "general": {
-                "walk_num": 40,
-            },
+        "cora": {"dropout": 0.6},
+        "citeseer": {"dropout": 0.6},
+    },
+    "unet": {
+        "general": {
+            "epochs": 1000,
+            "n_dropout": 0.90,
+            "adj_dropout": 0.05,
+            "hidden_size": 128,
+            "aug_adj": False,
+            "improved": False,
+            "n_pool": 4,
+            "pool_rate": [0.7, 0.5, 0.5, 0.4],
         },
-        "pte": {},
     },
-    "heterogeneous_node_classification": {
-        "gtn": {
-            "general": {
-                "hidden_size": 128,
-                "lr": 0.005,
-                "weight_decay": 0.001,
-            },
+    "ppnp": {
+        "general": {},
+        "flickr": {
+            "lr": 0.005,
+            "weight_decay": 0.001,
+            "dropout": 0.3,
+            "num_layers": 2,
+            "hidden_size": 256,
+            "epochs": 1000,
+            "num_iterations": 4,
+            "alpha": 0.5,
+            # 0.5227
+        },
+        "cora": {
+            "lr": 0.01,
+            "weight_decay": 0.000887,
+            "alpha": 0.1087,
+            "num_iterations": 7,
+            "num_layers": 2,
+            "epochs": 1000,
+            "hidden_size": 32,
+            "dropout": 0.8185,
+            #
+        },
+        "citeseer": {
+            "num_iterations": 4,
+            "hidden_size": 128,
+            "lr": 0.01,
+            "dropout": 0.3095,
+            "alpha": 0.171,
+            "weight_decay": 0.0004117,
+            "num_layers": 2,
+            "epochs": 1000,
+            # 0.7199
+        },
+        "pubmed": {
+            "num_iterations": 10,
+            "hidden_size": 256,
+            "dropout": 0.3766,
+            "alpha": 0.1201,
+            "weight_decay": 0.0002754,
+            "epochs": 1000,
+            "lr": 0.001,
         },
-        "han": {
-            "general": {
-                "hidden_size": 128,
-                "lr": 0.005,
-                "weight_decay": 0.001,
-            }
+        "reddit": {
+            "lr": 0.005,
+            "hidden_size": 256,
+            "dropout": 0.3751,
+            "alpha": 0.1995,
+            "num_layers": 3,
+            "weight_decay": 5.918e-6,
+            "num_iterations": 3,
+            # 96.26
         },
     },
-    "pretrain": {},
-    "similarity_search": {
-        "gcc": {
-            "general": {},
+    "correct_smooth_mlp": {
+        "general": {},
+        "ogbn_arxiv": {
+            "correct_norm": "row",
+            "smooth_norm": "col",
+            "correct_alpha": 0.9791632871592579,
+            "smooth_alpha": 0.7564990804200602,
+            "num_correct_prop": 50,
+            "num_smooth_prop": 50,
+            "autoscale": True,
+            "norm": "batchnorm",
+        },
+        "ogbn_products": {
+            "correct_norm": "sym",
+            "smooth_norm": "row",
+            "correct_alpha": 1.0,
+            "smooth_alpha": 0.8,
+            "num_correct_prop": 50,
+            "num_smooth_prop": 50,
+            "autoscale": False,
+            "scale": 10.0,
+            "norm": "batchnorm",
+            "act_first": True,
+        },
+    },
+    "sagn": {
+        "general": {
+            "data_gpu": True,
+            "lr": 0.001,
+            "hidden-size": 512,
+            "attn-drop": 0.0,
+            "dropout": 0.7,
+        },
+        "flickr": {
+            "threshold": 0.5,
+            "label-hop": 2,
+            "weight-decay": 3e-6,
+            "nstage": [50, 50, 50],
+            "nhop": 2,
+            "batch-size": 256,
+        },
+        "reddit": {
+            "threshold": 0.9,
+            "lr": 0.0001,
+            "batch-size": 1000,
+            "nhop": 2,
+            "label-nhop": 4,
+            "weight-decay": 0.0,
+            "nstage": [500, 500, 500],
+        },
+    },
+    "deepwalk": {
+        "general": {},
+    },
+    "dngr": {
+        "general": {
+            "hidden_size": 128,
+            "lr": 0.001,
+            "epochs": 500,
+            "hidden_size1": 1000,
+            "hidden_size2": 128,
+            "noise": 0.2,
+            "alpha": 0.1,
+            "step": 10,
         },
     },
-    "attributed_graph_clustering": {},
+    "grarep": {
+        "general": {},
+    },
+    "hope": {
+        "general": {},
+    },
+    "line": {
+        "general": {},
+        "blogcatalog": {"walk_num": 40},
+    },
+    "netmf": {
+        "general": {},
+        "ppi-ne": {"window_size": 10, "is_large": True},
+        "blogcatalog": {"window_size": 10, "is_large": True},
+        "wikipedia": {"window_size": 1},
+    },
+    "netsmf": {
+        "general": {"window_size": 10, "num_round": 1000},
+        "wikipedia": {"window_size": 1},
+        "blogcatalog": {"num_round": 10000},
+    },
+    "node2vec": {
+        "general": {},
+    },
+    "prone": {
+        "general": {"step": 10},
+        "ppi-ne": {"mu": 0.0},
+        "wikipedia": {"mu": -4.0},
+        "dblp-ne": {"mu": -1.2, "theta": 2.0},
+    },
+    "sdne": {
+        "general": {},
+    },
+    "spectral": {
+        "general": {},
+    },
+    "dgi": {
+        "general": {"weight_decay": 0},
+    },
+    "gcc": {
+        "general": {},
+    },
+    "grace": {
+        "general": {
+            "weight_decay": 0,
+            "epochs": 1000,
+            "patience": 20,
+        },
+        "cora": {
+            "lr": 0.0005,
+            "weight_decay": 0.00001,
+            "tau": 0.4,
+            "drop_feature_rates": [0.3, 0.4],
+            "drop_edge_rates": [0.2, 0.4],
+            "epochs": 200,
+            "hidden_size": 128,
+            "proj_hidden_size": 128,
+        },
+        "citeseer": {
+            "hidden_size": 256,
+            "proj_hidden_size": 256,
+            "drop_feature_rates": [0.3, 0.2],
+            "drop_edge_rates": [0.2, 0.0],
+            "lr": 0.001,
+            "_weight_decay": 0.00001,
+            "tau": 0.9,
+            "activation": "prelu",
+        },
+        "pubmed": {
+            "hidden_size": 256,
+            "proj_hidden_size": 256,
+            "drop_edge_rates": [0.4, 0.1],
+            "drop_feature_rates": [0.0, 0.2],
+            "tau": 0.7,
+            "lr": 0.001,
+            "weight_decay": 0.00001,
+        },
+    },
+    "unsup_graphsage": {
+        "lr": 0.001,
+        "weight_decay": 0,
+        "epochs": 3000,
+    },
+    "revgcn": {
+        "general": {},
+        "cora": {
+            "hidden_size": 128,
+            "lr": 0.001,
+            "dropout": 0.4706458854,
+            "weight_decay": 0.0008907,
+            "norm": "layernorm",
+            "num_layers": 10,
+            # 81.40
+        },
+    },
+    "gin": {
+        "general": {"lr": 0.001},
+        "imdb-b": {"degree_feature": True},
+        "imdb-m": {"degree_feature": True},
+        "collab": {"degree_feature": True},
+        "proteins": {
+            "num_layers": 5,
+            "dropout": 0.0,
+        },
+        "nci1": {
+            "num_layers": 5,
+            "dropout": 0.3,
+            "hidden_size": 64,
+        },
+    },
+    "infograph": {
+        "general": {
+            "lr": 0.0001,
+            "weight_decay": 5e-4,
+            "sup": False,
+        },
+        "mutag": {
+            "num_layers": 1,
+            "epochs": 20,
+        },
+        "imdb-b": {"degree_feature": True},
+        "imdb-m": {"degree_feature": True},
+        "collab": {"degree_feature": True},
+        "nci1": {"num_layers": 3},
+    },
+    "sortpool": {
+        "nci1": {
+            "dropout": 0.3,
+            "hidden_size": 64,
+            "num_layers": 5,
+        },
+    },
+    "patchy_san": {
+        "general": {
+            "lr": 0.001,
+            "hidden_size": 32,
+            "gamma": 0.5,
+            "dropout": 0.5,
+        },
+        "imdb-b": {"degree_feature": True},
+        "imdb-m": {"degree_feature": True},
+        "collab": {"degree_feature": True},
+    },
+    "graph2vec": {
+        "general": {},
+        "nci1": {
+            "lr": 0.001,
+            "window_size": 8,
+            "epochs": 10,
+            "iteration": 4,
+        },
+        "reddit-b": {
+            "lr": 0.01,
+            "degree_feature": True,
+            "hidden_size": 128,
+        },
+    },
+    "gatne": {
+        "general": {},
+        "twitter": {"eval_type": "1"},
+    },
+    "hin2vec": {
+        "general": {
+            "lr": 0.025,
+        },
+    },
+    "metapath2vec": {
+        "general": {
+            "walk_num": 40,
+        },
+    },
+    "pte": {},
+    "gtn": {
+        "general": {
+            "hidden_size": 128,
+            "lr": 0.005,
+            "weight_decay": 0.001,
+        },
+    },
+    "han": {
+        "general": {
+            "hidden_size": 128,
+            "lr": 0.005,
+            "weight_decay": 0.001,
+        }
+    },
 }
diff --git a/cogdl/datasets/__init__.py b/cogdl/datasets/__init__.py
index d888021a..f553ce8a 100644
--- a/cogdl/datasets/__init__.py
+++ b/cogdl/datasets/__init__.py
@@ -158,4 +158,20 @@ def build_dataset_from_path(data_path, dataset=None):
     "yelp2018": "cogdl.datasets.rec_data.Yelp2018Dataset",
     "ali": "cogdl.datasets.rec_data.AliDataset",
     "amazon-rec": "cogdl.datasets.rec_data.AmazonRecDataset",
+    "Github": "cogdl.datasets.rd2cd_data.Github",
+    "Elliptic": "cogdl.datasets.rd2cd_data.Elliptic",
+    "Film": "cogdl.datasets.rd2cd_data.Film",
+    "Wiki": "cogdl.datasets.rd2cd_data.Wiki",
+    "Clothing": "cogdl.datasets.rd2cd_data.Clothing",
+    "Electronics": "cogdl.datasets.rd2cd_data.Electronics",
+    "Dblp": "cogdl.datasets.rd2cd_data.Dblp",
+    "Yelpchi": "cogdl.datasets.rd2cd_data.Yelpchi",
+    "Alpha": "cogdl.datasets.rd2cd_data.Alpha",
+    "Weibo": "cogdl.datasets.rd2cd_data.Weibo",
+    "bgp": "cogdl.datasets.rd2cd_data.bgp",
+    "ssn5": "cogdl.datasets.rd2cd_data.ssn5",
+    "ssn7": "cogdl.datasets.rd2cd_data.ssn7",
+    "Aids": "cogdl.datasets.rd2cd_data.Aids",
+    "Nba": "cogdl.datasets.rd2cd_data.Nba",
+    "Pokec_z": "cogdl.datasets.rd2cd_data.Pokec_z",
 }
diff --git a/cogdl/datasets/rd2cd_data.py b/cogdl/datasets/rd2cd_data.py
new file mode 100644
index 00000000..2147bbb5
--- /dev/null
+++ b/cogdl/datasets/rd2cd_data.py
@@ -0,0 +1,186 @@
+import os
+import random
+import tarfile
+
+import numpy as np
+import requests
+import torch
+from torch import Tensor
+
+from cogdl.data import Graph
+from cogdl.datasets import NodeDataset
+
+base_url = "https://cloud.tsinghua.edu.cn/d/65d7c53dd8474d7091a9/files/?p=%2F"
+
+
+def get_whole_mask(y, ratio: str, seed: int = 1234567):
+    """split the whole dataset in proportion"""
+    y_have_label_mask = y != -1
+    total_node_num = len(y)
+    y_index_tensor = torch.tensor(list(range(total_node_num)), dtype=int)
+    masked_index = y_index_tensor[y_have_label_mask]
+    while True:
+        (train_mask, val_mask, test_mask) = get_order(ratio, masked_index, total_node_num, seed)
+        if check_train_containing(train_mask, y):
+            return (train_mask, val_mask, test_mask)
+        else:
+            seed += 1
+
+
+def get_order(ratio: str, masked_index: Tensor, total_node_num: int, seed: int = 1234567):
+    """
+    return：(train_mask,val_mask,test_mask)
+    """
+    random.seed(seed)
+
+    masked_node_num = len(masked_index)
+    shuffle_criterion = list(range(masked_node_num))
+    random.shuffle(shuffle_criterion)
+
+    train_val_test_list = [int(i) for i in ratio.split("-")]
+    tvt_sum = sum(train_val_test_list)
+    tvt_ratio_list = [i / tvt_sum for i in train_val_test_list]
+
+    train_end_index = int(tvt_ratio_list[0] * masked_node_num)
+    val_end_index = train_end_index + int(tvt_ratio_list[1] * masked_node_num)
+
+    train_mask_index = shuffle_criterion[:train_end_index]
+    val_mask_index = shuffle_criterion[train_end_index:val_end_index]
+    test_mask_index = shuffle_criterion[val_end_index:]
+
+    train_mask = torch.zeros(total_node_num, dtype=torch.bool)
+    train_mask[masked_index[train_mask_index]] = True
+    val_mask = torch.zeros(total_node_num, dtype=torch.bool)
+    val_mask[masked_index[val_mask_index]] = True
+    test_mask = torch.zeros(total_node_num, dtype=torch.bool)
+    test_mask[masked_index[test_mask_index]] = True
+
+    return (train_mask, val_mask, test_mask)
+
+
+def check_train_containing(train_mask, y):
+    for label in y.unique():
+        if label.item() == -1:
+            continue
+        if label.item() not in y[train_mask]:
+            return False
+    return True
+
+
+class RD2CD(NodeDataset):
+    def __init__(self, root, name):
+        self.root = root
+        self.name = name
+        self.source_path = root + "/" + name + "/raw"
+        if not os.path.exists(self.source_path):
+            os.makedirs(self.source_path)
+        dst_path = root + "/" + name + "/processed"
+        if not os.path.exists(dst_path):
+            os.makedirs(dst_path)
+        self.data_path = dst_path + "/data.pt"
+        if not os.path.exists(self.data_path):
+            self.download()
+        super(RD2CD, self).__init__(path=self.data_path, scale_feat=False)
+
+    def download(self):
+        r = requests.get(base_url + self.name + ".tgz&dl=1")
+        tarfile_path = self.source_path + "/" + self.name + ".tgz"
+        with open(tarfile_path, "wb") as f:
+            f.write(r.content)
+        with tarfile.open(tarfile_path, "r") as f:
+            f.extractall(self.source_path)
+
+    def process(self):
+        numpy_x = np.load(self.source_path + "/x.npy")
+        x = torch.from_numpy(numpy_x).to(torch.float)
+        numpy_y = np.load(self.source_path + "/y.npy")
+        y = torch.from_numpy(numpy_y).to(torch.long)
+        numpy_edge_index = np.load(self.source_path + "/edge_index.npy")
+        edge_index = torch.from_numpy(numpy_edge_index).to(torch.long)
+
+        # set train/val/test mask in node_classification task
+        random_seed = 14530529  # a fixed seed
+        (train_mask, val_mask, test_mask) = get_whole_mask(y, "6-2-2", random_seed)
+        data = Graph(x=x, edge_index=edge_index, y=y, train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)
+        torch.save(data, self.data_path)
+        return data
+
+
+class Github(RD2CD):
+    def __init__(self, root="data"):
+        super(Github, self).__init__(root, "Github")
+
+
+class Elliptic(RD2CD):
+    def __init__(self, root="data"):
+        super(Elliptic, self).__init__(root, "Elliptic")
+
+
+class Film(RD2CD):
+    def __init__(self, root="data"):
+        super(Film, self).__init__(root, "Film")
+
+
+class Wiki(RD2CD):
+    def __init__(self, root="data"):
+        super(Wiki, self).__init__(root, "Wiki")
+
+
+class Clothing(RD2CD):
+    def __init__(self, root="data"):
+        super(Clothing, self).__init__(root, "Clothing")
+
+
+class Electronics(RD2CD):
+    def __init__(self, root="data"):
+        super(Electronics, self).__init__(root, "Electronics")
+
+
+class Dblp(RD2CD):
+    def __init__(self, root="data"):
+        super(Dblp, self).__init__(root, "Dblp")
+
+
+class Yelpchi(RD2CD):
+    def __init__(self, root="data"):
+        super(Yelpchi, self).__init__(root, "Yelpchi")
+
+
+class Alpha(RD2CD):
+    def __init__(self, root="data"):
+        super(Alpha, self).__init__(root, "Alpha")
+
+
+class Weibo(RD2CD):
+    def __init__(self, root="data"):
+        super(Weibo, self).__init__(root, "Weibo")
+
+
+class bgp(RD2CD):
+    def __init__(self, root="data"):
+        super(bgp, self).__init__(root, "bgp")
+
+
+class ssn5(RD2CD):
+    def __init__(self, root="data"):
+        super(ssn5, self).__init__(root, "ssn5")
+
+
+class ssn7(RD2CD):
+    def __init__(self, root="data"):
+        super(ssn7, self).__init__(root, "ssn7")
+
+
+class Aids(RD2CD):
+    def __init__(self, root="data"):
+        super(Aids, self).__init__(root, "Aids")
+
+
+class Nba(RD2CD):
+    def __init__(self, root="data"):
+        super(Nba, self).__init__(root, "Nba")
+
+
+class Pokec_z(RD2CD):
+    def __init__(self, root="data"):
+        super(Pokec_z, self).__init__(root, "Pokec_z")
diff --git a/cogdl/experiments.py b/cogdl/experiments.py
index 8f5a9c5f..055c4e3f 100644
--- a/cogdl/experiments.py
+++ b/cogdl/experiments.py
@@ -75,10 +75,9 @@ def run(self):
 
 
 def set_best_config(args):
-    configs = BEST_CONFIGS[args.task]
-    if args.model not in configs:
+    if args.model not in BEST_CONFIGS:
         return args
-    configs = configs[args.model]
+    configs = BEST_CONFIGS[args.model]
     for key, value in configs["general"].items():
         args.__setattr__(key, value)
     if args.dataset not in configs:
diff --git a/cogdl/models/__init__.py b/cogdl/models/__init__.py
index 5221a112..d33177b5 100644
--- a/cogdl/models/__init__.py
+++ b/cogdl/models/__init__.py
@@ -70,6 +70,7 @@ def build_model(args):
     "mvgrl": "cogdl.models.nn.mvgrl.MVGRL",
     "patchy_san": "cogdl.models.nn.patchy_san.PatchySAN",
     "gcn": "cogdl.models.nn.gcn.GCN",
+    "actgcn": "cogdl.models.nn.actgcn.ActGCN",
     "gdc_gcn": "cogdl.models.nn.gdc_gcn.GDC_GCN",
     "graphsage": "cogdl.models.nn.graphsage.Graphsage",
     "compgcn": "cogdl.models.nn.compgcn.LinkPredictCompGCN",
diff --git a/cogdl/models/nn/actgcn.py b/cogdl/models/nn/actgcn.py
new file mode 100644
index 00000000..67bd57e9
--- /dev/null
+++ b/cogdl/models/nn/actgcn.py
@@ -0,0 +1,93 @@
+import torch.nn as nn
+
+try:
+    from cogdl.layers.actgcn_layer import ActGCNLayer
+except Exception:
+    print("Please install the actnn library first.")
+    exit(1)
+
+from .. import BaseModel
+
+
+class ActGCN(BaseModel):
+    r"""The GCN model from the `"Semi-Supervised Classification with Graph Convolutional Networks"
+    <https://arxiv.org/abs/1609.02907>`_ paper
+
+    Args:
+        in_features (int) : Number of input features.
+        out_features (int) : Number of classes.
+        hidden_size (int) : The dimension of node representation.
+        dropout (float) : Dropout rate for model training.
+    """
+
+    @staticmethod
+    def add_args(parser):
+        """Add model-specific arguments to the parser."""
+        # fmt: off
+        parser.add_argument("--num-features", type=int)
+        parser.add_argument("--num-classes", type=int)
+        parser.add_argument("--num-layers", type=int, default=2)
+        parser.add_argument("--hidden-size", type=int, default=64)
+        parser.add_argument("--dropout", type=float, default=0.5)
+        parser.add_argument("--residual", action="store_true")
+        parser.add_argument("--norm", type=str, default=None)
+        parser.add_argument("--activation", type=str, default="relu")
+        # fmt: on
+
+    @classmethod
+    def build_model_from_args(cls, args):
+        return cls(
+            args.num_features,
+            args.hidden_size,
+            args.num_classes,
+            args.num_layers,
+            args.dropout,
+            args.activation,
+            args.residual,
+            args.norm,
+            args.rp_ratio,
+        )
+
+    def __init__(
+        self,
+        in_feats,
+        hidden_size,
+        out_feats,
+        num_layers,
+        dropout,
+        activation="relu",
+        residual=False,
+        norm=None,
+        rp_ratio=1,
+    ):
+        super(ActGCN, self).__init__()
+        shapes = [in_feats] + [hidden_size] * (num_layers - 1) + [out_feats]
+        self.layers = nn.ModuleList(
+            [
+                ActGCNLayer(
+                    shapes[i],
+                    shapes[i + 1],
+                    dropout=dropout if i != num_layers - 1 else 0,
+                    residual=residual if i != num_layers - 1 else None,
+                    norm=norm if i != num_layers - 1 else None,
+                    activation=activation if i != num_layers - 1 else None,
+                    rp_ratio=rp_ratio,
+                )
+                for i in range(num_layers)
+            ]
+        )
+        self.num_layers = num_layers
+
+    def embed(self, graph):
+        graph.sym_norm()
+        h = graph.x
+        for i in range(self.num_layers - 1):
+            h = self.layers[i](graph, h)
+        return h
+
+    def forward(self, graph):
+        graph.sym_norm()
+        h = graph.x
+        for i in range(self.num_layers):
+            h = self.layers[i](graph, h)
+        return h
diff --git a/cogdl/models/nn/gcn.py b/cogdl/models/nn/gcn.py
index 70696df3..08a024b2 100644
--- a/cogdl/models/nn/gcn.py
+++ b/cogdl/models/nn/gcn.py
@@ -40,8 +40,6 @@ def build_model_from_args(cls, args):
             args.activation,
             args.residual,
             args.norm,
-            args.actnn,
-            args.rp_ratio,
         )
 
     def __init__(
@@ -54,29 +52,18 @@ def __init__(
         activation="relu",
         residual=False,
         norm=None,
-        actnn=False,
-        rp_ratio=1,
     ):
         super(GCN, self).__init__()
         shapes = [in_feats] + [hidden_size] * (num_layers - 1) + [out_feats]
-        Layer = GCNLayer
-        if actnn:
-            try:
-                from cogdl.layers.actgcn_layer import ActGCNLayer
-            except Exception:
-                print("Please install the actnn library first.")
-                exit(1)
-            Layer = ActGCNLayer
         self.layers = nn.ModuleList(
             [
-                Layer(
+                GCNLayer(
                     shapes[i],
                     shapes[i + 1],
                     dropout=dropout if i != num_layers - 1 else 0,
                     residual=residual if i != num_layers - 1 else None,
                     norm=norm if i != num_layers - 1 else None,
                     activation=activation if i != num_layers - 1 else None,
-                    rp_ratio=rp_ratio,
                 )
                 for i in range(num_layers)
             ]
@@ -96,6 +83,3 @@ def forward(self, graph):
         for i in range(self.num_layers):
             h = self.layers[i](graph, h)
         return h
-
-    def predict(self, data):
-        return self.forward(data)
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 2481e698..482c3d60 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -17,6 +17,7 @@ We summarize the contributions of CogDL as follows:
 ❗ News
 ------------
 
+- The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification. 🎉
 - The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 
 - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
 - The new **v0.4.0 release** refactors the data storage (from ``Data`` to ``Graph``) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this `link <https://kdd2021graph.github.io/>`_ for more details. 🎉
diff --git a/tests/datasets/test_rd2cd_data.py b/tests/datasets/test_rd2cd_data.py
new file mode 100644
index 00000000..89d73b7f
--- /dev/null
+++ b/tests/datasets/test_rd2cd_data.py
@@ -0,0 +1,55 @@
+from cogdl.datasets import build_dataset
+from cogdl.utils import build_args_from_dict
+
+
+def test_rd2cd_github():
+    args = build_args_from_dict({"dataset": "Github"})
+    assert args.dataset == "Github"
+    dataset = build_dataset(args)
+    data = dataset.data
+    assert data.num_nodes == 37700
+    assert dataset.num_features == 4005
+
+
+def test_rd2cd_elliptic():
+    args = build_args_from_dict({"dataset": "Elliptic"})
+    assert args.dataset == "Elliptic"
+    dataset = build_dataset(args)
+    data = dataset.data
+    assert data.num_nodes == 203769
+    assert dataset.num_features == 164
+
+
+def test_rd2cd_clothing():
+    args = build_args_from_dict({"dataset": "Clothing"})
+    assert args.dataset == "Clothing"
+    dataset = build_dataset(args)
+    data = dataset.data
+    assert data.num_nodes == 24919
+    assert dataset.num_features == 9034
+
+
+def test_rd2cd_electronics():
+    args = build_args_from_dict({"dataset": "Electronics"})
+    assert args.dataset == "Electronics"
+    dataset = build_dataset(args)
+    data = dataset.data
+    assert data.num_nodes == 42318
+    assert dataset.num_features == 8669
+
+
+def test_rd2cd_dblp():
+    args = build_args_from_dict({"dataset": "Dblp"})
+    assert args.dataset == "Dblp"
+    dataset = build_dataset(args)
+    data = dataset.data
+    assert data.num_nodes == 40672
+    assert dataset.num_features == 7202
+
+
+if __name__ == "__main__":
+    test_rd2cd_github()
+    test_rd2cd_elliptic()
+    test_rd2cd_clothing()
+    test_rd2cd_electronics()
+    test_rd2cd_dblp()