diff --git a/examples/GaAN/main.sh b/examples/GaAN/main.sh
index 338d977f3cba0705335888e173d34265611923a4..fb389445bb25bae7029a8365882d33efee1bc30b 100644
--- a/examples/GaAN/main.sh
+++ b/examples/GaAN/main.sh
@@ -1 +1 @@
-python3 train.py --epochs 100 --lr 1e-2 --rc 0 --batch_size 1024 --gpu_id 0 --exp_id 0
\ No newline at end of file
+python3 train.py --epochs 100 --lr 1e-2 --rc 0 --batch_size 1024 --exp_id 0
\ No newline at end of file
diff --git a/pgl/layers/conv.py b/pgl/layers/conv.py
index 4d17c323b1f338f185bd3c90d60ac36741664886..7423c079f5343107c40d6e8a619d7354b7a81a18 100644
--- a/pgl/layers/conv.py
+++ b/pgl/layers/conv.py
@@ -264,8 +264,8 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
     """Implementation of GaAN"""
 
     def send_func(src_feat, dst_feat, edge_feat):
-        # 计算每条边上的注意力分数
-        # E * (M * D1), 每个 dst 点都查询它的全部邻边的 src 点
+        # compute attention
+        # E * (M * D1)
         feat_query, feat_key = dst_feat['feat_query'], src_feat['feat_key']
         # E * M * D1
         old = feat_query
@@ -281,16 +281,15 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
                 'feat_gate': src_feat['feat_gate']}
 
     def recv_func(message):
-        # 每条边的终点的特征
+        # feature of src and dst node on each edge
         dst_feat = message['dst_node_feat']
-        # 每条边的出发点的特征
         src_feat = message['src_node_feat']
-        # 每个中心点自己的特征
+        # feature of center node
         x = fluid.layers.sequence_pool(dst_feat, 'average')
-        # 每个中心点的邻居的特征的平均值
+        # feature of neighbors of center node
         z = fluid.layers.sequence_pool(src_feat, 'average')
 
-        # 计算 gate
+        # compute gate
         feat_gate = message['feat_gate']
         g_max = fluid.layers.sequence_pool(feat_gate, 'max')
         g = fluid.layers.concat([x, g_max, z], axis=1)
@@ -318,10 +317,6 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
 
         return output
 
-    # feature N * D
-
-    # 计算每个点自己需要发送出去的内容
-    # 投影后的特征向量
     # N * (D1 * M)
     feat_key = fluid.layers.fc(feature, hidden_size_a * heads, bias_attr=False,
                      param_attr=fluid.ParamAttr(name=name + '_project_key'))
@@ -335,8 +330,7 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
     feat_gate = fluid.layers.fc(feature, hidden_size_m, bias_attr=False, 
                                 param_attr=fluid.ParamAttr(name=name + '_project_gate'))
 
-    # send 阶段
-
+    # send
     message = gw.send(
         send_func,
         nfeat_list=[('node_feat', feature), ('feat_key', feat_key), ('feat_value', feat_value),
@@ -344,7 +338,7 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
         efeat_list=None,
     )
 
-    # 聚合邻居特征
+    # recv
     output = gw.recv(message, recv_func)
     output = fluid.layers.fc(output, hidden_size_o, bias_attr=False,
                             param_attr=fluid.ParamAttr(name=name + '_project_output'))