fix parser

fastNLP/models/biaffine_parser.py

@@ -175,12 +175,11 @@ class LabelBilinear(nn.Module):
     def __init__(self, in1_features, in2_features, num_label, bias=True):
         super(LabelBilinear, self).__init__()
         self.bilinear = nn.Bilinear(in1_features, in2_features, num_label, bias=bias)
-        self.lin1 = nn.Linear(in1_features, num_label, bias=False)
-        self.lin2 = nn.Linear(in2_features, num_label, bias=False)
+        self.lin = nn.Linear(in1_features + in2_features, num_label, bias=False)
 
     def forward(self, x1, x2):
         output = self.bilinear(x1, x2)
-        output += self.lin1(x1) + self.lin2(x2)
+        output += self.lin(torch.cat([x1, x2], dim=2))
         return output
 
 
@@ -226,15 +225,16 @@ class BiaffineParser(GraphParser):
 
         rnn_out_size = 2 * rnn_hidden_size
         self.arc_head_mlp = nn.Sequential(nn.Linear(rnn_out_size, arc_mlp_size),
-                                          nn.ELU())
+                                          nn.ELU(),
+                                          TimestepDropout(p=dropout),)
         self.arc_dep_mlp = copy.deepcopy(self.arc_head_mlp)
         self.label_head_mlp = nn.Sequential(nn.Linear(rnn_out_size, label_mlp_size),
-                                            nn.ELU())
+                                            nn.ELU(),
+                                            TimestepDropout(p=dropout),)
         self.label_dep_mlp = copy.deepcopy(self.label_head_mlp)
         self.arc_predictor = ArcBiaffine(arc_mlp_size, bias=True)
         self.label_predictor = LabelBilinear(label_mlp_size, label_mlp_size, num_label, bias=True)
         self.normal_dropout = nn.Dropout(p=dropout)
-        self.timestep_dropout = TimestepDropout(p=dropout)
         self.use_greedy_infer = use_greedy_infer
         initial_parameter(self)
 
@@ -267,10 +267,10 @@ class BiaffineParser(GraphParser):
 
         # for arc biaffine
         # mlp, reduce dim
-        arc_dep = self.timestep_dropout(self.arc_dep_mlp(feat))
-        arc_head = self.timestep_dropout(self.arc_head_mlp(feat))
-        label_dep = self.timestep_dropout(self.label_dep_mlp(feat))
-        label_head = self.timestep_dropout(self.label_head_mlp(feat))
+        arc_dep = self.arc_dep_mlp(feat)
+        arc_head = self.arc_head_mlp(feat)
+        label_dep = self.label_dep_mlp(feat)
+        label_head = self.label_head_mlp(feat)
 
         # biaffine arc classifier
         arc_pred = self.arc_predictor(arc_head, arc_dep) # [N, L, L]