use implicit padding when possible (#152)

fairseq/models/fconv.py

@@ -98,9 +98,13 @@ class FConvEncoder(FairseqEncoder):
         for (out_channels, kernel_size) in convolutions:
             self.projections.append(Linear(in_channels, out_channels)
                                     if in_channels != out_channels else None)
+            if kernel_size % 2 == 1:
+               padding = kernel_size //2
+            else:
+               padding = 0
             self.convolutions.append(
                 ConvTBC(in_channels, out_channels * 2, kernel_size,
-                        dropout=dropout)
+                        dropout=dropout, padding=padding)
             )
             in_channels = out_channels
         self.fc2 = Linear(in_channels, embed_dim)
@@ -121,10 +125,14 @@ class FConvEncoder(FairseqEncoder):
         for proj, conv in zip(self.projections, self.convolutions):
             residual = x if proj is None else proj(x)
             x = F.dropout(x, p=self.dropout, training=self.training)
-            padding_l = (conv.kernel_size[0] - 1) // 2
-            padding_r = conv.kernel_size[0] // 2
-            x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
-            x = conv(x)
+            if conv.kernel_size[0] % 2 == 1:
+                # padding is implicit in the conv
+                x = conv(x)
+            else: 
+                padding_l = (conv.kernel_size[0] - 1) // 2
+                padding_r = conv.kernel_size[0] // 2
+                x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))
+                x = conv(x)
             x = F.glu(x, dim=2)
             x = (x + residual) * math.sqrt(0.5)