3bc7e38ed98f3b7a13fcee2726ec38b27e5c4e1b,onmt/Models.py,Encoder,forward,#Encoder#Any#Any#Any#,123

Before Change

                // feed forward
                ff_in, _ = self.multiattn[i](out, out, out)
                // ff_in = out
                ff_out = self.linear_final[i](F.relu(self.linear_out[i](ff_in)))
                out = self.layer_norm[i](ff_in + F.dropout(ff_out, p=0.1))
                // out = self.layer_norm[i](F.relu(ff_in + ff_out)).contiguous()
            return (mean, mean), out.transpose(0, 1).contiguous()
        if lengths:

After Change

                   .expand(self.layers, pre_emb.size(1), pre_emb.size(2))
            out = pre_emb.transpose(0, 1).contiguous()
            for i in range(self.layers):
                out = self.transformer[i](out, input[:, :, 0])
            return (mean, mean), out.transpose(0, 1).contiguous()
            
    

Instances