548c67bbe7598c3221b83cf6ef216b848debac2c,parlai/agents/seq2seq/modules.py,OutputLayer,forward,#OutputLayer#Any#,539

Before Change

            scores = self.e2s(e)

        if self.padding_idx == 0:
            pad_score = scores.new(scores.size(0),
                                   scores.size(1),
                                   1).fill_(-NEAR_INF)
            scores = torch.cat([pad_score, scores], dim=-1)

After Change

            // hsz => esz, good time for dropout
            e = self.dropout(self.o2e(input))
            // esz => num_features
            scores = F.linear(e, self.weight, self.bias)

        if self.padding_idx >= 0:
            scores[:, :, self.padding_idx] = -NEAR_INF

Instances