Models.py 文件源码

def forward(self, input, hidden, context, init_output):
        emb = self.word_lut(input)

        # n.b. you can increase performance if you compute W_ih * x for all
        # iterations in parallel, but that's only possible if
        # self.input_feed=False
        outputs = []
        output = init_output
        for emb_t in emb.split(1):
            emb_t = emb_t.squeeze(0)
            if self.input_feed:
                emb_t = torch.cat([emb_t, output], 1)

            output, hidden = self.rnn(emb_t, hidden)
            output, attn = self.attn(output, context.t())
            output = self.dropout(output)
            outputs += [output]

        outputs = torch.stack(outputs)
        return outputs, hidden, attn