dialog_model.py 文件源码

def forward_lm(self, inpt, lang_h, ctx_h):
        """Run forward pass for language modeling."""
        # embed words
        inpt_emb = self.word_encoder(inpt)

        # append the context embedding to every input word embedding
        ctx_h_rep = ctx_h.narrow(0, ctx_h.size(0) - 1, 1).expand(
            inpt.size(0), ctx_h.size(1), ctx_h.size(2))
        inpt_emb = torch.cat([inpt_emb, ctx_h_rep], 2)

        inpt_emb = self.dropout(inpt_emb)

        out, _ = self.reader(inpt_emb, lang_h)
        decoded = self.decoder(out.view(-1, out.size(2)))

        # tie weights between word embedding/decoding
        decoded = F.linear(decoded, self.word_encoder.weight)

        return decoded.view(out.size(0), out.size(1), decoded.size(1)), out