seq2seq

def forward(self, inp, targ=None):
        bs,sl = inp.size()
        hid = self.initHidden(bs)
        emb = self.emb_enc_drop(self.emb_enc(inp))
        enc_out, hid = self.encoder(emb, hid)
        
        hid = hid.view(2,self.n_layers, bs, self.n_hid).permute(1,2,0,3).contiguous()
        hid = self.out_enc(self.hid_dp(hid).view(self.n_layers, bs, 2*self.n_hid))

        dec_inp = inp.new_zeros(bs).long() + self.bos_idx
        res = []
        enc_att = self.enc_att(enc_out)
        for i in range(self.max_len):
            hid_att = self.hid_att(hid[-1])
            u = torch.tanh(enc_att + hid_att[:,None])
            attn_wgts = F.softmax(u @ self.V, 1)
            ctx = (attn_wgts[...,None] * enc_out).sum(1)
            emb = self.emb_dec(dec_inp)
            outp, hid = self.decoder(torch.cat([emb, ctx], 1)[:,None], hid)
            outp = self.out(self.out_drop(outp[:,0]))
            res.append(outp)
            dec_inp = outp.data.max(1)[1]
            if (dec_inp==self.pad_idx).all(): break
            if (targ is not None) and (random.random()<self.pr_force):
                if i>=targ.shape[1]: break
                dec_inp = targ[:,i]
        return torch.stack(res, dim=1)