def forward(self, incoming):
inp = Storage()
inp.attn_mask = incoming.data.sent_attnmask
inp.sent = incoming.data.sent
incoming.gen = gen = Storage()
self.teacherForcing(inp, gen)
w_o_f = flattenSequence(gen.w.transpose(0, 1),
incoming.data.sent_length - 1)
data_f = flattenSequence(
incoming.data.sent.transpose(0, 1)[1:],
incoming.data.sent_length - 1)
incoming.result.word_loss = self.lossCE(w_o_f, data_f)
incoming.result.perplexity = torch.exp(incoming.result.word_loss)
