code/src/model/lm.py [156:178]:
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                next_words = torch.multinomial(F.softmax(scores / temperature, dim=1), 1).squeeze(1)
            else:
                next_words = scores.max(1)[1]
            assert next_words.size() == (bs,)
            decoded[cur_len] = next_words
            cur_len += 1

            # stop when there is a </s> in each sentence
            if decoded.eq(self.eos_index).sum(0).ne(0).sum() == bs:
                break

        # compute the length of each generated sentence, and
        # put some padding after the end of each sentence
        lengths = torch.LongTensor(bs).fill_(cur_len)
        for i in range(bs):
            for j in range(cur_len):
                if decoded[j, i] == self.eos_index:
                    if j + 1 < max_len:
                        decoded[j + 1:, i] = self.pad_index
                    lengths[i] = j + 1
                    break
            if lengths[i] == max_len:
                decoded[-1, i] = self.eos_index
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code/src/model/seq2seq.py [319:341]:
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                next_words = torch.multinomial(F.softmax(scores / temperature, dim=1), 1).squeeze(1)
            else:
                next_words = scores.max(1)[1]
            assert next_words.size() == (bs,)
            decoded[cur_len] = next_words
            cur_len += 1

            # stop when there is a </s> in each sentence
            if decoded.eq(self.eos_index).sum(0).ne(0).sum() == bs:
                break

        # compute the length of each generated sentence, and
        # put some padding after the end of each sentence
        lengths = torch.LongTensor(bs).fill_(cur_len)
        for i in range(bs):
            for j in range(cur_len):
                if decoded[j, i] == self.eos_index:
                    if j + 1 < max_len:
                        decoded[j + 1:, i] = self.pad_index
                    lengths[i] = j + 1
                    break
            if lengths[i] == max_len:
                decoded[-1, i] = self.eos_index
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