Faster attention calculation in 4-2.Seq2Seq?

Thanks for sharing! Just found out `Attention.get_att_weight` is calculating attention in a for-loop? this looks rather slow isn't it?

`4-2.Seq2Seq(Attention)/Seq2Seq(Attention).ipynb`

```python
def get_att_weight(self, dec_output, enc_outputs): # get attention weight one 'dec_output' with 'enc_outputs'
n_step = len(enc_outputs)
attn_scores = torch.zeros(n_step) # attn_scores : [n_step]

for i in range(n_step):
attn_scores[i] = self.get_att_score(dec_output, enc_outputs[i])

# Normalize scores to weights in range 0 to 1
return F.softmax(attn_scores).view(1, 1, -1)

def get_att_score(self, dec_output, enc_output): # enc_outputs [batch_size, num_directions(=1) * n_hidden]
score = self.attn(enc_output) # score : [batch_size, n_hidden]
return torch.dot(dec_output.view(-1), score.view(-1)) # inner product make scalar value
```

Suggested parallel version

```python
def get_att_weight(self, dec_output, enc_outputs): # get attention weight one 'dec_output' with 'enc_outputs'
n_step = len(enc_outputs)
attn_scores = torch.zeros(n_step,device=self.device) # attn_scores : [n_step]

enc_t = self.attn(enc_outputs)
score = dec_output.transpose(1,0).bmm(enc_t.transpose(1,0).transpose(2,1))
out1 = score.softmax(-1)
return out1

```

从 4-2.Seq2Seq(Attention)/Seq2Seq(Attention).ipynb 开始，检查 get_att_weight 和 get_att_score。比较基于循环的 attention 计算与建议的批量计算，确认它们产生等价的权重，并测量修改后的计算是否更快。当 notebook 在没有逐步循环的情况下仍保留预期的 attention 输出时，即表示完成。

由索引模型根据 Issue 内容生成。