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

```

Start in 4-2.Seq2Seq(Attention)/Seq2Seq(Attention).ipynb and inspect get_att_weight alongside get_att_score. Compare the loop-based and suggested batched attention calculations for equivalent weights and measure whether the revised calculation is faster. Done means the notebook preserves the intended attention output without the per-step loop.

Do mô hình lập chỉ mục viết ra từ nội dung của issue.