| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| class ScaledDotProductAttention(nn.Module): | |
| """ Scaled Dot-Product Attention """ | |
| def __init__(self, temperature): | |
| super().__init__() | |
| self.temperature = temperature | |
| self.softmax = nn.Softmax(dim=2) | |
| def forward(self, q, k, v, mask=None): | |
| attn = torch.bmm(q, k.transpose(1, 2)) | |
| attn = attn / self.temperature | |
| if mask is not None: | |
| attn = attn.masked_fill(mask, -np.inf) | |
| attn = self.softmax(attn) | |
| output = torch.bmm(attn, v) | |
| return output, attn | |