import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from fastNLP.modules.utils import initial_parameter
[docs]class SelfAttention(nn.Module):
"""
Self Attention Module.
Args:
input_size: int, the size for the input vector
dim: int, the width of weight matrix.
num_vec: int, the number of encoded vectors
"""
def __init__(self, input_size, attention_unit=350, attention_hops=10, drop=0.5, initial_method=None,
use_cuda=False):
super(SelfAttention, self).__init__()
self.attention_hops = attention_hops
self.ws1 = nn.Linear(input_size, attention_unit, bias=False)
self.ws2 = nn.Linear(attention_unit, attention_hops, bias=False)
if use_cuda:
self.I = Variable(torch.eye(attention_hops).cuda(), requires_grad=False)
else:
self.I = Variable(torch.eye(attention_hops), requires_grad=False)
self.I_origin = self.I
self.drop = nn.Dropout(drop)
self.tanh = nn.Tanh()
initial_parameter(self, initial_method)
[docs] def penalization(self, attention):
"""
compute the penalization term for attention module
"""
baz = attention.size(0)
size = self.I.size()
if len(size) != 3 or size[0] != baz:
self.I = self.I_origin.expand(baz, -1, -1)
attentionT = torch.transpose(attention, 1, 2).contiguous()
mat = torch.bmm(attention, attentionT) - self.I[:attention.size(0)]
ret = (torch.sum(torch.sum((mat ** 2), 2), 1).squeeze() + 1e-10) ** 0.5
return torch.sum(ret) / size[0]
[docs] def forward(self, input, input_origin):
"""
:param input: the matrix to do attention. [baz, senLen, h_dim]
:param inp: then token index include pad token( 0 ) [baz , senLen]
:return output1: the input matrix after attention operation [baz, multi-head , h_dim]
:return output2: the attention penalty term, a scalar [1]
"""
input = input.contiguous()
size = input.size() # [bsz, len, nhid]
input_origin = input_origin.expand(self.attention_hops, -1, -1) # [hops,baz, len]
input_origin = input_origin.transpose(0, 1).contiguous() # [baz, hops,len]
y1 = self.tanh(self.ws1(self.drop(input))) # [baz,len,dim] -->[bsz,len, attention-unit]
attention = self.ws2(y1).transpose(1,
2).contiguous() # [bsz,len, attention-unit]--> [bsz, len, hop]--> [baz,hop,len]
attention = attention + (-999999 * (input_origin == 0).float()) # remove the weight on padding token.
attention = F.softmax(attention, 2) # [baz ,hop, len]
return torch.bmm(attention, input), self.penalization(attention) # output1 --> [baz ,hop ,nhid]