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model.py
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model.py
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_sequence
class MaskedNLLLoss(nn.Module):
def __init__(self, weight=None):
super(MaskedNLLLoss, self).__init__()
self.weight = weight
self.loss = nn.NLLLoss(weight=weight,
reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len, n_classes
target -> batch*seq_len
mask -> batch, seq_len
"""
mask_ = mask.view(-1,1) # batch*seq_len, 1
if type(self.weight)==type(None):
loss = self.loss(pred*mask_, target)/torch.sum(mask)
else:
loss = self.loss(pred*mask_, target)\
/torch.sum(self.weight[target]*mask_.squeeze())
return loss
class MaskedMSELoss(nn.Module):
def __init__(self):
super(MaskedMSELoss, self).__init__()
self.loss = nn.MSELoss(reduction='sum')
def forward(self, pred, target, mask):
"""
pred -> batch*seq_len
target -> batch*seq_len
mask -> batch*seq_len
"""
loss = self.loss(pred*mask, target)/torch.sum(mask)
return loss
class UnMaskedWeightedNLLLoss(nn.Module):
def __init__(self, weight=None):
super(UnMaskedWeightedNLLLoss, self).__init__()
self.weight = weight
self.loss = nn.NLLLoss(weight=weight,
reduction='sum')
def forward(self, pred, target):
"""
pred -> batch*seq_len, n_classes
target -> batch*seq_len
"""
if type(self.weight)==type(None):
loss = self.loss(pred, target)
else:
loss = self.loss(pred, target)\
/torch.sum(self.weight[target])
return loss
class SimpleAttention(nn.Module):
def __init__(self, input_dim):
super(SimpleAttention, self).__init__()
self.input_dim = input_dim
self.scalar = nn.Linear(self.input_dim,1,bias=False)
def forward(self, M, x=None):
"""
M -> (seq_len, batch, vector)
x -> dummy argument for the compatibility with MatchingAttention
"""
scale = self.scalar(M) # seq_len, batch, 1
alpha = F.softmax(scale, dim=0).permute(1,2,0) # batch, 1, seq_len
attn_pool = torch.bmm(alpha, M.transpose(0,1))[:,0,:] # batch, vector
return attn_pool, alpha
class MatchingAttention(nn.Module):
def __init__(self, mem_dim, cand_dim, alpha_dim=None, att_type='general'):
super(MatchingAttention, self).__init__()
assert att_type!='concat' or alpha_dim!=None
assert att_type!='dot' or mem_dim==cand_dim
self.mem_dim = mem_dim
self.cand_dim = cand_dim
self.att_type = att_type
if att_type=='general':
self.transform = nn.Linear(cand_dim, mem_dim, bias=False)
if att_type=='general2':
self.transform = nn.Linear(cand_dim, mem_dim, bias=True)
#torch.nn.init.normal_(self.transform.weight,std=0.01)
elif att_type=='concat':
self.transform = nn.Linear(cand_dim+mem_dim, alpha_dim, bias=False)
self.vector_prod = nn.Linear(alpha_dim, 1, bias=False)
def forward(self, M, x, mask=None):
"""
M -> (seq_len, batch, mem_dim)
x -> (batch, cand_dim)
mask -> (batch, seq_len)
"""
if type(mask)==type(None):
mask = torch.ones(M.size(1), M.size(0)).type(M.type())
if self.att_type=='dot':
# vector = cand_dim = mem_dim
M_ = M.permute(1,2,0) # batch, vector, seqlen
x_ = x.unsqueeze(1) # batch, 1, vector
alpha = F.softmax(torch.bmm(x_, M_), dim=2) # batch, 1, seqlen
elif self.att_type=='general':
M_ = M.permute(1,2,0) # batch, mem_dim, seqlen
x_ = self.transform(x).unsqueeze(1) # batch, 1, mem_dim
alpha = F.softmax(torch.bmm(x_, M_), dim=2) # batch, 1, seqlen
elif self.att_type=='general2':
M_ = M.permute(1,2,0) # batch, mem_dim, seqlen
x_ = self.transform(x).unsqueeze(1) # batch, 1, mem_dim
mask_ = mask.unsqueeze(2).repeat(1, 1, self.mem_dim).transpose(1, 2) # batch, seq_len, mem_dim
M_ = M_ * mask_
alpha_ = torch.bmm(x_, M_)*mask.unsqueeze(1)
alpha_ = torch.tanh(alpha_)
alpha_ = F.softmax(alpha_, dim=2)
# alpha_ = F.softmax((torch.bmm(x_, M_))*mask.unsqueeze(1), dim=2) # batch, 1, seqlen
alpha_masked = alpha_*mask.unsqueeze(1) # batch, 1, seqlen
alpha_sum = torch.sum(alpha_masked, dim=2, keepdim=True) # batch, 1, 1
alpha = alpha_masked/alpha_sum # batch, 1, 1 ; normalized
#import ipdb;ipdb.set_trace()
else:
M_ = M.transpose(0,1) # batch, seqlen, mem_dim
x_ = x.unsqueeze(1).expand(-1,M.size()[0],-1) # batch, seqlen, cand_dim
M_x_ = torch.cat([M_,x_],2) # batch, seqlen, mem_dim+cand_dim
mx_a = F.tanh(self.transform(M_x_)) # batch, seqlen, alpha_dim
alpha = F.softmax(self.vector_prod(mx_a),1).transpose(1,2) # batch, 1, seqlen
attn_pool = torch.bmm(alpha, M.transpose(0,1))[:,0,:] # batch, mem_dim
return attn_pool, alpha
class Attention(nn.Module):
def __init__(self, embed_dim, hidden_dim=None, out_dim=None, n_head=1, score_function='dot_product', dropout=0):
''' Attention Mechanism
:param embed_dim:
:param hidden_dim:
:param out_dim:
:param n_head: num of head (Multi-Head Attention)
:param score_function: scaled_dot_product / mlp (concat) / bi_linear (general dot)
:return (?, q_len, out_dim,)
'''
super(Attention, self).__init__()
if hidden_dim is None:
hidden_dim = embed_dim // n_head
if out_dim is None:
out_dim = embed_dim
self.embed_dim = embed_dim
self.hidden_dim = hidden_dim
self.n_head = n_head
self.score_function = score_function
self.w_k = nn.Linear(embed_dim, n_head * hidden_dim)
self.w_q = nn.Linear(embed_dim, n_head * hidden_dim)
self.proj = nn.Linear(n_head * hidden_dim, out_dim)
self.dropout = nn.Dropout(dropout)
if score_function == 'mlp':
self.weight = nn.Parameter(torch.Tensor(hidden_dim*2))
elif self.score_function == 'bi_linear':
self.weight = nn.Parameter(torch.Tensor(hidden_dim, hidden_dim))
else: # dot_product / scaled_dot_product
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.hidden_dim)
if self.weight is not None:
self.weight.data.uniform_(-stdv, stdv)
def forward(self, k, q):
if len(q.shape) == 2: # q_len missing
q = torch.unsqueeze(q, dim=1)
if len(k.shape) == 2: # k_len missing
k = torch.unsqueeze(k, dim=1)
mb_size = k.shape[0] # ?
k_len = k.shape[1]
q_len = q.shape[1]
# k: (?, k_len, embed_dim,)
# q: (?, q_len, embed_dim,)
# kx: (n_head*?, k_len, hidden_dim)
# qx: (n_head*?, q_len, hidden_dim)
# score: (n_head*?, q_len, k_len,)
# output: (?, q_len, out_dim,)
kx = self.w_k(k).view(mb_size, k_len, self.n_head, self.hidden_dim)
kx = kx.permute(2, 0, 1, 3).contiguous().view(-1, k_len, self.hidden_dim)
qx = self.w_q(q).view(mb_size, q_len, self.n_head, self.hidden_dim)
qx = qx.permute(2, 0, 1, 3).contiguous().view(-1, q_len, self.hidden_dim)
if self.score_function == 'dot_product':
kt = kx.permute(0, 2, 1)
score = torch.bmm(qx, kt)
elif self.score_function == 'scaled_dot_product':
kt = kx.permute(0, 2, 1)
qkt = torch.bmm(qx, kt)
score = torch.div(qkt, math.sqrt(self.hidden_dim))
elif self.score_function == 'mlp':
kxx = torch.unsqueeze(kx, dim=1).expand(-1, q_len, -1, -1)
qxx = torch.unsqueeze(qx, dim=2).expand(-1, -1, k_len, -1)
kq = torch.cat((kxx, qxx), dim=-1) # (n_head*?, q_len, k_len, hidden_dim*2)
# kq = torch.unsqueeze(kx, dim=1) + torch.unsqueeze(qx, dim=2)
score = torch.tanh(torch.matmul(kq, self.weight))
elif self.score_function == 'bi_linear':
qw = torch.matmul(qx, self.weight)
kt = kx.permute(0, 2, 1)
score = torch.bmm(qw, kt)
else:
raise RuntimeError('invalid score_function')
#score = F.softmax(score, dim=-1)
score = F.softmax(score, dim=0)
# print (score)
# print (sum(score))
output = torch.bmm(score, kx) # (n_head*?, q_len, hidden_dim)
output = torch.cat(torch.split(output, mb_size, dim=0), dim=-1) # (?, q_len, n_head*hidden_dim)
output = self.proj(output) # (?, q_len, out_dim)
output = self.dropout(output)
return output, score