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VIB_3_0.py
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import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from utils import constant
from utils.vocab import Vocab
SMALL = 1e-08
# 0: tokens, 1: pos, 2: mask_s, 3: labels
class ToyNet(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.K = args.K
self.rnn_hidden = args.rnn_hidden
self.max_sent_len = args.max_sent_len
print("loading pretrained emb......")
self.emb_matrix = np.load(args.dset_dir+'/'+args.dataset+'/embedding.npy')
print("loading dataset vocab......")
self.vocab = Vocab(args.dset_dir+'/'+args.dataset+'/vocab.pkl')
# create embedding layers
self.emb = nn.Embedding(self.vocab.size, args.emb_dim, padding_idx=constant.PAD_ID)
self.pos_emb = nn.Embedding(len(constant.POS_TO_ID), args.pos_dim) if args.pos_dim > 0 else None
# initialize embedding with pretrained word embeddings
self.init_embeddings()
# dropout
self.input_dropout = nn.Dropout(args.input_dropout)
# define r distribution
self.r_var = self.K * self.max_sent_len
self.r_mean = nn.Parameter(torch.randn(self.max_sent_len, self.K))
self.r_std = nn.Parameter(torch.randn(self.max_sent_len, self.K))
# define encoder
self.BiLSTM = LSTMRelationModel(args)
self.layer1 = nn.Linear(self.rnn_hidden*2, 500)
self.layer2 = nn.Linear(500, 400)
self.hidden2mean = nn.Linear(400, self.K)
self.hidden2std = nn.Linear(400, self.K)
# decoder
self.rc_cla = nn.Linear(args.K*2, len(constant.LABEL_TO_ID))
self.ner_cla = nn.Linear(args.K, len(constant.BIO_TO_ID))
self.logsoft_fn = nn.LogSoftmax(dim=3)
def init_embeddings(self):
if self.emb_matrix is None:
self.emb.weight.data[1:, :].uniform_(-1.0, 1.0)
else:
self.emb_matrix = torch.from_numpy(self.emb_matrix)
self.emb.weight.data.copy_(self.emb_matrix)
def get_statistics_batch(self, embeds):
mean = self.hidden2mean(embeds) # bsz, seqlen, dim
std = self.hidden2std(embeds) # bsz, seqlen, dim
cov = std * std + SMALL
return mean, cov
def get_sample_from_param_batch(self, mean, cov, sample_size):
bsz, seqlen, tag_dim = mean.shape
z = torch.randn(bsz, sample_size, seqlen, tag_dim).cuda()
z = z * torch.sqrt(cov).unsqueeze(1).expand(-1, sample_size, -1, -1) + \
mean.unsqueeze(1).expand(-1, sample_size, -1, -1)
return z
def kl_div(self, param1, param2, real_len, mask_kl):
"""
Calculates the KL divergence between a categorical distribution and a
uniform categorical distribution.
Parameters
----------
alpha : torch.Tensor
Parameters of the categorical or gumbel-softmax distribution.
Shape (N, D)
"""
mean1, cov1 = param1
mean2, cov2 = param2
bsz, seqlen, tag_dim = mean1.shape
var_len = tag_dim * real_len
cov2_inv = 1 / cov2
mean_diff = mean1 - mean2
mean_diff = mean_diff.view(bsz, -1)
cov1 = cov1.view(bsz, -1)
cov2 = cov2.view(bsz, -1)
cov2_inv = cov2_inv.view(bsz, -1)
mask_kl = mask_kl.view(bsz, -1)
temp = (mean_diff * cov2_inv*mask_kl).view(bsz, 1, -1)
KL = 0.5 * (torch.sum(torch.log(cov2)*mask_kl ,dim=1) - torch.sum(torch.log(cov1)*mask_kl, dim=1) - var_len
+ torch.sum(cov2_inv * cov1*mask_kl, dim=1) + torch.bmm(temp, mean_diff.view(bsz, -1, 1)).view(bsz))
return KL
# 0: tokens, 1: pos, 2: mask_s
def forward(self, inputs, num_sample=1):
# construct input feature X
tokens, pos, mask_s = inputs
tokens_emb = self.emb(tokens)
tokens_emb = [tokens_emb]
if self.args.pos_dim > 0:
tokens_emb += [self.pos_emb(pos)]
tokens_emb = torch.cat(tokens_emb, dim=2)
lens = mask_s.sum(dim=1)
tokens_emb = self.input_dropout(tokens_emb)
# forward into BiLSTM
temp = self.BiLSTM((tokens_emb, lens)) # bsz, len, K
temp = F.relu(self.layer1(temp))
temp = F.relu(self.layer2(temp))
# encode t
mean, cov = self.get_statistics_batch(temp)
encoding = self.get_sample_from_param_batch(mean, cov, num_sample)
# mask for output
s_len = encoding.size(2)
ner_mask = mask_s.unsqueeze(-1).expand(-1, -1, len(constant.BIO_TO_ID))
tmp_mask = mask_s.unsqueeze(-1).expand(-1, -1, len(constant.LABEL_TO_ID))
tmp_mask = tmp_mask.unsqueeze(1).expand(-1, s_len, -1, -1)
rc_mask = torch.zeros_like(tmp_mask)
real_len = mask_s.sum(dim=1).int()
for i in range(tmp_mask.size(0)):
rc_mask[i, :real_len[i], :real_len[i], :] = tmp_mask[i, :real_len[i], :real_len[i], :]
ner_mask = ner_mask.unsqueeze(1).expand(-1, num_sample, -1, -1)
rc_mask = rc_mask.unsqueeze(1).expand(-1, num_sample, -1, -1, -1)
# ner prediction
ner_logit = self.ner_cla(encoding)
ner_logit = self.logsoft_fn(ner_logit)
ner_logit = ner_logit * ner_mask
# rc prediction
encoding_e1 = encoding.unsqueeze(3).expand(-1, -1, -1, s_len, -1) # bsz, sample_num, len, len, K
encoding_e2 = encoding.unsqueeze(2).expand(-1, -1, s_len, -1, -1)
encoding_e = torch.cat([encoding_e1, encoding_e2], dim=4)
rc_logit = torch.sigmoid(self.rc_cla(encoding_e))
rc_logit = rc_logit * rc_mask
# caculate KL divergence
seqlen, bsz = s_len, mask_s.size(0)
mask_kl = mask_s.unsqueeze(-1).repeat(1, 1, self.K)
mean_r = self.r_mean[:seqlen].unsqueeze(0).expand(bsz, -1, -1)
std_r = self.r_std[:seqlen].unsqueeze(0).expand(bsz, -1, -1)
cov_r = std_r * std_r + SMALL
mean, cov = mean, cov
mean_r, cov_r = mean_r, cov_r
kl_div = self.kl_div((mean, cov), (mean_r, cov_r), real_len, mask_kl)
return kl_div.mean(), ner_logit.view(-1, len(constant.BIO_TO_ID)), rc_logit.view(-1, len(constant.LABEL_TO_ID))
# BiLSTM model
class LSTMRelationModel(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.in_dim = args.emb_dim + args.pos_dim
self.rnn = nn.LSTM(self.in_dim, self.args.rnn_hidden, 1, batch_first=True, \
dropout=0, bidirectional=True)
def encode_with_rnn(self, rnn_inputs, seq_lens, batch_size):
h0, c0 = rnn_zero_state(batch_size, self.args.rnn_hidden, 1, True)
rnn_inputs = nn.utils.rnn.pack_padded_sequence(rnn_inputs, seq_lens, batch_first=True)
rnn_outputs, (ht, ct) = self.rnn(rnn_inputs, (h0, c0))
rnn_outputs, _ = nn.utils.rnn.pad_packed_sequence(rnn_outputs, batch_first=True)
return rnn_outputs
def forward(self, inputs):
# unpack inputs
inputs, lens = inputs[0], inputs[1]
return self.encode_with_rnn(inputs, lens, inputs.size()[0])
# Initialize zero state
def rnn_zero_state(batch_size, hidden_dim, num_layers, bidirectional=True, use_cuda=True):
total_layers = num_layers * 2 if bidirectional else num_layers
state_shape = (total_layers, batch_size, hidden_dim)
h0 = c0 = Variable(torch.zeros(*state_shape), requires_grad=False)
if use_cuda:
return h0.cuda(), c0.cuda()
else:
return h0, c0