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model.py
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model.py
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import torch as T
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from data_util import config
import torch.nn.functional as F
from train_util import get_cuda
def init_lstm_wt(lstm):
for name, _ in lstm.named_parameters():
if 'weight' in name:
wt = getattr(lstm, name)
wt.data.uniform_(-config.rand_unif_init_mag, config.rand_unif_init_mag)
elif 'bias' in name:
# set forget bias to 1
bias = getattr(lstm, name)
n = bias.size(0)
start, end = n // 4, n // 2
bias.data.fill_(0.)
bias.data[start:end].fill_(1.)
def init_linear_wt(linear):
linear.weight.data.normal_(std=config.trunc_norm_init_std)
if linear.bias is not None:
linear.bias.data.normal_(std=config.trunc_norm_init_std)
def init_wt_normal(wt):
wt.data.normal_(std=config.trunc_norm_init_std)
class Encoder(nn.Module):
def __init__(self):
super(Encoder, self).__init__()
self.lstm = nn.LSTM(config.emb_dim, config.hidden_dim, num_layers=1, batch_first=True, bidirectional=True)
init_lstm_wt(self.lstm)
self.reduce_h = nn.Linear(config.hidden_dim * 2, config.hidden_dim)
init_linear_wt(self.reduce_h)
self.reduce_c = nn.Linear(config.hidden_dim * 2, config.hidden_dim)
init_linear_wt(self.reduce_c)
def forward(self, x, seq_lens):
packed = pack_padded_sequence(x, seq_lens, batch_first=True)
enc_out, enc_hid = self.lstm(packed)
enc_out,_ = pad_packed_sequence(enc_out, batch_first=True)
enc_out = enc_out.contiguous() #bs, n_seq, 2*n_hid
h, c = enc_hid #shape of h: 2, bs, n_hid
h = T.cat(list(h), dim=1) #bs, 2*n_hid
c = T.cat(list(c), dim=1)
h_reduced = F.relu(self.reduce_h(h)) #bs,n_hid
c_reduced = F.relu(self.reduce_c(c))
return enc_out, (h_reduced, c_reduced)
class encoder_attention(nn.Module):
def __init__(self):
super(encoder_attention, self).__init__()
self.W_h = nn.Linear(config.hidden_dim * 2, config.hidden_dim * 2, bias=False)
self.W_s = nn.Linear(config.hidden_dim * 2, config.hidden_dim * 2)
self.v = nn.Linear(config.hidden_dim * 2, 1, bias=False)
def forward(self, st_hat, h, enc_padding_mask, sum_temporal_srcs):
''' Perform attention over encoder hidden states
:param st_hat: decoder hidden state at current time step
:param h: encoder hidden states
:param enc_padding_mask:
:param sum_temporal_srcs: if using intra-temporal attention, contains summation of attention weights from previous decoder time steps
'''
# Standard attention technique (eq 1 in https://arxiv.org/pdf/1704.04368.pdf)
et = self.W_h(h) # bs,n_seq,2*n_hid
dec_fea = self.W_s(st_hat).unsqueeze(1) # bs,1,2*n_hid
et = et + dec_fea
et = T.tanh(et) # bs,n_seq,2*n_hid
et = self.v(et).squeeze(2) # bs,n_seq
# intra-temporal attention (eq 3 in https://arxiv.org/pdf/1705.04304.pdf)
if config.intra_encoder:
exp_et = T.exp(et)
if sum_temporal_srcs is None:
et1 = exp_et
sum_temporal_srcs = get_cuda(T.FloatTensor(et.size()).fill_(1e-10)) + exp_et
else:
et1 = exp_et/sum_temporal_srcs #bs, n_seq
sum_temporal_srcs = sum_temporal_srcs + exp_et
else:
et1 = F.softmax(et, dim=1)
# assign 0 probability for padded elements
at = et1 * enc_padding_mask
normalization_factor = at.sum(1, keepdim=True)
at = at / normalization_factor
at = at.unsqueeze(1) #bs,1,n_seq
# Compute encoder context vector
ct_e = T.bmm(at, h) #bs, 1, 2*n_hid
ct_e = ct_e.squeeze(1)
at = at.squeeze(1)
return ct_e, at, sum_temporal_srcs
class decoder_attention(nn.Module):
def __init__(self):
super(decoder_attention, self).__init__()
if config.intra_decoder:
self.W_prev = nn.Linear(config.hidden_dim, config.hidden_dim, bias=False)
self.W_s = nn.Linear(config.hidden_dim, config.hidden_dim)
self.v = nn.Linear(config.hidden_dim, 1, bias=False)
def forward(self, s_t, prev_s):
'''Perform intra_decoder attention
Args
:param s_t: hidden state of decoder at current time step
:param prev_s: If intra_decoder attention, contains list of previous decoder hidden states
'''
if config.intra_decoder is False:
ct_d = get_cuda(T.zeros(s_t.size()))
elif prev_s is None:
ct_d = get_cuda(T.zeros(s_t.size()))
prev_s = s_t.unsqueeze(1) #bs, 1, n_hid
else:
# Standard attention technique (eq 1 in https://arxiv.org/pdf/1704.04368.pdf)
et = self.W_prev(prev_s) # bs,t-1,n_hid
dec_fea = self.W_s(s_t).unsqueeze(1) # bs,1,n_hid
et = et + dec_fea
et = T.tanh(et) # bs,t-1,n_hid
et = self.v(et).squeeze(2) # bs,t-1
# intra-decoder attention (eq 7 & 8 in https://arxiv.org/pdf/1705.04304.pdf)
at = F.softmax(et, dim=1).unsqueeze(1) #bs, 1, t-1
ct_d = T.bmm(at, prev_s).squeeze(1) #bs, n_hid
prev_s = T.cat([prev_s, s_t.unsqueeze(1)], dim=1) #bs, t, n_hid
return ct_d, prev_s
class Decoder(nn.Module):
def __init__(self):
super(Decoder, self).__init__()
self.enc_attention = encoder_attention()
self.dec_attention = decoder_attention()
self.x_context = nn.Linear(config.hidden_dim*2 + config.emb_dim, config.emb_dim)
self.lstm = nn.LSTMCell(config.emb_dim, config.hidden_dim)
init_lstm_wt(self.lstm)
self.p_gen_linear = nn.Linear(config.hidden_dim * 5 + config.emb_dim, 1)
#p_vocab
self.V = nn.Linear(config.hidden_dim*4, config.hidden_dim)
self.V1 = nn.Linear(config.hidden_dim, config.vocab_size)
init_linear_wt(self.V1)
def forward(self, x_t, s_t, enc_out, enc_padding_mask, ct_e, extra_zeros, enc_batch_extend_vocab, sum_temporal_srcs, prev_s):
x = self.x_context(T.cat([x_t, ct_e], dim=1))
s_t = self.lstm(x, s_t)
dec_h, dec_c = s_t
st_hat = T.cat([dec_h, dec_c], dim=1)
ct_e, attn_dist, sum_temporal_srcs = self.enc_attention(st_hat, enc_out, enc_padding_mask, sum_temporal_srcs)
ct_d, prev_s = self.dec_attention(dec_h, prev_s) #intra-decoder attention
p_gen = T.cat([ct_e, ct_d, st_hat, x], 1)
p_gen = self.p_gen_linear(p_gen) # bs,1
p_gen = T.sigmoid(p_gen) # bs,1
out = T.cat([dec_h, ct_e, ct_d], dim=1) # bs, 4*n_hid
out = self.V(out) # bs,n_hid
out = self.V1(out) # bs, n_vocab
vocab_dist = F.softmax(out, dim=1)
vocab_dist = p_gen * vocab_dist
attn_dist_ = (1 - p_gen) * attn_dist
# pointer mechanism (as suggested in eq 9 https://arxiv.org/pdf/1704.04368.pdf)
if extra_zeros is not None:
vocab_dist = T.cat([vocab_dist, extra_zeros], dim=1)
final_dist = vocab_dist.scatter_add(1, enc_batch_extend_vocab, attn_dist_)
return final_dist, s_t, ct_e, sum_temporal_srcs, prev_s
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.encoder = Encoder()
self.decoder = Decoder()
self.embeds = nn.Embedding(config.vocab_size, config.emb_dim)
init_wt_normal(self.embeds.weight)
self.encoder = get_cuda(self.encoder)
self.decoder = get_cuda(self.decoder)
self.embeds = get_cuda(self.embeds)