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lstm_dec_v1.py
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import torch
import torch as T
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from utils_pg import *
class LSTMAttentionDecoder(nn.Module):
def __init__(self, input_size, hidden_size, ctx_size, device, copy, coverage, is_predicting):
super(LSTMAttentionDecoder, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.ctx_size = ctx_size
self.is_predicting = is_predicting
self.device = device
self.copy = copy
self.coverage = coverage
self.lstm_1 = nn.LSTMCell(self.input_size, self.hidden_size)
self.Wc_att = nn.Parameter(torch.Tensor(self.ctx_size, self.ctx_size))
self.b_att = nn.Parameter(torch.Tensor(self.ctx_size))
self.W_comb_att = nn.Parameter(torch.Tensor(self.ctx_size, 2*self.hidden_size))
self.U_att = nn.Parameter(torch.Tensor(1, self.ctx_size))
if self.coverage:
self.W_coverage= nn.Parameter(torch.Tensor(self.ctx_size, 1))
self.init_weights()
def init_weights(self):
init_lstm_weight(self.lstm_1)
init_ortho_weight(self.Wc_att)
init_bias(self.b_att)
init_ortho_weight(self.W_comb_att)
init_ortho_weight(self.U_att)
if self.coverage:
init_ortho_weight(self.W_coverage)
def forward(self, y_emb, context, init_state, x_mask, y_mask, xid=None, init_coverage=None):
def _get_word_atten(pctx, h1, x_mask, acc_att=None): #acc_att: B * len(B)
if acc_att is not None:
h = F.linear(h1, self.W_comb_att) + F.linear(T.transpose(acc_att, 0, 1).unsqueeze(2), self.W_coverage) # len(x) * B * ?
else:
h = F.linear(h1, self.W_comb_att)
unreg_att = T.tanh(pctx + h) * x_mask
unreg_att = F.linear(unreg_att, self.U_att)
word_atten = T.exp(unreg_att - T.max(unreg_att, 0, keepdim = True)[0]) * x_mask
sum_word_atten = T.sum(word_atten, 0, keepdim = True)
word_atten = word_atten / sum_word_atten
return word_atten
def recurrence(x, y_mask, hidden, pctx, context, x_mask, acc_att=None):
pre_h, pre_c = hidden
h1, c1 = self.lstm_1(x, hidden)
h1 = y_mask * h1 + (1.0 - y_mask) * pre_h
c1 = y_mask * c1 + (1.0 - y_mask) * pre_c
# len(x) * batch_size * 1
s = T.cat((h1.view(-1, self.hidden_size), c1.view(-1, self.hidden_size)), 1)
if self.coverage:
word_atten = _get_word_atten(pctx, s, x_mask, acc_att)
else:
word_atten = _get_word_atten(pctx, s, x_mask)
atted_ctx = T.sum(word_atten * context, 0)
word_atten_ = T.transpose(word_atten.view(x_mask.size(0), -1), 0, 1)
if self.coverage:
acc_att += word_atten_
return (h1, c1), h1, atted_ctx, word_atten_, acc_att
else:
return (h1, c1), h1, atted_ctx, word_atten_
hs, cs, ss, atts, dists, xids, Cs = [], [], [], [], [], [], []
hidden = init_state
acc_att = init_coverage
if self.copy:
xid = T.transpose(xid, 0, 1) # B * len(x)
pctx = F.linear(context, self.Wc_att, self.b_att)
x = y_emb
steps = range(y_emb.size(0))
for i in steps:
if self.coverage:
Cs += [acc_att]
hidden, s, att, att_dist, acc_att = recurrence(x[i], y_mask[i], hidden, pctx, context, x_mask, acc_att)
else:
hidden, s, att, att_dist = recurrence(x[i], y_mask[i], hidden, pctx,context, x_mask)
hs += [hidden[0]]
cs += [hidden[1]]
ss += [s]
atts += [att]
dists += [att_dist]
xids += [xid]
if self.coverage:
if self.is_predicting :
Cs += [acc_att]
Cs = Cs[1:]
Cs = T.stack(Cs).view(y_emb.size(0), *Cs[0].size())
hs = T.stack(hs).view(y_emb.size(0), *hs[0].size())
cs = T.stack(cs).view(y_emb.size(0), *cs[0].size())
ss = T.stack(ss).view(y_emb.size(0), *ss[0].size())
atts = T.stack(atts).view(y_emb.size(0), *atts[0].size())
dists = T.stack(dists).view(y_emb.size(0), *dists[0].size())
if self.copy:
xids = T.stack(xids).view(y_emb.size(0), *xids[0].size())
if self.copy and self.coverage:
return (hs, cs), ss, atts, dists, xids, Cs
elif self.copy:
return (hs, cs), ss, atts, dists, xids
elif self.coverage:
return (hs, cs), ss, atts, dists, Cs
else:
return (hs, cs), ss, atts