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model.py
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model.py
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from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.init as I
import torch.nn.utils.rnn as R
import torch.nn.functional as F
import re
import logging
import constant as C
logger = logging.getLogger()
def log_sum_exp(tensor, dim=0):
"""LogSumExp operation."""
m, _ = torch.max(tensor, dim)
m_exp = m.unsqueeze(-1).expand_as(tensor)
return m + torch.log(torch.sum(torch.exp(tensor - m_exp), dim))
def sequence_mask(lens, max_len=None):
batch_size = lens.size(0)
if max_len is None:
max_len = lens.max().item()
ranges = torch.arange(0, max_len).long()
ranges = ranges.unsqueeze(0).expand(batch_size, max_len)
if lens.data.is_cuda:
ranges = ranges.cuda()
lens_exp = lens.unsqueeze(1).expand_as(ranges)
mask = ranges < lens_exp
return mask
def load_embedding(path: str,
dimension: int,
vocab: dict = None,
skip_first_line: bool = True,
):
logger.info('Scanning embedding file: {}'.format(path))
embed_vocab = set()
lower_mapping = {} # lower case - original
digit_mapping = {} # lower case + replace digit with 0 - original
digit_pattern = re.compile('\d')
with open(path, 'r', encoding='utf-8') as r:
if skip_first_line:
r.readline()
for line in r:
try:
token = line.split(' ')[0].strip()
if token:
embed_vocab.add(token)
token_lower = token.lower()
token_digit = re.sub(digit_pattern, '0', token_lower)
if token_lower not in lower_mapping:
lower_mapping[token_lower] = token
if token_digit not in digit_mapping:
digit_mapping[token_digit] = token
except UnicodeDecodeError:
continue
token_mapping = defaultdict(list) # embed token - vocab token
for token in vocab:
token_lower = token.lower()
token_digit = re.sub(digit_pattern, '0', token_lower)
if token in embed_vocab:
token_mapping[token].append(token)
elif token_lower in lower_mapping:
token_mapping[lower_mapping[token_lower]].append(token)
elif token_digit in digit_mapping:
token_mapping[digit_mapping[token_digit]].append(token)
logger.info('Loading embeddings')
weight = [[.0] * dimension for _ in range(len(vocab))]
with open(path, 'r', encoding='utf-8') as r:
if skip_first_line:
r.readline()
for line in r:
try:
segs = line.rstrip().split(' ')
token = segs[0]
if token in token_mapping:
vec = [float(v) for v in segs[1:]]
for t in token_mapping.get(token):
weight[vocab[t]] = vec.copy()
except UnicodeDecodeError:
continue
except ValueError:
continue
embed = nn.Embedding(
len(vocab),
dimension,
padding_idx=C.PAD_INDEX,
sparse=True,
_weight=torch.FloatTensor(weight)
)
return embed
class Linear(nn.Linear):
def __init__(self,
in_features: int,
out_features: int,
bias: bool = True):
super(Linear, self).__init__(in_features, out_features, bias=bias)
I.orthogonal_(self.weight)
class Linears(nn.Module):
def __init__(self,
in_features: int,
out_features: int,
hiddens: list,
bias: bool = True,
activation: str = 'tanh'):
super(Linears, self).__init__()
assert len(hiddens) > 0
self.in_features = in_features
self.out_features = self.output_size = out_features
in_dims = [in_features] + hiddens[:-1]
self.linears = nn.ModuleList([Linear(in_dim, out_dim, bias=bias)
for in_dim, out_dim
in zip(in_dims, hiddens)])
self.output_linear = Linear(hiddens[-1], out_features, bias=bias)
self.activation = getattr(F, activation)
def forward(self, inputs):
linear_outputs = inputs
for linear in self.linears:
linear_outputs = linear.forward(linear_outputs)
linear_outputs = self.activation(linear_outputs)
return self.output_linear.forward(linear_outputs)
class Highway(nn.Module):
def __init__(self,
size: int,
layer_num: int = 1,
activation: str = 'relu'):
super(Highway, self).__init__()
self.size = self.output_size = size
self.layer_num = layer_num
self.activation = getattr(F, activation)
self.non_linear = nn.ModuleList([Linear(size, size)
for _ in range(layer_num)])
self.gate = nn.ModuleList([Linear(size, size)
for _ in range(layer_num)])
def forward(self, inputs):
for layer in range(self.layer_num):
gate = F.sigmoid(self.gate[layer](inputs))
non_linear = self.activation(self.non_linear[layer](inputs))
inputs = gate * non_linear + (1 - gate) * inputs
return inputs
class LSTM(nn.LSTM):
def __init__(self,
input_size: int,
hidden_size: int,
num_layers: int = 1,
bias: bool = True,
batch_first: bool = False,
dropout: float = 0,
bidirectional: bool = False,
forget_bias: float = 0
):
super(LSTM, self).__init__(input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
bias=bias,
batch_first=batch_first,
dropout=dropout,
bidirectional=bidirectional)
self.output_size = hidden_size * 2 if bidirectional else hidden_size
self.forget_bias = forget_bias
def initialize(self):
for n, p in self.named_parameters():
if 'weight' in n:
I.orthogonal_(p)
elif 'bias' in n:
bias_size = p.size(0)
p[bias_size // 4:bias_size // 2].fill_(self.forget_bias)
class CharCNN(nn.Module):
def __init__(self, embedding_num, embedding_dim, filters):
super(CharCNN, self).__init__()
self.output_size = sum([x[1] for x in filters])
self.embedding = nn.Embedding(embedding_num,
embedding_dim,
padding_idx=0,
sparse=True)
self.convs = nn.ModuleList([nn.Conv2d(1, x[1], (x[0], embedding_dim))
for x in filters])
def forward(self, inputs):
inputs_embed = self.embedding.forward(inputs)
inputs_embed = inputs_embed.unsqueeze(1)
conv_outputs = [F.relu(conv.forward(inputs_embed)).squeeze(3)
for conv in self.convs]
max_pool_outputs = [F.max_pool1d(i, i.size(2)).squeeze(2)
for i in conv_outputs]
outputs = torch.cat(max_pool_outputs, 1)
return outputs
class CRF(nn.Module):
def __init__(self, label_size):
super(CRF, self).__init__()
self.label_size = label_size
self.start = self.label_size - 2
self.end = self.label_size - 1
transition = torch.randn(self.label_size, self.label_size)
self.transition = nn.Parameter(transition)
self.initialize()
def initialize(self):
self.transition.data[:, self.end] = -100.0
self.transition.data[self.start, :] = -100.0
def pad_logits(self, logits):
# lens = lens.data
batch_size, seq_len, label_num = logits.size()
# pads = Variable(logits.data.new(batch_size, seq_len, 2).fill_(-1000.0),
# requires_grad=False)
pads = logits.new_full((batch_size, seq_len, 2), -1000.0,
requires_grad=False)
logits = torch.cat([logits, pads], dim=2)
return logits
def calc_binary_score(self, labels, lens):
batch_size, seq_len = labels.size()
# labels_ext = Variable(labels.data.new(batch_size, seq_len + 2))
labels_ext = labels.new_empty((batch_size, seq_len + 2))
labels_ext[:, 0] = self.start
labels_ext[:, 1:-1] = labels
mask = sequence_mask(lens + 1, max_len=(seq_len + 2)).long()
# pad_stop = Variable(labels.data.new(1).fill_(self.end))
pad_stop = labels.new_full((1,), self.end, requires_grad=False)
pad_stop = pad_stop.unsqueeze(-1).expand(batch_size, seq_len + 2)
labels_ext = (1 - mask) * pad_stop + mask * labels_ext
labels = labels_ext
trn = self.transition
trn_exp = trn.unsqueeze(0).expand(batch_size, *trn.size())
lbl_r = labels[:, 1:]
lbl_rexp = lbl_r.unsqueeze(-1).expand(*lbl_r.size(), trn.size(0))
trn_row = torch.gather(trn_exp, 1, lbl_rexp)
lbl_lexp = labels[:, :-1].unsqueeze(-1)
trn_scr = torch.gather(trn_row, 2, lbl_lexp)
trn_scr = trn_scr.squeeze(-1)
mask = sequence_mask(lens + 1).float()
trn_scr = trn_scr * mask
score = trn_scr
return score
def calc_unary_score(self, logits, labels, lens):
labels_exp = labels.unsqueeze(-1)
scores = torch.gather(logits, 2, labels_exp).squeeze(-1)
mask = sequence_mask(lens).float()
scores = scores * mask
return scores
def calc_gold_score(self, logits, labels, lens):
unary_score = self.calc_unary_score(logits, labels, lens).sum(
1).squeeze(-1)
binary_score = self.calc_binary_score(labels, lens).sum(1).squeeze(-1)
return unary_score + binary_score
def calc_norm_score(self, logits, lens):
batch_size, seq_len, feat_dim = logits.size()
# alpha = logits.data.new(batch_size, self.label_size).fill_(-10000.0)
alpha = logits.new_full((batch_size, self.label_size), -100.0)
alpha[:, self.start] = 0
# alpha = Variable(alpha)
lens_ = lens.clone()
logits_t = logits.transpose(1, 0)
for logit in logits_t:
logit_exp = logit.unsqueeze(-1).expand(batch_size,
*self.transition.size())
alpha_exp = alpha.unsqueeze(1).expand(batch_size,
*self.transition.size())
trans_exp = self.transition.unsqueeze(0).expand_as(alpha_exp)
mat = logit_exp + alpha_exp + trans_exp
alpha_nxt = log_sum_exp(mat, 2).squeeze(-1)
mask = (lens_ > 0).float().unsqueeze(-1).expand_as(alpha)
alpha = mask * alpha_nxt + (1 - mask) * alpha
lens_ = lens_ - 1
alpha = alpha + self.transition[self.end].unsqueeze(0).expand_as(alpha)
norm = log_sum_exp(alpha, 1).squeeze(-1)
return norm
def viterbi_decode(self, logits, lens):
"""Borrowed from pytorch tutorial
Arguments:
logits: [batch_size, seq_len, n_labels] FloatTensor
lens: [batch_size] LongTensor
"""
batch_size, seq_len, n_labels = logits.size()
# vit = logits.data.new(batch_size, self.label_size).fill_(-10000)
vit = logits.new_full((batch_size, self.label_size), -100.0)
vit[:, self.start] = 0
# vit = Variable(vit)
c_lens = lens.clone()
logits_t = logits.transpose(1, 0)
pointers = []
for logit in logits_t:
vit_exp = vit.unsqueeze(1).expand(batch_size, n_labels, n_labels)
trn_exp = self.transition.unsqueeze(0).expand_as(vit_exp)
vit_trn_sum = vit_exp + trn_exp
vt_max, vt_argmax = vit_trn_sum.max(2)
vt_max = vt_max.squeeze(-1)
vit_nxt = vt_max + logit
pointers.append(vt_argmax.squeeze(-1).unsqueeze(0))
mask = (c_lens > 0).float().unsqueeze(-1).expand_as(vit_nxt)
vit = mask * vit_nxt + (1 - mask) * vit
mask = (c_lens == 1).float().unsqueeze(-1).expand_as(vit_nxt)
vit += mask * self.transition[self.end].unsqueeze(
0).expand_as(vit_nxt)
c_lens = c_lens - 1
pointers = torch.cat(pointers)
scores, idx = vit.max(1)
# idx = idx.squeeze(-1)
paths = [idx.unsqueeze(1)]
for argmax in reversed(pointers):
idx_exp = idx.unsqueeze(-1)
idx = torch.gather(argmax, 1, idx_exp)
idx = idx.squeeze(-1)
paths.insert(0, idx.unsqueeze(1))
paths = torch.cat(paths[1:], 1)
scores = scores.squeeze(-1)
return scores, paths
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.gpu = False
def cuda(self, device=None):
self.gpu = True
for module in self.children():
module.cuda(device)
return self
def cpu(self):
self.gpu = False
for module in self.children():
module.cpu()
return self
class LstmCrf(Model):
def __init__(self,
token_vocab,
label_vocab,
char_vocab,
word_embedding,
char_embedding,
crf,
lstm,
input_layer=None,
univ_fc_layer=None,
spec_fc_layer=None,
output_layer=None,
embed_dropout_prob=0,
lstm_dropout_prob=0,
use_char_embedding=True,
char_highway=None
):
super(LstmCrf, self).__init__()
self.token_vocab = token_vocab
self.label_vocab = label_vocab
self.char_vocab = char_vocab
self.idx_label = {idx: label for label, idx in label_vocab.items()}
self.embed_dropout_prob = embed_dropout_prob
self.lstm_dropout_prob = lstm_dropout_prob
self.use_char_embedding = use_char_embedding
self.word_embedding = word_embedding
self.char_embedding = char_embedding
self.feat_dim = word_embedding.embedding_dim
if use_char_embedding:
self.feat_dim += char_embedding.output_size
self.lstm = lstm
self.input_layer = input_layer
self.univ_fc_layer = univ_fc_layer
self.spec_fc_layer = spec_fc_layer
self.output_layer = output_layer
self.crf = crf
self.char_highway = char_highway
self.lstm_dropout = nn.Dropout(p=lstm_dropout_prob)
self.embed_dropout = nn.Dropout(p=embed_dropout_prob)
self.label_size = len(label_vocab)
if spec_fc_layer:
self.spec_gate = Linear(spec_fc_layer.in_features,
spec_fc_layer.out_features)
def forward_model(self, inputs, lens, chars=None, char_lens=None):
batch_size, seq_len = inputs.size()
# Word embedding
inputs_embed = self.word_embedding.forward(inputs)
# Character embedding
if self.use_char_embedding:
chars_embed = self.char_embedding.forward(chars)
if self.char_highway:
chars_embed = self.char_highway.forward(chars_embed)
chars_embed = chars_embed.view(batch_size, seq_len, -1)
inputs_embed = torch.cat([inputs_embed, chars_embed], dim=2)
inputs_embed = self.embed_dropout.forward(inputs_embed)
# LSTM layer
inputs_packed = R.pack_padded_sequence(inputs_embed, lens.tolist(),
batch_first=True)
lstm_out, _ = self.lstm(inputs_packed)
lstm_out, _ = R.pad_packed_sequence(lstm_out, batch_first=True)
lstm_out = lstm_out.contiguous().view(-1, self.lstm.output_size)
lstm_out = self.lstm_dropout.forward(lstm_out)
# Fully-connected layer
univ_feats = self.univ_fc_layer.forward(lstm_out)
if self.spec_fc_layer is not None:
spec_feats = self.spec_fc_layer.forward(lstm_out)
gate = F.sigmoid(self.spec_gate.forward(lstm_out))
outputs = gate * spec_feats + (1 - gate) * univ_feats
else:
outputs = univ_feats
outputs = outputs.view(batch_size, seq_len, self.label_size)
return outputs
def predict(self, inputs, labels, lens, chars=None, char_lens=None):
self.eval()
loglik, logits = self.loglik(inputs, labels, lens, chars, char_lens)
loss = -loglik.mean()
scores, preds = self.crf.viterbi_decode(logits, lens)
self.train()
return preds, loss
def loglik(self, inputs, labels, lens, chars=None, char_lens=None):
logits = self.forward_model(inputs, lens, chars, char_lens)
logits = self.crf.pad_logits(logits)
norm_score = self.crf.calc_norm_score(logits, lens)
gold_score = self.crf.calc_gold_score(logits, labels, lens)
loglik = gold_score - norm_score
return loglik, logits