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model.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from transformers import AutoModel
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True, epsilon=None, conditional=False,
hidden_units=None, hidden_activation='linear', hidden_initializer='xaiver', **kwargs):
super(LayerNorm, self).__init__()
"""
input_dim: inputs.shape[-1]
cond_dim: cond.shape[-1]
"""
self.center = center
self.scale = scale
self.conditional = conditional
self.hidden_units = hidden_units
self.hidden_initializer = hidden_initializer
self.epsilon = epsilon or 1e-12
self.input_dim = input_dim
self.cond_dim = cond_dim
if self.center:
self.beta = nn.Parameter(torch.zeros(input_dim))
if self.scale:
self.gamma = nn.Parameter(torch.ones(input_dim))
if self.conditional:
if self.hidden_units is not None:
self.hidden_dense = nn.Linear(in_features=self.cond_dim, out_features=self.hidden_units, bias=False)
if self.center:
self.beta_dense = nn.Linear(in_features=self.cond_dim, out_features=input_dim, bias=False)
if self.scale:
self.gamma_dense = nn.Linear(in_features=self.cond_dim, out_features=input_dim, bias=False)
self.initialize_weights()
def initialize_weights(self):
if self.conditional:
if self.hidden_units is not None:
if self.hidden_initializer == 'normal':
torch.nn.init.normal(self.hidden_dense.weight)
elif self.hidden_initializer == 'xavier': # glorot_uniform
torch.nn.init.xavier_uniform_(self.hidden_dense.weight)
if self.center:
torch.nn.init.constant_(self.beta_dense.weight, 0)
if self.scale:
torch.nn.init.constant_(self.gamma_dense.weight, 0)
def forward(self, inputs, cond=None):
if self.conditional:
if self.hidden_units is not None:
cond = self.hidden_dense(cond)
for _ in range(len(inputs.shape) - len(cond.shape)):
cond = cond.unsqueeze(1) # cond = K.expand_dims(cond, 1)
if self.center:
beta = self.beta_dense(cond) + self.beta
if self.scale:
gamma = self.gamma_dense(cond) + self.gamma
else:
if self.center:
beta = self.beta
if self.scale:
gamma = self.gamma
outputs = inputs
if self.center:
mean = torch.mean(outputs, dim=-1).unsqueeze(-1)
outputs = outputs - mean
if self.scale:
variance = torch.mean(outputs ** 2, dim=-1).unsqueeze(-1)
std = (variance + self.epsilon) ** 0.5
outputs = outputs / std
outputs = outputs * gamma
if self.center:
outputs = outputs + beta
return outputs
class ConvolutionLayer(nn.Module):
def __init__(self, input_size, channels, dilation, dropout=0.1):
super(ConvolutionLayer, self).__init__()
self.base = nn.Sequential(
nn.Dropout2d(dropout),
nn.Conv2d(input_size, channels, kernel_size=1),
nn.GELU(),
)
self.convs = nn.ModuleList(
[nn.Conv2d(channels, channels, kernel_size=3, groups=channels, dilation=d, padding=d) for d in dilation])
def forward(self, x):
x = x.permute(0, 3, 1, 2).contiguous()
x = self.base(x)
outputs = []
for conv in self.convs:
x = conv(x)
x = F.gelu(x)
outputs.append(x)
outputs = torch.cat(outputs, dim=1)
outputs = outputs.permute(0, 2, 3, 1).contiguous()
return outputs
class Biaffine(nn.Module):
def __init__(self, n_in, n_out=1, bias_x=True, bias_y=True):
super(Biaffine, self).__init__()
self.n_in = n_in
self.n_out = n_out
self.bias_x = bias_x
self.bias_y = bias_y
weight = torch.zeros((n_out, n_in + int(bias_x), n_in + int(bias_y)))
nn.init.xavier_normal_(weight)
self.weight = nn.Parameter(weight, requires_grad=True)
def extra_repr(self):
s = f"n_in={self.n_in}, n_out={self.n_out}"
if self.bias_x:
s += f", bias_x={self.bias_x}"
if self.bias_y:
s += f", bias_y={self.bias_y}"
return s
def forward(self, x, y):
if self.bias_x:
x = torch.cat((x, torch.ones_like(x[..., :1])), -1)
if self.bias_y:
y = torch.cat((y, torch.ones_like(y[..., :1])), -1)
# [batch_size, n_out, seq_len, seq_len]
s = torch.einsum('bxi,oij,byj->boxy', x, self.weight, y)
# remove dim 1 if n_out == 1
s = s.permute(0, 2, 3, 1)
return s
class MLP(nn.Module):
def __init__(self, n_in, n_out, dropout=0):
super().__init__()
self.linear = nn.Linear(n_in, n_out)
self.activation = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.dropout(x)
x = self.linear(x)
x = self.activation(x)
return x
class CoPredictor(nn.Module):
def __init__(self, cls_num, hid_size, biaffine_size, channels, ffnn_hid_size, dropout=0):
super().__init__()
self.mlp1 = MLP(n_in=hid_size, n_out=biaffine_size, dropout=dropout)
self.mlp2 = MLP(n_in=hid_size, n_out=biaffine_size, dropout=dropout)
self.biaffine = Biaffine(n_in=biaffine_size, n_out=cls_num, bias_x=True, bias_y=True)
self.mlp_rel = MLP(channels, ffnn_hid_size, dropout=dropout)
self.linear = nn.Linear(ffnn_hid_size, cls_num)
self.dropout = nn.Dropout(dropout)
def forward(self, x, y, z):
ent_sub = self.dropout(self.mlp1(x))
ent_obj = self.dropout(self.mlp2(y))
o1 = self.biaffine(ent_sub, ent_obj)
z = self.dropout(self.mlp_rel(z))
o2 = self.linear(z)
return o1 + o2
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.use_bert_last_4_layers = config.use_bert_last_4_layers
self.lstm_hid_size = config.lstm_hid_size
self.conv_hid_size = config.conv_hid_size
lstm_input_size = 0
self.bert = AutoModel.from_pretrained(config.bert_name, cache_dir="./cache/", output_hidden_states=True)
lstm_input_size += config.bert_hid_size
self.dis_embs = nn.Embedding(20, config.dist_emb_size)
self.reg_embs = nn.Embedding(3, config.type_emb_size)
self.encoder = nn.LSTM(lstm_input_size, config.lstm_hid_size // 2, num_layers=1, batch_first=True,
bidirectional=True)
conv_input_size = config.lstm_hid_size + config.dist_emb_size + config.type_emb_size
self.convLayer = ConvolutionLayer(conv_input_size, config.conv_hid_size, config.dilation, config.conv_dropout)
self.dropout = nn.Dropout(config.emb_dropout)
self.predictor = CoPredictor(config.label_num, config.lstm_hid_size, config.biaffine_size,
config.conv_hid_size * len(config.dilation), config.ffnn_hid_size,
config.out_dropout)
self.cln = LayerNorm(config.lstm_hid_size, config.lstm_hid_size, conditional=True)
def forward(self, bert_inputs, grid_mask2d, dist_inputs, pieces2word, sent_length):
'''
:param bert_inputs: [B, L']
:param grid_mask2d: [B, L, L]
:param dist_inputs: [B, L, L]
:param pieces2word: [B, L, L']
:param sent_length: [B]
:return:
'''
bert_embs = self.bert(input_ids=bert_inputs, attention_mask=bert_inputs.ne(0).float())
if self.use_bert_last_4_layers:
bert_embs = torch.stack(bert_embs[2][-4:], dim=-1).mean(-1)
else:
bert_embs = bert_embs[0]
length = pieces2word.size(1)
min_value = torch.min(bert_embs).item()
# Max pooling word representations from pieces
_bert_embs = bert_embs.unsqueeze(1).expand(-1, length, -1, -1)
_bert_embs = torch.masked_fill(_bert_embs, pieces2word.eq(0).unsqueeze(-1), min_value)
word_reps, _ = torch.max(_bert_embs, dim=2)
word_reps = self.dropout(word_reps)
packed_embs = pack_padded_sequence(word_reps, sent_length.cpu(), batch_first=True, enforce_sorted=False)
packed_outs, (hidden, _) = self.encoder(packed_embs)
word_reps, _ = pad_packed_sequence(packed_outs, batch_first=True, total_length=sent_length.max())
cln = self.cln(word_reps.unsqueeze(2), word_reps)
dis_emb = self.dis_embs(dist_inputs)
tril_mask = torch.tril(grid_mask2d.clone().long())
reg_inputs = tril_mask + grid_mask2d.clone().long()
reg_emb = self.reg_embs(reg_inputs)
conv_inputs = torch.cat([dis_emb, reg_emb, cln], dim=-1)
conv_inputs = torch.masked_fill(conv_inputs, grid_mask2d.eq(0).unsqueeze(-1), 0.0)
conv_outputs = self.convLayer(conv_inputs)
conv_outputs = torch.masked_fill(conv_outputs, grid_mask2d.eq(0).unsqueeze(-1), 0.0)
outputs = self.predictor(word_reps, word_reps, conv_outputs)
return outputs