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import logging
import torch
import torch.nn as nn
from transformers import BertModel
from utils.nn_utils import gelu
logger = logging.getLogger(__name__)
class BertEncoder(nn.Module):
"""This class using pretrained `Bert` model to encode token,
then fine-tuning `Bert` model
"""
def __init__(self, bert_model_name, trainable=False, output_size=0, activation=gelu, dropout=0.0):
"""This function initialize pertrained `Bert` model
Arguments:
bert_model_name {str} -- bert model name
Keyword Arguments:
output_size {float} -- output size (default: {None})
activation {nn.Module} -- activation function (default: {gelu})
dropout {float} -- dropout rate (default: {0.0})
"""
super().__init__()
self.bert_model = BertModel.from_pretrained(bert_model_name)
logger.info("Load bert model {} successfully.".format(bert_model_name))
self.output_size = output_size
if trainable:
logger.info("Start fine-tuning bert model {}.".format(bert_model_name))
else:
logger.info("Keep fixed bert model {}.".format(bert_model_name))
for param in self.bert_model.parameters():
param.requires_grad = trainable
if self.output_size > 0:
self.mlp = BertLinear(input_size=self.bert_model.config.hidden_size,
output_size=self.output_size,
activation=activation)
else:
self.output_size = self.bert_model.config.hidden_size
self.mlp = lambda x: x
if dropout > 0:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = lambda x: x
def get_output_dims(self):
return self.output_size
def forward(self, seq_inputs, token_type_inputs=None):
"""forward calculates forward propagation results, get token embedding
Args:
seq_inputs {tensor} -- sequence inputs (tokenized)
token_type_inputs (tensor, optional): token type inputs. Defaults to None.
Returns:
tensor: bert output for tokens
"""
if token_type_inputs is None:
token_type_inputs = torch.zeros_like(seq_inputs)
mask_inputs = (seq_inputs != 0).long()
outputs = self.bert_model(input_ids=seq_inputs, attention_mask=mask_inputs, token_type_ids=token_type_inputs)
last_hidden_state = outputs[0]
pooled_output = outputs[1]
return self.dropout(self.mlp(last_hidden_state)), self.dropout(self.mlp(pooled_output))
class BertLayerNorm(nn.Module):
"""This class is LayerNorm model for Bert
"""
def __init__(self, hidden_size, eps=1e-12):
"""This function sets `BertLayerNorm` parameters
Arguments:
hidden_size {int} -- input size
Keyword Arguments:
eps {float} -- epsilon (default: {1e-12})
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
"""This function propagates forwardly
Arguments:
x {tensor} -- input tesor
Returns:
tensor -- LayerNorm outputs
"""
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class BertLinear(nn.Module):
"""This class is Linear model for Bert
"""
def __init__(self, input_size, output_size, activation=gelu, dropout=0.0):
"""This function sets `BertLinear` model parameters
Arguments:
input_size {int} -- input size
output_size {int} -- output size
Keyword Arguments:
activation {function} -- activation function (default: {gelu})
dropout {float} -- dropout rate (default: {0.0})
"""
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.linear = nn.Linear(input_size, output_size)
self.linear.weight.data.normal_(mean=0.0, std=0.02)
self.linear.bias.data.zero_()
self.activation = activation
self.layer_norm = BertLayerNorm(self.output_size)
if dropout > 0:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = lambda x: x
def get_input_dims(self):
return self.input_size
def get_output_dims(self):
return self.output_size
def forward(self, x):
"""This function propagates forwardly
Arguments:
x {tensor} -- input tensor
Returns:
tenor -- Linear outputs
"""
output = self.activation(self.linear(x))
return self.dropout(self.layer_norm(output))
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