|
import torch.nn as nn |
|
import torch |
|
import math |
|
|
|
class TokenEmbedding(nn.Embedding): |
|
def __init__(self, vocab_size, embed_size=512): |
|
super().__init__(vocab_size, embed_size, padding_idx=0) |
|
|
|
|
|
class SegmentEmbedding(nn.Embedding): |
|
def __init__(self, embed_size=512): |
|
super().__init__(3, embed_size, padding_idx=0) |
|
|
|
|
|
class PositionalEmbedding(nn.Module): |
|
|
|
def __init__(self, d_model, max_len=512): |
|
super().__init__() |
|
|
|
|
|
pe = torch.zeros(max_len, d_model).float() |
|
pe.require_grad = False |
|
|
|
position = torch.arange(0, max_len).float().unsqueeze(1) |
|
div_term = (torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model)).exp() |
|
|
|
pe[:, 0::2] = torch.sin(position * div_term) |
|
pe[:, 1::2] = torch.cos(position * div_term) |
|
|
|
pe = pe.unsqueeze(0) |
|
self.register_buffer('pe', pe) |
|
|
|
def forward(self, x): |
|
return self.pe[:, :x.size(1)] |
|
|
|
class BERTEmbedding(nn.Module): |
|
""" |
|
BERT Embedding which consisted of following features |
|
1. TokenEmbedding : normal embedding matrix |
|
2. PositionalEmbedding : adding positional information using sin, cos |
|
2. SegmentEmbedding : adding sentence segment info, (sent_A:1, sent_B:2) |
|
|
|
sum of all these features are output of BERTEmbedding |
|
""" |
|
|
|
def __init__(self, vocab_size, embed_size, dropout=0.1): |
|
""" |
|
:param vocab_size: total vocab size |
|
:param embed_size: embedding size of token embedding |
|
:param dropout: dropout rate |
|
""" |
|
super().__init__() |
|
self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size) |
|
self.position = PositionalEmbedding(d_model=self.token.embedding_dim) |
|
self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim) |
|
self.dropout = nn.Dropout(p=dropout) |
|
self.embed_size = embed_size |
|
|
|
def forward(self, sequence, segment_label): |
|
x = self.token(sequence) + self.position(sequence) + self.segment(segment_label) |
|
return self.dropout(x) |
