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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class PositionalEmbedding(torch.nn.Module):
def __init__(self, d_model, max_len=128):
super().__init__()
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model).float()
pe.require_grad = False
for pos in range(max_len):
# for each dimension of the each position
for i in range(0, d_model, 2):
pe[pos, i] = math.sin(pos / (10000 ** ((2 * i)/d_model)))
pe[pos, i + 1] = math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
# include the batch size
self.pe = pe.unsqueeze(0)
# self.register_buffer('pe', pe)
def forward(self, x):
return self.pe
class BERTEmbedding(torch.nn.Module):
"""
BERT Embedding which is consisted with under 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, seq_len=64, dropout=0.1):
"""
:param vocab_size: total vocab size
:param embed_size: embedding size of token embedding
:param dropout: dropout rate
"""
super().__init__()
self.embed_size = embed_size
# (m, seq_len) --> (m, seq_len, embed_size)
# padding_idx is not updated during training, remains as fixed pad (0)
self.token = torch.nn.Embedding(vocab_size, embed_size, padding_idx=0)
self.segment = torch.nn.Embedding(3, embed_size, padding_idx=0)
self.position = PositionalEmbedding(d_model=embed_size, max_len=seq_len)
self.dropout = torch.nn.Dropout(p=dropout)
def forward(self, sequence, segment_label):
x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)
return self.dropout(x)
### attention layers
class MultiHeadedAttention(torch.nn.Module):
def __init__(self, heads, d_model, dropout=0.1):
super(MultiHeadedAttention, self).__init__()
assert d_model % heads == 0
self.d_k = d_model // heads
self.heads = heads
self.dropout = torch.nn.Dropout(dropout)
self.query = torch.nn.Linear(d_model, d_model)
self.key = torch.nn.Linear(d_model, d_model)
self.value = torch.nn.Linear(d_model, d_model)
self.output_linear = torch.nn.Linear(d_model, d_model)
def forward(self, query, key, value, mask):
"""
query, key, value of shape: (batch_size, max_len, d_model)
mask of shape: (batch_size, 1, 1, max_words)
"""
# (batch_size, max_len, d_model)
query = self.query(query)
key = self.key(key)
value = self.value(value)
# (batch_size, max_len, d_model) --> (batch_size, max_len, h, d_k) --> (batch_size, h, max_len, d_k)
query = query.view(query.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
key = key.view(key.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
value = value.view(value.shape[0], -1, self.heads, self.d_k).permute(0, 2, 1, 3)
# (batch_size, h, max_len, d_k) matmul (batch_size, h, d_k, max_len) --> (batch_size, h, max_len, max_len)
scores = torch.matmul(query, key.permute(0, 1, 3, 2)) / math.sqrt(query.size(-1))
# fill 0 mask with super small number so it wont affect the softmax weight
# (batch_size, h, max_len, max_len)
scores = scores.masked_fill(mask == 0, -1e9)
# (batch_size, h, max_len, max_len)
# softmax to put attention weight for all non-pad tokens
# max_len X max_len matrix of attention
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
# (batch_size, h, max_len, max_len) matmul (batch_size, h, max_len, d_k) --> (batch_size, h, max_len, d_k)
context = torch.matmul(weights, value)
# (batch_size, h, max_len, d_k) --> (batch_size, max_len, h, d_k) --> (batch_size, max_len, d_model)
context = context.permute(0, 2, 1, 3).contiguous().view(context.shape[0], -1, self.heads * self.d_k)
# (batch_size, max_len, d_model)
return self.output_linear(context)
class FeedForward(torch.nn.Module):
"Implements FFN equation."
def __init__(self, d_model, middle_dim=2048, dropout=0.1):
super(FeedForward, self).__init__()
self.fc1 = torch.nn.Linear(d_model, middle_dim)
self.fc2 = torch.nn.Linear(middle_dim, d_model)
self.dropout = torch.nn.Dropout(dropout)
self.activation = torch.nn.GELU()
def forward(self, x):
out = self.activation(self.fc1(x))
out = self.fc2(self.dropout(out))
return out
class EncoderLayer(torch.nn.Module):
def __init__(
self,
d_model=768,
heads=12,
feed_forward_hidden=768 * 4,
dropout=0.1
):
super(EncoderLayer, self).__init__()
self.layernorm = torch.nn.LayerNorm(d_model)
self.self_multihead = MultiHeadedAttention(heads, d_model)
self.feed_forward = FeedForward(d_model, middle_dim=feed_forward_hidden)
self.dropout = torch.nn.Dropout(dropout)
def forward(self, embeddings, mask):
# embeddings: (batch_size, max_len, d_model)
# encoder mask: (batch_size, 1, 1, max_len)
# result: (batch_size, max_len, d_model)
interacted = self.dropout(self.self_multihead(embeddings, embeddings, embeddings, mask))
# residual layer
interacted = self.layernorm(interacted + embeddings)
# bottleneck
feed_forward_out = self.dropout(self.feed_forward(interacted))
encoded = self.layernorm(feed_forward_out + interacted)
return encoded
class BERT(torch.nn.Module):
"""
BERT model : Bidirectional Encoder Representations from Transformers.
"""
def __init__(self, vocab_size, d_model=768, n_layers=12, heads=12, dropout=0.1):
"""
:param vocab_size: vocab_size of total words
:param hidden: BERT model hidden size
:param n_layers: numbers of Transformer blocks(layers)
:param attn_heads: number of attention heads
:param dropout: dropout rate
"""
super().__init__()
self.d_model = d_model
self.n_layers = n_layers
self.heads = heads
# paper noted they used 4 * hidden_size for ff_network_hidden_size
self.feed_forward_hidden = d_model * 4
# embedding for BERT, sum of positional, segment, token embeddings
self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=d_model)
# multi-layers transformer blocks, deep network
self.encoder_blocks = torch.nn.ModuleList(
[EncoderLayer(d_model, heads, d_model * 4, dropout) for _ in range(n_layers)])
def forward(self, x, segment_info):
# attention masking for padded token
# (batch_size, 1, seq_len, seq_len)
mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)
# embedding the indexed sequence to sequence of vectors
x = self.embedding(x, segment_info)
# running over multiple transformer blocks
for encoder in self.encoder_blocks:
x = encoder.forward(x, mask)
return x
class NextSentencePrediction(torch.nn.Module):
"""
2-class classification model : is_next, is_not_next
"""
def __init__(self, hidden):
"""
:param hidden: BERT model output size
"""
super().__init__()
self.linear = torch.nn.Linear(hidden, 2)
self.softmax = torch.nn.LogSoftmax(dim=-1)
def forward(self, x):
# use only the first token which is the [CLS]
return self.softmax(self.linear(x[:, 0]))
class MaskedLanguageModel(torch.nn.Module):
"""
predicting origin token from masked input sequence
n-class classification problem, n-class = vocab_size
"""
def __init__(self, hidden, vocab_size):
"""
:param hidden: output size of BERT model
:param vocab_size: total vocab size
"""
super().__init__()
self.linear = torch.nn.Linear(hidden, vocab_size)
self.softmax = torch.nn.LogSoftmax(dim=-1)
def forward(self, x):
return self.softmax(self.linear(x))
class BERTLM(torch.nn.Module):
"""
BERT Language Model
Next Sentence Prediction Model + Masked Language Model
"""
def __init__(self, bert: BERT, vocab_size):
"""
:param bert: BERT model which should be trained
:param vocab_size: total vocab size for masked_lm
"""
super().__init__()
self.bert = bert
self.next_sentence = NextSentencePrediction(self.bert.d_model)
self.mask_lm = MaskedLanguageModel(self.bert.d_model, vocab_size)
def forward(self, x, segment_label):
x = self.bert(x, segment_label)
return self.next_sentence(x), self.mask_lm(x) |