Spaces:
Runtime error
Runtime error
File size: 2,713 Bytes
7900c16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
import torch
import torch.nn as nn
from tencentpretrain.layers.layer_norm import LayerNorm
from tencentpretrain.utils import *
class MlmTarget(nn.Module):
"""
BERT exploits masked language modeling (MLM)
and next sentence prediction (NSP) for pretraining.
"""
def __init__(self, args, vocab_size):
super(MlmTarget, self).__init__()
self.vocab_size = vocab_size
self.hidden_size = args.hidden_size
self.emb_size = args.emb_size
self.factorized_embedding_parameterization = args.factorized_embedding_parameterization
self.act = str2act[args.hidden_act]
if self.factorized_embedding_parameterization:
self.linear_1 = nn.Linear(args.hidden_size, args.emb_size)
self.layer_norm = LayerNorm(args.emb_size)
self.linear_2 = nn.Linear(args.emb_size, self.vocab_size)
else:
self.linear_1 = nn.Linear(args.hidden_size, args.hidden_size)
self.layer_norm = LayerNorm(args.hidden_size)
self.linear_2 = nn.Linear(args.hidden_size, self.vocab_size)
self.softmax = nn.LogSoftmax(dim=-1)
self.criterion = nn.NLLLoss()
def mlm(self, memory_bank, tgt_mlm):
# Masked language modeling (MLM) with full softmax prediction.
output_mlm = self.act(self.linear_1(memory_bank))
output_mlm = self.layer_norm(output_mlm)
if self.factorized_embedding_parameterization:
output_mlm = output_mlm.contiguous().view(-1, self.emb_size)
else:
output_mlm = output_mlm.contiguous().view(-1, self.hidden_size)
tgt_mlm = tgt_mlm.contiguous().view(-1)
output_mlm = output_mlm[tgt_mlm > 0, :]
tgt_mlm = tgt_mlm[tgt_mlm > 0]
output_mlm = self.linear_2(output_mlm)
output_mlm = self.softmax(output_mlm)
denominator = torch.tensor(output_mlm.size(0) + 1e-6)
if output_mlm.size(0) == 0:
correct_mlm = torch.tensor(0.0)
else:
correct_mlm = torch.sum((output_mlm.argmax(dim=-1).eq(tgt_mlm)).float())
loss_mlm = self.criterion(output_mlm, tgt_mlm)
return loss_mlm, correct_mlm, denominator
def forward(self, memory_bank, tgt, seg):
"""
Args:
memory_bank: [batch_size x seq_length x hidden_size]
tgt: [batch_size x seq_length]
Returns:
loss: Masked language modeling loss.
correct: Number of words that are predicted correctly.
denominator: Number of masked words.
"""
# Masked language model (MLM).
loss, correct, denominator = self.mlm(memory_bank, tgt)
return loss, correct, denominator
|