This is a PyTorch implementation of the Masked Language Model (MLM) used to pre-train the BERT model introduced in the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
BERT model is a transformer model. The paper pre-trains the model using MLM and with next sentence prediction. We have only implemented MLM here.
In next sentence prediction, the model is given two sentences
B and the model
makes a binary prediction whether
B is the sentence that follows
A in the actual text.
The model is fed with actual sentence pairs 50% of the time and random pairs 50% of the time.
This classification is done while applying MLM. We haven’t implemented this here.
This masks a percentage of tokens at random and trains the model to predict
the masked tokens.
They mask 15% of the tokens by replacing them with a special
The loss is computed on predicting the masked tokens only.
This causes a problem during fine-tuning and actual usage since there are no
at that time.
Therefore we might not get any meaningful representations.
To overcome this 10% of the masked tokens are replaced with the original token,
and another 10% of the masked tokens are replaced with a random token.
This trains the model to give representations about the actual token whether or not the
input token at that position is a
And replacing with a random token causes it to
give a representation that has information from the context as well;
because it has to use the context to fix randomly replaced tokens.
MLMs are harder to train than autoregressive models because they have a smaller training signal. i.e. only a small percentage of predictions are trained per sample.
Another problem is since the model is bidirectional, any token can see any other token.
This makes the “credit assignment” harder.
Let’s say you have the character level model trying to predict
home *s where i want to be.
At least during the early stages of the training, it’ll be super hard to figure out why the
* should be
i, it could be anything from the whole sentence.
Whilst, in an autoregressive setting the model will only have to use
h to predict
hom to predict
e and so on. So the model will initially start predicting with a shorter context first
and then learn to use longer contexts later.
Since MLMs have this problem it’s a lot faster to train if you start with a smaller sequence length
initially and then use a longer sequence length later.
Here is the training code for a simple MLM model.
67from typing import List 68 69import torch
This class implements the masking procedure for a given batch of token sequences.
padding_tokenis the padding token `[PAD]. We will use this to mark the labels that shouldn’t be used for loss calculation.
mask_tokenis the masking token
no_mask_tokensis a list of tokens that should not be masked. This is useful if we are training the MLM with another task like classification at the same time, and we have tokens such as
[CLS]that shouldn’t be masked.
n_tokenstotal number of tokens (used for generating random tokens)
masking_probis the masking probability
randomize_probis the probability of replacing with a random token
no_change_probis the probability of replacing with original token
79 def __init__(self, *, 80 padding_token: int, mask_token: int, no_mask_tokens: List[int], n_tokens: int, 81 masking_prob: float = 0.15, randomize_prob: float = 0.1, no_change_prob: float = 0.1, 82 ):
95 self.n_tokens = n_tokens 96 self.no_change_prob = no_change_prob 97 self.randomize_prob = randomize_prob 98 self.masking_prob = masking_prob 99 self.no_mask_tokens = no_mask_tokens + [padding_token, mask_token] 100 self.padding_token = padding_token 101 self.mask_token = mask_token
xis the batch of input token sequences. It’s a tensor of type
103 def __call__(self, x: torch.Tensor):
masking_prob of tokens
110 full_mask = torch.rand(x.shape, device=x.device) < self.masking_prob
112 for t in self.no_mask_tokens: 113 full_mask &= x != t
A mask for tokens to be replaced with original tokens
116 unchanged = full_mask & (torch.rand(x.shape, device=x.device) < self.no_change_prob)
A mask for tokens to be replaced with a random token
118 random_token_mask = full_mask & (torch.rand(x.shape, device=x.device) < self.randomize_prob)
Indexes of tokens to be replaced with random tokens
120 random_token_idx = torch.nonzero(random_token_mask, as_tuple=True)
Random tokens for each of the locations
122 random_tokens = torch.randint(0, self.n_tokens, (len(random_token_idx),), device=x.device)
The final set of tokens that are going to be replaced by
124 mask = full_mask & ~random_token_mask & ~unchanged
Make a clone of the input for the labels
127 y = x.clone()
note that this doesn’t include the tokens that will have the original token unchanged and
those that get replace with a random token.
132 x.masked_fill_(mask, self.mask_token)
Assign random tokens
134 x[random_token_idx] = random_tokens
[PAD] to all the other locations in the labels.
The labels equal to
[PAD] will not be used in the loss.
138 y.masked_fill_(~full_mask, self.padding_token)
Return the masked input and the labels
141 return x, y