when training the masked LM, the unmasked words (have label 0) were trained together with masked words?

[coddinglxf]

According to the code

    def random_word(self, sentence):
        tokens = sentence.split()
        output_label = []

        for i, token in enumerate(tokens):
            prob = random.random()
            if prob < 0.15:
                # 80% randomly change token to make token
                if prob < prob * 0.8:
                    tokens[i] = self.vocab.mask_index

                # 10% randomly change token to random token
                elif prob * 0.8 <= prob < prob * 0.9:
                    tokens[i] = random.randrange(len(self.vocab))

                # 10% randomly change token to current token
                elif prob >= prob * 0.9:
                    tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)

                output_label.append(self.vocab.stoi.get(token, self.vocab.unk_index))

            else:
                tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index)
                output_label.append(0)

        return tokens, output_label

Do we need to exclude the unmasked words when training the LM?

[codertimo]

@coddinglxf I just solved that problem with nn.NLLLoss(ignore_index=0) which 0 is equal to pad_index. Even if we target the 0(unmasked_value), it doesn't affect to the loss of propagation

[coddinglxf]

got it，thanks. however，when batch size is large，the final LM output will be “batch_size * seq_len * vacab_size”, such matrix is too big. maybe we can record the index of masked works when preprocessing the text， and use index technology to save memory.

[codertimo]

@coddinglxf that's what I thought at first, but can't implement it efficiently as much as GPU computation time. If you have any idea please implement and pull request plez :) It would be really cool to do it 👍

[coddinglxf]

@codertimo the output of transformer block will be : (batch, seq, hidden)

step 1: we can reshape the transformer output as : (batch*seq, hidden)

step 2: index the masked words for 0-th sentence， the i-th masked word will have a index: 0 * seq + i for j-th sentence, the i-th masked word will have a index: j * seq + i

combine step1 and step2: we can get the hidden output of masked words (using fancy indexing)

the question is we can not do this with multi-gpu

[leon-cas]

@coddinglxf I just solved that problem with nn.NLLLoss(ignore_index=0) which 0 is equal to pad_index. Even if we target the 0(unmasked_value), it doesn't affect to the loss of propagation

why here output_label is 0, but not "tokens[i]"? if we set output_label =0, that means, just 15% of training data is used to train MaskedLM? tokens[i] = self.vocab.stoi.get(token, self.vocab.unk_index) output_label.append(0)

[codertimo]

@leon-cas yes #36 it's solved with your question