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I am trying to implement an autoregressive transformer model similar to the paper attention is all you need. From what I have understood, in order to replicate the architecture fully, I need to give the transformer decoder 3 masks.

  1. Target subsequent mask: this is for causality.

  2. Target padding indexes: just to look at non-padded indices.

  3. Encoder padding indices: just to look at non-padded inputs from the encoder.

The snippet is here:

y = self.decoder(y, x,
                         tgt_mask=tgt_causal_mask,
                         tgt_key_padding_mask=tgt_padding_mask,
                         memory_key_padding_mask=src_padding_mask)

With masks being generated like this:

def generate_no_peek_mask(self, sz):
    mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
    mask = mask.float().masked_fill(mask == 0, float("-inf")).masked_fill(mask == 1, float(0.0))
    mask = mask.to(self.device)
    return mask

def generate_padding_mask(self, seq, pad_idx):
    return (seq != pad_idx).to(self.device)

The problem is that using these masks leads to issues with the Softmax function because of NaN values. Without these masks, the model does not generate any NaN value. I have tried toying with various input lengths and seeing what happens when I make sure my inputs are moderately big, but it still does not work. The only thing that works is not giving the decoder the masks.

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I found out what the problem was. It was not from subsequent mask. It was caused by bad key_padding_mask. PyTorch expects the key_padding_mask to be true wherever there is a padding token and false wherever there is none. I was generating the padding mask in exactly the opposite way. So the correct mask will be:

def generate_padding_mask(self, seq, pad_idx):
    return (seq == pad_idx).to(self.device)
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