Isn't loss only supposed to be calculated on masked tokens?

[EmaadKhwaja]

In the training loop we have:

imgs = imgs.to(device=args.device)
logits, target = self.model(imgs)
loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
loss.backward()

However, the output of the transformer is:

  _, z_indices = self.encode_to_z(x)
.
.
.
  a_indices = mask * z_indices + (~mask) * masked_indices

  a_indices = torch.cat((sos_tokens, a_indices), dim=1)

  target = torch.cat((sos_tokens, z_indices), dim=1)

  logits = self.transformer(a_indices)

  return logits, target

which means the returned target is the original unmasked image tokens.

The MaskGIT paper seems to suggest that loss was only calculated on the masked tokens

[darius-lam]

I've attempted both strategies for a simple MaskGIT on CIFAR10 but the generation quality seems to still be bad. There are tricks that the authors are not telling us in the paper for their training scheme

[xuesongnie]

I have the same issue. Why loss was calculated on all tokens？

[EmaadKhwaja]

@Lamikins I believe the training issues come from an error in the masking formula. I've ammended the error: https://github.com/dome272/MaskGIT-pytorch/pull/16.

@xuesongnie

[xuesongnie]

@EmaadKhwajareturn logits[~mask], target[~mask] seems a bit problematic， we should calculate masked token loss return logits[mask], target[mask]

[EmaadKhwaja]

@xuesongnie it's because the mask calculated is applied to the wrong values. The other option would be to do r = math.floor(1-self.gamma(np.random.uniform()) * z_indices.shape[1]), but I don't like that because it's different from how the formula appears in the paper

[xuesongnie]

@xuesongnie it's because the mask calculated is applied to the wrong values. The other option would be to do r = math.floor(1-self.gamma(np.random.uniform()) * z_indices.shape[1]), but I don't like that because it's different from how the formula appears in the paper

Hi, bro. I find that poor performance after modifying return logits[mask], target[mask]. It is weird. I guess the embedding layer also needs to train the corresponding unmasked token.