How can i compute seq2seq loss using mask?

Isn’t it a simple solution to use the arg weight to CrossEntropy or NLLLoss? AFAIK loss corresponding to padding steps will be set to zero if its corresponding weight is set to 0.

weight = torch.ones(vocab_size)
weight[pad_id] = 0.0
loss = nn.CrossEntropy(weight=weight)

or am I misunderstanding something?


That is correct. Simply set ignore_index to your padding index. Also set size_average to false and divide the loss by the number of non padding tokens.