In patch_trg, i cant understand why do you change the data shape like that

my dataset is composed with horizontal

i didn't use transpose(0,1)

so i changed your code like below

def patch_trg(trg, pad_idx):
       trg , gold = trg[:, :-1], trg[:, 1:].contiguous().view(-1)
       return trg, gold

And my dataset example is composed with below sample_1 = bos, 346, 32, 124, 214, eos sample_2 = bos, 346, 124, 214, eos ... sample_N = bos, 346, 32, 32, 32, 124, 214, eos

every length of sample data is different

so, this is my question. if i running your code, when making trg parameter, the eos token of longest sample is deleted that means, in every batch, the longest sample will be trained without eos token

so i want to know the correct role of that code(trg[:, :-1] and trg[:, 1:])?

i think that gold made for to get rid of bos token but i dont know the trg parameter