#

Utilities for Transformer

10import torch

#

Subsequent mask to mask out data from future (subsequent) time steps

13def subsequent_mask(seq_len):

#

17    mask = torch.tril(torch.ones(seq_len, seq_len)).to(torch.bool).unsqueeze(-1)
18    return mask

#

21def _subsequent_mask():
22    from labml.logger import inspect
23    inspect(subsequent_mask(10)[:, :, 0])
24
25
26if __name__ == '__main__':
27    _subsequent_mask()