TopKDecoder about pytorch-seq2seq HOT 6 OPEN

I studied the codes these days, and I thought you can use the torch.repeat_interleave. Such as follow:
hidden = tuple([torch.repeat_interleave(h, self.k, dim=1) for h in encoder_hidden])
inflated_encoder_outputs = torch.repeat_interleave(encoder_outputs, self.k, dim=0)

from pytorch-seq2seq.

hi, I am studying the code and have similar doubts. However, can you be clear what you mean by decoder_output? do you actually mean log_softmax_output?

from pytorch-seq2seq.

@JojoFisherman Yeah, I mean the output probability of decoder, i.e. log_softmax_output.

from pytorch-seq2seq.

I have the same question. It surprised me that no one has answered this. If theres really something wrong in the beam search, surely it will output some weird sequence. Do you have any conclusion about this?

from pytorch-seq2seq.

It seems some issues have referred that beam search doesn't work correctly. Unfortunately, maybe this repo is not active maintained now. Currently, I use fairseq (pytorch version) to conduct some related experiments.

from pytorch-seq2seq.

I studied the codes these days, and I thought you can use the torch.repeat_interleave. Such as follow:
hidden = tuple([torch.repeat_interleave(h, self.k, dim=1) for h in encoder_hidden])
inflated_encoder_outputs = torch.repeat_interleave(encoder_outputs, self.k, dim=0)

I had the problem with batch_size > 1, but after applying this comment, then it works now.

Thank you!!

from pytorch-seq2seq.