Comments (3)
Hi,
Turns out the discrepancy in the behaviour between CPU and GPU is a bug in pytorch. We filed a bug for the same pytorch/pytorch#4002
Currently, we only support encoders and decoders with the same number of hidden layers. We created an issue to support what you are doing #107.
from pytorch-seq2seq.
Hi,
Please include n_layers=2 in the decoder too. That seems to have fixed the issue.
encoder = EncoderRNN(len(src.vocab), max_len, hidden_size, n_layers=2,
bidirectional=bidirectional, variable_lengths=True)
decoder = DecoderRNN(len(tgt.vocab), max_len, hidden_size * 2 if bidirectional else 1,
n_layers=2, dropout_p=0.2, use_attention=True,
bidirectional=bidirectional, eos_id=tgt.eos_id,
sos_id=tgt.sos_id)
from pytorch-seq2seq.
Hey,
Thanks for looking into it. Did you run this also on a GPU? Because I tried this already but it didn't solve the problem (well, no errors on the CPU, but only on GPU). Why do you think this should help? I don't think there is any theoretical reason that the encoder should have the same number of layers as the decoder (and it is in fact also not what I would like to have in my model).
from pytorch-seq2seq.
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from pytorch-seq2seq.