4 point interval between my finetune model and shared model about genre HOT 7 CLOSED

As I read the [fairseq document](https://fairseq.readthedocs.io/en/latest/command_line_tools.html#fairseq-train). They defaultly use distributed data parallel. You use 32 GPU, the batch size is 8 when --required-batch-size-multiple 1 . So for each time of gradient descent, the gradient is learned from 32*8=256 cases. But when I use 1 gpu, the real batch size is 1*8=8. So this make some different.

I changed the learning rate, as it going lower, the performance getting better. From the table below, we can say 1e-06 is a proper lr for 1 GPU.

By the way. Thanks for you always quick answer for this issue and the issue #56 . I get the same trie tree with kilt_titles_trie_dict.pkl and get similar result with the model you shared.

Thanks again! Your guidances help a lot.

from genre.

It looks like in the first table you are using a "trie tree build specially for aida" where in the second you are using "WIKI trie tree 'kilt_titles_trie_dict.pkl'". So these two tables do not look comparable.

from genre.

no, the first table also use WIKI trie tree, not a tree build specially for aida. I add the word "without" before it now.

from genre.

Try to lower the learning rate. I used many GPUs so the gradient might have been more precise.

from genre.

hi, i guess maybe this is because you use a smaller trie tree (tree build for aida) for pretrain and finetune, so the searching space is smaller for the model. This will bring some performance improvement.
Do you think this assumption is reasonable?

from genre.

I did not train with a trie. Not during pertaining nor during finetuning.

from genre.

Yes, you didn't. I re-read the section 4.1, you train and finetune just like normal generation task. But when testing, use a trie to constrain the output.
But in Appendix A.1, the paper mentioned we fine-tune on AIDA without resetting the learning rate nor the optimizer statistics for 10k steps.
I just use 1 GPU, and you use 32 GPU, will this lead the result difference? I will try to lower the learning rate and see.

from genre.