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xiamengzhou avatar xiamengzhou commented on June 19, 2024 1

Hi! It's normal to get nan for some batches when the sampled batch does not contain data for a specific domain, usually because the sampling ratio for that domain is low.

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xiamengzhou avatar xiamengzhou commented on June 19, 2024

It's weird to me why it happens.. Have you tried the original set up with 7b domains? Does it cause problems? Meanwhile I will try out the 2 domain set up once I get some compute ready.

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PengWenChen avatar PengWenChen commented on June 19, 2024

Hi @xiamengzhou,
I also encounter this issue with the original dynamic loading setup in pruning.sh.
set_names=[cc,github,book,stackexchange,wiki,arxiv,c4]
proportion=[0.67,0.045,0.045,0.02,0.045,0.025,0.15]

And NaN happens in the first batch when calculating metric/train/stackexchange_LanguageCrossEntropy.

The environment I use is the same as yours except that flash attn is 2.3.6.
The sample data for pruning is 0.1B.

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xiamengzhou avatar xiamengzhou commented on June 19, 2024

Could you try the processed data I have here: https://drive.google.com/drive/folders/1WPIRx2NGkNBDswqZZh-hwI1h-QiKVCuN
And see if the same issue occurs again?
@PengWenChen @YanxiZSQ

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PengWenChen avatar PengWenChen commented on June 19, 2024

Hi @xiamengzhou! Thanks for your reply.
However, I can not access google drive where I am working :(
Could you please upload the processed data to this repository?
It would really help a lot!

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PengWenChen avatar PengWenChen commented on June 19, 2024

Hi, @xiamengzhou!
The proportion updating fails because of NaN loss on evaluation data. And it is because of the missing data of some subdatasets.
I solved this issue by increasing the number of evaluation sequence to 3500!

However, during normal training (update L_prune), the nan still happens due to the same reason (missing data of some subdatasets), but L_prune can still be updated.
I would like to confirm the correctness of this part!
Is this normal to get nan in train/metric/xx_LanguageCrossEntropy ?
Thank you.

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