Comments (2)
Tsingularity
commented on September 27, 2024
Hi Shun,
Thanks for your interest in our work! And your understanding is correct. Although prompt tuning could save storage cost after training, it still requires similar amount of (even more when you have many prompt tokens) GPU memory compared to full-finetuning, because just as you said, it still need a full back-propogation to do the optimization during training. You can find more discussion on this in our paper appendix "Empirical computational cost" part and figure 18.
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ShunLu91
commented on September 27, 2024
Thank you so much for taking the time to respond to my question. Your prompt and positive feedback means a lot to me!
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Related Issues (20)
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