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microsoft / mup

maximal update parametrization (µP)
https://arxiv.org/abs/2203.03466
MIT License
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Finetuning a Pretrained Model Using MuP #31

Closed zanussbaum closed 1 year ago

zanussbaum commented 1 year ago

Somewhat of a naive question, but say we have pretrained a model and now want to finetune it on a downstream task. Is there any reason we shouldn't replace the MuP layers with the equivalent torch layers? I have to imagine that we don't need to use MuP here, but want to make sure that this doesn't break anything if we replace them

thegregyang commented 1 year ago

If it's already pretrained, you can replace torch layers with muP layers to allow you to use muP optimizers (that can scale per layer lr with shape info), as long as you make sure to keep the model forward pass invariant when you switch out layers.

On Wed, Dec 7, 2022, 6:13 AM Zach Nussbaum @.***> wrote:

Somewhat of a naive question, but say we have pretrained a model and now want to finetune it on a downstream task. Is there any reason we shouldn't replace the MuP layers with the equivalent torch layers? I have to imagine that we don't need to use MuP here, but want to make sure that this doesn't break anything if we replace them

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zanussbaum commented 1 year ago

Sorry I was not clear! If we pretrained using MuP, we should replace the Readout layers with normal torch layers when fine tuning correct?

On Wed, Dec 7, 2022 at 11:42 AM Greg Yang @.***> wrote:

If it's already pretrained, you can replace torch layers with muP layers to allow you to use muP optimizers (that can scale per layer lr with shape info), as long as you make sure to keep the model forward pass invariant when you switch out layers.

On Wed, Dec 7, 2022, 6:13 AM Zach Nussbaum @.***> wrote:

Somewhat of a naive question, but say we have pretrained a model and now want to finetune it on a downstream task. Is there any reason we shouldn't replace the MuP layers with the equivalent torch layers? I have to imagine that we don't need to use MuP here, but want to make sure that this doesn't break anything if we replace them

— Reply to this email directly, view it on GitHub https://github.com/microsoft/mup/issues/31, or unsubscribe < https://github.com/notifications/unsubscribe-auth/AMWHHMY2DARLGILJMLQBUQDWMCZRLANCNFSM6AAAAAASXAFWV4

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thegregyang commented 1 year ago

I think this is up to you. It's possible to replacing muP layer with torch layers can make it easier to apply established hyperparameters for fine-tuning. On the other hand, the muP layers themselves can open up better hyperparameter choices for fine-tuning as well.

On Wed, Dec 7, 2022, 6:47 AM Zach Nussbaum @.***> wrote:

Sorry I was not clear! If we pretrained using MuP, we should replace the Readout layers with normal torch layers when fine tuning correct?

On Wed, Dec 7, 2022 at 11:42 AM Greg Yang @.***> wrote:

If it's already pretrained, you can replace torch layers with muP layers to allow you to use muP optimizers (that can scale per layer lr with shape info), as long as you make sure to keep the model forward pass invariant when you switch out layers.

On Wed, Dec 7, 2022, 6:13 AM Zach Nussbaum @.***> wrote:

Somewhat of a naive question, but say we have pretrained a model and now want to finetune it on a downstream task. Is there any reason we shouldn't replace the MuP layers with the equivalent torch layers? I have to imagine that we don't need to use MuP here, but want to make sure that this doesn't break anything if we replace them

— Reply to this email directly, view it on GitHub https://github.com/microsoft/mup/issues/31, or unsubscribe <

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