Why using the gradient norm to control the dynamic loss scale
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Description
Hi everyone. Right now I am modifying the original FusedAdam( ) optimizer. I noticed that the dynamic scale factor is controlled by the norm of the gradients. Is there any specific reason why we are using this to control the scale factor, instead of using an element-wise metric? I mean can we check each element in the gradient and decay the scale factor whenever we find one Inf or NaN?
I would really appreciate it if you could help me with this! Thanks!
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Research direction
Read the existing FusedAdam implementation to understand how the gradient norm controls the dynamic loss scale. Compare that behavior with element-wise Inf/NaN detection; the issue is done only once the project provides a maintainer-confirmed rationale or a clearly scoped change request.
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Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100