-
Notifications
You must be signed in to change notification settings - Fork 115
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Add a note about data-aware mixed precision assignment #1075
Add a note about data-aware mixed precision assignment #1075
Conversation
The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks @nikita-savelyevv !
Co-authored-by: Helena Kloosterman <[email protected]>
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks for the addition @nikita-savelyevv !
…yevv/optimum-intel into ns/update-ratio-description
What does this PR do?
Recently there appeared a confusion regarding when the
dataset
argument actually affects the resulting model. I've added some notes to help clarifying this.Namely, if users provide compression
ratio<1
and a dataset at the same time, according to NNCF data-aware mixed precision usingMAX_ACTIVATION_VARIANCE
criterion will be applied. https://github.com/openvinotoolkit/nncf/blob/develop/nncf/quantization/algorithms/weight_compression/algorithm.py#L96Otherwise, if no data-aware compression algorithm is selected,
dataset
argument is not used.Before submitting