-
Notifications
You must be signed in to change notification settings - Fork 197
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
GPFQ #666
GPFQ #666
Conversation
src/brevitas_examples/imagenet_classification/ptq/ptq_common.py
Outdated
Show resolved
Hide resolved
src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py
Outdated
Show resolved
Hide resolved
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.
We should potentially account for updating float layers (e.g. last unquantized) based on the activation quantization error at the input.
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.
For act_order, we could consider weights x activation magnitude as ordering criteria
We need to define an order for quantizing the input channels. When multiplying weight x activation, the input channel is the inner dimension of the matmul which means that we "lose" that information |
No description provided.