xinghaochen / slab Goto Github PK
View Code? Open in Web Editor NEW[ICML 2024] Official PyTorch implementation of "SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization"
[ICML 2024] Official PyTorch implementation of "SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization"
Thank you for the very nice work.
Iām currently exploring various normalization techniques and noticed that RMSNorm is generally considered a less computationally intensive alternative compared to Layer Norm. Given that Iām not deeply familiar with the intricacies of these normalization methods, I would appreciate some clarification on how your method compares in terms of efficiency with RMSNorm.
Could you provide some insights or benchmarks on this? Any information you can share would be greatly helpful.
Thank you very much for your work, this work should be very scalable!
I have observed that the superparameter step (total step) of linearnorm in the code seems to be only related to the model, but not to the dataset, whether it is because these are fixed by your implementation.
linearnorm = partial(LinearNorm, norm1=ln, norm2=RepBN, step=60000)
in slab_swin
linearnorm = partial(LinearNorm, norm1=nn.LayerNorm, norm2=RepBN, step=120000)
in slab_cswin
linearnorm = partial(LinearNorm, norm1=ln, norm2=RepBN)
in others
Should I set my total step to epoch * len (train_dataloader)
in linearnorm when I train it? or any tricks here?
Could you please give me the link of the data set and the processing method of the data, and tell me which directory the data set should be placed in ?
A declarative, efficient, and flexible JavaScript library for building user interfaces.
š Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ššš
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ā¤ļø Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.