This page shows how different projects use kernels.
karpathy/autoresearch uses kernels to
integrate Flash-Attention 3 through the get_kernel() method.
inclusionAI/AReaL uses kernels in an opt-in manner to integrate
optimized attention mechanisms.
huggingface/transformers primarily
depends on kernels for all optimizations related to optimized kernels, including
optimized attention implementations, MoE blocks, and quantization. Besides
get_kernel(), it also uses kernel layers to optimize the forward passes
of common layers involved in the modeling blocks. Some references are available
here
and here.
Refer to the following posts to know more:
Similar to transformers, huggingface/diffusers uses
kernels for integrating optimized kernels to compute attention.
Besides leveraging pre-built compute kernels, different projects
rely on kernels to also package, build, and distribute their
kernels on the Hugging Face Hub platform. This is made possible by the
“builder” component of kernels.
Visit huggingface.co/kernels to browse
the pre-built compute kernels available on the Hub.
Feel free to open a PR enlisting your project to show how kernels
is leveraged there.