Add autoawq to requirements

[casper-hansen]

@oobabooga I have re-added autoawq to requirements because I recently overhauled the requirements for autoawq. This means the requirements are extremely minimal since we only rely on Triton kernels. We also have autoawq[kernels] for a more speedy inference solution, but I have chosen to leave it out of here due to torch having no backwards compatible support between minor versions.

Checklist:

• I have read the Contributing guidelines.

[oobabooga]

@casper-hansen thanks for the PR. I removed AutoAWQ from the requirements because, according to the AutoAWQ Kernels README, the provided wheels are for CUDA 12.4.1, while the project currently uses CUDA 12.1:

1a870b3

Is CUDA 12.4 really necessary? Wheels are usually not backwards compatible across CUDA versions and it becomes hard to conciliate ExLlamaV2 wheels with AutoAWQ wheels in the same environment.

[casper-hansen]

@oobabooga I may not have perfectly communicated this - but AutoAWQ kernels are now optional and does not get installed unless you specify pip install autoawq[kernels]. It's only needed if you want faster inference, so I would prefer for superusers to go do that themselves are compile the kernels if needed.

EDIT: To answer your question more directly: yes. If you want to use the latest compiled kernels, here is the versions list with torch and CUDA. However, I am looking into options of how to distibute these kernels in such a way that is independent of CUDA and Torch version.

• 0.0.7: torch 2.3.x with cuda 12.1
• 0.0.8: torch 2.4.x with cuda 12.4
• 0.0.9: torch 2.5.x with cuda 12.4

[oobabooga]

The thing about triton is that it doesn't work on Windows, and I guess most people who install this project are on Windows (unfortunately). So to add AutoAWQ back, I'd need either autoawq-kernels wheels that work with CUDA 12.1, or to update the whole project to CUDA 12.4.

[casper-hansen]

Seems there is a working triton installation for Windows below. This would be preferred since Windows is not compatible with most CUDA kernels either way.
https://github.com/woct0rdho/triton-windows/tree/v3.1.x-windows

[oobabooga]

Having additional installation steps defeats the purpose of adding the library to the requirements.txt. I would need wheels that work with the current environment to add the requirement back. Maybe I should do that in a fork, assuming the library does in fact work with cuda 12.1.