[FEAT]: EETQ quantizer support
Author: dtlzhuangz
Pull request: yes

# What does this PR do?

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[EETQ](https://github.com/NetEase-FuXi/EETQ) supports int8 per-channel weight-only quantization for NVIDIA GPUS. The high-performance GEMM and GEMV kernels are from FasterTransformer and TensorRT-LLM. It requires no calibration dataset and does not need to pre-quantize your model. Moreover, the accuracy degradation is negligible owing to the per-channel quantization.
https://github.com/NetEase-FuXi/EETQ/issues/13

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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- [ ] Did you write any new necessary tests?


## Who can review?

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @muellerzr and @pacman100

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc and @younesbelkada

Documentation: @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1

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Fixes https://github.com/NetEase-FuXi/EETQ/issues/13

Comments:
@younesbelkada 
Please review the code and document to see if there is anything inappropriate.

> Hi @dtlzhuangz, thanks for the fast response. I've answered your questions. After fixing the issues pointed by @younesbelkada, we will ask a core maintainer for a final review.

Sorry, could you help me fix the error of 'Import block is un-sorted or un-formatted'? I'm not quite familiar with the CI.

Yes, I took care of that. You just needed to do `make style`. I will try to run the test on my setup to see if everything works ! 

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_30262). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

> Yes, I took care of that. You just needed to do `make style`. I will try to run the test on my setup to see if everything works !

Thank you so much for your guidance and effort!

> I was able to build the dockerfile and the tests are passing 🔥 Thanks again @dtlzhuangz for the clean PR. Do you any plan to release the package on pypi ? Installing from source is not ideal since it takes quite a lot of time to build the wheels + users are subject to breaking changes since there is no release yet.

Sorry for replying to the question late. My colleague and I are setting out to do it but the built files depend on the version of torch. Error occurs if the version mismatches. If there is no solution, we have to install a specific version of torch when installing EETQ 

Hi @SunMarc @younesbelkada @amyeroberts, we have released the .whl in the [release page](https://github.com/NetEase-FuXi/EETQ/releases) and updated the document. Please make a review. Thanks!

Hi @amyeroberts . I have fixed all the comments. I think ci errors should not be because of me, the errors occured after I modified the quantization.md. Please make a check.

@dtlzhuangz Regarding the failing tests - a fix has been merged into main. Could you rebase?

Re-ran the testing suite and tests seem to pass now ! 🤞 

Thank you all for your help! @SunMarc  @amyeroberts  @younesbelkada 

Great work thanks everyone involved in this !