Exllama kernels support for AWQ models
Author: IlyasMoutawwakil
Pull request: yes

# What does this PR do?

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Following https://github.com/casper-hansen/AutoAWQ/pull/313
ExllamaV2 offers up to 2x speedup compared to GEMM, while also compatible with AMD ROCm.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?
@SunMarc and @younesbelkada

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

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

 -->

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

I guess all points are addressed.
@casper-hansen when is 0.1.9 planned ?

@younesbelkada The next release will be 0.2.0 🤗. For T4 support, I have not tested it. If AutoGPTQ supports T4 with ExLlama v1 and v2 kernels, AutoAWQ should too as the kernels are the same.

EDIT: To answer the timeline question. There is no set-in-stone plan for the next release. PRs to be merged before release include AMD support, Marlin support, Qwen2 support, and hopefully PEFT support. I expect this could be done in <1-2 weeks.

Awesome! Per my understanding ex-llama + AutoGPTQ should be supported on T4 so it should be all good ! 
Let me know whenever you have some progress for the PEFT support so that I'll dive in to add AWQ + PEFT support directly in PEFT 

@SunMarc on it!

works on rocm5.6 with torch 2.2
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AwqConfig

quantization_config = AwqConfig(version="exllama")
model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/Mistral-7B-Instruct-v0.1-AWQ",
    quantization_config=quantization_config,
    device_map="auto" or torch.device("cuda"),
)

input_ids = torch.randint(0, 100, (1, 128), dtype=torch.long, device="cuda")
output = model(input_ids)
print(output.logits)

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.1-AWQ")
input_ids = tokenizer.encode("How to make a cake", return_tensors="pt").to(model.device)
output = model.generate(input_ids, do_sample=True, max_length=50, pad_token_id=50256)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
The `device_map` is mandatory since `exllamav2_post_init` scratch space tensors allocation needs to check which qweights are on which cuda devices. I believe this is a requirement in GPTQ as well.