[Feature]: GPTQ/AWQ quantization is not fully optimized yet. The speed can be slower than non-quantized models.

[ShubhamVerma16]

🚀 The feature, motivation and pitch

While running the vLLM server with quantized models specifying the quantization type, the below mentioned Warning is shown:

WARNING 04-25 12:26:07 config.py:169] gptq quantization is not fully optimized yet. The speed can be slower than non-quantized models.

Is the a feature in progress or is there any workaround that can be done to handle the same.
Let me know if any more details are required from my end.

Alternatives

No response

Additional context

No response

[robertgshaw2-redhat]

You can use the Marlin kernels for int4 inference

We have a PR to automatically support GPTQ models with Marlin. Should be merged imminently

#3922

[davidgxue]

Would this have support for 8 bit quants as well or just 4 bit?

[robertgshaw2-redhat]

Right now, 4 bits only. But @alexm-nm is working on 8 bit version of Marlin ATM and should be done relatively soon

Marlin supports act_order=True and grouping as well

[alexm-redhat]

@davidgxue We have initial correctness on 8bit marlin, will do some perf checks and more testing and will put PR in a couple of days.

[alexm-redhat]

Btw, the new 8bit marlin will support all group_sizes and act_order

[davidgxue]

Awesome!! Thank you guys for the hard work!

[alexm-redhat]

@davidgxue here we add 8-bit support #4533

[davidgxue]

Thank you!!

[vidhyat98]

when can we expect awq models to be optimized for inference?

[jugodfroy]

+1 :)
(thank you for your work btw)

[alexm-redhat]

@vidhyat98 AWQ is added to marlin.

[mgoin]

Resolved! :)

[alexdauenhauer]

so it looks like we should pass quantization='awq_marlin' for awq quantized models?

[mgoin]

so it looks like we should pass quantization='awq_marlin' for awq quantized models?

@alexdauenhauer you don't need to pass any quantization argument, in fact it is best if you don't! vLLM will automatically choose the best kernel it can use for your quantized model

[alexdauenhauer]

@mgoin great to know thanks!