AQLM quantizer support

[BlackSamorez]

What does this PR do?

Fixes Vahe1994/AQLM#11

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@younesbelkada

[BlackSamorez]

A model to test it: BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch

[BlackSamorez]

A Google Colab demo: Mixtral in 2 bits.

[younesbelkada]

I saw in the config of the model you pushed on the Hub that you also included layer norm weights inside linear_weights_not_to_quantize , I think these can be excluded from the config as they are not an insitance of nn.Linear right?

[BlackSamorez]

They certainly can be excluded. It's just that converting from a freshly quantized AQLM format it would be troublesome to check if an unquantized .weight parameter is of nn.Linear or not. So I simply included all of them just in case. That Mixtral config can, indeed, be made somewhat shorter.

[younesbelkada]

Looks pretty clean already ! Thanks so much for working on this and converting Mixtral using AQLM format - thanks also for sharing the Google Colab ! Amazing work !
I assume the method works also on a T4 since you shared a colab demo, would you mind adding simple tests?
You can simply copy paste the tests from AWQ: https://github.com/huggingface/transformers/blob/main/tests/quantization/autoawq/test_awq.py and simply have Config tests and very simple model tests that tests that the model has been successfully converted to Aqlm format + a generation test. I would also add a simple test to make sure the model loads well on CPU
Can you also share some insights on generation speed for CPU & GPU ? 🙏

[younesbelkada]

One last thing; could you add installation instructions on our testing dockerfile: https://github.com/huggingface/transformers/blob/main/docker/transformers-all-latest-gpu/Dockerfile

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[younesbelkada]

cc @oobabooga this might be of your interest !

[BlackSamorez]

I updated the docked recipe and added tests, but they are skipped because aqlm is not installed in the testing environment.

[younesbelkada]

Thanks a lot ! I left two minor comments ! Can you also run make fixup ? This will redirect you to run make fix-copies which should fix the tests !

[younesbelkada]

you can move this method and make it public under integrations/aqlm.py and import locally the method inside _process_model_before_weight_loading

[BlackSamorez]

Done

[younesbelkada]

Suggested change

	class AwqConfigTest(unittest.TestCase):
	class AqlmConfigTest(unittest.TestCase):

[BlackSamorez]

Done

[younesbelkada]

Looks very clean ! Thanks so much for the integration !
As discussed with @ArthurZucker offline, we could leverage # Copied from on the tests but this is clearly not a blocker for me and we can merge as is! Looking forward to the integration !

[ArthurZucker]

LGTM thanks for the clean PR 🤗

[younesbelkada]

I also left one nit ! I tested it and seems to work only for python >=3.10

[younesbelkada]

@BlackSamorez on a google colab env the inference script works great, however on my VM, on a python 3.10 env with latest torch + cuda11.8 I constantly get:

Traceback (most recent call last):
  File "/transformers/scratch.py", line 11, in <module>
    output = quantized_model.generate(tokenizer("", return_tensors="pt")["input_ids"].cuda(), max_new_tokens=10)
  File "/miniconda3/envs/aqlm/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/transformers/src/transformers/generation/utils.py", line 1495, in generate
    return self.greedy_search(
  File "/transformers/src/transformers/generation/utils.py", line 2366, in greedy_search
    next_tokens = torch.argmax(next_tokens_scores, dim=-1)
RuntimeError: CUDA error: device kernel image is invalid
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Do you have an idea what might be wrong here?

[younesbelkada]

The only difference I see between the colab instance and mine is the CUDA version, I'll update it to 12.1 and loop back here

[BlackSamorez]

@younesbelkada
The kernel is compiled in runtime with

import os
from typing import Optional

import torch
from torch.utils.cpp_extension import load

CUDA_FOLDER = os.path.dirname(os.path.abspath(__file__))
CUDA_KERNEL = load(
    name="codebook_cuda",
    sources=[os.path.join(CUDA_FOLDER, "cuda_kernel.cpp"), os.path.join(CUDA_FOLDER, "cuda_kernel.cu")],
)

Maybe your nvcc is sourced from an incorrect cuda installment. I'm not really sure how to test it. Maybe you could try specifying an nvcc path somehow with an environmental variable.
I'll try to reproduce it as well.

[BlackSamorez]

CUDA 11.8 seems to work fine on my machine on an a100 GPU.

[BlackSamorez]

FYI: I've released aqlm version 1.0.1 where I added device guards to fix CUDA errors when running in the multi-gpu setup. I've added the corresponding tests similar to autoawq ones

[SunMarc]

Thanks for the integration !

[BlackSamorez]

Looks like some network error occured

[younesbelkada]

Thanks a lot @BlackSamorez !

[ArthurZucker]

🤗 🚀

[younesbelkada]

Hi @BlackSamorez !
Thanks again for your great work ! I was wondering if you could update the installation cell on the shared notebook to install transformers from source instead of your fork - that way we could catch potential bugs in the future before the release 🙏

[BlackSamorez]

@younesbelkada
Looks like this commit outside of the PR broke something.

AttributeError                            Traceback (most recent call last)

[<ipython-input-2-68b1b199d504>](https://localhost:8080/#) in <cell line: 3>()
      1 from transformers import AutoTokenizer, AutoModelForCausalLM
      2 
----> 3 quantized_model = AutoModelForCausalLM.from_pretrained(
      4     "BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch",
      5     torch_dtype="auto", device_map="auto", low_cpu_mem_usage=True,

4 frames

[/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py](https://localhost:8080/#) in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    565         elif type(config) in cls._model_mapping.keys():
    566             model_class = _get_model_class(config, cls._model_mapping)
--> 567             return model_class.from_pretrained(
    568                 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
    569             )

[/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py](https://localhost:8080/#) in from_pretrained(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)
   3561 
   3562         if hf_quantizer is not None:
-> 3563             hf_quantizer.postprocess_model(model)
   3564             model.hf_quantizer = hf_quantizer
   3565 

[/usr/local/lib/python3.10/dist-packages/transformers/quantizers/base.py](https://localhost:8080/#) in postprocess_model(self, model, **kwargs)
    177                 The keyword arguments that are passed along `_process_model_after_weight_loading`.
    178         """
--> 179         return self._process_model_after_weight_loading(model, **kwargs)
    180 
    181     @abstractmethod

[/usr/local/lib/python3.10/dist-packages/transformers/quantizers/quantizer_aqlm.py](https://localhost:8080/#) in _process_model_after_weight_loading(self, model, **kwargs)
     78 
     79     def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
---> 80         model._is_quantized_training_enabled = False
     81         return model
     82 

[/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py](https://localhost:8080/#) in __setattr__(self, name, value)
   1745                     buffers[name] = value
   1746                 else:
-> 1747                     super().__setattr__(name, value)
   1748 
   1749     def __delattr__(self, name):

AttributeError: can't set attribute '_is_quantized_training_enabled'