[quantization] feat: support aobaseconfig classes in TorchAOConfig

[sayakpaul]

What does this PR do?

The AOBaseConfig classes introduced in torchao (since 0.9.0) are more flexible. Similar to Transformers, this PR adds support for allowing them in Diffusers:

from diffusers import DiffusionPipeline, TorchAoConfig, PipelineQuantizationConfig
from torchao.quantization import Int8WeightOnlyConfig
import torch 

ckpt_id = "black-forest-labs/FLUX.1-dev"
pipeline_quant_config = PipelineQuantizationConfig(
    quant_mapping={"transformer": TorchAoConfig(Int8WeightOnlyConfig())}
)
pipe = DiffusionPipeline.from_pretrained(
    ckpt_id, quantization_config=pipeline_quant_config, torch_dtype=torch.bfloat16
).to("cuda")
_ = pipe("dog", num_inference_steps=2)

@stevhliu, would it be possible for you to propagate the relevant changes to our TorchAO docs from Transformers? Can happen in a later PR.

[HuggingFaceDocBuilderDev]

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[jerryzh168]

I think we can deprecate this one since this is less scalable than AOBaseConfig

[sayakpaul]

Yes, will do so after this PR. Meanwhile, if you could review the PR, it'd be helpful.

[SunMarc]

Thanks for updating this !

[jerryzh168]

nit: merge this with the branch in L521 to keep relevant things relevant together?

[jerryzh168]

we should probably clean this up in the future..

[jerryzh168]

looks good, would be good to put up a deprecation plan for torchao version support and clean up the old code for str support I think, I don't think there is any reason why people can't upgrade to the most recent version currently

[sayakpaul]

@jerryzh168 thanks for your reviews! I agree that it will make the library codebase cleaner if we started a deprecation cycle to promote the AoBaseConfig more and more. Will let @DN6 also review and then merge.