Add support for generic data sets to SliceGPT pass

[shaahji]

Add support for generic data sets to SliceGPT pass

Implementation of SliceGPT supported only a handful of specific datasets. Widen the support for any generic dataset via the data_config configuration.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass.
• Update documents if necessary.
• Lint and apply fixes to your code by running lintrunner -a
• Is this a user-facing change? If yes, give a description of this change to be included in the release notes.
• Is this PR including examples changes? If yes, please remember to update example documentation in a follow-up PR.

(Optional) Issue link

[jambayk]

is the squeeze to remove the batch dimension so that you can apply the config.calibration_batch_size later? Does this mean the data config must use batch size 1?

[shaahji]

Yes, the batch size has to be 1. However, even with batch size of 1, the generated output needs to be squeezed to drop the extra dimension.

[jambayk]

thanks! that makes sense. the default_dataloader from data config always inserts a batch dimension even if it is 1.

You could try this dataloader

Olive/olive/data/component/dataloader.py

Line 22 in 40845a3

def no_auto_batch_dataloader(dataset, **kwargs):

if you don't want the dataloader to batch the data.

[jambayk]

This looks okay for now. But we can probably add an option in the dataloader section of dataconfig in the future for random sampling so that this extra work of rewrapping the data with a dataloader is not needed.

Will also remove the potential confusion between batch_size, max_samples in the data config and calibration_batch_size, calibration_nsamples here.