Refactor KTO [3/N]: Extract dataset processing to _prepare_dataset method#4788
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albertvillanova wants to merge 1 commit into
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Refactor KTO [3/N]: Extract dataset processing to _prepare_dataset method#4788albertvillanova wants to merge 1 commit into
albertvillanova wants to merge 1 commit into
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qgallouedec
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Jan 8, 2026
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| if is_processed: | ||
| logger.info( | ||
| f"{dataset_name} dataset is already processed (contains 'completion_input_ids'), skipping preprocessing." | ||
| ) | ||
| return dataset |
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I think we shouldn't allow pre-tokenized dataset. We do support it for SFT, but IMO it should remain the only trainer to support this
qgallouedec
reviewed
Jan 8, 2026
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| # Tokenize the dataset | ||
| dataset = dataset.map( | ||
| _tokenize, |
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the function _tokenize could also be included inside this method, like in SFTTrainer
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Refactor KTO [3/N]: Extract dataset processing to _prepare_dataset method.
This PR extracts inline dataset preprocessing logic from
KTOTrainer.__init__()into a dedicated_prepare_dataset()method, aligning with SFTTrainer's architecture and improving code organization.SFTTrainer._prepare_datasetpatternPart of:
Problem
Before:
__init__()__init__()with mixed concernsAfter:
_prepare_dataset()method__init__()