fix: use_liger_kernel with IterableDataset

[jue-jue-zi]

What does this PR do?

When using load_dataset(.., streaming=True) to load the training set as an IterableDataset, the dataset.column_names may be None.

If use_liger_kernel is enabled at the same time, SFTTrainer will attempt to remove unused columns during data preparation:

collator_expected_keys = {"input_ids", "seq_lengths", "completion_mask", "assistant_masks"}
dataset.select_columns(collator_expected_keys.intersection(dataset.column_names))

Since dataset.column_names is None in this case, this operation causes a training error.

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