* Fix ORPO text tokenization with processors
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* Guard ORPO tokenizer rewrite anchor
* Resolve processor pad_token_id and preserve preference data collators for ORPO
Two follow-ups so the text-only ORPO + VL processor path works end to end on
top of the build_tokenized_answer and tokenize_row rewrites:
1. Add orpo_trainer_processor_pad_token to rewrite processing_class.pad_token_id
in ORPOTrainer.__init__ to fall back to processing_class.tokenizer.pad_token_id
when the processor itself has no pad_token_id (Qwen3-VL, Gemma-3, etc.).
Without this, DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id)
raises AttributeError before training starts.
2. Stop the outer UnslothORPOTrainer.__init__ collator-swap from clobbering
DPODataCollatorWithPadding when the tokenizer is a processor without .pad.
The swap to TransformersDataCollatorForLanguageModeling is now only applied
to LM-style collators, so ORPO/DPO/CPO/KTO keep their own prompt/chosen/
rejected handling. Otherwise the collator can't pad ORPO rows and raises
"You should supply an encoding ... that includes input_ids" at train time.
Verified with Qwen3-VL-2B-Instruct ORPO + text-only data (training completes
to max_steps, no AttributeError, no collator error) and Llama-3.2-1B-Instruct
ORPO (losses and grad-norms bit-exact identical to main, so the change is a
true no-op for plain text tokenizers).
Extends tests/python/test_orpo_processor_text_tokenizer.py with three new
unit tests covering the pad_token_id rewriter.
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
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