Fix crash and tokenization mismatch in speculative decoding with different tokenizers
Author: rohanbalsaraf
Pull request: yes

# What does this PR do?

This PR fixes two critical issues in `AssistedCandidateGeneratorDifferentTokenizers` and `UniversalSpeculativeDecodingGenerator` used for speculative decoding when the assistant and target models have different tokenizers/vocabularies.

### The Problems
1. **Runtime Crash:** The assistant model would often crash with a `RuntimeError` (size mismatch in RoPE/positional embeddings). This happened because it was incorrectly inheriting stale `position_ids` and `token_type_ids` from the main model'\''s context, rather than generating its own based on its specific tokenizer'\''s sequence length.
2. **Tokenization Mismatch:** The code was unintentionally adding a BOS (Beginning of Sentence) token during every iterative re-tokenization step. This led to "stuttering" in the generated sequence (e.g., `
 Hello 
 world`). Additionally, aggressive space cleaning was causing BPE-based tokenizers to get out of sync.

### The Fix
- **Kwarg Management:** Explicitly `pop` stale `position_ids`, `attention_mask`, and `token_type_ids` before the assistant model'\''s forward pass, allowing it to correctly recalculate them for its own sequence length.
- **Controlled Tokenization:** Updated `convert_source_tokens_to_target_tokens` to include an `add_special_tokens` flag. It now uses `False` during iterative generation to prevent BOS duplication and sets `clean_up_tokenization_spaces=False` to preserve BPE consistency.
- **Unified Logic:** Aligned the sampling path (`UniversalSpeculativeDecodingGenerator`) with the greedy path for consistency.
- **Test Improvement:** Updated `test_speculative_decoding_equals_regular_decoding` to use a string-based prompt. Since different-tokenizer speculation relies on a text bottleneck, random integer IDs are not a valid test case for this specific architecture.

These issues were discovered through a proactive analysis of the `tests/generation/` suite where `test_speculative_decoding_equals_regular_decoding` was failing.

## Code Agent Policy
- [x] I confirm that this is not a pure code agent PR.

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that'\''s the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)?
- [ ] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests? (Updated existing failing test to be robust).

## Who can review?
@zucchini-nlp @gante

Comments:
View the CircleCI Test Summary for this PR:

https://huggingface.co/spaces/transformers-community/circle-ci-viz?pr=46124&sha=f42500

Not sure who's maintaining the universal speculative decoder rn, maybe @cyrilvallez?

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Please provide a clear repro of the issue so I can investigate