[Bugfix] Transformers backend: tokenize multimodal prompts once - #50899
[Bugfix] Transformers backend: tokenize multimodal prompts once#50899molbap wants to merge 4 commits into
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Signed-off-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
Signed-off-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
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Signed-off-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
Signed-off-by: Pablo Montalvo <pablo.montalvo.leroux@gmail.com>
| else: | ||
| # Chat templates render special tokens themselves; mirror the | ||
| # fallback in `ProcessorMixin.apply_chat_template`. | ||
| bos_token = getattr( | ||
| getattr(hf_processor, "tokenizer", None), "bos_token", None | ||
| ) | ||
| if bos_token is not None and prompt.startswith(bos_token): | ||
| tokenization_kwargs = { | ||
| **tokenization_kwargs, | ||
| "add_special_tokens": False, | ||
| } |
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i am not sure we should do it here. On hf side this is passed from jinja render which is always set to None whenever a template is applied
The rule of thumb is for devs to add their special tokens in jinja, and don't rely on tokenizer
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Yeah, this is a bit hacky. Not sure where else to put it at the moment to quickly fix double encode/decode issue.
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btw it just got to me, is it too expensive to encode-decode, i..e do we have some numbers?
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we don't yet, it's probably not much compared to prefill/decode of course, but it's unnecessary
| tokenization_kwargs: NotRequired[dict[str, Any]] | ||
| """ | ||
| Overrides for the tokenization performed by the multi-modal processor. | ||
| Set by the renderer when tokenization is deferred to the processor. | ||
| """ | ||
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btw, this shouldn't be needed anymore when #50107 is merged. We can use now accept mm/ids-only and use base class methods to process it correctly
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This pull request has merge conflicts that must be resolved before it can be |
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This PR is now superseded by #51827. That PR adds a new offsets based processor which removes the need for retokenization entirely, therefore rendering delayed tokenization unnecessary. |
3 participants
Purpose
Multimodal requests on the Transformers backend tokenize twice and duplicate special tokens.
Current flow: the renderer tokenizes the text and adds BOS. The backend gets ids, but HF processors want text, so it decodes back to text. Then the HF processor tokenizes again with add_special_tokens=True: second BOS. decode→encode is also not an identity op for SentencePiece tokenizers, it inserts an extra ▁ after
<s>for instance. So every request has one decode + one extra encode and can get a corrupted prompt.Fix: tokenize once in the processor.
BaseMultiModalProcessor.prefers_prompt_text, alwaysTrueon the Transformers backend. The renderer then skips its own tokenization for mm prompts and hands the text to the processor. Engine ids are now exactly processor(text=..., images=...) output. Same pattern as the existingEncDecMultiModalProcessor.skip_decoder_start_tokenflag.add_special_tokens=Falseso BOS is not added twice.truncate_prompt_tokens/pad_prompt_tokenskeep the old path. Native models unaffected (flag is False).No open PR covers this AFAIK. Will also open a sibling PR on transformers side to test the backend e2e.
Test Plan
pytest tests/models/multimodal/processing/test_transformers_image.pyTest Result
Before patch, test flags a double BOS token for 2 test models:
def test_ids_prompt_does_not_duplicate_special_tokens(model_id): ... ids_token_ids = ids_processed_inputs["prompt_token_ids"] > assert ids_token_ids.count(tokenizer.bos_token_id) == 1 E assert 2 == 1After patch: passes.
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.