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Scope clarification: #57781 addresses the generic validation path by rejecting prompts whose placeholder-token occurrences exceed the recorded media placeholders. This PR addresses the Qwen2.5-VL-specific wrapper expansion and embedding-position handling.\n\nThe two changes are intended to be complementary: #57781 turns the generic mismatch into a validation error, while this PR preserves the correct Qwen2.5-VL media wrapper behavior. Please let me know if maintainers prefer these to be consolidated. |
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This pull request has merge conflicts that must be resolved before it can be |
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Match the complete vision wrapper when expanding Qwen2.5-VL media placeholders, so a literal image pad token in user text cannot consume a media update. Preserve the wrapper and mark only pad tokens as embedding positions. Fixes vllm-project#57740 Co-authored-by: OpenAI Codex <noreply@openai.com> Signed-off-by: cross2pro <qq1091192337@gmail.com>
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Rebased onto the latest (base 362a64b) and resolved the upstream changes in . The PR is now mergeable; GitHub reports pending review/CI authorization.\n\nValidation after the rebase:\n- Qwen2.5-VL regression test: 1 passed\n- : passed\n- : passed\n- : passed\n- : passed\n\nThe test runner needed a local workaround to register the missing schema because this environment's CPU Torch/torchvision build is mismatched; the test itself passed. |
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Please ignore the previous status comment; shell quoting mangled its Markdown. Rebased onto the latest Validation after the rebase:
The test runner needed a local workaround to register the missing |
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Hi @sighingnow @AndreasKaratzas @DarkLight1337 @ywang96 @vadiklyutiy — could you please add the |
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Sorry for missing this, please see #57740 (comment) |
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Thanks for taking a look! Just to clarify, this PR doesn't touch Under the The fix matches the complete 3-token wrapper as the replacement target instead, so a stray |
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Your PR makes sense but it doesn't actually fix the root cause of the issue reported by #57740. Could you update your PR description to indicate that this fix is related to the issue but doesn't close that issue? |
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Also please check which models inherit from the Qwen2.5-VL stack, investigate whether they also use this placeholder format, and update them accordingly as well |
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Thanks, I've updated the PR description to mark this as related to #57740 rather than closing it. On the inheritance question, I checked the Qwen2.5-VL stack:
Would you like me to extend this PR to cover Qwen2-VL (+ ExaOne 4.5) and OpenPangu-VL, or would you prefer those as follow-up PRs? Touching Qwen2-VL means changing the parent class, so I wanted to check with you first. |
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Yes, please do that in this PR. But make sure you have verified that the chat template of the official templates handle the placeholder tokens in the way you described |
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Done — extended to Qwen2-VL (+ ExaOne 4.5, which reuses Qwen2-VL's processor) and OpenPangu-VL in c8aa3d4. Template verification against the official checkpoints:
Added regression tests for both models mirroring the Qwen2.5-VL one. |
…u-VL Signed-off-by: cross2pro <qq1091192337@gmail.com>
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Codex review: Two affected models use different wrappers, and all six affected implementations still have incompatible dummy prompts. Qwen2-VL Qwen2.5-VL EXAONE 4.5 dots.ocr Jina VL / jina-reranker-m0 OpenPangu-VL The two substantive compatibility regressions are:
The dummy-input issue remains unresolved: the shared builder emits bare pad tokens. Qwen2.5-VL, EXAONE, Jina, and OpenPangu reuse or inherit that behavior; dots.ocr has its own bare-token override. Updating the shared builder must also handle their different wrappers. Unaffected despite related inheritance/reuse: OpenCUA, Qwen2.5-Omni, Qwen3-Omni, and Qwen3-VL use separate prompt-update implementations. OpenCUA’s model-class inheritance does not route it through the changed processor. I ran isolated checks (/tmp/check_pr57763.py) using the PR’s exact update methods and vLLM’s prompt-matching machinery, including multi-frame video and embedding masks. These reproduced the mismatches and dummy failures; EXAONE’s The PR needs model-specific wrapper handling for EXAONE and dots.ocr, plus compatible dummy builders and regression coverage for all six implementations. |
Address review feedback: derive the prompt-replacement target from each model class's own get_placeholder_str instead of hardcoding <|vision_start|>/<|vision_end|>. This fixes EXAONE 4.5 (whose tokenizer lacks those tokens and would raise KeyError) and dots.ocr (which uses <|img|><|imgpad|><|endofimg|>), and covers Jina-VL via fallback. - Qwen2VLMultiModalProcessor._get_prompt_updates now builds targets via get_wrapper_token_ids(); pad-token counting moved to an overridable _get_num_pad_tokens() hook so Qwen2.5-VL keeps only its EVS logic. - Dummy inputs emit the complete wrapper so profiling prompts match. - Regression tests for EXAONE/dots.ocr/Jina-style wrappers and dummies. Signed-off-by: cross2pro <qq1091192337@gmail.com>
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The MM processors themselves look OK overall, but M-RoPE needs to be updated accordingly with respect to the new offsets. From codex: Qwen2-VL Qwen2.5-VL EXAONE 4.5 dots.ocr Jina VL / jina-reranker-m0 OpenPangu-VL The M-RoPE problem comes from the changed placeholder offset. It now points to the opening wrapper token, while the unchanged position-generation methods treat it as the first pad token. The embedding mask does not adjust that offset. I reproduced different position tensors for all five affected M-RoPE models using ordinary prompts without stray pad tokens. The Jina problem comes from inconsistent fallbacks: get_wrapper_token_ids() falls back to a bare video pad token, but get_dummy_text() falls back to an empty string. Default profiling includes video unless its limit is explicitly set to zero. Validation: The PR’s eight tests pass. Additional CPU checks using the actual model classes reproduced both remaining problems. No end-to-end GPU inference was run. |
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GPU validation on Kaggle (Tesla T4), branch @
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Update: the environment-fixed re-run is complete.
The earlier Kaggle failures were purely notebook environment setup issues on my side (PYTHONPATH shadowing the wheel's compiled extensions; pip mixing torch cu130 with torchaudio cu128), unrelated to this PR. |
Address review follow-ups on the per-model wrapper targets: - M-RoPE: mm_position.offset now points at the opening wrapper token (e.g. <|vision_start|>), while position generation expects the first vision token. Derive the vision-token offset from mm_position.is_embed via _get_vision_token_offset() and use it in iter_mm_grid_thw, restoring the pre-PR position semantics. - Remove the byte-identical iter_mm_grid_thw / get_mrope_input_positions overrides in Qwen2.5-VL so it inherits the fixed parent implementation (also covering EXAONE 4.5 and Jina-VL, which share it). - Apply the same offset fix to OpenPangu-VL's iter_mm_grid_thw. - Jina-VL video: get_dummy_text() now falls back to the bare pad token when the model defines no placeholder string, consistent with the fallback in get_wrapper_token_ids(), so profiling with video matches the video replacement target. - Regression tests: M-RoPE positions skip the wrapper prefix, the offset helper edge cases, and the Jina-style video dummy fallback. Signed-off-by: cross2pro <qq1091192337@gmail.com>
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Thanks for the follow-up review — both findings were correct, fixed in acbadf2:
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This pull request has merge conflicts that must be resolved before it can be |
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It fixes several previous findings, but introduces a blocking regression: The PR deletes Qwen2.5-VL’s implementation of get_mrope_input_positions(), apparently expecting it to inherit Qwen2-VL’s implementation. But only the processor inherits from the Qwen2-VL processor. The Qwen2.5-VL model class does not inherit from the Qwen2-VL model class. After deletion, calling get_mrope_input_positions() reaches the empty method in SupportsMRoPE. That method returns None. The runner expects two values: the position tensor and its delta. When it unpacks None, it raises: TypeError: cannot unpack non-iterable NoneType object This blocks inference for Qwen2.5-VL and its model-class descendants, EXAONE 4.5 and OpenCUA. It affects normal requests, independently of the stray-placeholder issue this PR addresses. The fix is to restore Qwen2.5-VL’s methods with the corrected offsets, or explicitly give it a shared implementation. The passing tests miss this because they test M-RoPE on Qwen2-VL, not Qwen2.5-VL or those descendants. SummaryQwen2-VL: Qwen2.5-VL: EXAONE 4.5: dots.ocr: Jina VL: OpenPangu-VL: OpenCUA: |
The previous commit wrongly deleted Qwen2.5-VL's iter_mm_grid_thw and get_mrope_input_positions: only the processor inherits from Qwen2-VL's processor, while Qwen2_5_VLForConditionalGeneration does not inherit from Qwen2VLForConditionalGeneration. After deletion, M-RoPE fell back to SupportsMRoPE's empty method returning None, breaking inference for Qwen2.5-VL, EXAONE 4.5 and OpenCUA. Restore both methods with the _get_vision_token_offset() correction applied, and add a regression test covering Qwen2.5-VL's M-RoPE (previously only Qwen2-VL was tested). Signed-off-by: cross2pro <qq1091192337@gmail.com>
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@claude review |
Resolve conflicts in qwen2_vl.py and qwen2_5_vl.py iter_mm_grid_thw: keep upstream's data-extraction refactor, apply the M-RoPE _get_vision_token_offset() correction on top. Signed-off-by: cross2pro <qq1091192337@gmail.com>
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You were right — that deletion was wrong. I confused processor inheritance with model-class inheritance: only
Also resolved the merge conflicts with upstream/main (kept upstream's Tests: 12 passed in |
![claude[bot]](https://avatars.githubusercontent.com/in/1236702?s=60&v=4){kind=small}
Per review feedback, avoid the model_loader.get_model_cls indirection in OpenPanguVLMultiModalProcessor._get_prompt_updates. Reference OpenPanguVLForConditionalGeneration directly (defined in the same file) when deriving wrapper token ids. Signed-off-by: cross2pro <qq1091192337@gmail.com>
Per review feedback, avoid the model_loader.get_model_cls indirection. Processors and dummy builders now expose the model class via an explicit _get_model_cls() method: the Qwen2-VL base returns Qwen2VLForConditionalGeneration, and each subclass (Qwen2.5-VL, Jina, EXAONE 4.5, dots.ocr, OpenPangu) overrides it with its own model class. EXAONE 4.5 and dots.ocr get dedicated processor subclasses instead of aliasing the base directly. Signed-off-by: cross2pro <qq1091192337@gmail.com>
| vocab = tokenizer.get_vocab() | ||
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| model_cls = get_model_cls(self.info.ctx.model_config) | ||
| model_cls = self._get_model_cls() |
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Can we just extract the tokens from hf_processor instead of the model class?
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Good question — I checked, and hf_processor doesn't have the full wrapper. Qwen2VLProcessor only exposes image_token (<|image_pad|>) and video_token (<|video_pad|>); the surrounding <|vision_start|>/<|vision_end|> (or <vision>/</vision>, <|img|>/<|endofimg|>, [unused18]/[unused20]) are emitted by the chat template, not stored on the processor object. That's exactly why the model class's get_placeholder_str exists in vLLM — it's the explicit record of what each model's template emits, which is what we need to match. Deriving it from the template at runtime (e.g. applying it to a dummy message and parsing) would be fragile; the per-model placeholder string is the ground truth we already maintain.
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Then in that case, it seems that the HF processor doesn't actually consider <|vision_start|> and <|vision_end|> to be part of the replacement tokens. We should try to follow the conventions from HF Transformers
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I see the concern, but there's a structural reason: in vLLM, _get_prompt_updates runs after the chat template has been applied, so the prompt already contains the full wrapper (<|vision_start|><|image_pad|><|vision_end|>). If the replacement were just the pads (following HF's processor convention), the vs/ve tokens in the target would be deleted from the prompt, which the model doesn't expect.
HF can keep vs/ve out of the processor's replacement because the template adds them afterwards. vLLM does it in the opposite order: template first, then prompt replacement to expand the pads. Matching the full wrapper as the target is what lets us distinguish a template-emitted placeholder from a bare pad token typed by the user (the #57740 mitigation) — with a pads-only target we'd be back to the original bug.
If you're suggesting we move vLLM's replacement to pre-template (HF's order), that's a much larger architectural change — happy to discuss it separately, but it's out of scope for this mitigation PR. Does the current approach work for you?
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We consider HF as the reference implementation. If you feel that this is something that needs to be fixed, perhaps it would be more approprate to fix on HF upstream first?
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I agree HF is the reference, and this PR doesn't diverge from it — it doesn't change what tokens the model receives, only which token sequences vLLM's prompt replacement treats as placeholders.
The issue #57740 is vLLM-specific: it comes from vLLM's _get_prompt_updates, which has no equivalent in HF Transformers. HF never needs to distinguish "template-emitted placeholder" from "user-typed pad token" because HF doesn't do post-template prompt replacement. So there's nothing to fix on HF upstream for this — the fix (or mitigation) has to live in vLLM.
The wrapper matching doesn't invent new tokens or change HF's conventions; it just narrows vLLM's match target from the bare pad (which collides with user text) to the full template-emitted wrapper. The model still receives exactly the same tokens as before.
If you'd rather not take this mitigation approach at all, I'm happy to close the PR — just let me know which direction you'd prefer.
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We would like our search/replace to be based on the HF code. As I mentioned in the original issue, I'd prefer the mitigation to be done on the client side: vLLM shouldn't reject input that is considered valid for HF Transformers - even if incorrect model behavior occurs because of the limited validation from HF Transformers, it's not our job to enforce further validation on top of what HF provides.
- Preserve the original per-frame video replacement layout ([1:-1] stripping outer wrapper tokens) in OpenPanguVLMultiModalProcessor; the PR only changes the match target, not the replacement token arrangement. - OpenPanguVLDummyInputsBuilder now resolves to OpenPanguVLForConditionalGeneration (its placeholder strings differ from Qwen2.5-VL's). Signed-off-by: cross2pro <qq1091192337@gmail.com>
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Closing per your feedback — I understand the position that vLLM shouldn't add server-side validation beyond HF. Thanks for the detailed reviews; the discussion is recorded here for reference. |
Summary
get_placeholder_str(viaget_wrapper_token_ids()), not hardcoded:<|vision_start|><|image_pad|><|vision_end|>(official Qwen2-VL chat template is byte-identical to Qwen2.5-VL's).<vision><|image_pad|></vision>(its tokenizer lacks<|vision_start|>, so a hardcoded lookup would raiseKeyError).<|img|><|imgpad|><|endofimg|>.[unused18][unused19][unused20]/[unused18][unused32][unused20](official chat template verified)._get_num_pad_tokens()hook.Related to #57740 (mitigates the binding mismatch but does not fully close the issue — see discussion)
Tests
pytest tests/models/multimodal/processing/test_qwen2_5_vl.py— 8 passed (covers Qwen2-VL, Qwen2.5-VL, EXAONE-style, dots.ocr-style, Jina-style fallback, OpenPangu-VL wrappers, and dummy inputs).ruff checkandruff format --checkpass on all changed files.AI disclosure
This pull request was developed with AI assistance. The changes were reviewed and tested by the submitter.