[Bugfix][Model] Pad SigLIP text prompts to the trained sequence length - #51157
[Bugfix][Model] Pad SigLIP text prompts to the trained sequence length#51157Hert4 wants to merge 1 commit into
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@DarkLight1337 @njhill CODEOWNERS for One thing worth a reviewer's eye: @DarkLight1337, you added the padding block in f0a1c84. Force-pushed 3ff12dc → a5b4609:
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The official checkpoint is indeed like this, but this isn't really inherent to the model definition, I don't think it's proper to hardcode this for all SigLIP checkpoints. Instead it would be better to show this inside the example files (e.g. |
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It's not a hardcoded value, text_config.max_position_embeddings comes from the checkpoint config, so a different checkpoint gets its own length. What isn't checkpoint-specific: SiglipTextTransformer.forward takes no attention mask and get_text_features flips the sequence so CLS pooling reads the last position. The embedding shifts with however many pads trail the text. Examples won't cover though, there's no padding field on the request and no CLI flag. If the worry is a finetune trained unpadded, I can add an opt-out instead. |
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@DarkLight1337, you're right, max_position_embeddings isn't in any SigLIP config.json, it's the SiglipTextConfig default of 64, so every checkpoint gets 64 anyway. The tokenizer config does say something useful though: "model_input_names": ["input_ids"] on both siglip1 and siglip2, so no attention mask. Gating on that, a finetune trained with masking would opt out on its own: Length still needs max_position_embeddings though model_max_length is 64 on siglip1 but 1e30 on siglip2. |
| def _tokenize_singleton_prompt( | ||
| self, | ||
| prompt: SingletonDictPrompt, | ||
| params: TokenizeParams, | ||
| ) -> SingletonTokPrompt: | ||
| params = self._apply_default_padding(prompt, params) |
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I think this should be applied in _tokenize_prompt instead
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_tokenize_prompt doesn't actually apply the padding itself. That's handled later in apply_post_tokenization (base.py:569 and :607), which works with the caller's params. Reassigning params inside _tokenize_prompt wouldn't be visible to the caller, so the padding would never be applied.
Updating those two call sites was the smallest change I could come up with without changing _tokenize_prompt to return the updated params as well. If you'd rather go that route, I'm happy to do it. Or if there's a cleaner approach I'm missing, let me know.
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I'm wondering, why can't we just rely on get_default_tok_params to do the job?
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I checked this path as well, /v1/embeddings doesn't read this value. It builds params directly from the request (pooling/embed/io_processor.py:311 → _build_pooling_tok_params) rather than going through default_cmpl_tok_params.
I could move the logic there, but that would introduce model specific behavior into the pooling layer. Would that be the preferred approach?
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Moving this into _build_pooling_tok_params looks harder than I assumed; it only gets model_config, and the only reads of info.default_tok_params are in renderers/base.py via self.mm_processor. So that route means plumbing the processor into the pooling protocol.
Happy to try it anyway if @noooop prefers.
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Does it lead to unnecessary complexity?
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One helper plus two call sites, sync and async.
I tried _tokenize_prompt first, as @DarkLight1337 suggested. It doesn't work: the padding is applied by apply_post_tokenization (base.py:569 and :607), which runs on the caller's params. Rebinding inside _tokenize_prompt never reaches it, so nothing gets padded.
Happy to restructure if you have a shape in mind that keeps it out of the renderer.
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@noooop @DarkLight1337 I looked at moving this into the pooling layer, and I had the reason wrong earlier. The renderer is available at the call site (embed/io_processor.py has self.renderer two lines above), so plumbing isn't the blocker.
Granularity is. tok_params is built once per HTTP request at embed/io_processor.py:311 and shared by every prompt in the batch (:323-335). Padding has to be per-prompt, since multimodal prompts must not be padded, and only _tokenize_singleton_prompt sees individual prompts. Setting it there would pad the image prompts in a mixed batch.
That's why it sits in the tokenize path. Separately: pre-run-check is still blocking CI on the label gate, so nothing has run on this yet. Could someone add ready?
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This pull request has merge conflicts that must be resolved before it can be |
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Rebased — the conflict was from #50907 removing @noooop any preference on renderer vs pooling layer? @DarkLight1337 if noooop is tied up, happy to go with whichever you prefer. Also, CI has never run on this — |
| @@ -52,7 +55,9 @@ def _run_test( | |||
| gpu_memory_utilization=0.7, | |||
| ) as vllm_model: | |||
| vllm_outputs = vllm_model.embed( | |||
| input_texts, images=input_images, tokenization_kwargs=tokenization_kwargs | |||
| input_texts, | |||
| images=input_images, | |||
| tokenization_kwargs=vllm_tokenization_kwargs, | |||
| ) | |||
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Why are duplicate tokenization_kwargs and vllm_tokenization_kwargs needed here?
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Not duplicates, they feed different sides of the comparison.
tokenization_kwargs goes to HF, which always pads to build the reference embedding. vllm_tokenization_kwargs goes to vLLM, and with explicit_padding=False it's empty, so vLLM is told nothing about padding.
That's the regression being guarded: vLLM has to land on the same embedding without being asked. If both sides shared one dict the test would pass on main too, and prove nothing.
| @cached_property | ||
| def _model_pad_prompt_tokens(self) -> int | None: | ||
| mm_processor = self.mm_processor | ||
| if mm_processor is None: | ||
| return None | ||
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| return mm_processor.info.default_tok_params.pad_prompt_tokens |
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I think pad_prompt_tokens is not unique to multimodal models, so this design prevents language models from using this feature.
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You're right. Fixed: reads default_cmpl_tok_params now, so both branches work.
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This pull request has merge conflicts that must be resolved before it can be |
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| HF_TEXT_PROMPTS, | ||
| text_images, | ||
| { | ||
| "padding": "max_length", | ||
| "max_length": 64, | ||
| }, | ||
| padding_kwargs, | ||
| padding_kwargs if explicit_padding else {}, | ||
| ), |
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I think it's not worth adding so much code just to implement "padding": "max_length" and "max_length": 64. Could you further reduce the code to, say, 20 lines?
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@noooop review round is done from my side (23 lines). Anything else, or is this good to go? CI still hasn't run could someone add ready?
SigLIP is trained with padding="max_length" and without an attention mask, so padding tokens are part of the input and the pooled embedding is read from the last position. Text embeddings computed without padding are not aligned with the image embeddings, which makes image-text similarity unusable. Callers that cannot pass tokenization kwargs — notably /v1/embeddings, which builds TokenizeParams from scratch in entrypoints/pooling/base/protocol.py — silently get wrong values: the request still returns 200 with a correctly shaped vector. Gate the default on the tokenizer's declared inputs, so a finetune trained with masking (attention_mask in model_input_names) opts out on its own. Test: each case in _run_test now carries separate HF and vLLM tokenization kwargs. With explicit_padding=False vLLM is told nothing about padding and still has to reach the same embedding as HF, which is the regression being guarded. Co-authored-by: Claude <noreply@anthropic.com> Signed-off-by: tmduc3 <tmduc3@rd.misa.com.vn>
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3 participants
Purpose
SigLIP is trained with
padding="max_length"and without an attention mask, so padding tokensare part of the input and the pooled embedding is taken from the last position. Text embeddings
computed without padding are not aligned with the image embeddings, which makes image-text
similarity unusable.
The existing test only passes because it opts into padding explicitly:
Callers that cannot pass tokenization kwargs get silently wrong results. The request still
returns 200 with a correctly shaped vector; only the values are wrong, so the failure is easy to
miss in production.
Two entry points need covering, and they take different routes:
TokenizeParamsLLM.embedrenderer.default_cmpl_tok_params/v1/embeddings(text and chat)request.build_tok_params()entrypoints/pooling/base/protocol.py:97-116buildsTokenizeParamsfrom scratch, so amodel-level default alone does not reach the OpenAI-compatible server.
Note that padding text-only prompts inside
SiglipMultiModalProcessordoes not work:inputs/preprocess.py:169-181routes text-only prompts straight to_tokenize_promptand neverreaches the multi-modal processor. I verified this by instrumenting
apply()— during the texttest it is only ever called with dummy profiling data.
Fix
Two small pieces.
1. The model declares its requirement through the existing
BaseProcessingInfo.get_default_tok_paramsextension point, which ten models already override(
paligemma,gemma4_mm,ovis,ovis2_5,whisper,ultravox,cohere_asr,lfm2_vl,nano_nemotron_vl,nemotron_parse):2. The renderer applies that default when a frontend builds
TokenizeParamswithout one, in_tokenize_singleton_promptand its async twin:Both guards matter. Explicitly requested padding wins. Multi-modal prompts are skipped because
renderers/base.py:786tokenizes the prompt before multi-modal processing on the online path,so without the guard image requests would be padded too.
The padding machinery itself already exists (
TokenizeParams.pad_prompt_tokens,_token_padding); this only turns it on for a model that needs it. Models that declare nothingget
Noneand the helper returns the params unchanged.Known trade-off
A caller who deliberately passes
padding=Falsefor SigLIP will still get the model defaultreapplied, so there is no opt-out.
Note this is not caused by the fallback alone.
TokenizeParams.with_kwargsguards the paddingmapping with a truthiness check:
padding=Falseis falsy, so the key is popped and theelifnever runs —padding=Falseisalready a no-op on
mainfor any model whose default setspad_prompt_tokens. Consequently"disabled" and "unspecified" are indistinguishable to the fallback, and it reapplies the default.
I judged that acceptable because unpadded SigLIP is simply incorrect, but I am happy to add an
explicit sentinel if maintainers prefer to keep the opt-out. Fixing the dead
elifbranch itselflooks like a separate change; happy to send one if that is wanted.
Alternative considered
Setting
pad_prompt_tokensinside_build_pooling_tok_paramswould avoid touching the renderer,but it only fixes frontends that build
TokenizeParamsand puts a model-specific rule in theentrypoints layer. Happy to move the fix there if preferred.
Test plan
test_models_textis parametrized overexplicit_padding. Both cases compare against the sameHF reference computed with padding, so
explicit_padding=Falsefails onmain.tests/renderers/test_default_padding.pycovers the server route by buildingTokenizeParamsexactly the way the pooling endpoints do (no padding field). It runs on CPU in seconds.
Test result
All three models in
MODELS, on an NVIDIA GB10 (sm_121, aarch64, CUDA 13.0):The same suite passes on an RTX 4070 SUPER (sm_89, x86_64, CUDA 12.8, vLLM built from source)
for the two base models — 8 passed, giant skipped there for disk reasons.
Full renderer suite on that machine, to check the shared
renderers/base.pychange against everyother model:
The single failure is
test_hf.py::test_resolve_content_format_fallbacks[facebook/chameleon-7b-string],which fails identically on unpatched
main— unrelated to this change.Reverse check
Reverting only the changed source files and rerunning:
maintest_models_text[False-...siglip-base-patch16-224]test_models_text[True-...siglip-base-patch16-224]test_default_padding::test_model_default_is_appliedassert 9 == 64test_default_padding::test_explicit_padding_is_not_overriddentest_default_padding::test_multimodal_prompts_are_not_paddedLint
pre-commit run --files <the four changed files>passes, includingmypyfor Python 3.10 andmypy-3.12 --hook-stage manual.Model evaluation
Zero-shot classification through
LLM.embedongoogle/siglip-base-patch16-224, two imagesfrom
vllm.assets.imageagainst three labels. Cosine similarity, argmax picks the label.Before (unpatched
main):After:
The accuracy change (1/2 to 2/2) understates it: before the change every similarity is negative
and near zero, so there is no signal at all and the one correct pick is accidental. SigLIP's
logit_scale(~118) andlogit_bias(~−12.7) put the 0.5 probability threshold at a cosine ofabout 0.107, which only the patched matching pairs clear.
Image-image cosine is 0.5879 in both runs, so the vision tower is untouched and only the
cross-modal alignment changed.
Duplicate check
Checked 2026-08-05, no overlapping work found:
Open PRs mentioning siglip are unrelated (MiniCPM-RobotTrack, a Gemma 4 fp16 overflow fix,
PaddleOCR-VL CUDA graph support, DeepSeek-VL2). Open issues mentioning siglip are unrelated.
#29794 added
tokenization_kwargsto the offline API and is already merged; this PR covers thecase where the caller cannot pass them.
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