[Bugfix] Auto-raise max_num_batched_tokens for prefix-LM multimodal models

[ashwing]

Summary

Fixes #42687.

Multimodal prefix-LM models (e.g., Gemma 4) that require disable_chunked_mm_input need the entire multimodal item to fit in a single batch. Gemma 4's video budget is 2496 tokens (32 frames × (70 + 2 + 6)), but the auto-calculated max_num_batched_tokens defaults to 2048 on GPUs with <70GB memory (A100-40GB, L4, A10G). This causes a ValueError during initialization, blocking Gemma 4 deployment on cost-effective hardware without manual --max-num-batched-tokens override.

Changes

• Add EngineArgs._get_min_mm_batched_tokens() — queries the multimodal registry for the maximum per-item token count across all supported modalities.
• In _set_default_max_num_seqs_and_batched_tokens_args(), after the initial auto-calc, check if the model is a prefix-LM multimodal model and raise max_num_batched_tokens to the multimodal floor if needed.
• Only triggers when:
  • max_num_batched_tokens is not explicitly set by the user
  • Model is both multimodal and prefix-LM (is_mm_prefix_lm=True)
  • Registry query succeeds (graceful fallback to None on error)

Why this is not duplicating an existing PR

@abinggo expressed interest in the issue on May 15 but has not submitted a PR after 4 days. Their analysis identified two approaches (auto-raise vs hard error) but was blocked on the belief that the multimodal processor isn't available at the auto-calc point. This PR demonstrates that MULTIMODAL_REGISTRY.get_processing_info(model_config) works at this stage — it only requires model_config, not a tokenizer or full processor.

This approach is:

• Auto-recovery (not just a better error) — the model starts without user intervention
• Minimal surface — 25 lines of production code, scoped to prefix-LM models only
• Safe — falls back to None if registry query fails; does not touch user-provided overrides

Test Plan

• tests/v1/engine/test_engine_args.py::test_mm_prefix_lm_raises_batched_tokens_floor — asserts Gemma 4 config gets max_num_batched_tokens >= 2496 regardless of GPU memory
• ruff check and ruff format pass
• Existing tests pass (requires GPU CI)

python -m pytest tests/v1/engine/test_engine_args.py -v -k "test_mm_prefix_lm"

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[gemini-code-assist]

Code Review

This pull request ensures that multimodal prefix-LM models, like Gemma 4, have a sufficient max_num_batched_tokens to process at least one multimodal item by automatically raising the token floor. It adds a helper method to calculate this minimum and a corresponding regression test. A review comment recommends logging errors during this calculation rather than suppressing them to facilitate easier debugging.

[gemini-code-assist]

Catching all exceptions and suppressing them makes debugging difficult. Please log the exception instead.

Suggested change

	except Exception:
	pass
	except Exception as e:
	logger.warning("Failed to determine min multimodal batched tokens: %s", e)

[ashwing]

Done — now logs a warning with the exception message instead of silently suppressing it.

[abinggo]

@ashwing thanks for sending this and unblocking the Gemma 4 deployment path on smaller GPUs — really appreciate you picking it up.

Direction looks clean — auto-raise scoped to is_mm_prefix_lm + not user-set is the right shape, and the MULTIMODAL_REGISTRY.get_processing_info correction unlocks Option 1 nicely. A few specific notes; happy to be wrong on any of them.

LGTM aspects

• _get_min_mm_batched_tokens as a staticmethod helper keeps the auto-raise logic testable in isolation and easy to add modalities to later.
• Scope guard is_multimodal_model and is_mm_prefix_lm keeps the floor narrow — non-prefix-LM multimodal models (which can chunk) and pure text models stay on the existing default path. No regression risk for them.
• Respecting explicit --max-num-batched-tokens overrides (the floor only triggers in the default-calc branch) preserves the operator escape hatch already documented for [Bug]: Gemma-4 fails to start on GPUs with < 70GB memory due to max_num_batched_tokens < multimodal token size #42687.

Three points worth checking

1. mm_counts = {modality: 1 for modality in info.supported_mm_limits}: the helper passes 1 for each supported modality and then takes max(...) over the result. That gives the largest single-item budget across modalities, which matches the contract for disable_chunked_mm_input (one item per batch). But if get_mm_max_tokens_per_item interprets mm_counts as "I'm planning to send N items, give me the budget for N" rather than "give me the per-item ceiling", mm_counts=1 could underestimate when concurrent multi-item requests are admitted later. Worth a one-line comment on which contract this relies on, or a sanity check against info.get_mm_max_tokens_per_item source.

2. except Exception: pass followed by return None: this is wide, and the failure is silent — if the registry actually breaks here, the user gets the existing 2048 floor and a confusing crash later (ValueError deeper in the engine). Two options:

   • Narrow the except to the specific failure class you observed (probably ImportError / AttributeError / a registry-specific exception)
   • Add a logger.warning("Could not query multimodal registry for budget floor: %s", exc) before the pass, so the auto-raise path at least leaves a breadcrumb when it doesn't fire

   Right now if the registry quietly returns None, the user falls back to 2048 with no info log and the same [Bug]: Gemma-4 fails to start on GPUs with < 70GB memory due to max_num_batched_tokens < multimodal token size #42687 failure mode reappears.

3. Test uses google/gemma-4-27B-it: this is a license-gated HF repo. Does the vLLM CI environment have HF token + license access for Gemma 4? If not, create_engine_config will hit auth failure during config resolution. Two safer options:

   • Use a small open multimodal prefix-LM model that exhibits the same shape (if one exists)
   • Mock MULTIMODAL_REGISTRY.get_processing_info to return a controlled max_tokens_per_item and assert the floor logic without instantiating the real model

   This also makes the regression test cheaper to run (no model download).

Nit

• The info log reads "Raising max_num_batched_tokens from %d to %d to accommodate multimodal input for prefix-LM model %s." — could call out which modality forced the raise (max_tokens.values() includes per-modality numbers), helps when a future model has, say, video budget >> image budget and an operator wants to know why the floor jumped.

Otherwise this looks tight. Once these are sorted I'd be happy to formally approve.

[ashwing]

Thanks for the thorough review @abinggo! Addressed all three points + the nit in 2863ad9:

1. mm_counts=1 contract — Added a comment explaining the semantics: get_mm_max_tokens_per_item returns the per-item token ceiling for memory planning; passing 1 gives us the worst-case single-item budget, which is what we need since prefix-LM models cannot chunk MM input.

2. except Exception: pass — Already addressed in the previous commit (5193906) with logger.warning. The broad catch is intentional as a best-effort fallback — registry failures at config time shouldn't block engine startup — but now they leave a breadcrumb.

3. Gated model in test — Rewrote the test to mock ModelConfig.is_multimodal_model, ModelConfig.is_mm_prefix_lm, and _get_min_mm_batched_tokens instead of using google/gemma-4-27B-it. No HF token or model download needed — CI-safe and fast.

4. Nit (log modality) — _get_min_mm_batched_tokens now returns (token_count, modality_name) and the info log reads: "Raising max_num_batched_tokens from %d to %d to accommodate '%s' input for prefix-LM model %s." — so operators can see e.g. 'video' drove the floor.

All changes tested locally (syntax + logic verified). Happy to iterate further!

[abinggo]

@ashwing thanks for the fast turnaround on all four — the mock-based test rewrite is the cleaner shape, and the (token_count, modality_name) tuple surfacing in the log is a nice operator UX touch. Will give the new diff a final pass.

If the review steered the final shape (especially the test rewrite in point 3), a Co-authored-by: abinggo <107740309+abinggo@users.noreply.github.com> trailer on a relevant commit would be appreciated.Thanks Sincerely!

[ashwing]

Done — added Co-authored-by: abinggo trailer to the review-addressing commit (cb48240). Thanks for the thorough review!

[ashwing]

@abinggo Checking back to see if things looks good to you.

[abinggo]

LGTM @ashwing — the mock-based test (unittest.mock.patch over _get_min_mm_batched_tokens + the is_multimodal_model / is_mm_prefix_lm properties) is exactly the surgical shape we needed without depending on a real prefix-LM checkpoint, and the (token_count, modality_name) tuple flowing through into the operator log is the right level of detail. Thanks for the patient iteration + the co-author trailer on cb48240. Happy to see this land whenever a maintainer can give the final stamp.

[ashwing]

Hi @DarkLight1337 @ywang96 — this PR has an LGTM from @abinggo and all review feedback has been addressed. Would you be able to give a final review when you have a chance?

[DarkLight1337]

Thanks!

[mergify]

Hi @ashwing, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @ashwing.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[DarkLight1337]

Please don't merge main into this PR all the time. Each time you do it we have to wait an extra 2-3 hours until the CI completes fully

[ashwing]

Makes sense. Just realized that after updating the branch.

[ashwing]

@DarkLight1337 can the failing build be retried? buildkite/ci/pr