[Bugfix] Shared MM text-LoRA mapper fallback for language_model wrappers

[fooSynaptic]

Supersedes #49464, which was accidentally closed during DCO history repair. This PR restores the change on a clean branch with signed-off commits and includes a review follow-up: instead of hardcoding language_model.model., the fallback now derives its destination from the wrapper's existing model.language_model. mapping and requires that destination to be rooted at language_model.. For the currently affected in-tree wrappers, the resulting mappings and model coverage are unchanged.

Purpose

Fixes #48019.

Addresses #49354, including the Qwen3.5 / Qwen3.6 text-LoRA no-op path described there.

This PR fixes text-only PEFT LoRAs silently loading as no-ops on multimodal and conditional-generation wrappers that mount their language model under self.language_model.

The broader affected-model analysis and the motivation for moving this fallback into a shared mapper path were provided by @ErenAta16 in the PR comment: #48022 (comment)

After PEFT strips base_model.model., text-LoRA keys look like:

model.layers.0.self_attn.q_proj

Affected wrappers typically map model.language_model. to language_model.model. but have no bare model. fallback. The key therefore resolves to a module that does not exist, while adapter loading completes without an error.

Add WeightsMapper.with_mm_text_lora_fallback() to derive the bare model. fallback from the wrapper's existing model.language_model. destination:

lm_dst = prefixes.get("model.language_model.")
if isinstance(lm_dst, str) and lm_dst.startswith("language_model."):
    prefixes["model."] = lm_dst

The fallback is added last only when the mapper contains the wrapper pattern and has no existing model. or model catch-all. It is applied only in the LoRA loading path after get_unstacked_mapper(), so model mapper definitions and base-weight loading remain unchanged.

This is a shared alternative to adding the same fallback to individual model classes as each affected wrapper is discovered.

Test Plan

Lint the changed files:

ruff check vllm/model_executor/models/utils.py vllm/lora/worker_manager.py
ruff format --check vllm/model_executor/models/utils.py vllm/lora/worker_manager.py

Static mapper probe for the post-PEFT text-LoRA key model.layers.0.self_attn.q_proj on each wrapper's hf_to_vllm_mapper, before and after get_unstacked_mapper().with_mm_text_lora_fallback():

.venv/bin/python - <<'PY'
from vllm.model_executor.models.utils import WeightsMapper

probe = "model.layers.0.self_attn.q_proj"
# Example: Qwen3-VL style mapper missing the bare model. catch-all.
mapper = WeightsMapper(
    orig_to_new_prefix={
        "model.visual.": "visual.",
        "lm_head.": "language_model.lm_head.",
        "model.language_model.": "language_model.model.",
    }
)
before = mapper._map_name(probe)
after = mapper.get_unstacked_mapper().with_mm_text_lora_fallback()._map_name(probe)
print("before:", before)
print("after:", after)
assert before == probe
assert after == "language_model.model.layers.0.self_attn.q_proj"
print("ok")
PY

Repeat the same probe across the SupportsLoRA wrapper table from the @ErenAta16 analysis, and confirm:

1. affected wrappers resolve under language_model.model
2. wrappers with an existing model. / model catch-all stay unchanged
3. existing prefix-rule sample keys map identically before and after

GPU generation parity on Qwen3.5-4B with a text PEFT LoRA, using the same protocol as #49354 (temperature=0, label match rate, n=64). Adapters and prompts are private fine-tunes; the public reproduction shape is:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

BASE = "Qwen/Qwen3.5-4B"
ADAPTER = "/path/to/peft-adapter"
PROMPTS = ["..."]  # identical across arms

# HF base vs HF LoRA, then:
llm_base = LLM(model=BASE, trust_remote_code=True, dtype="bfloat16",
               language_model_only=True, enforce_eager=True)
llm_lora = LLM(model=BASE, trust_remote_code=True, dtype="bfloat16",
               enable_lora=True, max_lora_rank=16,
               language_model_only=True, enforce_eager=True)
sp = SamplingParams(temperature=0.0, max_tokens=64)
out_base = llm_base.generate(PROMPTS, sp)
out_lora = llm_lora.generate(
    PROMPTS, sp, lora_request=LoRARequest("adapter", 1, ADAPTER)
)
# Expect vLLM base↔lora similar to HF base↔lora, and HF_lora↔vLLM_lora ~= 1.0

Test Result

Static mapper verification:

• 13 affected wrappers: MAP_BUG → FIXED
• 2 wrappers with an existing catch-all: unchanged
• 1 different-layout wrapper: not targeted
• Remaining mapping failures after the shared fallback: 0
• Existing prefix-rule sample mappings: identical before and after

Full table:

model	map issue?	before ok	after ok	status	fallback applied	base-rule regression
qwen2_vl	no (already OK)	yes	yes	OK_ALREADY→OK_UNCHANGED	no	yes
gemma4_mm	no (already OK; has catch-all)	yes	yes	OK_ALREADY→OK_UNCHANGED	no	yes
qwen3_vl	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
gemma3_mm	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
llava	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
llava_next_video	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
paligemma	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
pixtral	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
interns1	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
lfm2_vl	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
glm4_1v	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
granite4_vision	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
minicpmv4_6	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
cheers	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes
gemma4	no (DIFF_LAYOUT; not this bug)	no†	no†	DIFF_LAYOUT→N/A_LAYOUT	no	yes
qwen3_asr	yes (MAP_BUG)	no	yes	MAP_BUG→FIXED	yes	yes

† before/after ok is only vs the probe “lands under language_model.*”: gemma4 maps "model.language_model." → "model.", so the probe stays at model.layers.* — that is the correct layout for text CausalLM, not a MAP_BUG. Online Gemma4 LoRA silent failure was alias wipe (39815/39816), orthogonal to this helper; skipping gemma4-shaped destinations avoids double-map.

Lint:

ruff check: PASS
ruff format --check: PASS

Qwen3.5-4B generation parity, 64 samples:

HF base   ↔ HF LoRA:   0.6094
vLLM base ↔ vLLM LoRA: 0.6094
HF base   ↔ vLLM base: 0.9688
HF LoRA   ↔ vLLM LoRA: 1.0000

The GPU evaluation used a build containing the existing LoRA zeroing fix from #39816 plus the shared mapper fallback. The remaining listed wrappers were verified at the prefix-mapping layer but were not individually evaluated with GPU text-LoRA checkpoints.

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[linitra24]

Currently, the guard accepts any destination starting with language_model, but the fallback always maps to language_model.model.. This works for all affected in-tree models today, but could mis-map a future or out-of-tree wrapper whose language model is mounted at something like language_model.transformer..

Would this be safer?

if not isinstance(lm_dst, str) or not lm_dst.startswith("language_model."):
    return self
prefixes["model."] = lm_dst

Alternatively, if only the current layout is intentionally supported, the guard could require lm_dst == "language_model.model.".

[fooSynaptic]

Good catch — agreed. The hardcoded language_model.model. destination is fine for current in-tree mappers, but the guard is wider and could mis-map a future wrapper. I'll update this later to use startswith("language_model.") and prefixes["model."] = lm_dst, and tighten the unit tests accordingly.

[fooSynaptic]

Updated in the latest commit: startswith("language_model.") and prefixes["model."] = lm_dst. Also added unit tests for a non-model destination (language_model.transformer.) and to ensure language_modeling. does not incorrectly enable the fallback.

[ErenAta16]

Picking this up since it supersedes #49464, which I reviewed in depth. The core guard verification from there carries over: I ran each SupportsLoRA wrapper's real orig_to_new_prefix through the guard, confirmed 13 wrappers get the fallback and resolve a post-PEFT model.layers... key under language_model., that qwen2_vl/gemma4_mm are correctly skipped (already have a catch-all), that gemma4 is correctly skipped (its model.language_model. maps to model., not a language_model destination), and that no existing rule output gets re-caught into a double-map. That all still holds here.

One correction to the description though: this is not implementation-unchanged from #49464. The destination line changed, and for the better:

# #49464
prefixes["model."] = "language_model.model."

# this PR
lm_dst = prefixes.get("model.language_model.")
if not isinstance(lm_dst, str) or not lm_dst.startswith("language_model."):
    return self
prefixes["model."] = lm_dst

#49464 hardcoded language_model.model. as the fallback destination; this derives it from the wrapper's own existing model.language_model. rule. For every affected model in my scan that's identical in effect, since they all map model.language_model. → language_model.model.. But deriving it is the more correct shape: a wrapper whose LM sits at a different destination would now get the right target instead of a hardcoded assumption. Worth fixing the "implementation is unchanged" line in the description so a reviewer doesn't skip the diff on that basis.

The guard also tightened from startswith("language_model") to startswith("language_model.") (trailing dot). I checked this doesn't drop anything: none of the SupportsLoRA wrappers in my earlier scan map model.language_model. to a bare language_model-without-dot destination, they all carry the .model. suffix, so the set of models that receive the fallback is unchanged.

The diff here is also just the three files that matter (utils.py, worker_manager.py, the new test), where #49464's diff had picked up a large amount of unrelated .buildkite/ churn. This is the cleaner base to review and land.

Still worth what I noted on #49464: none of this proves the adapters were no-ops rather than merely unresolvable at the mapper level, so the GPU parity run on Qwen3.5-4B from #49354 is the part that actually closes that. And a warning when a LoRA loads but matches zero modules would turn this whole class of bug from a silent no-op into a one-line diagnosis, independently of the mapping fix.

[fooSynaptic]

Thanks @ErenAta16. @VBS2004 has already brought up the idea of adding a zero-module-match warning in #48022, so it would be better to leave this feature for them.

BTW, pr body has changed for the new commit.

[ErenAta16]

Agreed on leaving the zero-module-match warning to #48022, that's the better home for it. It's orthogonal to the mapper fix and would only muddy the diff here, and @VBS2004 raised it first. Thanks for updating the PR body too, the "implementation is unchanged" line was the only thing that could have led a reviewer to skip the diff.

Also worth noting for the record, since it's buried in a resolved review thread: @linitra24's catch about the hardcoded destination is what makes this version robust, not just tidier. The earlier prefixes["model."] = "language_model.model." would have mis-mapped any wrapper that mounts its LM somewhere other than language_model.model., while the guard accepted it. Deriving the destination from the existing model.language_model. rule closes that, and the added tests for language_model.transformer. plus the language_modeling. non-match are exactly the two cases that pin the behavior down. I checked the language_modeling. one specifically because it's the kind of prefix collision that's easy to miss: without the trailing dot in the guard it would have falsely enabled the fallback.

Nothing further from me. This looks ready for a maintainer whenever CI can be run on it.

[fooSynaptic]

Hi @jeejeelee ! Could you please add ready (or verified) so pre-run-check can pass and CI can run? thanks!

[mergify]

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

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

[linitra24]

Hi, this PR looks good now, and I also tested it locally — it works as expected.

However, I still have one question. The issue this PR addresses is that when training LoRA for a multimodal model, users may train only the language model part instead of the full model (for example, using Qwen3_5ForCausalLM instead of Qwen3_5ForConditionalGeneration for Qwen3.5). This causes the prefix of the trained LoRA weights to be inconsistent with the prefixes supported by vLLM.

But should this problem really be fixed on the vLLM side? Shouldn't we avoid this situation during the training process instead?

[ErenAta16]

Fair question to raise, but I think vLLM has already answered it, and not once.

WeightsMapper exists for exactly this problem. Its whole job is reconciling an
upstream checkpoint's naming with vLLM's internal naming, and it is a first-class
abstraction rather than a workaround: orig_to_new_prefix, a merge operator so
mappers compose, get_rename_mapper(), ignore_unexpected_prefixes. Over 170
files under vllm/model_executor/models carry an hf_to_vllm_mapper, and the
repo has a built-in one for a naming quirk that has nothing to do with LoRA
(REMOVE_UNUSED_ROTARY_EMBEDS_MAPPER).

So "the producer should have named it correctly" is not the policy this codebase
runs on. It has decided, a hundred and seventy times over, that adapting to how
checkpoints are actually named upstream belongs on the vLLM side. Pushing this
one case back onto training would be a new rule, not the existing one, and it
would be a rule vLLM applies to LoRA adapters and to nothing else.

The specific case is also not a user mistake. Training only the language tower of
a multimodal model is a normal thing to do, and the adapter that comes out is
correct. It is named for the model it was trained on, which is Qwen3_5ForCausalLM,
because that is the model that was trained. Asking people to retrain against a
wrapper they did not use is a large cost for a naming difference.

Where I do think the concern lands is scope, which is what my earlier note here
was about. A fallback that guesses too widely is worse than no fallback, because
it mis-maps silently instead of failing. The current version avoids that by
deriving the destination from the existing model.language_model. rule rather
than hardcoding it, and the tests pin both the accepted case and the
language_modeling. near-miss. That is the part worth holding the line on, not
whether vLLM should be doing the mapping at all.