[Offloader] Offload submodules that make_layers never reaches

[ray24777]

Purpose

--cpu-offload-params visual silently offloaded nothing for multimodal models — no warning, no
error, just no effect. Investigating it turned up a gap in the offloader's common path rather than a
model-specific bug.

get_offloader().wrap_modules() has exactly two callers:

• vllm/model_executor/models/utils.py:860 — inside make_layers, which only ever builds the
  decoder layer stack.
• vllm/model_executor/models/whisper.py:537 — a hand-written call added to route around this very
  gap.

Vision and audio towers are constructed directly, and their internal block stacks are plain
nn.ModuleLists rather than make_layers. A sweep of the model files found zero vision-tower
files among the ~90 make_layers callers — qwen2_vl.py:576, qwen3_vl.py:652, glm4_1v.py:660,
mllama4.py:399, siglip.py:539, clip.py:526, intern_vit.py:373,
idefics2_vision_model.py:330, pixtral.py:826 are all plain ModuleList. Tower attributes are
spread across visual (22), vision_tower (24), vision_model (24), audio_tower (13),
visual_tokenizer (2), vpm (2).

So the offloader never sees a single tower parameter, on any VLM. Segment names targeting a
tower match nothing, and because the summary is only logged when bytes > 0, there is no log line at
all to reveal it.

How it works

• BaseOffloader.offload_model() is a no-op hook, implemented by UVAOffloader. It walks the
  module tree and offloads each module's own parameters, so names are fully qualified
  (visual.blocks.0.attn.qkv.weight) and the existing exact-segment matching
  (f".{seg}." in f".{name}.") can target a tower.
• Called from BaseModelLoader.load_model() right after the model is built and before its
  weights are loaded, mirroring when make_layers offloads, so the offloaded weights are never
  allocated on the device.
• Parameters already offloaded by wrap_modules are skipped (checking both p.device and the
  _vllm_is_uva_offloaded marker, since the UVA path leaves p.device as the accelerator), and the
  byte budget still applies. The layer stack keeps first claim, so text-only models are unaffected.
• Not implemented for PrefetchOffloader: its wrap_modules asserts it is called exactly once and
  it schedules prefetches over a circular layer stack, which a tower is not.

No model implementation is touched — this addresses @Isotr0py's review comments on previous commits, asking for the fix on the common code path rather than in qwen3_5.py specifically.

The Qwen3.5-specific approach from the earlier revisions of this PR has been dropped entirely; the
branch has been squashed so only the common-path fix remains.

Why this is not a duplicate

Searched vllm-project/vllm issues and PRs for cpu-offload-params visual, vision tower offload,
wrap_modules visual, UVA offload vision, offload visual encoder. No existing issue or PR
addresses offloading vision-tower weights.

• qwen2.5-omni: offload multimodal features to CPU after GPU processing #41891 (open): offloads multimodal feature tensors to CPU after the HF processor runs.
  Different problem — runtime features vs. resident tower weights.
• [Bugfix] Wire up weight offloader in V2 Model Runner (--cpu-offload-gb was silently a no-op) #48468 / [Bugfix] Install the weight offloader in the V2 model runner #51462: wire up the weight offloader in the V2 model runner. Different root cause; in
  this checkout the V2 runner already installs the offloader
  (vllm/v1/worker/gpu/model_runner.py:329).
• [Offloader] Skip per-tensor scale parameters from CPU offload #43453 (open): skips per-tensor scale parameters inside the UVA offloader. Independent
  offloader-internal change.

Test Plan

.venv/bin/python -m pytest tests/basic_correctness/test_cpu_offload.py -v -k "offload_model or mrv2"
.venv/bin/python -m ruff check vllm/model_executor/offloader/ vllm/model_executor/model_loader/base_loader.py tests/basic_correctness/test_cpu_offload.py

Six new unit tests were added to the existing tests/basic_correctness/test_cpu_offload.py rather
than a new file. They need no real model and cover: the tower being reached, unmatched parameters
being left resident, idempotency against wrap_modules, the layer stack's first claim on the
budget, the non-UVA fallback hook producing correct output with CPU-resident weights, and the other
backends being unaffected.

Test Result

Unit tests — 8 passed (6 new + the 2 existing test_mrv2_weight_offloading cases, covering
both the UVA and prefetch backends through a real model load):

test_mrv2_weight_offloading[offload_kwargs0-UVAOffloader] PASSED
test_mrv2_weight_offloading[offload_kwargs1-PrefetchOffloader] PASSED
test_offload_model_reaches_directly_constructed_tower PASSED
test_offload_model_leaves_unmatched_params_resident PASSED
test_offload_model_skips_params_already_offloaded_by_wrap_modules PASSED
test_offload_model_respects_budget_consumed_by_layer_stack PASSED
test_offload_model_fallback_hook_restores_weights_for_forward PASSED
test_offload_model_is_a_noop_for_other_backends PASSED

Lint — All checks passed! (ruff check + format).

End-to-end on unsloth/Qwen3.8-27B-NVFP4 (Qwen3_5ForConditionalGeneration, 27B NVFP4, single
RTX 5090), launched with --cpu-offload-gb 2 --cpu-offload-params visual. Measured as a
before/after on the same host, where "before" is this PR's base commit:

	Before	After
Total CPU offloaded parameters	(no log line — nothing offloaded)	0.86 GiB
Model loading	22.13 GiB	21.26 GiB
GPU memory in use	30786 MiB	29880 MiB

The "before" run confirms the bug: Offloader set to UVAOffloader appears, but no parameters are
ever matched.

Both modalities verified correct after the change, with the vision tower resident on CPU via UVA
zero-copy:

• Image understanding — described the test image correctly ("A red square and a blue circle sit
  side by side on a white background, with the word 'TEST' written beneath the square."),
  finish_reason: stop. Byte-identical to the "before" run's description.
• Text follow-up — correct arithmetic (391 + 9 → 400), finish_reason: stop, confirming the
  sweep does not disturb language-model weights.

No errors, no fallback.

Notes

TensorizerLoader.load_model overrides load_model and returns without calling
super().load_model(), so it already bypasses process_weights_after_loading today and does not
get the sweep. Wiring it up would mean touching an unrelated loader for a pre-existing gap, so it is
left out of scope here.

The four test_cpu_offload[*] parametrizations in that same file fail in my local environment with
FileNotFoundError: 'vllm' — they spawn a bare vllm binary that is not on my PATH. I confirmed
these fail identically on this PR's base commit, so the failure is environmental and pre-existing,
not introduced here.

AI assistance was used in preparing this change (implementation, tests, and this description). The
submitting human has reviewed every changed line and run the tests above.

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR.
• The test plan, providing test commands.
• The test results (unit + e2e on real hardware).
• Why this is not a duplicate of existing PRs.
• AI-assistance statement (per AGENTS.md).

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[claude]

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[Isotr0py]

/ci run

[github-actions]

✅ Triggered Buildkite CI #85596 for commit 9f02ede23310.

[ray24777]

@Isotr0py, thanks for your implmentation!
However, I noticed that buildkite/ci/pr/nvidia-h200-basic-correctness has failed, which was caused by allocating only 5% of VRAM on a sliced 16 GB H200, causing CI to fail on test_tower_weight_offloading. That would be only 0.8 GB RAM for Qwen 3.5 0.8B, which is too small. I made a small commit that fix it by increase gpu_memory_utilization to 0.3. The modified test passed successfully on my local 5090 32GB GPU.

[ray24777]

/ci run

[github-actions]

✅ Triggered Buildkite CI #85622 for commit f60471e63747.