[tryryry]

Overview

Fix reduced KV cache capacity when IPC weight loading falls back to disk after external weights have already been charged against the engine's memory budget.

Follow-up to #62583.

Claims

• Restore the engine budget after disk fallback.
• Remove only the external weight charge for the model that falls back to disk, then recheck the engine’s memory budget.

Validation

Qwen3-0.6B on NVIDIA B300, TP1, eager mode, gpu_memory_utilization=0.1, maximum length 1024, and FlashInfer autotune disabled.

The daemon uses BF16; both engines use FP16. The dtype mismatch triggers IPC fallback. The same daemon remains running during both disk and fallback runs.

Metric	Direct disk load	Fallback before	Fallback after
Engine memory budget	26.77 GiB	25.65 GiB	26.77 GiB
Available KV cache	25.50 GiB	24.38 GiB	25.50 GiB
Generated output	Reference	Identical	Identical

The regression test fails without the fix:

AssertionError: Disk fallback retained the external weight charge
assert 27539026292 == 28741612340

With the fix:

• Focused regression and accounting tests: 6 passed.
• MRV1 regression: 1 passed; the default MRV2 path also passes.
• Related worker and weight-cache tests: 41 passed, 1 skipped.
• Separate disk, zero-copy, absent-daemon fallback, and copy-mode runs completed with identical eight-token outputs and expected accounting.
• All pre-commit checks passed.

Reproduce the regression:

CUDA_VISIBLE_DEVICES=7 OMP_NUM_THREADS=1 \
VLLM_WORKER_MULTIPROC_METHOD=spawn \
.venv/bin/python -m pytest \
  tests/model_executor/model_loader/test_weight_cache.py::test_ipc_fallback_restores_memory_budget \
  -v -s

Repeat with VLLM_USE_V2_MODEL_RUNNER=0 for MRV1.

The regression extends the existing weight-cache suite selected by Model Executor CI. Partial target/draft fallback is covered by accounting unit tests; mixed-role end-to-end validation has not been performed.

Details

The worker queries external weight memory before loading the model. If IPC loading later falls back to disk, the original external charge remains while locally loaded weights are included in profiling, unnecessarily reducing KV capacity.

A scoped accounting object records charges by daemon socket and role. After disk loading succeeds, the IPC loader removes that source's charge once. The worker then recalculates its budget before KV profiling, retaining the original memory snapshot and capacity checks.

gpu_worker.py changes are limited to querying the accounting object, activating it during loading, and applying an updated budget. The KV profiling formula and daemon lifecycle are unchanged. The preload documentation describes the corrected behavior.

Duplicate-work searches found no equivalent budget fix. Related #59813 restores model state after IPC construction failures; this change corrects memory accounting after successful fallback.

AI assistance was used for implementation, review, and validation.

———

Pull Request Checklist

• I used vLLM's /pr-checklist skill. (Mandatory for agents, optional for humans).
• AI assistance was used during the creation of this PR.
• Design Fit: Minimizes impact on core components, reuses existing functionality, and justifies added complexity.
• Testing and Validation: Validates the change and ensures any added tests are meaningful and reliable, with CI coverage or documented CI resource constraints and validation performed outside CI.
• Code Quality and Style: Keeps code and comments clear and concise, and updates relevant documentation and examples.
• Pull Request Contents: Includes a brief summary and relevant links, supports claims with evidence, explains root causes and implementation trade-offs, and follows the contributing guide.

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[mergify]

Documentation preview: https://vllm--63488.org.readthedocs.build/en/63488/

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[mergify]

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

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[mergify]

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

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