[Bugfix][LoRA] Support ark_linear base layer in _get_lora_device

[AlejandroParedesLT]

Summary

Fixes bug 1 from #47650: _get_lora_device doesn't recognize the ark_linear base-layer shape produced by INCARKLinearMethod (INC WNA16 / AutoRound scheme), so --enable-lora crashes at model load with ValueError: Unsupported base layer for any AutoRound/INC ark-backed checkpoint.

INCARKLinearMethod.process_weights_after_loading (vllm/model_executor/layers/quantization/inc/schemes/inc_wna16_linear.py) repacks quantized weights into a layer.ark_linear submodule and deletes qweight/qzeros/scales from the parent layer. _get_lora_device's existing hasattr probes only check the parent layer, so they all miss. This adds a branch that reads the device off ark_linear.qweight instead, which is preserved (just repacked in place).

• Not duplicating existing work: searched gh issue view 47650 and gh pr list for _get_lora_device, ark_linear, _mcp_apply, and the issue number — no open PR addresses this. Note bug 2 from the same issue (unconditional all_gather in _mcp_apply) is already fixed on main by [LoRA] Gate all_gather on fully_sharded_loras inside _mcp_apply; rewrite regression test #45715 (merged before this issue was filed against an older release tag), so this PR only addresses bug 1.
• AI assistance disclosure: this change (fix + test) was drafted with Claude (Claude Code) and reviewed/tested by me before submitting.

Test plan

• .venv/bin/python -m pytest tests/lora/test_layers_utils.py -v — new unit tests covering _get_lora_device's existing branches (unquantized, GPTQ/AWQ) plus the new ark_linear branch and the unsupported-layer error path — 4 passed.
• .venv/bin/python -m ruff check vllm/lora/layers/utils.py tests/lora/test_layers_utils.py — clean.
• .venv/bin/python -m ruff format --check vllm/lora/layers/utils.py tests/lora/test_layers_utils.py — clean.

Not able to run a full E2E repro (requires Intel XPU hardware, auto_round_kernel, and an AutoRound int4 checkpoint, none of which are available in this environment); the fix and tests target the platform-agnostic attribute-detection logic directly.

Co-authored-by: Claude

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

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[marcorigodanzo]

Reporter of #47650 here — thanks for picking this up, @AlejandroParedesLT. Confirming your patch is exactly the fix, independently verified end-to-end on the same class of hardware.

Rig: 2× Intel Arc Pro B70 (Battlemage G31, 8086:e223), TP=2, EPYC 7413, Ubuntu 26.04 / kernel 7.0.0-27 (xe), vLLM 0.24.0+xpu, model Intel/Qwen3.6-27B-int4-AutoRound (INC ark_linear / W4A16).

Verified with this exact ark_linear device branch:

• Clean boot with --enable-lora --tensor-parallel-size 2 --enforce-eager (without the branch: crash-loop at load_model → _get_lora_device → Unsupported base layer).
• 12/12 guided-JSON (llguidance, json_schema) requests valid, no mojibake.
• Zero-weight adapter (lora_B = 0, exact no-op) loaded at runtime via POST /v1/load_lora_adapter: outputs at temperature 0 are bit-identical to the base model through the sliced TP=2 path — so device lookup + slicing are correct end-to-end, not just at boot.

Re your Bug-2 note (#45715): confirmed. We only hit the unconditional all_gather in _mcp_apply because we build from the v0.24.0 release tag, which predates #45715 on main; the caller-side fully_sharded_loras gating on main supersedes it, so this PR needing only Bug 1 is correct.

LGTM. Happy to run any additional check on this hardware if useful.

[AlejandroParedesLT]

@jeejeelee Can you please review my pull request?