[Bugfix] Fix HYV3 shared_mlp prefix for compressed-tensors ignore matching

[DCoEngine]

What does this PR do?

Fixes a KeyError: 'layers.N.mlp.shared_mlp.down_proj.weight' raised at load time when serving a W4A16 compressed-tensors hy_v3 checkpoint whose shared expert (shared_mlp) is intentionally kept in BF16.

Root cause

HYV3MoEFused builds its shared expert as an HYV3FeedForward with prefix=f"{prefix}", omitting the .shared_mlp segment. The Linear layers inside it (gate_up_proj, down_proj) register their parameters under ...mlp.shared_mlp.<proj>.* — the shared_mlp attribute name is appended by nn.Module regardless of the prefix argument.

CompressedTensorsConfig decides whether a Linear is quantized by matching the prefix against its ignore list. With the missing segment, the ignore check saw ...mlp.down_proj instead of ...mlp.shared_mlp.down_proj, so a re:.*shared_mlp.* ignore entry failed to match.

Consequence: the shared expert was built as a quantized Linear expecting weight_packed/scale/shape, while real W4A16 hy_v3 checkpoints (produced by llm-compressor) correctly keep shared_mlp in BF16 as .weight. This raised the KeyError at load.

Fix

Pass prefix=f"{prefix}.shared_mlp" so the ignore check matches the real parameter registration path and the shared expert is built unquantized (UnquantizedLinearMethod), aligning with the BF16 checkpoint. Parameter registration names are unchanged.

Duplicate-work check

Searched vllm-project/vllm:

• Open PRs matching shared_mlp / hy_v3 compressed / shared_mlp prefix: only [NPU][0-DAY] Hy3 0-day adaptation on Ascend NPU #47813 ([NPU][0-DAY] Hy3 0-day adaptation on Ascend NPU) — a hardware-backend adaptation, unrelated to compressed-tensors prefix matching.
• Issues matching hy_v3 shared_mlp / hy_v3 compressed tensors: none.

No existing PR addresses this fix.

Test commands and results

This is a one-line fix to the prefix passed at module construction. Validation:

• End-to-end: on a CUDA server, before the fix the W4A16 hy_v3 checkpoint failed to load with the KeyError above; after applying this one-line fix the checkpoint loads and the vLLM service starts successfully.

The fix changes which Linear method the shared expert is built with; it does not alter the forward computation (the unquantized shared expert computes exactly the BF16 path the checkpoint was quantized against). No model-eval accuracy regression is expected; reviewer may request evals if deemed necessary.

AI assistance

AI assistance was used in producing this change: root-cause diagnosis was developed with AI assistance. The submitting human has reviewed every changed line and validated the fix end-to-end on a CUDA server.

[claude]

Claude Code Review

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[jeejeelee]

We don't need to add this test script, please remove it