[Bugfix][Gemma4] Fix ModelOpt mixed-precision MoE config mapping

[wangqia0309]

Purpose

Fix ModelOpt MIXED_PRECISION dispatch for Gemma4 checkpoints whose
quantized_layers records the MoE parent module as
model.language_model.layers.N.experts.

The issue was reproduced and validated with
wangqia0309/gemma-4-26B-A4B-it_nvfp4_experts_fp8_dense_fp8_attn-kv_fp8,
which uses NVFP4 experts, FP8 dense and attention layers, and an FP8 KV cache.

vLLM registers the runtime FusedMoE module under
language_model.model.layers.N.moe.experts. Per-tensor expert names are
already remapped by the Gemma4 weight iterator, but that path does not rewrite
the parent-module keys in quantized_layers. The lookup therefore misses the
NVFP4 entry and falls back to an unquantized MoE method.

This change:

• maps the Gemma4 parent-module suffix from .experts to .moe.experts for
  both the conditional and causal model wrappers;
• makes ModelOptMixedPrecisionConfig.apply_vllm_mapper idempotent because
  the outer Gemma4ForConditionalGeneration and inner Gemma4ForCausalLM
  configure the same quantization config; and
• adds a regression test for the real two-stage mapper sequence and verifies
  that the resulting prefix resolves to NVFP4.

This PR does not change Gemma4 GELU activation behavior.

Duplicate-work check

No open PR was found for the quantized_layers parent-prefix issue.

• [Model] Gemma4: robust quantized MoE expert weight loading #39406 and fix(model_loader): deterministic suffix mapping for Gemma4 MoE quantization #41988 address per-tensor expert weight and scale loading, not
  mixed-precision parent-module config lookup.
• [BugFix] Fix ModelOpt mixed-precision quantization for sparse quantized_layers configs. #47318 fixed sparse mixed-precision inference for packed projection
  siblings, but does not map .experts to .moe.experts.
• Open-PR searches were run for Gemma4 ModelOpt mixed precision MoE,
  quantized_layers experts Gemma4, and moe.moe.experts.

AI assistance was used to inspect the current main branch, adapt the original
patch, add the regression test, and draft this PR description. The human
submitter provided the original patch and performed the RTX 5090 runtime
validation.

Test Plan

.venv/bin/python -m pytest \
  tests/quantization/test_modelopt.py \
  -k test_modelopt_mixed_precision_maps_gemma4_experts_once -v

.venv/bin/pre-commit run --files \
  tests/quantization/test_modelopt.py \
  vllm/model_executor/layers/quantization/modelopt.py \
  vllm/model_executor/models/gemma4.py \
  vllm/model_executor/models/gemma4_mm.py

.venv/bin/pre-commit run mypy-3.12 --hook-stage manual --files \
  tests/quantization/test_modelopt.py \
  vllm/model_executor/layers/quantization/modelopt.py \
  vllm/model_executor/models/gemma4.py \
  vllm/model_executor/models/gemma4_mm.py

Test Result

• Targeted pre-commit hooks: passed.
• Python 3.12 mypy hook for the four changed files: passed.
• Manual end-to-end validation by the submitter on an RTX 5090: the target
  mixed-precision checkpoint started successfully with this fix.
• The pytest command above was not run in the local checkout because the
  local machine does not have a CUDA-capable vLLM test environment.

---

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