[Bugfix][Spec Decode] Avoid temporary Qwen3.5 MTP vocab allocations

[Chishenzheng]

Purpose

Fixes #53887.

With pipeline parallelism disabled (PP=1), the generic MTP proposer shares the target model's token embedding and LM head with the draft model after the draft model has loaded. Qwen3_5MTP still constructs both full-vocabulary modules up front, and its weight loader maps the target checkpoint's embedding and LM-head tensors into them before the proposer replaces them with the target modules.

For RedHatAI/Qwen3.8-27B-INT4, each unquantized BF16 vocabulary table is 248320 x 5120 x 2 bytes = 2425 MiB. These two temporary modules therefore add 4850 MiB to the loading peak and can cause an OOM before the existing sharing step is reached.

This PR leaves embed_tokens and lm_head unallocated for PP=1 and filters their tensors out of the draft-model weight loader. The existing proposer then attaches the target modules before inference. PP>1 retains its existing allocation and loading paths unchanged.

Relationship to existing PRs

#47953, and the alternative #47833, address a different point in the lifecycle: the generic proposer's post-load decision to share MTP embeddings. They do not prevent Qwen3.5 MTP from allocating and loading its temporary embedding and LM head before that decision is reached.

I also checked the #53887 discussion and searched open vLLM PRs on 2026-08-28; no open PR addresses these pre-sharing allocations.

Test Plan

python -m pytest \
  tests/model_executor/test_qwen3_5_mtp.py \
  tests/model_executor/test_qwen3_5_quantization.py -q

python -m pytest \
  tests/v1/spec_decode/test_mtp.py::test_mtp_load_model_unified -q

pre-commit run --files \
  vllm/model_executor/models/qwen3_5_mtp.py \
  tests/model_executor/test_qwen3_5_mtp.py \
  tests/model_executor/test_qwen3_5_quantization.py

End-to-end validation used RedHatAI/Qwen3.8-27B-INT4@2fb0debc365fb6c1683d7d3ad7722470919627a8 with PP=1 on an NVIDIA B300. GPU memory was sampled with nvidia-smi every 100 ms during model loading. Both the legacy and V2 model runners were exercised.

Test Result

• Model-specific unit tests: 5 passed.
• Existing MTP post-load sharing test: 1 passed on a GPU node.
• All applicable pre-commit hooks passed, including Ruff and mypy.

Configuration	Loading-window peak	Reported model memory
Upstream, legacy model runner	25826 MiB	18.50 GiB
Patched, legacy model runner	20974 MiB	18.50 GiB
Patched, V2 model runner	20934 MiB	18.52 GiB

The upstream and patched legacy-runner measurements used the same vLLM base revision. The patch reduced the loading peak by 4852 MiB, matching the theoretical 4850 MiB for the two omitted BF16 tables within sampling and allocator granularity. The unchanged reported model memory shows that only the temporary copies were removed; the MTP layer itself remains resident.

For four temperature-zero prompts, the patched legacy and V2 runners produced the same text and token usage as upstream. All three MTP runs created 137 draft tokens and accepted 116 (84.7%).

AI assistance disclosure

OpenAI Codex assisted with investigation, testing, model evaluation. The submitter reviewed every changed line and the reported results before submission.

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