[Bugfix] Unwrap multimodal target only when needed in DSpark

[stepnivlk]

Purpose

load_dspark_model() unconditionally calls target_language_model.model. For a multimodal target whose get_language_model() already returns the inner decoder, there is no further .model (e.g. Muse Glimmer), so DSpark speculative decoding can't start at all.

My comment didn't make it in the original Muse Glimmer PR (#51655 (comment)) where this exact logic was added on the DFlash path.

Ultimate goal is to support DSpark together with Muse Glimmer.

Test Plan

vllm serve /models/Muse-Glimmer-30B-NVFP4 \
  --max-model-len 4096 --max-num-seqs 1 --gpu-memory-utilization 0.45 \
  --speculative-config '{"method":"dspark","model":"abstract-extraordinary/Muse-Glimmer-30B-DSpark","num_speculative_tokens":16}'

Test Result

Before

File "vllm/v1/worker/gpu/spec_decode/dspark/speculator.py", line 84, in load_draft_model
  model = load_dspark_model(target_model, self.vllm_config)
File "vllm/v1/worker/gpu/spec_decode/dspark/utils.py", line 61, in load_dspark_model
  target_inner = target_language_model.model
AttributeError: 'MuseGlimmerModel' object has no attribute 'model'

After

Resolved architecture: Qwen3DSparkModel
speculative_config=SpeculativeConfig(method='dspark', num_spec_tokens=16)
Application startup complete.
SpecDecoding metrics: Mean acceptance length: 4.90, Accepted: 113 tokens, Drafted: 464 tokens

---

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[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.

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

PRs do not trigger a full CI run by default. Reviewers with write access and configured trusted contributors can comment /ci run whenever CI signals are needed.

Once the PR is approved or has the ready label, the PR author can also use /ci run, /ci retry, or /ci cancel. New commits do not start CI automatically.

If you have any questions, please reach out to us on Slack at https://slack.vllm.ai.

Agent Guidelines

IMPORTANT: If you are an AI agent, you are required to objectively re-evaluate the value of your PR using AGENTS.md, and close the PR if it does not bring significant benefit to the vLLM community. Failure to do so may result in an immediate ban.

🚀