[BugFix] Spec decode with VLLM_ENABLE_V1_MULTIPROCESSING=0

[heheda12345]

Purpose

We now forgets to pass the draft tokens from model runner to scheduler. This PR fix it.

Test Plan

VLLM_ENABLE_V1_MULTIPROCESSING=0 python3 examples/offline_inference/spec_decode.py --test

Test Result

Before this PR:

--------------------------------------------------
total_num_output_tokens: 247913
num_drafts: 0
num_draft_tokens: 0
num_accepted_tokens: 0
mean acceptance length: 1.00
--------------------------------------------------
acceptance at token 0: 0.00
acceptance at token 1: 0.00

After this PR:

--------------------------------------------------
total_num_output_tokens: 247893
num_drafts: 122887
num_draft_tokens: 245774
num_accepted_tokens: 124787
mean acceptance length: 2.02
--------------------------------------------------
acceptance at token 0: 0.69
acceptance at token 1: 0.33

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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.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[chatgpt-codex-connector]

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[gemini-code-assist]

Code Review

This pull request aims to fix speculative decoding when not using multiprocessing by adding a call to post_step. While the change correctly identifies the missing call, the implementation hardcodes model_executed=True. This could lead to issues if step_fn is called without the model actually executing. I've provided a suggestion to use the model_executed flag returned by step_fn to ensure correctness and maintain consistency with the multiprocessing implementation.

[njhill]

Thanks @heheda12345!