[Core][V1] Reuse request token storage for trace replay

[chengcuiping]

Purpose

Trace Replay currently keeps its forced decode tokens in a dedicated
max_num_reqs * max_model_len pinned/UVA matrix even though RequestState already
owns an all-token matrix with the same shape. Reuse the existing request token
storage and remove the duplicate allocation.

Changes

• Append the unconsumed trace suffix to RequestState.all_token_ids during
  request admission on the sampling pipeline rank.
• Read replay tokens from all_token_ids[prompt_len + step] in the existing
  post-sampling overwrite kernel.
• Keep normal sampling and logprob computation unchanged; this does not add a
  sampling bypass.
• Cover real RequestState admission wiring, full admission batches, resume,
  slot reuse, empty suffix behavior, and non-trace requests.

At max_num_reqs=256 and max_model_len=40960, this removes exactly
41,943,040 bytes (40 MiB) of logical pinned/UVA storage per worker.

Duplicate-work check

Open-PR searches for trace_decode_token_ids and trace replay found no PR
implementing this storage reuse. PR #53357 mentions the parameter for HTTP error
classification only and changes unrelated files.

Tests

• Latest-official-main compatibility: commit 463be19d8 applied without
  conflict, without a new commit, to a detached worktree at vLLM
  main@903a02192fca19e4c89705af7017c0f24971ea4f.
• The candidate is 54 upstream commits behind that main. Of the five candidate
  files, upstream changed only vllm/v1/worker/gpu/model_runner.py; those
  adaptive speculative verification and PCP/CUDA graph changes do not overlap
  or alter the candidate add_requests/trace-suffix admission path.
• git diff --check and the staged-patch equivalent: passed.
• pre-commit run --files <five changed files>: all applicable hooks passed,
  including ruff, formatting, mypy, SPDX, forbidden-import, and CUDA API checks.
• .venv/bin/python -m pytest tests/v1/sample/test_trace_replay_params.py tests/v1/engine/test_input_processor_trace_replay.py -q:
  17 passed.
• .venv/bin/python -m pytest tests/v1/worker/test_gpu_trace_replay.py -q:
  12 passed through the real CUDA/UVA path, including admission, resume, full
  batches, and slot reuse.
• .venv/bin/python -m pytest tests/v1/worker/test_gpu_sampler_flags.py tests/v1/worker/test_gpu_bad_words.py -q:
  17 passed for adjacent non-trace behavior.
• harness/correctness_ab.py --tp 1 --pp 1 on Qwen3-4B latest main: all trace
  tokens matched, all requests finished, preemption/resume observed (50
  preemptions), and all compared output fields were strictly equal to the frozen
  candidate artifact.
• harness/correctness_ab.py --tp 1 --pp 2 on Qwen3-4B latest main: all trace
  tokens matched, all requests finished, preemption/resume observed (23
  preemptions), and all compared output fields were strictly equal to the frozen
  candidate artifact.
• Frozen-base correctness comparisons for Qwen3-4B TP1, TP2, and PP2 and
  Qwen3-8B TP1: strict output equality. Every configuration exercised primary
  replay, slot reuse, normal-after-trace, mixed trace/non-trace requests, and
  forced-pressure preemption/resume.

Frozen-base A/B confirmation

The performance baseline is vLLM main@06ecec7a8424106dc80c5c40bb0cc22bcc6da667.
On that frozen base, Qwen3-4B with prompt length 1024 and replay trace length
8192 was measured for seven alternating, GPU-counterbalanced rounds per
concurrency:

• Concurrency 16: throughput -0.289%, p50 latency +0.292%.
• Concurrency 64: throughput +0.018%, p50 latency -0.005%.
• 56 included observations, zero failed or token-mismatched requests.

The throughput changes at concurrency 16 and 64 are close to run-to-run noise;
this change is not claimed to improve throughput. The evidence supports
performance neutrality on the frozen base while removing exactly 40 MiB of
logical pinned/UVA storage per worker. The full 56-observation A/B was not rerun
on the latest main because its relevant execution path and functional results
remain consistent in the compatibility checks above.

AI assistance

This change was prepared with OpenAI Codex assistance. The human submitter must
review every changed line and be prepared to defend the change end-to-end before
opening the PR.

[claude]

Claude Code Review

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