[Bugfix][V1][TurboQuant] Reserve workspace before CUDA graph capture

[Bot1822]

Summary

Supersedes #40798. This PR fixes a TurboQuant CUDA graph workspace bug: TurboQuant can lazily request larger decode / continuation-prefill scratch buffers after CUDA graph capture has already locked the v1 workspace, which triggers runtime locked-workspace assertions on long-context requests.

The fix reserves the maximum TurboQuant workspace from the TurboQuant attention backend initialization path, before CUDA graph capture locks the workspace. The reservation is handled in TurboQuantMetadataBuilder.__init__, so the TurboQuant-specific logic stays inside the attention backend rather than GPUModelRunner.

This PR is scoped to TurboQuant workspace reservation for CUDA graph safety. It does not claim to fix unrelated TurboQuant speculative-decoding correctness issues.

Note on #40798: while adding the DCO sign-off, I used a --depth 1 shallow clone and amended the commit there. That produced a root commit with no parent and temporarily made GitHub treat the old PR as a full-repository diff. The fork branch was restored, but GitHub reports that #40798 cannot be reopened because the branch was force-pushed/recreated. This PR recreates the same fix from a clean branch based on current main, with the DCO sign-off included.

Motivation

On stock TurboQuant, a long-context request can hit _continuation_prefill after the workspace has been locked for CUDA graph replay. If the previously allocated workspace is smaller than the runtime request, vLLM raises an assertion like:

AssertionError: Workspace is locked but allocation from 'turboquant_attn.py:747:_continuation_prefill' requires 16.00 MB, current size is 0.51 MB

This breaks practical TurboQuant serving for long-context requests even when model loading succeeds.

Changes

• Reserve TurboQuant decode scratch workspace during TurboQuantMetadataBuilder initialization.
• Reserve continuation-prefill dequant workspace before CUDA graph capture can lock the workspace.
• Keep TurboQuant-specific reservation logic inside the TurboQuant attention backend.
• Add behavior tests for TurboQuant metadata-builder workspace reservation.

Testing

• git diff --check
• H200 real serving regression, baseline without this reservation:
  • model: /mnt/afs/models/Llama/Meta-Llama-3.1-8B
  • --kv-cache-dtype turboquant_3bit_nc
  • --max-model-len 16384
  • --max-num-batched-tokens 4096
  • --max-num-seqs 1
  • request: /v1/completions with a 12k-token prompt
  • result: reproduced AssertionError: Workspace is locked but allocation from 'turboquant_attn.py:747:_continuation_prefill' requires 16.00 MB, current size is 0.51 MB
• H200 real serving validation with the backend-init reservation:
  • same model, server args, and 12k-token /v1/completions request
  • result: HTTP 200, prompt_tokens=12001, completion_tokens=1, finish_reason=length
  • server log had no Workspace is locked, AssertionError, or Traceback
• H200 startup/capacity validation on Llama-3.1-70B, TP=2, kv_cache_dtype=turboquant_3bit_nc, max_model_len=65536, gpu_memory_utilization=0.90:
  • model loading memory reduced from 105.23 GiB to 65.74 GiB
  • GPU KV cache size increased from 400,128 to 1,478,384 tokens
• H200 serving benchmark, /v1/completions, random dataset, 32 prompts, input length 4096, output length 128, request_rate=inf, max_concurrency=8, ignore_eos=true:
  • before: 0.3186 req/s, 40.78 output tok/s, mean TTFT 16809 ms, mean TPOT 65.16 ms
  • after: 0.3343 req/s, 42.80 output tok/s, mean TTFT 15629 ms, mean TPOT 65.14 ms
• Sampled MMLU-Pro regression, leaderboard_mmlu_pro, limit=20, 5-shot, local-completions, num_concurrent=4:
  • before: acc=0.60 +/- 0.1124
  • after: acc=0.60 +/- 0.1124
  • same predicted option on 20/20 samples and same correctness on 20/20 samples

AI Assistance

AI assistance was used to prepare this patch and PR text. Guipeng Zhang is the human submitter responsible for reviewing and defending the change.

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[MidasMining]

+1 — also affected by the same workspace lock-violation (#41565) on our 8× RTX A4000 SM_86 stack (Nemotron-3-Super-120B-AWQ-4bit, TP=8 EP=8, --kv-cache-dtype turboquant_3bit_nc, max-model-len 131072). We're pinned to a pre-PR#40941 fork specifically because of this bug.

Tracked the move from #40798 → here. Same patch idea, smaller scope, fewer files touched — nice cleanup.

Happy to run cross-platform validation on 8× A4000 once the pre-commit check is green and the PR is ready for review. Already did the same validation work on the parallel #42215 (comment) so the bench harness is set up. Just ping when you'd like a runtime report.

@njhill @LucasWilkinson @mgoin — pinging since this is the structurally-smallest fix for #41565 and the TQ workspace lock has been blocking production migrations for several users for ~2-3 releases now (see comment thread on closed #40798).

[mergify]

Hi @Bot1822, the pre-commit checks have failed. Please run:

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[Bot1822]

@mgoin Quick update: I fixed the formatting issue and the GitHub pre-commit check is now passing. The remaining failures seem limited to three B200 Buildkite jobs with exit status -1, while the relevant TurboQuant / quantization / v1 checks are passing. Could you take a look when you have a chance, and rerun the B200 jobs if these look like infra flakes?

[alankessler]

I confirmed this fixes the workspace-lock crash on Intel Arc B70 (BMG) too. Unpatched, with turboquant_4bit_nc and enforce_eager off, graph mode compiles and captures fine but the first decode dies in _decode_attention -> workspace.py with Workspace is locked but allocation requires 0.76 MB, current size 0.00 MB. With #44053 applied I get a clean start and a correct first decode.

One note: decode is fast at short context (~28 tok/s) but falls off hard as context grows (~0.1 tok/s by ~40K), while eager stays steady (~13 tok/s). Separate from this PR, but now that graph mode is correct I can dig into the perf. I'll open a focused issue once I've narrowed it.

Qwen3.6-27B int4 (hybrid GDN+MoE), turboquant_4bit_nc, vLLM 0.20.2.