[TurboQuant] Share decode scratch workspace across layers

[Bot1822]

Summary

TurboQuant currently registers three decode scratch buffers on every attention layer. These buffers are temporary decode workspace, but because they are registered per layer they scale with the number of TurboQuant layers and with max_num_seqs.

This PR moves TurboQuant decode scratch allocation to the v1 workspace manager so the scratch tensors are shared across layers. It also reserves the maximum TurboQuant decode workspace before CUDA graph capture locks the workspace, preventing locked-workspace growth at runtime.

This PR is scoped to TurboQuant decode scratch memory usage. It does not claim to fix TurboQuant speculative-decoding correctness issues.

Motivation

On large models and large H100/H200 server defaults, the per-layer scratch buffers can consume tens of GiB of non-KV memory and significantly reduce KV cache capacity.

Measured on H200 with Llama-3.1-70B, TP=2, kv_cache_dtype=turboquant_3bit_nc, max_model_len=65536, gpu_memory_utilization=0.90:

version	model loading memory	available KV memory	GPU KV cache size	max concurrency @ 65,536
before	105.23 GiB	14.61 GiB	400,128 tokens	6.11x
after	65.74 GiB	53.97 GiB	1,478,384 tokens	22.56x

The old per-layer buffer cost for this setup is about 39.5 GiB:

B = 1024
Hq = 32
S = 32
D = 128
TQ layers = 76
mid_o ~= B * Hq * S * (D + 1) * 4 bytes * 76 ~= 39.5 GiB

Changes

• Keep TurboQuant centroids as per-layer state.
• Stop registering _tq_mid_o_buf, _tq_output_buf, and _tq_lse_buf on every attention layer.
• Allocate TurboQuant decode scratch through WorkspaceManager.get_simultaneous().
• Reserve a max-size TurboQuant decode workspace before lock_workspace() in CUDA graph capture.
• Reserve across all attention groups so hybrid model layouts do not miss TurboQuant groups outside the first group.
• Add behavior tests for the workspace allocation and reservation paths.

Duplicate-work note

I checked the open TurboQuant scratch/workspace work before publishing this PR. This PR is intentionally scoped to the v1 workspace-manager reservation path and the measured H200 Llama-3.1-70B TP=2 memory-capacity regression. It is not a general cleanup and does not claim to solve unrelated TurboQuant speculative-decoding correctness issues.

Testing

• python -m py_compile tests/quantization/test_turboquant.py vllm/v1/worker/gpu_model_runner.py vllm/model_executor/layers/attention/attention.py vllm/v1/attention/ops/triton_turboquant_decode.py vllm/v1/attention/backends/turboquant_attn.py
• git diff --check
• H200 container: python3 -m pytest tests/quantization/test_turboquant.py -k TurboQuantDecodeWorkspace -q
  • Result: 4 passed, 117 deselected, 17 warnings in 2.03s
• H200 startup/capacity validation:
  • model: /mnt/afs/models/Llama/Llama-3.1-70B
  • GPUs: H200 2x, TP=2
  • --kv-cache-dtype turboquant_3bit_nc
  • --max-model-len 65536
  • --gpu-memory-utilization 0.90
  • result: 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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[Bot1822]

Related: #40706 is also addressing TurboQuant decode scratch deduplication. This PR focuses on the H200/Llama-3.1-70B TP=2 case and explicitly reserves the max-size decode workspace before the v1 workspace is locked after CUDA graph capture.

[gemini-code-assist]

Code Review

This pull request refactors TurboQuant decode scratch space management by transitioning from per-layer registered buffers to a shared workspace manager. This change prevents excessive memory usage in models with many layers by sharing scratch buffers across layers. Feedback was provided regarding the workspace reservation logic in gpu_model_runner.py, which currently only checks the first attention group and may fail to reserve memory for hybrid models where TurboQuant layers are present in subsequent groups.

[gemini-code-assist]

The current implementation only iterates over the first KV cache group (self.attn_groups[0]). In hybrid models (e.g., Mamba + Attention), TurboQuant layers might be assigned to a subsequent KV cache group. If the TurboQuant group is not in the first list, the workspace will not be reserved at its maximum size before the CUDA graph lock, potentially leading to runtime AssertionError crashes when the batch size increases. Iterating over all groups in self.attn_groups ensures the workspace is correctly reserved for any model configuration.

Suggested change

	for group in self.attn_groups[0]:
	if group.backend.get_name() != "TURBOQUANT":
	continue
	max_num_reqs = self.scheduler_config.max_num_seqs
	num_heads = self.model_config.get_num_attention_heads(self.parallel_config)
	head_size = self.model_config.get_head_size()
	max_num_splits = (
	self.vllm_config.attention_config.tq_max_kv_splits_for_cuda_graph
	)
	current_workspace_manager().get_simultaneous(
	(
	(max_num_reqs, num_heads, max_num_splits, head_size + 1),
	torch.float32,
	),
	((max_num_reqs, num_heads, head_size), torch.float32),
	((max_num_reqs, num_heads), torch.float32),
	)
	return
	for groups in self.attn_groups:
	for group in groups:
	if group.backend.get_name() != "TURBOQUANT":
	continue
	max_num_reqs = self.max_num_reqs
	num_heads = self.model_config.get_num_attention_heads(self.parallel_config)
	head_size = self.model_config.get_head_size()
	max_num_splits = (
	self.vllm_config.attention_config.tq_max_kv_splits_for_cuda_graph
	)
	current_workspace_manager().get_simultaneous(
	(
	(max_num_reqs, num_heads, max_num_splits, head_size + 1),
	torch.float32,
	),
	((max_num_reqs, num_heads, head_size), torch.float32),
	((max_num_reqs, num_heads), torch.float32),
	)
	return

[Bot1822]

Addressed the AI review comment about hybrid models: _reserve_turboquant_decode_workspace() now iterates over all attention groups instead of only self.attn_groups[0], so TurboQuant groups outside the first group also reserve max decode workspace before lock_workspace().

I kept self.scheduler_config.max_num_seqs rather than switching to self.max_num_reqs because this reservation is intentionally tied to the configured scheduler maximum that caused the original per-layer allocation issue. I also added a regression check to prevent indexing only the first attention group again.

[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.

[Bot1822]

Additional H200 validation on Llama-3.1-70B, TP=2, kv_cache_dtype=turboquant_3bit_nc, max_model_len=65536, gpu_memory_utilization=0.90.

Startup / capacity:

version	model loading memory	available KV memory	GPU KV cache size	max concurrency @ 65,536
before	105.23 GiB	14.61 GiB	400,128 tokens	6.11x
after	65.74 GiB	53.97 GiB	1,478,384 tokens	22.56x

Serving benchmark, /v1/completions, random dataset, 32 prompts, input length 4096, output length 128, request_rate=inf, max_concurrency=8, ignore_eos=true:

version	req/s	output tok/s	mean TTFT	median TTFT	p99 TTFT	mean TPOT	median TPOT	p99 TPOT
before	0.3186	40.78	16809 ms	2412 ms	64133 ms	65.16 ms	62.87 ms	90.35 ms
after	0.3343	42.80	15629 ms	2415 ms	59396 ms	65.14 ms	62.83 ms	90.20 ms

Sampled MMLU-Pro regression, leaderboard_mmlu_pro, limit=20, 5-shot, local-completions, num_concurrent=4:

version	acc	stderr
before	0.60	0.1124
after	0.60	0.1124

Sample-level comparison: same predicted option on 20/20 samples, same correctness on 20/20 samples.

[noonghunna]

Heads-up — this PR may be the upstream fix for #40831 (TurboQuant × spec-decode degenerate token loops), incidentally rather than by design.

We isolated #40831 through a six-probe ladder (summary in #40831) to CUDA graph capture/replay specifically:

• --enforce-eager (cudagraph + torch.compile both off): bug fixed (23 TPS)
• --compilation-config '{"cudagraph_mode":"NONE"}' (cudagraph off, torch.compile on): bug fixed (33 TPS)
• Default (cudagraph + torch.compile both on): degenerate token loops on tools, structured outputs, long-recall

Triton kernels and torch.compile inductor output are correct when invoked dynamically. Only the captured graph is wrong, which strongly points at buffer-pointer instability between capture and replay.

@Sandermage flagged this PR as the likely fix because it moves _tq_mid_o_buf / _tq_output_buf / _tq_lse_buf from per-layer register_buffer(..., persistent=False) into WorkspaceManager.get_simultaneous(). The captured cudagraph would then reference a stable data_ptr — a persistent base buffer that runtime spec-decode shapes can slice into without the underlying address changing.

The current pre-allocation in attention.py::_init_turboquant_buffers sizes these buffers at B = max_num_seqs (typically 1) but spec-decode verify routes through _prefill_attention with B = q_len (4 for MTP n=3). Under the existing reuse logic, that reallocates the buffer and replaces buf_holder._tq_mid_o_buf, but the captured cudagraph from warmup still references the original data_ptr. Stale pointer → corrupted reads at replay.

If this PR is the right fix, we expect:

• The cudagraph + torch.compile + spec-decode + TurboQuant configuration to produce coherent output (no <parameter=parameter=> corruption on tool calls, no for for / age age token duplication on code/XML, no first-token loops on long-context recall).
• TPS to recover from our workaround's 33 TPS back to the original ~85 TPS.

Worth a focused validation pass on this PR before merging:

1. Run with spec-decode enabled (--speculative-config '{"method":"mtp","num_speculative_tokens":3}' or ngram).
2. Run a structured-output probe (tool calls, JSON, XML, code) in addition to the throughput benchmarks.
3. Check needle-in-haystack at multiple depths (10K, 30K, 60K, 90K) — token duplication on retrieval is the most sensitive signal.

Independent reproductions of #40831 came in from at least four rigs (1× 3090, 1× 4090, 2× A5000, RTX 5090) across different models and TurboQuant presets, so the bug is robust enough to confirm against this PR.

[noonghunna]

Update — backported this PR onto our pinned vLLM nightly digest and tested against the originally-failing #40831 config (Qwen3.6 + turboquant_3bit_nc + MTP n=3 + cudagraph ON). The bug persists with all of #40798's structural changes applied.

Full details + the disk-edit patch script we used + per-test results: #40831 follow-up.

Symptoms unchanged:

• Tool calls: still produce <tool_call> inline-cascade with empty tool_calls[]
• Long-context recall: still produces first-token loops (amber amber amber...)
• Streaming: still shows occasional token duplication (above above above above)
• TPS at ~96 confirms cudagraph + torch.compile genuinely active — not silently falling back to eager

This means either (a) PR #40798 is necessary but not sufficient on its own, (b) there's a companion change in main we'd need to also backport, or (c) our backport has a subtle anchor mismatch that maintainers with branch-local CI could rule in/out.

Sorry for the earlier signal-boosting — wanted to update the thread now that we have data either way. The PR's core claim (memory savings from sharing workspace across layers) is still valid; we're not commenting on that. Just the side-effect "this might fix #40831" hypothesis didn't pan out on our test.

[Bot1822]

Confirmed based on the backport result in #40831. This PR is scoped to reducing TurboQuant decode scratch memory by sharing temporary decode workspace across layers and reserving it before the workspace is locked. I will not claim it fixes #40831. The TurboQuant + speculative decoding + CUDA graph correctness issue appears separate and should be investigated independently.

[MidasMining]

This PR's stated goal — "Reserve a max-size TurboQuant decode workspace before lock_workspace()... preventing locked-workspace growth at runtime" — directly addresses Issue #41565 that I filed today. The assertion message in #41565 is Workspace is locked but allocation from 'turboquant_attn.py:757:_continuation_prefill' requires 2.00 MB, current size is 0.26 MB, which is exactly the failure mode this PR prevents.

Repro is small: any TQ-enabled prompt past ~6-8K cached tokens crashes the engine on stock v0.20.0. The same workloads run cleanly on the pre-#40941 fork. Details + threshold sweep in #41565.

Happy to validate this PR on 8× RTX A4000 SM86 / Nemotron-3-Super-120B-AWQ-4bit / vLLM 0.20.0 — your H200 / Llama-3.1-70B numbers are great but the failure also reproduces on Ampere with hybrid Mamba+MoE+Attention models, and that's where I have a deterministic crash to test against. If you'd like Ampere validation before merge, give me a thumbs-up here and I'll run a sweep at 8K / 16K / 32K / 64K cached KV and post the results.

Also potentially related: Issue #40420 (jhsmith409, Apr 21) — different symptom (CUDA OOM at 185K tokens) but same _continuation_prefill function and Sandermage's lazy-buffer-allocation analysis. May share root cause; worth checking whether your reservation also covers that path.

— MidasMining, 8× RTX A4000 SM86 / vLLM 0.20.0

[gaby]

@Bot1822 Fix the merge conflict

[Bot1822]

@MidasMining @gaby The merge conflict has been resolved and the PR branch has been updated.

The updated version keeps the WorkspaceManager-based path from main and preserves this PR fix: TurboQuant workspace is reserved before lock_workspace(), including the _continuation_prefill path mentioned in #41565.

[gaby]

@mgoin @JartX @njhill @sfeng33 Ping, TurboQuant does not work at all without this.