[Platform] Allow pinning the attention backend for components that auto-select

[jianyi85]

The MTP spec-decode drafter does not receive the user's --attention-backend; it reaches CudaPlatform.get_attn_backend_cls with selected_backend=None and auto-selects. On DGX Spark (GB10 / SM121) auto-selection picks FLASHINFER, whose kernels fault with MTP + fp8 KV (#37754), so a user who sets --attention-backend TRITON_ATTN still crashes — the drafter is a second, unpinnable door to the faulting kernels.

This adds a VLLM_FORCE_ATTN_BACKEND env that pins the auto-selection path as well. The forced backend is validated against each component's configuration and falls back to auto-selection where it is invalid, so components with other constraints keep working.

Production data (Qwen3 MoE NVFP4, fp8 KV, MTP num_speculative_tokens=5, structured-JSON tool workload at 8-concurrent, single GB10): with the drafter auto-selected onto FLASHINFER, CUDA error: an illegal memory access reproduces within ~2 minutes. In the crash dumps preempted_req_ids=[], and the fault survived a per-step torch.cuda.synchronize(), record_stream hardening on the cross-stream D2H copies, and cudagraph_mode=PIECEWISE — pointing at in-step kernel corruption rather than a scheduling race. With both decode paths pinned to TRITON_ATTN via this env, five consecutive reproducer rounds ran clean and throughput was at parity or better. Relevant to #40756 and #37754.

A fuller fix would plumb attention_config.backend into the draft model's selection; this env is the minimal escape hatch and is also useful for bisecting backend-specific faults.

[claude]

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[jianyi85]

Test data and evidence

Environment: single NVIDIA DGX Spark (GB10, SM121, 121GB unified, driver 580.x), vLLM v0.26.0 (vllm/vllm-openai:v0.26.0-ubuntu2404 + the backports listed below), nvidia/Qwen3.6-35B-A3B-NVFP4, --kv-cache-dtype fp8, --moe-backend marlin, --speculative-config '{"method":"mtp","num_speculative_tokens":5,"moe_backend":"triton"}', --async-scheduling, --enable-chunked-prefill, --enable-prefix-caching, --kv-cache-memory 34307139482, --max-num-seqs 16.

Reproducer: 5 single-stream + 3×8-concurrent structured-JSON tool-calling requests (~400 tok each). Unpatched stack at K=5: ~10 EngineCore deaths in 5 minutes; every run below that says "crash" died mid-run with HTTP 500s.

Elimination matrix (all at K=5, same reproducer)

stack	result	AcceleratorError count
v0.26.0 + #48245 backport + #38551 fallback	crash	5
+ record_stream on cross-stream D2H copies, OOB guard on draft scatter, full torch.cuda.synchronize() after every drafted step	crash	3
+ cudagraph_mode=PIECEWISE	crash	3
+ --attention-backend TRITON_ATTN + enable_flashinfer_autotune=false (main model pinned, drafter still auto-selects FLASHINFER)	crash	3
+ VLLM_FORCE_ATTN_BACKEND=TRITON_ATTN (this PR — both decode paths pinned)	clean ×3 rounds	0
same, with FULL_AND_PIECEWISE restored and the per-step sync removed	clean ×2 rounds	0

Why the flag alone is not enough — backend selection log lines

With --attention-backend TRITON_ATTN on the command line:

(EngineCore pid=395) INFO ... [cuda.py:422] Using AttentionBackendEnum.TRITON_ATTN backend.
(EngineCore pid=395) INFO ... [cuda.py:482] Using FLASHINFER attention backend out of potential backends: ['FLASHINFER', 'TRITON_ATTN'].

The second selection (the MTP drafter's) arrives at get_attn_backend_cls with selected_backend=None and auto-selects. With this PR's env set:

(EngineCore pid=393) INFO ... [cuda.py:422] Using AttentionBackendEnum.TRITON_ATTN backend.
(EngineCore pid=393) INFO ... [cuda.py:443] Using AttentionBackendEnum.TRITON_ATTN backend (VLLM_FORCE_ATTN_BACKEND).

Crash dump excerpt (drafter on FLASHINFER, no preemption in flight)

(EngineCore pid=394) ERROR ... [dump_input.py:79] Dumping scheduler output for model execution: SchedulerOutput(
  scheduled_cached_reqs=CachedRequestData(req_ids=[... 6 requests ...], resumed_req_ids=set(), ...),
  num_scheduled_tokens={...: 6, ...: 6, ...: 6, ...: 6, ...: 6, ...: 6}, total_num_scheduled_tokens=36,
  scheduled_spec_decode_tokens={...: [-1, -1, -1, -1, -1], ... all 6 requests ...},
  preempted_req_ids=[], num_invalid_spec_tokens=null, num_spec_tokens_to_schedule=5)
(EngineCore pid=394) ERROR ... [core.py:1332]   File ".../vllm/v1/attention/backend.py", line 510, in seq_lens_cpu
(EngineCore pid=394) ERROR ... [core.py:1332]     self._seq_lens_cpu = self.seq_lens.to("cpu")
(EngineCore pid=394) ERROR ... [core.py:1332] torch.AcceleratorError: CUDA error: an illegal memory access was encountered

preempted_req_ids=[] in every dump we captured, and the fault survives a full per-step device sync — i.e. the corruption happens inside a single step's kernels, which is why scheduler-side fixes (#48245, which we carry) don't stop this instance of the signature.

No performance regression from pinning TRITON_ATTN (K=3 production config, same box)

workload	single tok/s (TRITON_ATTN)	single (prev FLASHINFER baseline)	8-conc (TRITON_ATTN)	8-conc (baseline)
tool/JSON	116.3	117.2	360.8	329.5
code	109.7	111.6	337.8	300.4
chinese	80.4	79.4	234.7	222.2
prose	90.8	92.0	300.6	293.8
math	118.8	121.5	381.9	393.0

MTP acceptance after the switch: mean accepted length 3.20–3.66 at K=3 (per-position 0.955/0.899/0.809 on one sample). 16-concurrent burst load test and a 112-turn agent-platform suite both pass on the pinned config; RestartCount stable at 0 since.

All numbers measured 2026-08-23 on the production deployment; happy to provide full logs or re-run specific configurations.

[jianyi85]

Added unit tests in tests/v1/attention/test_forced_attn_backend_env.py (mock-based, same style as test_cuda_backend_probe_errors.py, no GPU needed beyond the CUDA-platform gate):

• forced backend valid → used, auto-selection never entered;
• forced backend invalid for the component's configuration → falls back to auto-selection;
• unknown backend name in the env → warning + fall back (also hardened the implementation with a KeyError guard for this case);
• env unset → auto-selection unchanged;
• an explicitly selected_backend still takes precedence over the env.

[cadamcat]

@jianyi85 The same on ROCm, where it is silent rather than fatal.

Two RX 7900 XT (gfx1100), TP=2, vLLM 0.27.1.dev5, Qwen3.8-27B-AWQ-INT4 with MTP k=3, launched with --attention-backend TRITON_ATTN. The target takes the flag; the drafter does not:

[api_utils.py:273]         non-default args: {..., 'attention_backend': 'TRITON_ATTN', 'speculative_config': {'method': 'mtp', ...}}
[rocm.py:606]              Using TRITON_ATTN backend (selected via --attention-backend).
[gpu_model_runner.py:5332] Loading drafter model...
(Worker_TP0) [rocm.py:651] Found incompatible backend(s) [TURBOQUANT] with AttentionType.DECODER. Overriding with ROCM_ATTN out of potential backends: ['ROCM_ATTN', 'TRITON_ATTN'].
(Worker_TP1) [rocm.py:651] ... identical ...

RocmPlatform.get_attn_backend_cls is reached with the same selected_backend=None you describe for CudaPlatform: TRITON_ATTN is in the candidate list and the flag is not consulted. Both ranks, and two runs the same day.

What ROCm adds is the failure mode. On SM90 (#48495) and on your GB10 the drafter lands on a kernel that faults, so the bug announces itself. Here it lands on ROCM_ATTN, which works — the server starts, an eleven-rung ladder runs to 32 K, every request returns. An arm labelled TRITON_ATTN is then a mixture that reports numbers, and we withdrew two arms of a backend comparison after publishing the ranking they produced.

Which is a second reason for the fuller fix you name in the description over the env var: someone who has already passed --attention-backend has no reason to go looking for a second control.

Serve logs: https://github.com/cadamcat/dual-radeon-vllm/tree/main/benchmarks/campaign-2026-08-29/logs