[Distributed] Make --disable-custom-all-reduce fall back to NCCL

[roy6n23]

Overview

Fixes #59987. --disable-custom-all-reduce is documented as "Disable the custom all-reduce kernel and fall back to NCCL", but on Hopper and Blackwell it only removes the CUSTOM backend. The TP group keeps the FlashInfer and symmetric-memory all-reduce, and at the default -O2 the FlashInfer all-reduce + RMSNorm fusion stays on. This PR makes the flag turn those off too, so all-reduce falls back to NCCL.

Claims

• With --disable-custom-all-reduce, the TP group's all-reduce backends are ['PYNCCL'] and the all-reduce + RMSNorm fusion is off (an explicit pass_config.fuse_allreduce_rms=True still turns it on).
• Without the flag nothing changes.
• GSM8K accuracy is the same with and without the change.

Validation

2x H100 SXM (NV18), vllm/vllm-openai:nightly-18f8f96025b556071eb627076f94df560fbd3a22 with this PR's diff applied to the installed package (18f8f9602 is 15 commits behind this PR's base and none of them touch the changed files), Qwen/Qwen2.5-1.5B-Instruct, TP=2, default -O2:

	All-reduce backends (startup log)	allreduce_rms fusion	All-reduce kernels in one 32-token request, per rank (torch profiler)	GSM8K, 1319 questions, 5-shot
main + --disable-custom-all-reduce	['FLASHINFER', 'PYNCCL']	on	1824 FlashInfer allreduce_fusion_kernel_oneshot_lamport, 0 NCCL all-reduce	0.540
this PR + --disable-custom-all-reduce	['PYNCCL']	off	1824 ncclDevKernel_AllReduce_Sum_bf16_RING_LL	0.542
this PR, no flag	['FLASHINFER', 'CUSTOM', 'PYNCCL']	on	1824 FlashInfer allreduce_fusion_kernel_oneshot_lamport	0.540
this PR + flag + -cc.pass_config.fuse_allreduce_rms=True	['PYNCCL']	on	1824 FlashInfer allreduce_fusion_kernel_oneshot_lamport	-

1824 is 57 all-reduces per forward pass (the embedding plus two per layer, 28 layers) times 32 forward passes, so on main with the flag every all-reduce in the model ran in FlashInfer and none in NCCL. The greedy completion of the profiled request was the same in all four runs.

SYMM_MEM is missing from these lists because multicast does not work on this host: SymmMemCommunicator logs "symmetric memory multicast operations are not supported" after the CUDA driver returns error 401, the same error that made NCCL's NVLS setup fail in ncclCommInitRank, so all four runs used NCCL_NVLS_ENABLE=0. On the host in #59987 (2x H100, vLLM 0.30.0) the default list was ['FLASHINFER', 'CUSTOM', 'SYMM_MEM', 'PYNCCL'] and the flag left ['FLASHINFER', 'SYMM_MEM', 'PYNCCL']. The unit test below covers the symmetric-memory gate.

Reproduction commands

All four runs were on the same pod. The first row used the image as is; for the other three the vllm/ part of this PR's diff was applied to the installed package with patch -p1.

export NCCL_NVLS_ENABLE=0            # this host only, see above
export VLLM_DISABLE_COMPILE_CACHE=1  # no compiled graph reused across runs

# EXTRA_ARGS per row: "--disable-custom-all-reduce" (rows 1 and 2), "" (row 3),
# "--disable-custom-all-reduce -cc.pass_config.fuse_allreduce_rms=True" (row 4)
vllm serve Qwen/Qwen2.5-1.5B-Instruct --tensor-parallel-size 2 --port 8000 --max-model-len 4096 \
  --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./trace", "torch_profiler_with_stack": false, "ignore_frontend": true}' \
  $EXTRA_ARGS > serve.log 2>&1 &

# Backends and fusion columns
grep "all-reduce backends" serve.log
grep "Enabled custom fusions" serve.log

# One profiled 32-token request
curl -X POST localhost:8000/start_profile
curl localhost:8000/v1/completions -H 'Content-Type: application/json' \
  -d '{"model": "Qwen/Qwen2.5-1.5B-Instruct", "prompt": "The capital of France is", "max_tokens": 32, "temperature": 0}'
curl -X POST localhost:8000/stop_profile

# GSM8K (rows 1-3)
python tests/evals/gsm8k/gsm8k_eval.py --port 8000 --num-questions 1319

# Unit tests, after applying this PR on the same host
pytest -v tests/distributed/test_comm_ops.py tests/test_config.py \
  -k "disable_custom_all_reduce or precedes_nccl or allreduce_rms_fusion_default or (multi_process_tensor_parallel and not pipeline)"

Counting the all-reduce kernels in each rank's trace:

import collections, glob, gzip, json

for f in sorted(glob.glob("trace/**/*.pt.trace.json.gz", recursive=True)):
    events = json.load(gzip.open(f, "rt"))["traceEvents"]
    counts = collections.Counter(
        e["name"].split("(")[0] for e in events
        if e.get("cat") == "kernel" and any(s in e["name"].lower() for s in ("nccl", "allreduce"))
    )
    print(f, dict(counts))

It also prints 32 ncclDevKernel_AllGather_RING_LL launches in every run: the logits processor's all-gather across vocab shards, one per forward pass, not all-reduces.

Tests:

• New test_disable_custom_all_reduce_falls_back_to_nccl (tests/distributed/test_comm_ops.py, runs on CPU with stand-in backends): builds a TP CudaCommunicator with custom all-reduce disabled and checks that an all-reduce goes to pynccl, not to FlashInfer, FlashInfer PCIe IPC or symmetric memory. It fails on main and passes with the fix.
• New test_allreduce_rms_fusion_default_follows_disable_custom_all_reduce (tests/test_config.py): on a platform patched to look like SM90 with FlashInfer, the fusion default is on without the flag and off with it. The flag case fails on main.
• On the 2x H100 host with the fix: the two new tests, test_flashinfer_all_reduce_precedes_nccl and the three test_multi_process_tensor_parallel[*-2] cases, 7 passed.
• In a CPU-only container, tests/distributed/test_comm_ops.py and tests/test_config.py: 435 passed, 17 skipped, 19 failed. The same 19 fail on main in that container (gated HF repos and CPU-platform defaults).
• pre-commit run --files <changed files>: all hooks pass.

Duplicate check: no open PR references #59987. Open PRs that match disable_custom_all_reduce or VLLM_ALLREDUCE_USE_FLASHINFER touch other things (#55835 cross-node MNNVL groups, #59730 IPC all-gather, #47751 custom all-reduce on sm_12x).

Details

The flag only reaches _ENABLE_CUSTOM_ALL_REDUCE, which gates CustomAllreduce, and on ROCm also AITER and QuickReduce all-reduce. FlashInfer all-reduce (on by default since #52998) and symmetric-memory all-reduce were added later with their own env vars and never tied to the flag. The fusion default, enable_allreduce_rms_fusion, doesn't look at it either. So on SM 9.0 / 10.x the flag leaves most all-reduces on FlashInfer: at -O2 the ones followed by RMSNorm run inside the fused kernel, and the rest go through ['FLASHINFER', 'SYMM_MEM', 'PYNCCL'].

The change:

• CudaCommunicator: FlashInfer, FlashInfer PCIe IPC and symmetric-memory all-reduce also require _ENABLE_CUSTOM_ALL_REDUCE, the way AITER and QuickReduce already do.
• enable_allreduce_rms_fusion returns False when the flag is set. This only changes the default; an explicit fuse_allreduce_rms still wins.
• The flag's docstring (also the --help text) and docs/design/fusions.md now say what it turns off.

Things to be aware of:

• Anyone who sets the flag today and is getting FlashInfer or symmetric-memory all-reduce will get NCCL instead, which is what the flag says it does. There is no longer a way to turn off only vLLM's custom kernel and keep FlashInfer; if that combination is needed, an env var for the custom kernel (like the existing VLLM_ALLREDUCE_USE_* ones) would be the natural counterpart.
• Not covered: some models call FlashInfer's fused all-reduce directly in their eager paths through _can_use_flashinfer in fused_allreduce_gemma_rms_norm.py (DeepSeek V3.2, Kimi K3, MiniMax M3). I left those out to keep this PR small and can gate them in a follow-up if you want.
• Tests that pass the flag (the tensorizer tests, Whisper) now run on NCCL. The fusion e2e and full-graph test helpers also set it by default, but their only TP=2 tests, in test_tp2_ar_rms.py, set it to False.
• A correction to the issue: it said the flag plus VLLM_ALLREDUCE_USE_FLASHINFER=0 VLLM_ALLREDUCE_USE_SYMM_MEM=0 gives NCCL only. At -O2 the fusion also has to be off (-cc.pass_config.fuse_allreduce_rms=False).

AI assistance: Claude Code (Claude Opus 5.5) drafted the patch and the tests and ran the validation above in a local CPU container and on a rented 2x H100 pod. I reviewed every changed line and the results.

---

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[claude]

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[mergify]

Documentation preview: https://vllm--60011.org.readthedocs.build/en/60011/

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[roy6n23]

A user-side data point: in #60660 (a FlashInfer MNNVL all-reduce crash on 4x H20 with vLLM 0.30.0) the reporter kept --disable-custom-all-reduce set while ruling out causes, and the startup log still shows Initialized FlashInfer Allreduce norm fusion workspace with backend=mnnvl; the report points to #59987 for this. The crash itself went away after upgrading vLLM and FlashInfer, so this PR has nothing to do with its cause. It is only an example of the flag being used to rule out vLLM's own all-reduce while FlashInfer all-reduce stayed on.

AI assistance: Claude Code found the issue and drafted this comment; I checked it against the issue.