[Core] Disable fuse_allreduce_rms under VLLM_BATCH_INVARIANT (non-deterministic under TP)

[tolleybot]

Purpose

Fixes #51290.

VLLM_BATCH_INVARIANT=1 is bit-stable on a single GPU but non-deterministic under
tensor parallelism: repeating a byte-identical workload against the same engine returns
different logprobs (and, on near-ties, different tokens) once tensor_parallel_size > 1.

The cause is the fused all-reduce + RMSNorm pass (fuse_allreduce_rms, a FlashInfer
kernel), which reduces in a run-to-run-varying order under TP. It is enabled by
enable_allreduce_rms_fusion() exactly when TP > 1 on Hopper/Blackwell with flashinfer,
i.e. the regime where batch invariance is expected to hold.

This disables the fusion when batch invariance is requested, mirroring the existing
disable_custom_all_reduce = True guard in ParallelConfig (which handles the analogous
non-batch-invariant custom all-reduce path). It is an 8-line early return; TP=1 and
non-batch-invariant runs are unaffected.

Relationship to #50505

This is the Hopper/Blackwell-side complement to #50505. On cc≥90 the fuse_allreduce_rms
pass replaces the TP all-reduce with a FlashInfer fused kernel, bypassing the custom
all-reduce that #50505 pins to its deterministic 1-stage path. Disabling the fusion here
lets the all-reduce fall through to that path. The two compose: with #50505 the fallback
is the fast 1-stage custom all-reduce (~97% throughput); without it, NCCL (see
Performance). On A100 the fusion pass is inactive (cc<90), so #50505 alone covers that
hardware; this PR is what extends the fix to Hopper/Blackwell.

Test Plan

Run the same workload 8 times against one engine and count distinct bit-exact outputs
(from #51290). Requires the if __name__ == "__main__": guard because TP>1 uses spawn.

import os, itertools
os.environ["VLLM_BATCH_INVARIANT"] = "1"
from vllm import LLM, SamplingParams

def main():
    llm = LLM(model="Qwen/Qwen2.5-7B-Instruct", tensor_parallel_size=4,
              gpu_memory_utilization=0.9, enable_prefix_caching=True)
    sp = SamplingParams(temperature=0.0, max_tokens=1, logprobs=20)
    prompts = [f"{'the quick brown fox ' * 50} item {i}:" for i in range(2000)]
    runs = []
    for _ in range(8):
        out = llm.generate(prompts, sp, use_tqdm=False)
        runs.append([(o.outputs[0].token_ids[0],
                      o.outputs[0].logprobs[0][o.outputs[0].token_ids[0]].logprob)
                     for o in out])
    groups = []
    for i, r in enumerate(runs):
        if not any(runs[g[0]] == r and g.append(i) is None for g in groups):
            groups.append([i])
    print(f"{len(groups)} distinct bit-exact outputs over {len(runs)} identical runs")

if __name__ == "__main__":
    main()

Test Result

Qwen2.5-7B-Instruct, H100, vLLM 0.25.1 (the fix function is byte-identical on main):

config	distinct bit-exact outputs / 8	verdict
tensor_parallel_size=1	1	deterministic (unaffected)
tensor_parallel_size=4, before	8	non-deterministic
tensor_parallel_size=4, after this PR	1	deterministic

With the fix, VLLM_BATCH_INVARIANT=1 at TP=4 auto-disables the fusion (the
Enabled custom fusions: allreduce_rms / Auto-selected flashinfer allreduce log lines
disappear) and the 8 identical runs collapse to a single bit-exact output. Also confirmed
on Llama-3.1-70B (dense, TP=4), and the bug reproduces on both 0.20.2 and 0.25.1.

Performance

Disabling the fusion has a throughput cost, confined to VLLM_BATCH_INVARIANT=1 + TP>1
(non-batch-invariant runs and TP=1 are unaffected). Decode-heavy generation, TP=4, H100:

model	fusion on (baseline)	fusion off (this PR)	cost
Qwen2.5-7B (128 x 256 tok)	18,355 tok/s	7,450 tok/s	~2.5x slower
Llama-3.1-70B (64 x 128 tok)	1,618 tok/s	935 tok/s	~1.7x slower

This cost reflects the NCCL fallback used before #50505. With #50505's deterministic
1-stage custom all-reduce enabled, the all-reduce falls through to that path instead,
which runs at ~97% of fused throughput at default dispatch. So the number above is a
pre-#50505 artifact, not an inherent cost of this PR.

The cost is largest for decode-heavy workloads, where the all-reduce runs every layer per
token; prefill-heavy workloads (long prompt, few output tokens) see considerably less.

I checked whether a cheaper, performance-preserving fix inside the fused kernel exists, and
none does. The fused kernel is nondeterministic by construction, and no config knob
restores determinism:

• Backend: both mnnvl and trtllm are nondeterministic on 0.25.1 (H100).
  mnnvl (multicast/Lamport) diverges with token flips. trtllm (fixed-order fp32
  reduction, but Lamport/two-shot) is smaller-magnitude yet still not bit-exact: 8 distinct
  logprob groups over 8 identical runs (0 token flips that run, but the sub-bit-exact
  residual still tips near-ties at scale).
• fp32_acc: already True by default, insufficient.
• Autotune: enable_flashinfer_autotune=False does not help. The divergence is
  intra-process (repeated generate() against one engine diverge with the kernel selection
  already fixed), so it is the kernel's runtime reduction order, not autotune selection.
• One-shot: forcing use_oneshot=True overflows the one-shot workspace on large
  tensors, and the one-shot path is itself nondeterministic on small tensors.

So disabling the fusion under BI is the correct trade, consistent with the existing
disable_custom_all_reduce guard. Performance is recovered by #50505 (the deterministic
1-stage custom all-reduce this PR falls through to). A fully-fused deterministic kernel
would only claw back the last few percent over that and is not needed here.

[claude]

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[tolleybot]

Cross-referencing #50505 (@mosafariuk), which this composes with.

Following discussion there, we're landing the two separately with cross-references — they're unrelated judgement calls and keeping them apart keeps each review scoped:

• [Core][Distributed] Size custom all-reduce buffers at init so batch-invariant mode can use them #50505 sizes the custom all-reduce buffers at init and pins a deterministic 1-stage path. On A100 (cc<90) that alone covers batch-invariant TP, since the fuse_allreduce_rms pass is inactive there.
• [Core] Disable fuse_allreduce_rms under VLLM_BATCH_INVARIANT (non-deterministic under TP) #51292 (this PR) is the Hopper/Blackwell complement. On cc≥90 the fusion pass rewrites the TP all-reduce into a FlashInfer fused kernel above the communicator, so should_custom_ar() / the communicator-backend filter never see it. Disabling the fusion under BI lets the reduction fall through to [Core][Distributed] Size custom all-reduce buffers at init so batch-invariant mode can use them #50505's 1-stage path.

The two compose cleanly: with #50505 the fallback is the fast 1-stage custom all-reduce (~97% of fused throughput), so the ~2.5x cost measured here is a pre-#50505 artifact rather than an inherent cost of this PR.

Now marked ready for review.

[tolleybot]

/ci run

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[yewentao256]

Thanks for the work, could you take a deeper look what is the root cause?

[tolleybot]

Sure thing, here is a more in-depth explanation of the root cause with the isolating experiments.

It is a run-to-run reduction race, not a batch-composition effect. The same 8 byte-identical generate() calls against one engine, TP=4, H100, VLLM_BATCH_INVARIANT=1, stock 0.25.1, produce 8 distinct bitwise outputs. Nothing about the input changes between calls, so this is a genuine nondeterministic reduction, not the size-gated kernel-selection issue from #50136.

I then isolated it to the FlashInfer all-reduce reduction itself rather than the RMSNorm fusion, with three cells over the same identical batch run 8 times. With the fused allreduce_rms pass on, which is the default, the FlashInfer fused all-reduce plus RMSNorm runs on mnnvl and gives 8 distinct groups, non-deterministic. With fuse_allreduce_rms=False but VLLM_ALLREDUCE_USE_FLASHINFER=1, so a plain FlashInfer all-reduce on mnnvl with no fusion at all, still 8 groups, non-deterministic. With both off, so NCCL carries the all-reduce, 1 group, deterministic. The middle cell removes the RMSNorm fusion entirely and keeps only the FlashInfer mnnvl all-reduce, and it is still non-deterministic, so the epilogue is not the source, the reduction is. A fixed-order reduction like NCCL is bitwise-stable.

One thing I could not settle from outside the kernel, and where your read would help. FlashInfer 0.6.13 looks like it intends determinism here, trtllm_mnnvl_allreduce.cuh has reduceOneshotDeterministic and comments stating "every rank uses the exact same reduction order," and the fused RMSNorm uses shuffle and block reductions. Yet the mnnvl path is not run-to-run bit-exact on this config. My guess is that the deterministic one-shot path is size-gated and this workload lands on the two-shot path, or that the guarantee is cross-rank consistency rather than run-to-run reproducibility, but that is your area. Happy to dump the per-call tensor sizes and which mnnvl path is selected if that helps.

I also ruled out the cheaper fixes, all still non-deterministic. Both backends, mnnvl and trtllm. fp32_acc, which is already True. enable_flashinfer_autotune=False. And forcing use_oneshot=True, which overflows the one-shot workspace on larger tensors and is itself non-deterministic on small tensors.

On why disabling the fusion is the right fix, on cc>=9 the fuse_allreduce_rms pass is what routes the TP all-reduce into FlashInfer in the first place. Disabling it under VLLM_BATCH_INVARIANT lets the all-reduce fall back to a fixed-order reduction, NCCL today, or the 1-stage custom all-reduce once #50505 lands. That PR pins the order-fixed kernel but does not touch this fusion, so the two are complementary, and only the pair is deterministic at full performance on Hopper.

[yewentao256]

Thanks for the work! Will user config overwrite this? eg --compilation-config '{"pass_config":{"fuse_allreduce_rms":true}}'

[yewentao256]

Suggested change

	# The fused all-reduce + RMSNorm path is not batch-invariant: under tensor
	# parallelism it reduces in a run-to-run-varying order, so with
	# VLLM_BATCH_INVARIANT set the same request can produce different logprobs
	# across otherwise-identical runs. Disable the fusion in that mode, mirroring
	# the disable_custom_all_reduce guard in ParallelConfig.
	if envs.VLLM_BATCH_INVARIANT:
	return False
	# The fused all-reduce + RMSNorm path is not batch-invariant
	if envs.VLLM_BATCH_INVARIANT:
	return False

[mosafariuk]

That override case is exactly what the fail-loud guard discussed in #50505 would catch: if VLLM_BATCH_INVARIANT is set and the fusion pass is active — whether by default or by explicit --compilation-config — raise with a message naming the incompatibility, rather than silently producing non-reproducible output. Config-level disabling (this PR) handles the default; the guard handles the explicit override. Happy to carry that guard in #50505 if that split makes sense.

[tolleybot]

Good question, and yes it does. The guard changes the default, since enable_allreduce_rms_fusion is only consulted as the -O2/-O3 default factory, and _set_config_default skips it when the field is already set. So --compilation-config '{"pass_config":{"fuse_allreduce_rms":true}}' re-enables the fusion and batch invariance goes non-deterministic again. That is intentional here, because this PR should not silently override an explicit user choice. The explicit override case is handled loudly in #50505, which raises when VLLM_BATCH_INVARIANT is set and the fusion pass is active. In other words, the default is fixed here, and misuse is caught there.

[tolleybot]

Agreed, the guard belongs in #50505. Thanks for carrying it there. That keeps the default fix and the fail-loud override check in their respective PRs.

[yewentao256]

LGTM, thanks for the work!

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[yewentao256]

/ci run

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[tolleybot]

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