[Distributed] Tune TP8 SM80 one-shot all-reduce crossover

[haosdent]

Purpose

Tune the existing custom-allreduce one-shot/two-shot selection for the exact
TP=8 SM80 fully connected case. Cudagraph measurements on 8xA100 NVLink place
the crossover above the current 256 KiB generic threshold, so the current
policy switches to two-shot too early on this topology.

This PR changes only the algorithm-selection heuristic inside the existing
barrier-based custom all-reduce. It does not add a communicator or increase the
custom-allreduce payload cap.

Exact scope and nonduplication

Publication commit: 09d8eb1d74a5e2096675d5439142e3b171e5ccbe.
Relative to origin/main@dd11df04f3b7046c40f13e586ac38a3725bc3c03,
the PR changes three files (+71/-21):

• a constexpr one-shot-bound policy and compile-time boundary assertions;
• propagation of exact SM80 device capability into the stable custom-AR C++
  object;
• a ca_auto arm in the existing communicator benchmark.

Relationship to existing open work:

• perf: add push-based allreduce for small tensor reductions vllm-project/vllm#44891 adds a new higher-priority push all-reduce backend for small messages.
  This PR instead tunes the existing CustomAllreduce fallback. The payload
  regions overlap, so the gains are not cumulative when push all-reduce accepts
  a tensor. Before promotion or upstream submission, verify perf: add push-based allreduce for small tensor reductions vllm-project/vllm#44891's A100
  support/dispatch; if it handles this exact configuration, this heuristic may
  only benefit fallback cases and its standalone value should be reconsidered.
• [Core][Distributed] Size custom all-reduce buffers at init so batch-invariant mode can use them vllm-project/vllm#50505 pins custom all-reduce to one-stage under batch-invariant mode. This
  PR affects only automatic selection when no explicit one/two-stage override
  is active; it does not solve or modify batch invariance.
• [Feature] TRITON_MLA_SPARSE backend for SM8x/11x/12x DSA Sparse MLA Support vllm-project/vllm#38476, [WIP][New Model] Add A100/SM80 support for DeepSeek V4 Flash vllm-project/vllm#40906, and [Feature]: DeepSeek-V4 Flash sm_80 (A100/A800) support vllm-project/vllm#40851 are model/attention support work and do not overlap
  these three distributed-runtime files.

Changed behavior

For the automatic custom-allreduce path, with the surrounding existing
fully-connected check still required:

Case	One-shot selection
world size <= 4	payload < 512 KiB (unchanged)
world size = 8 and exact SM80	payload <= 448 KiB
every other world-size/architecture case	payload < 256 KiB (unchanged)

The SM80 TP=8 bound is intentionally inclusive because one-shot measured 6.7%
faster at exactly 448 KiB. 448 KiB + 1 and larger payloads select two-shot;
the next measured tile, 464 KiB, was already 1.2% faster with two-shot. Forced
VLLM_CUSTOM_ALLREDUCE_ALGO behavior is unchanged.

Measured evidence

Dense crossover sweep: 8xA100 NVLink, TP=8, BF16, hidden size 8192,
cudagraph-captured, 50 warmups and 1000 trials:

Payload	One-shot	Two-shot	Faster arm
432 KiB	20.895 us	22.002 us	one-shot by 5.0%
448 KiB	21.549 us	23.102 us	one-shot by 6.7%
464 KiB	22.146 us	21.878 us	two-shot by 1.2%
480 KiB	22.723 us	21.860 us	two-shot by 3.8%
512 KiB	23.776 us	21.871 us	two-shot by 8.0%

The raw sweep is stored locally as
benchmark/custom-ar-sm80-crossover-dense.json. A separate automatic-policy
sweep (benchmark/custom-ar-sm80-crossover-auto.json, 20 warmups, 300 trials)
covered both sides of the boundary. A broader sweep is in
benchmark/custom-ar-sm80-crossover.json.

These are collective microbenchmarks. No TTFT, TPOT, ITL, model throughput, or
combined result with vllm-project#44891 is claimed.

Build and benchmark commands run

Targeted CUDA build:

cmake --build build/temp.linux-x86_64-cpython-312 \
  --target _C_stable_libtorch --parallel 12

Result: exit 0; both custom-allreduce CUDA objects compiled and
_C_stable_libtorch.abi3.so linked.

Dense crossover:

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
VLLM_ALLREDUCE_USE_FLASHINFER=0 \
VLLM_ALLREDUCE_USE_SYMM_MEM=0 \
/root/vllm/.venv/bin/torchrun --standalone --nproc-per-node=8 \
  benchmarks/kernels/benchmark_device_communicators.py \
  --sequence-lengths 24 25 26 27 28 29 30 31 32 \
  --num-warmup 50 --num-trials 1000 \
  --output-json /root/vllm-pr-evidence/20260803-deepseek-v4-a100/benchmark/custom-ar-sm80-crossover-dense.json

Automatic-policy verification used the same environment and executable with:

--sequence-lengths 16 24 27 28 29 32 --num-warmup 20 --num-trials 300
--output-json /root/vllm-pr-evidence/20260803-deepseek-v4-a100/benchmark/custom-ar-sm80-crossover-auto.json

Compile-time assertions cover SM80/non-SM80 and both sides of the special
inclusive boundary.
Changed-range pre-commit hooks pass.

Model evaluation status

Not run. Moving 256-448 KiB reductions from two-shot to one-shot changes
the BF16 reduction order, so a model-level output/evaluation comparison is
required before merge. No DeepSeek-V4 128k/256k server result is attributed to
this microbenchmark PR.

Risks and limitations

• Qualified only on one 8xA100 fully connected NVLink system. A800, PCIe-only
  systems, other TP sizes, and other dtypes are unmeasured.
• The policy is exact SM80 + world-size 8, but topology remains controlled by
  the surrounding fully-connected check.
• The special case makes one boundary inclusive while existing generic bounds
  remain exclusive; compile-time assertions guard 448 KiB and
  448 KiB + 1 explicitly.
• perf: add push-based allreduce for small tensor reductions vllm-project/vllm#44891 may preempt the same sizes with push all-reduce; [Core][Distributed] Size custom all-reduce buffers at init so batch-invariant mode can use them vllm-project/vllm#50505 bypasses this
  automatic policy under batch invariance.
• This PR has build-time/static boundary checks and benchmark evidence, but no
  standalone numerical pytest or model eval yet.

Stack and review order

• Branch: pr/custom-ar-sm80-crossover
• Head: 09d8eb1d74a5e2096675d5439142e3b171e5ccbe
• Base: origin/main@dd11df04f3b7046c40f13e586ac38a3725bc3c03
• Standalone from the DeepSeek-V4 support/prefill stack and from the payload-cap
  PR
• Reassess value/dispatch against open perf: add push-based allreduce for small tensor reductions vllm-project/vllm#44891 and rebase against [Core][Distributed] Size custom all-reduce buffers at init so batch-invariant mode can use them vllm-project/vllm#50505 if it
  changes the same custom-AR policy surface

AI assistance and human ownership

AI assistance was used for research, implementation, benchmark analysis,
review, and drafting (Anthropic Claude and OpenAI Codex).

This body and the current code were prepared by code agents. @haosdent has not
yet attested to a line-by-line human review. This PR must remain a draft until
a human reviews every changed line, reviews or reruns the reported build and
measurements, understands the topology/reduction-order risks and overlap with
vllm-project#44891/vllm-project#50505, and explicitly takes responsibility for the contribution.

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[haosdent]

Closing this draft on duplicate/value grounds rather than republishing its malformed trailer commit. Upstream vllm-project#44891 gives non-SM100 architectures (including SM80) a higher-priority TP8 push-allreduce path through 512 KiB, which intercepts this draft’s entire measured 256–448 KiB region on the target fully connected A100 topology. Revisit only with evidence for the fallback case where push AR is disabled or unavailable.