[anish-shanbhag]

Overview

Select the MiniMax-M3 CUTLASS MSA decode path using measured SM103 EAGLE3
crossover points instead of a fixed batch-16 gate. The current patch is
model-local and keeps the original floor for unmeasured shapes and
architectures.

No other open PR changes this decode dispatch gate; #60684 changes prefill
query quantization and does not overlap this work.

Claims in the current patch

• On SM103 with decode query lengths 1–8 and measured rank-local 64/4, 32/2,
  or 16/1 query/KV heads, use CUTLASS once
  batch_size * decode_query_len * num_q_heads >= 1024.
• Keep the existing batch-16 floor for query lengths 9–32, SM100, and unknown
  head geometries.
• Preserve all existing kernel constraints: explicit CUTLASS selection, FP8
  E4M3 KV cache, supported head geometry, page size 128, top-k 16, supported
  query length, and the planner row bound.
• Allow deployments to override the automatic crossover with the model-local
  VLLM_MINIMAX_M3_MSA_CUTLASS_MIN_BATCH; unset uses the automatic policy,
  and setting the integer to 16 restores the original floor.

Validation

Full pr-checklist audit: read the condensed report.

Current PR refresh (2026-10-08)

• Added the review update as a fast-forward commit; no existing commits were
  rewritten.
• Removed the global envs.py registration and tests/test_envs.py changes.
  The final PR changes only the MiniMax MSA implementation and its existing
  kernel test.
• pre-commit run --files tests/kernels/attention/test_minimax_m3_msa_cutlass_sparse_decode.py vllm/models/minimax_m3/nvidia/msa_cutlass_sparse_decode.py vllm/models/minimax_m3/nvidia/sparse_attention_msa.py:
  passed, including Ruff, formatting, mypy, test tethering, SPDX, and
  forbidden-import checks.
• CPU-safe direct checks: seven dispatch cases and two model-local override
  cases passed. git diff --check also passed.
• The local host does not have the PyTorch/CUDA test runtime, so the existing
  GB300 correctness and benchmark results below remain the GPU validation.

Original query-length-4 GB300 evidence

The isolated benchmark used one NVIDIA GB300, EAGLE3 query length 4, FP8 E4M3
KV cache, 16 selected pages per token/KV head, CUDA graph replay, 30 warmups,
300 iterations, and five trials. It covered request batches 1/2/4/8/16, 8K
and 200K cached contexts, TP1/TP2/TP4, and concurrent DEP2/DEP4 ranks.

Local heads	Batch 4	Batch 8	Batch 16	First measured win	Query-head rows at win
64/4	1.069x	1.545x	1.875x	4	1,024
32/2	0.800x	1.100x	1.642x	8	1,024
16/1	0.600x	0.800x	1.099x	16	1,024

These conservative ratios charge CUTLASS for standalone BF16-to-FP8 query
quantization. All 130 rank-level CUTLASS-versus-Triton comparisons passed at
atol=0.02, rtol=0.02; maximum absolute error was 0.00006104. The
focused GB300 kernel suite reported 41 passed in 237.40 seconds, and the
focused environment suite reported 9 passed.

Query-length 1–8 follow-up (2026-10-08)

A second GB300 microbenchmark swept decode query lengths 1–8, TP1/TP2/TP4,
request batches 1/2/3/4/6/8/12/16/24/32/48/64, and cached contexts 8,192 and
200,064. It used interleaved CUDA-graph replay with the same 30 warmups, 300
iterations, and five trials. Query lengths 1/2/4/8 fit the rule; 3/5/6/7 were
held out. All 1,344 rank-level comparisons passed correctness at
atol=rtol=0.02; maximum absolute error was 0.00430298.

The safe direct-kernel crossover is the first measured batch for which every
larger measured batch wins at both context lengths:

Decode query length	TP1 / 64 Q heads	TP2 / 32 Q heads	TP4 / 16 Q heads
1	12	24	48
2	6	12	24
3	4	8	16
4	3	6	12
5	2	4	8
6	2	4	6
7	2	3	6
8	2	3	6

The follow-up image (vLLM 0.25.0) first won at 768 query-head rows for query
length 4. The newer vLLM 0.28.1rc1 benchmark used only power-of-two batches,
so its first measured win at 1,024 rows did not test 768. At the shared 512-
and 1,024-row points, the two datasets agree to rounding (approximately
0.889x/0.800x and 1.222x/1.099x for direct/conservative speedup). This is not
evidence of a version shift, but the matching-version evidence does not resolve
the intermediate boundary. The merge-safe rule is therefore the conservative,
directly measured newer-image boundary:

query_head_rows = batch_size * decode_query_len * num_q_heads
if 1 <= decode_query_len <= 8 and measured_head_geometry:
    automatic_min_batch = ceil_div(1024, decode_query_len * num_q_heads)
else:
    automatic_min_batch = 16
use_cutlass = batch_size >= automatic_min_batch

The 1,024-row rule produced zero false-positive CUTLASS regressions across all
576 new aggregated cases. It left 61 direct-kernel wins unselected on the
older image, an intentional conservative trade-off. This supports replacing
the decode_query_len == 4 special case and three head-geometry entries with a
single query-head-row threshold, while retaining every existing capability,
dtype, head, page-size, top-k, maximum-query-length, and maximum-row guard.
Query lengths 9–32 retain the existing batch-16 floor rather than extrapolating
beyond the measured sweep. Unknown head geometries also retain 16.

The selected region has clear margin: among all 318 selected cases, the minimum
direct-kernel speedup was 1.221x and the minimum quantization-inclusive speedup
was 1.098x. The 24 cases at exactly 1,024 rows measured 1.221–1.223x direct and
1.098–1.101x with standalone quantization. Median per-case trial CV at the
boundary was 0.020% for Triton and 0.019% for the conservative CUTLASS path;
the maximum CVs were 0.199% and 0.063%, respectively.

The current commit implements the conservative 1,024 query-head-row rule for
query lengths 1–8. The 768-row rule remains a later candidate, but it should be
adopted only after measuring that intermediate point on the same vLLM build
used by the PR.

Complete tables, all trial JSON/CSV artifacts, methodology, and runnable
benchmark/analysis scripts are in the benchmark Gist.

Matched MiniMax-M3-NVFP4 disaggregated EAGLE3 runs of the original fixed-16
policy versus an earlier blanket batch-4 experiment showed approximately
+3–5% p90 per-user output-token rate and +1–2.5% total TPS/GPU. That experiment
supports the opportunity but does not isolate the final dispatch policy; the
microbenchmarks above provide the shape-specific evidence.

Details

Root cause and implementation

The existing metadata builder used a fixed batch_size >= 16 dispatch gate,
so common EAGLE3 verification steps fell back to Triton even when CUTLASS was
faster. A blanket lower floor is unsafe because tensor parallelism changes
rank-local query/KV head geometry and therefore the crossover. The current
model-local helper selects a conservative floor from query length, device
capability, and head geometry. The metadata builder resolves the optional
environment override once at initialization and passes it to the existing
dispatch predicate.

Design fit and trade-offs

The implementation stays within MiniMax-M3's NVIDIA sparse-attention path and
does not change shared scheduler, model-runner, or KV-cache interfaces. The
automatic speedup is deliberately limited to the measured SM103 EAGLE3
query-length and head-geometry region; all other shapes keep the prior
batch-16 behavior.

Checklist

• Change is model-local and does not add complexity to shared core components.
• Dispatch boundaries have targeted regression coverage; the integer
  override remains a model-local rollback.
• Existing CUDA correctness and benchmark evidence exercises the changed path.
• Query lengths 1–8 now have a genuine CUTLASS-versus-Triton microbenchmark.
• Current lint, typing, test-tethering, and CPU-safe checks pass after rebasing.
• Performance scope, fallback behavior, version calibration, and end-to-end limitations are explicit.
• Commits are signed off and include AI-assistance attribution.

AI assistance disclosure

This change, benchmark support, rebase, and PR refresh used OpenAI Codex. The
human submitter remains responsible for reviewing every changed line,
reproducing GPU validation as needed, and defending the implementation and
evidence end to end.

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[claude]

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @anish-shanbhag.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @anish-shanbhag.

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[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @anish-shanbhag.

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[wzhao18]

Overall I think this PR can be kept minimal as it is primarily a heuristic update. please polish. Thanks!

[anish-shanbhag]

thanks for the review @wzhao18 !

[wzhao18]

LGTM

[github-actions]

✅ @anish-shanbhag, CI is now available for this PR.

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[anish-shanbhag]

/ci run

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[anish-shanbhag]

/ci run

[github-actions]

✅ Triggered Buildkite CI #94532 for commit 8b9473da755e.