[tuananhlfc]

Purpose

Fixes #48076.

Problem

The Triton unified-attention 2D/3D split-KV selector uses a batch-only heuristic
(MIN_LAUNCH_GRID_SIZE_2D // num_kv_heads) that ignores KV length and device SM count.
Above ~8 sequences, long-context decode drops from 3D split-KV to 2D, roughly doubling
inter-token latency (hard cliff at batch >= 12).

Fix

Derive the threshold at init from the static facts the heuristic should have used, in
vllm/v1/attention/backends/triton_attn.py:

1. Launch-grid occupancy — the 2D grid is (num_q_blocks, num_heads_kv) and
   num_q_blocks is lower-bounded by the number of sequences, so the target is sized
   off the device's actual compute-unit count instead of a fixed 128-CTA grid.
2. Serial KV reduction — for long-context decode the 2D kernel reduces the whole
   KV serially per CTA, so split-KV keeps winning well past the occupancy point. For
   long-context deployments (max_model_len >= 8192) the target is biased further
   toward 3D by a configurable multiplier (new env var
   VLLM_TRITON_ATTN_LONGCTX_3D_MULT, default 4, set to 1 to disable the bias).

Capped at 64 (bounds the softmax_segm_* scratch buffers) and clamped to never fall
below the stock value, so short-context / small-GPU shapes never regress. Kept
init-time — the 2D/3D choice must stay fixed per CUDA-graph capture.

stock_threshold = MIN_LAUNCH_GRID_SIZE_2D // self.num_heads_kv
grid_target = max(
    MIN_LAUNCH_GRID_SIZE_2D, current_platform.num_compute_units(device.index)
)
if model_config.max_model_len >= SEQ_THRESHOLD_3D_LONG_CTX_MIN_MODEL_LEN:
    grid_target *= SEQ_THRESHOLD_3D_LONG_CTX_GRID_MULT
self.seq_threshold_3D = max(
    stock_threshold,
    min(grid_target // self.num_heads_kv, SEQ_THRESHOLD_3D_CAP),
)

A companion change adds VLLM_TRITON_ATTN_LONGCTX_3D_MULT to vllm/envs.py so the
long-context bias can be tuned or disabled without a code change.

This is the principled form of a direct MIN_LAUNCH_GRID_SIZE_2D 128->512 probe — it
reproduces that probe's win exactly, without baking in a workload-specific constant, so
other shapes/GPUs are unaffected.

Duplicate-work check

• gh pr list --search "seq_threshold_3D" / "MIN_LAUNCH_GRID_SIZE_2D": no matches.
• Related open PRs [Triton][Spec Decode] Non-causal support + relax 3D-launch gate for multi-query verify in unified attention #44652, Admit MTP/EAGLE spec-decode steps and sliding-window layers into the Triton 3D flash-decoding path (B300, NVFP4) #45450, [Attention] Occupancy-gated 3D segmented decode for multi-query (diffusion-LM canvas) over long KV #46724 modify the 3D-path query-length admission
  gate (multi-query verify / spec-decode / diffusion-LM); none touch the batch-size
  threshold this PR changes. This PR is complementary, not competing.

Test plan

• pytest tests/kernels/attention/ -k triton -v
• pytest tests/v1/attention/ -k triton -v
• Benchmarked end-to-end on B200x1 — numbers above.
• Re-profiled (nsys) to confirm the 3D path is active and the targeted kernel's
  time dropped.
• Accuracy validated via gsm8k (lm_eval, full-sample, strict-match) — no
  regression.

Test Result

Benchmark setup

Model nvidia/Gemma-4-31B-IT-NVFP4, B200 x1 (TP=1), base image
vllm/vllm-openai:v0.23.0. Workload: RAG/chat — ISL 2400 + shared-system-prompt 5600
(total ISL ~ 8000) / OSL 1000, prefix caching exercised via the shared prefix.
Concurrency sweep [1, 4, 8, 16, 32].

Throughput at the SLA (>=50 tok/s/user, i.e. ITL <= 20 ms)

cc	stock tok/s/gpu	stock tok/s/user	stock ITL	patched tok/s/gpu	patched tok/s/user	patched ITL	SLA (patched)
1	80	85.3	11.7 ms	81	86.4	11.6 ms	OK
4	294	79.3	12.6 ms	296	80.0	12.5 ms	OK
8	504	69.9	14.3 ms	512	71.0	14.1 ms	OK
16	527	35.9 (fail)	27.9 ms	777	55.3	18.1 ms	OK (was fail)
19-20	-	-	-	848	51.2	19.6 ms	OK (SLA-max op point)
32	843	29.1 (fail)	34.4 ms	1060	37.5 (fail)	26.7 ms	fail

+68% aggregate output tok/s/gpu at the SLA operating point (stock cc8, 505 tok/s/gpu
-> patched cc19, 848 tok/s/gpu). The cc8->cc16 throughput plateau and the ITL cliff at
batch >= 12 are eliminated; ITL now scales smoothly 14->20 ms over cc 8->20.

Mechanism (nsys)

• Stock (cc16): kernel_unified_attention = 51.7% of GPU kernel time, avg 169us (>>
  median 81us — long-KV tail dominates)
• Patched (cc16): kernel_unified_attention = 38.1%, avg 101us (-40%); total
  attention GPU-time 10.2s -> 6.1s
• Confirms the 3D path is active and the targeted kernel's cost dropped as predicted.

Accuracy — gsm8k (lm_eval, full-sample, strict-match)

	score
stock	0.9272 +/- 0.0072
patched	0.9340 +/- 0.0068

Delta is within stderr — numerically equivalent, no regression. 3D split-KV is the same
online-softmax math as 2D, just parallelized across segments.

Generalization (no regression on other shapes)

Output tok/s/gpu, cc16-32 band where stock picks 2D and patched picks 3D (cc<=8 and
cc>=48 tie — same path on both):

workload	cc16	cc24	cc32
isl1024/osl1024 (stock->patched)	953->1111 (+17%)	1163->1414 (+22%)	1510->1643 (+9%)
isl8192/osl1024 (stock->patched)	425->600 (+41%)	518->699 (+35%)	612->752 (+23%)

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[hclsys]

I own a DGX Spark (GB10, sm_121, 48 SMs) and went looking for this PR because the stock threshold is wrong on my card. It is — but not in the direction the clamps assume, so I think this lands as a regression here.

Decode-only, 32 q / 8 KV heads, head dim 128, BF16, best of 30. Ratio is 2D/3D, so >1 means split-KV wins:

nseq   CTAs_2D    kv1K    kv8K   kv32K   kv131K
   4        32   1.23x   1.03x   1.06x    1.07x
   8        64   0.89x   0.97x   0.99x    1.00x
  16       128   0.91x   0.97x   0.99x    0.99x
  32       256   0.87x   0.95x   0.97x    0.97x
  64       512   0.83x   0.93x   0.96x    0.97x

Split-KV wins only at nseq 4. The crossover is right around 48 CTAs — num_seqs * num_kv_heads ~ the SM count, i.e. exactly your effect #1, just at 48 instead of 128. And effect #2 doesn't rescue it: at 131K KV split-KV is still 0.97-1.00x from nseq 8 up, because with 48 SMs the 2D grid is already full at 64 CTAs and there's no idle capacity for the split to use.

Two things follow for the formula:

grid_target = max(MIN_LAUNCH_GRID_SIZE_2D, current_platform.num_compute_units(...))

max(128, 48) is 128, so on any part with fewer than 128 SMs the SM count never reaches the result — the derivation is inert precisely where it would change something. And the outer max(stock_threshold, ...) means it can only ever raise the threshold, while my numbers say GB10 needs it lowered: stock is 16 here, and nseq 8-15 already picks split-KV at 0.86-0.91x today.

With the long-context bias on a max_model_len >= 8192 model this becomes max(16, min(max(128,48)*4//8, 64)) = 64, which puts nseq 8-63 on the split-KV path — the 0.83-0.97x rows above. @lucamotz reports the same shape independently in #58141 ("on GB10, main already runs 16-64-sequence decode with 1K KV on split-KV at 0.74-0.78x of 2D") and adds an SM-count bound for the multi-token path for that reason.

So the comment "clamped to never fall below the stock value so short-context / small-GPU shapes never regress" has it backwards for a small GPU: the exposure there is the threshold being too high, not too low. Dropping the max(128, ...) floor and the max(stock_threshold, ...) clamp would make the SM-count reasoning do the right thing on both ends — 512 on a B200, 6 on a GB10 — but that changes big-GPU behaviour too, so it's your call rather than mine.

Not opening anything competing; happy to re-run any shape on the GB10 if it helps.