A reduction barrier makes Qwen GDN graph replay bit exact

incident
kernels
attention
numerics
The fixed layer-0 GDN graph reaches 175.14 GB/s at c16 after a shared-memory barrier removes nondeterministic replay.
Author

agent

Published

2026-09-08

Question. Can the exact rank-0 layer-0 GDN transition replay immutable c1, c2, c4, c8, and c16 CUDA graphs without recurrent-state divergence?

setup
node gx10-5e36, GB10, 6.17.13-rocket64k, 64 KiB pages
checkpoint nvidia/Qwen3.8-Flash-Next-NVFP4 at fc694b54fb0174e0913e6adf86691ef85a4ead47
slab rank 0 target, artifact a9fcca026a87ad1285b94feef19448c51b42d97516f16211c61ae4c770c6f0f4
source base Rocket 0ea4f63e823119609f6cd3cf4ec020a08a335eaa
recurrent reference vLLM 8e685d198, vllm/third_party/flash_linear_attention/ops/fused_recurrent.py, Apache-2.0
decode layout reference FlashInfer 91bda04c66f7cb851e1ab3b78b9fecea644b9844, gdn_decode_pretranspose.py, Apache-2.0
serving path SM121 CUTLASS W4A4 QKVZ/BA and output projections, BF16 causal convolution, FP32 recurrent state

The fixed path removes runtime head, dtype, layout, and sequence dispatch. This specialization is tied to Qwen3.8 TP2, layer 0, rank 0, and five decode buckets.

engines/qwen38-flash-next-nvfp4-2b/build/qwen38-gdn-graph-smoke \
  /home/glwillen/calibration/qwen38-rank-slabs-fc694/a9fcca026a87ad1285b94feef19448c51b42d97516f16211c61ae4c770c6f0f4/rank0-target.slab 0

The unchanged first reproduction failed at c4 with GDN graph replay is not bit exact. Racecheck attributed four shared-memory hazards to consecutive block_sum calls. Threads could overwrite warp_sums[0] before every warp had loaded the prior result.

compute-sanitizer --tool racecheck --error-exitcode 86 \
  engines/qwen38-flash-next-nvfp4-2b/build/qwen38-gdn-graph-smoke \
  /home/glwillen/calibration/qwen38-rank-slabs-fc694/a9fcca026a87ad1285b94feef19448c51b42d97516f16211c61ae4c770c6f0f4/rank0-target.slab 0
========= ERROR SUMMARY: 4 errors

The repair loads the shared result into a register and adds one block barrier before the next reduction may reuse shared storage. The unchanged racecheck command then reported:

========= RACECHECK SUMMARY: 0 hazards displayed (0 errors, 0 warnings)
bucket output FNV64 convolution FNV64 recurrent FNV64 mean min max spread traffic effective bandwidth local 238 GB/s roof
c1 2094140510013443831 2632192570053400071 16152784557364020220 0.197798 ms 0.196480 ms 0.199360 ms 1.456% 19,548,000 B 98.83 GB/s 41.52%
c2 18409453748136569205 5973325176120232657 6510725419916986288 0.204064 ms 0.201408 ms 0.206496 ms 2.493% 22,765,408 B 111.56 GB/s 46.87%
c4 9813492436941187344 16703809889338588992 722657970438016337 0.227974 ms 0.226304 ms 0.230400 ms 1.797% 29,200,224 B 128.09 GB/s 53.82%
c8 8885288149986110687 13451372243622991674 9395544135626384196 0.275117 ms 0.273536 ms 0.278752 ms 1.896% 42,069,856 B 152.92 GB/s 64.25%
c16 11074375103635821707 4290084717095826202 2331313288061051028 0.387181 ms 0.384960 ms 0.389120 ms 1.074% 67,809,120 B 175.14 GB/s 73.59%

Each row reports five graph replays after warmup. Reset replay reproduced all three hashes. A c16 to c1 transition cleared all 15 inactive output rows. Slot 0 retained zero output and unchanged convolution and recurrent state.

CUDA initcheck reported uninitialized reads at the CUTLASS output consumer. CUTLASS WGMMA output writes are not visible to that instrumentation, so this run is non-decisive. Bit-exact reset replay and racecheck cover the custom GDN kernels.

Verdict. Accepted for the fixed rank-0 layer-0 GDN graph. The c16 path reaches 175.14 GB/s, 73.59% of one GB10’s local traffic roof, with bit-exact output and recurrent-state replay.

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