AstrAI/docs/developer/swiglu_benchmark.md

Fused SwiGLU benchmark

csrc/bench/benchmark_swiglu.py compares the directly callable fused BF16 SwiGLU primitive with both F.linear and the existing two-GEMV chain. It covers the native AstrAI 1B MLP plus LLaMA 2 7B/13B, LLaMA 3 8B, and GPT-NeoX 20B up/gate shapes at M=1/2/4/8 in eager and CUDA Graph modes.

CUDA_VISIBLE_DEVICES=0 python csrc/bench/benchmark_swiglu.py \
  --output results/swiglu.json \
  --markdown-output results/swiglu.md \
  --m-values 1,2,4,8 --mode both \
  --warmup 20 --iterations 100 --trials 10

Each trial uses A-B-C-C-B-A ordering to balance clock, cache, and temperature drift. The generated JSON records every timing sample, p50/p90/p99, CUDA launch count, maximum/mean absolute error, and cosine similarity.

L20 findings

Hardware was one NVIDIA L20 (sm_89), PyTorch 2.11.0+cu128, CUDA 12.8. The existing GPU5 inference service remained resident (15.4 GiB) but idle at the sampling boundaries; no process or container was stopped.

For AstrAI 1B (N,K)=(6912,1536), CUDA Graph medians were:

M	torch (ms)	GEMV chain (ms)	fused (ms)	vs best unfused
1	0.02564	0.02298	0.01375	+67.13%
2	0.02484	0.02628	0.01416	+75.40%
4	0.02507	0.03839	0.01806	+38.82%
8	0.02563	0.07007	0.03339	-23.24%

The wide traditional shapes are weight-bandwidth dominated. CTA reuse keeps the fused primitive within roughly -1.2% to +0.9% of the best unfused chain, so none is eligible for automatic selection. This negative crossover is kept in the raw evidence rather than hidden by a favorable subset.

The real 24-layer AstrAI checkpoint was then run through InferenceEngine, including scheduler, sampling, and CUDA Graph. A-B-B-A medians were:

Batch	unfused (ms/step)	forced fused (ms/step)	throughput gain
1	4.125	3.925	+5.10%
2	4.245	4.055	+4.69%
4	4.475	4.305	+3.95%

Dispatch decision

Direct correctness stayed close (max_abs <= 2.4e-4, cosine approximately 1.0), but deterministic greedy generations changed at M=1, M=2, and M=4.

For that reason no SM89 shape is enabled in auto. The default path stays on the existing unfused linear backend, including any independently qualified GEMV dispatch. ASTRAI_SWIGLU=1 remains an explicit benchmark/experimentation switch for callers that accept normal BF16 reduction-order variation. A future automatic band must repeat both the performance and checkpoint-output gates.

HBM re-measurement and kernel simplification

The operator numbers above are L2-resident: the AstrAI pair is 40.5 MB, smaller than the 96 MB L2, so a tight timing loop re-reads warm weights (13.75 us implies ~3.1 TB/s, far above the 864 GB/s spec). Real decode rotates ~1 GB of per-layer weights through L2 every step, so every call is cold.

Re-measuring with rotated weight copies (>= 240 MB working set) on the same L20 showed:

• The fused CTA-reuse kernel sits at the dual-stream cold-read floor (702 vs 699 GB/s at (6912,1536); 369 vs 370 GB/s at (11008,4096)). Wide LLaMA matrices cap at ~370-400 GB/s regardless of kernel, even for a pure-read loop, so the old per-variant gaps there were noise.
• The (6912,1536) warp-per-row variant (formerly M=2/4/8) is 2-6% slower than CTA reuse at M=2/4 under cold weights and no longer wins at M=8 once the CTA drops to 128 threads. It and its dispatch table were deleted.
• New rule: 256 threads for M in [1, 7], 128 threads for M=8. End-to-end through the built module at (6912,1536): 738-752 GB/s for M in [1, 4] and 702 GB/s at M=8 (+6% over the removed warp path).

The M=8 CUDA-Graph regression reported above (-23.24%) does not survive the cold-weight regime: cuBLAS reaches L2 bandwidth in the warm loop while both fused paths converge to the same HBM floor.