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d4a292b36b
- deepen common-shape BF16 GEMV tuning with warp-row tiling for LLaMA/Qwen2/GPT-NeoX/OPT decode projections - add fused BF16 up/gate SwiGLU CUDA primitive with ASTRAI_SWIGLU=0/1/auto dispatch - keep the unfused linear backend as the default path; auto enables no shape until per-architecture checkpoint gates pass - fall back to the linear/torch chain when kernels are absent, on CPU, in training, or outside supported M/K/dtype shapes - add gemv/swiglu benchmark scripts, dispatch and parity tests, and kernel documentation Benchmark: NVIDIA L20 (sm_89), CUDA 12.8, PyTorch 2.11.0+cu128, idle GPU. AstrAI 1B config (24 layers, hidden 1536, vocab 100000), BF16, prompt 128, 32 greedy decode tokens, CUDA graphs enabled, A/B in separate interleaved processes (3 rounds, 8 trials each, medians). Default vs ASTRAI_SWIGLU=1 per generate call: batch 1 134.8->129.1 ms (+4.44%), batch 2 136.2->130.9 ms (+4.06%), batch 4 145.5->140.3 ms (+3.66%). Greedy output identical at batch 1, differs at batch 2/4, so auto stays unfused by default; kernelless fallback verified bit-identical greedy.
2.5 KiB
Fused SwiGLU benchmark
scripts/tools/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 scripts/tools/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.