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- 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.
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Decode linear shape benchmark
scripts/tools/benchmark_gemv.py records the F.linear baseline used to decide
whether a BF16 GEMV or small-M kernel should enter automatic inference dispatch.
It does not change model execution or select a custom kernel.
The default matrix covers the AstrAI 1B q/k/v/out projections, MLP up/gate/down,
and LM head for M=1,2,4,8,16,32. Each shape runs in eager and CUDA Graph replay
modes. Results include device-event latency samples, p50/p90/p99, estimated
effective IO bandwidth, and CUDA kernel launches per call.
CUDA_VISIBLE_DEVICES=0 python scripts/tools/benchmark_gemv.py \
--output results/decode_linear.json \
--markdown-output results/decode_linear.md
Use --shape NAME:N:K repeatedly to override the preset and --m-values to
change the decode batch sizes. Compare each GPU architecture only with its own
baseline; do not use absolute A100-versus-L20 numbers as a dispatch criterion.
Keep the raw JSON as the source of truth and generate tables with
--markdown-output rather than transcribing measurements by hand.
For direct A/B coverage of the custom kernel and guarded dispatcher across traditional LLaMA and GPT-NeoX decode shapes, use:
CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python scripts/tools/benchmark_gemv_common.py \
--suite all --family traditional --m 2 4 \
--output results/gemv_common.json
The kernel suite compares the directly callable primitive with F.linear.
Use repeatable --shape-label and --chain-label filters for a focused run.
The synthetic-chain suite alternates ASTRAI_GEMV=0 and auto, includes
dependent MLP work and Python dispatch, and rotates through distinct weights.
Pass --candidate-mode 1 to characterize a family before adding it to the
automatic shape table; the checked-in final evidence always uses auto.
It is deliberately not labeled a whole-model throughput benchmark. Both
suites report median/p90 CUDA-event latency plus maximum absolute error,
relative L2 error, and row-wise argmax parity.