0z5a
a144d7f306
- add decode-shape benchmark harness - add bf16 GEMV CUDA primitive with head-dim generic kernel - dispatch decode-time linear layers to gemv for M=1 - extend gemv coverage to small decode batches
23 lines
1.1 KiB
Markdown
23 lines
1.1 KiB
Markdown
# Decode linear shape benchmark
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`scripts/tools/benchmark_gemv.py` records the `F.linear` baseline used to decide
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whether a BF16 GEMV or small-M kernel should enter automatic inference dispatch.
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It does not change model execution or select a custom kernel.
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The default matrix covers the AstrAI 1B q/k/v/out projections, MLP up/gate/down,
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and LM head for `M=1,2,4,8,16,32`. Each shape runs in eager and CUDA Graph replay
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modes. Results include device-event latency samples, p50/p90/p99, estimated
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effective IO bandwidth, and CUDA kernel launches per call.
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```bash
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CUDA_VISIBLE_DEVICES=0 python scripts/tools/benchmark_gemv.py \
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--output results/decode_linear.json \
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--markdown-output results/decode_linear.md
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```
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Use `--shape NAME:N:K` repeatedly to override the preset and `--m-values` to
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change the decode batch sizes. Compare each GPU architecture only with its own
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baseline; do not use absolute A100-versus-L20 numbers as a dispatch criterion.
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Keep the raw JSON as the source of truth and generate tables with
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`--markdown-output` rather than transcribing measurements by hand.
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