ViperEkura
28d11f1610
- Move benchmark_gemv.py, benchmark_swiglu.py, and benchmark_gemv_common.py from scripts/tools/ to csrc/bench/ so kernel benchmarks live next to the kernels they measure - Update reproduction commands in decode_linear_benchmark.md, swiglu_benchmark.md, and cuda_kernels.md - Codify the placement convention in AGENTS.md: kernel benchmarks in csrc/bench/, pure-CUDA harnesses in csrc/tests/*.cu, engine and evaluation benchmarks in scripts/
43 lines
2.1 KiB
Markdown
43 lines
2.1 KiB
Markdown
# Decode linear shape benchmark
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`csrc/bench/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 csrc/bench/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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For direct A/B coverage of the custom kernel and guarded dispatcher across
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traditional LLaMA and GPT-NeoX decode shapes, use:
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```bash
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CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python csrc/bench/benchmark_gemv_common.py \
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--suite all --family traditional --m 2 4 \
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--output results/gemv_common.json
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```
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The kernel suite compares the directly callable primitive with `F.linear`.
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Use repeatable `--shape-label` and `--chain-label` filters for a focused run.
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The synthetic-chain suite alternates `ASTRAI_GEMV=0` and `auto`, includes
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dependent MLP work and Python dispatch, and rotates through distinct weights.
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Automatic dispatch is keyed on the decode batch size alone (`M` in `[2, 4]` on
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compute capability 8.0+); use `--candidate-mode 1` to characterize a family
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before widening that band. The checked-in final evidence always uses `auto`.
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It is deliberately not labeled a whole-model throughput benchmark. Both
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suites report median/p90 CUDA-event latency plus maximum absolute error,
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relative L2 error, and row-wise argmax parity.
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