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chore: relocate kernel benchmarks to csrc/bench

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- 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/
This commit is contained in:
ViperEkura
2026-09-03 21:06:33 +08:00
parent 76f1c10feb
commit 28d11f1610
6 changed files with 6 additions and 6 deletions
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docs/developer/decode_linear_benchmark.md
+3 -3
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@@ -1,6 +1,6 @@
# Decode linear shape benchmark
`scripts/tools/benchmark_gemv.py` records the `F.linear` baseline used to decide
`csrc/bench/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.
@@ -10,7 +10,7 @@ modes. Results include device-event latency samples, p50/p90/p99, estimated
effective IO bandwidth, and CUDA kernel launches per call.
```bash
CUDA_VISIBLE_DEVICES=0 python scripts/tools/benchmark_gemv.py \
CUDA_VISIBLE_DEVICES=0 python csrc/bench/benchmark_gemv.py \
--output results/decode_linear.json \
--markdown-output results/decode_linear.md
```
@@ -25,7 +25,7 @@ For direct A/B coverage of the custom kernel and guarded dispatcher across
traditional LLaMA and GPT-NeoX decode shapes, use:
```bash
CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python scripts/tools/benchmark_gemv_common.py \
CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python csrc/bench/benchmark_gemv_common.py \
--suite all --family traditional --m 2 4 \
--output results/gemv_common.json
```