Use kernels benchmark to run benchmark scripts shipped with a kernel repository.
The command:
benchmarks/benchmark*.py scriptsbenchmark_* workload and prints a results tablekernels benchmark requires extra dependencies:
uv pip install 'kernels[benchmark]' # or pip install 'kernels[benchmark]'kernels benchmark kernels-community/activation --version 1
Example output:
Downloading kernels-community/activation@v1... Running benchmark.py... GPU Apple M3 Max (30 cores) CPU Apple M3 Max OS Darwin 25.2.0 PyTorch 2.10.0 Running SiluWorkloads on mps ┌───────────────┬────────────┬─────┬───────────┬────────────┬───────────┬───────────┬───────────┬───────────┬────────────┬───────────┬─────────┐ │ Benchmark │ Workload │ N │ Speedup │ Mean(ms) │ Std(ms) │ Min(ms) │ Max(ms) │ IQR(ms) │ Outliers │ Ref(ms) │ Match │ ├───────────────┼────────────┼─────┼───────────┼────────────┼───────────┼───────────┼───────────┼───────────┼────────────┼───────────┼─────────┤ │ SiluWorkloads │ large │ 100 │ 1.72x │ 6.5153 │ 0.4343 │ 6.2883 │ 8.4699 │ 0.1701 │ 8 │ 11.2048 │ ✓ │ │ SiluWorkloads │ medium │ 100 │ 2.48x │ 1.1813 │ 0.3976 │ 1.04 │ 4.2146 │ 0.0698 │ 5 │ 2.9332 │ ✓ │ │ SiluWorkloads │ small │ 100 │ 1.96x │ 0.4909 │ 0.2175 │ 0.4407 │ 2.6438 │ 0.0085 │ 16 │ 0.9622 │ ✓ │ └───────────────┴────────────┴─────┴───────────┴────────────┴───────────┴───────────┴───────────┴───────────┴────────────┴───────────┴─────────┘ large: 1.72x faster (95% CI: 6.4302-6.6004ms vs ref 11.2048ms) ✓ significant medium: 2.48x faster (95% CI: 1.1034-1.2592ms vs ref 2.9332ms) ✓ significant small: 1.96x faster (95% CI: 0.4483-0.5335ms vs ref 0.9622ms) ✓ significant Kernel: 2385e44 Benchmark: 5b53516
You must specify which revision to benchmark, either via flags or with @... in the repo id:
kernels benchmark <repo_id> --version <N> kernels benchmark <repo_id> --branch <name> kernels benchmark <repo_id>@v<N> kernels benchmark <repo_id>@<branch>
Benchmark a tagged kernel version:
kernels benchmark kernels-community/activation --version 1
Equivalent shorthand:
kernels benchmark kernels-community/activation@v1
Benchmark a branch:
kernels benchmark kernels-community/activation --branch main
Tune warmup and iteration count:
kernels benchmark kernels-community/activation@v1 --warmup 20 --iterations 200
Save results to a file (JSON):
kernels benchmark kernels-community/activation@v1 --output results.json
Benchmark a local kernel checkout (must contain benchmarks/):
kernels benchmark ./my_kernel
--output <file>.json writes a JSON payload to disk.Benchmark scripts must live under benchmarks/ in the kernel repository and match benchmark*.py.
Each script should define one or more subclasses of kernels.benchmark.Benchmark.
Minimal example (benchmarks/benchmark_activation.py):
import torch
from kernels.benchmark import Benchmark
class ActivationBenchmark(Benchmark):
seed = 0
def setup(self):
self.x = torch.randn(128, 1024, device=self.device, dtype=torch.float16)
self.out = torch.empty(128, 512, device=self.device, dtype=torch.float16)
def benchmark_silu_and_mul(self):
self.kernel.silu_and_mul(self.out, self.x)
def verify_silu_and_mul(self):
# Return reference tensor; runner compares with self.out
return torch.nn.functional.silu(self.x[..., :512]) * self.x[..., 512:]The runner will:
setup() once per workload (or setup_<workload>() if present)--warmup)benchmark_<workload>() for --iterationsverify_<workload>() exists, check that outputs match (torch.allclose(..., atol=1e-2)) and show a speedup vs the reference computationbenchmarks/ directory (or no benchmark*.py files), the command exits with an error.Benchmark subclasses, the command exits with an error.verify_<workload>() exists and the outputs do not match, the command exits with an error.