mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-06-29 02:37:36 +00:00
44ec7de6bd
* Add decorators for registering benchmarks and adding axis
cuda.bench.register(fn) continues returning Benchmark, and supports
legacy use.
New signature added:
cuda.bench.register():
Returns a decorator
```
@bench.register()
@bench.axis.float64("Duration (s)", [7e-5, 1e-4, 5e-4])
@bench.option.min_samples(120)
def single_float64_axis(state: bench.State):
...
```
* Remove example/auto_throughput.py
The C++ counterpart's purpose is to demonstrate use of CUPTI
metrics, but these are not supported in Python bindings, so
this example is a duplicate of example/throughput.py
* Add wrong decorator order test for bench.axis.*
* Strengthen type annotation for register function
Acting on code rabbit nit-pick require that function being
registered take cuda.bench.State object as an argument.
Verified the fix as
```
(py313) :~/repos/nvbench/python$ python -m mypy --ignore-missing-import /tmp/t.py
/tmp/t.py:8: error: Argument 1 has incompatible type "Callable[[], None]"; expected "Callable[[State], None]" [arg-type]
Found 1 error in 1 file (checked 1 source file)
(py313) :~/repos/nvbench/python$ nl -ba /tmp/t.py
1 # /tmp/check_nvbench_register.py
2 import cuda.bench as bench
3
4 @bench.register()
5 def good(state: bench.State) -> None:
6 pass
7
8 @bench.register()
9 def bad() -> None:
10 pass
```
* Replace use of global variable with thread-safe lru_cache
This improves thread-safety of module initialization.
* Abide by RUF005 linting rule
* Expand docstrings regarding cuda.bench.register() decorator
It explains to the user what the decorator does and provides
a concise usage example.
* Sharpen wording on exception maybe-thrown by decorator
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
# Copyright 2025-2026 NVIDIA Corporation
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#
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# Licensed under the Apache License, Version 2.0 with the LLVM exception
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# (the "License"); you may not use this file except in compliance with
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# the License.
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#
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# You may obtain a copy of the License at
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#
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# http://llvm.org/foundation/relicensing/LICENSE.txt
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import cuda.bench as bench
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import cuda.cccl.headers as headers
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import cuda.core as core
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def as_core_Stream(cs: bench.CudaStream) -> core.Stream:
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"Create view into native stream provided by NVBench"
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return core.Stream.from_handle(cs.addressof())
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def make_sleep_kernel():
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"""JITs sleep_kernel(seconds)"""
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src = r"""
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#include <cuda/std/cstdint>
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#include <cuda/std/chrono>
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// Each launched thread just sleeps for `seconds`.
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__global__ void sleep_kernel(double seconds) {
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namespace chrono = ::cuda::std::chrono;
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using hr_clock = chrono::high_resolution_clock;
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auto duration = static_cast<cuda::std::int64_t>(seconds * 1e9);
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const auto ns = chrono::nanoseconds(duration);
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const auto start = hr_clock::now();
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const auto finish = start + ns;
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auto now = hr_clock::now();
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while (now < finish)
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{
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now = hr_clock::now();
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}
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}
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"""
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incl = headers.get_include_paths()
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opts = core.ProgramOptions(include_path=str(incl.libcudacxx))
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prog = core.Program(src, code_type="c++", options=opts)
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mod = prog.compile("cubin", name_expressions=("sleep_kernel",))
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return mod.get_kernel("sleep_kernel")
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@bench.register()
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@bench.axis.float64("Duration", [1e-4 + k * 0.25e-3 for k in range(5)])
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@bench.axis.string("Kramble", ["Foo", "Bar", "Baz"])
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def runtime_skip(state: bench.State):
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duration = state.get_float64("Duration")
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kramble = state.get_string("Kramble")
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# Skip Baz benchmarks with 0.8 ms duration
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if kramble == "Baz" and duration < 0.8e-3:
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state.skip("Short 'Baz' benchmarks are skipped")
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return
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# Skip Foo benchmark with > 0.3 ms duration
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if kramble == "Foo" and duration > 0.3e-3:
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state.skip("Long 'Foo' benchmarks are skipped")
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return
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krn = make_sleep_kernel()
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launch_cfg = core.LaunchConfig(grid=1, block=1, shmem_size=0)
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def launcher(launch: bench.Launch):
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s = as_core_Stream(launch.get_stream())
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core.launch(s, launch_cfg, krn, duration)
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state.exec(launcher)
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if __name__ == "__main__":
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bench.run_all_benchmarks(sys.argv)
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