mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-05-13 17:55:39 +00:00
e07f87910a
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):
...
```
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
# Copyright 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 torch
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def as_torch_cuda_Stream(
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cs: bench.CudaStream, dev: int | None
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) -> torch.cuda.ExternalStream:
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return torch.cuda.ExternalStream(
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stream_ptr=cs.addressof(), device=torch.cuda.device(dev)
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)
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@bench.register()
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@bench.option.throttle_threshold(0.25)
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def torch_bench(state: bench.State) -> None:
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dev_id = state.get_device()
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tc_s = as_torch_cuda_Stream(state.get_stream(), dev_id)
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dt = torch.float32
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scalar_shape: tuple = tuple()
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n = 2**28
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with torch.cuda.stream(tc_s):
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a3 = torch.randn(scalar_shape, dtype=dt)
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a2 = torch.randn(scalar_shape, dtype=dt)
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a1 = torch.randn(scalar_shape, dtype=dt)
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a0 = torch.randn(scalar_shape, dtype=dt)
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x = torch.linspace(-3, 3, n, dtype=dt)
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y = torch.sin(x)
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learning_rate = 1e-4
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def launcher(launch: bench.Launch) -> None:
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tc_s = as_torch_cuda_Stream(launch.get_stream(), dev_id)
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with torch.cuda.stream(tc_s):
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x2 = torch.square(x)
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y_pred = (a3 + x2 * a1) + x * (a2 + a0 * x2)
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_ = torch.square(y_pred - y).sum()
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grad_y_pred = 2 * (y_pred - y)
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grad_a3 = grad_y_pred.sum()
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grad_a2 = (grad_y_pred * x).sum()
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grad_a1 = (grad_y_pred * x2).sum()
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grad_a0 = (grad_y_pred * x2 * x).sum()
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_ = a3 - grad_a3 * learning_rate
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_ = a2 - grad_a2 * learning_rate
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_ = a1 - grad_a1 * learning_rate
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_ = a0 - grad_a0 * learning_rate
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state.exec(launcher, sync=True)
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if __name__ == "__main__":
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bench.run_all_benchmarks(sys.argv)
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