mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-03-14 20:27:24 +00:00
b5e4b4ba31
Per PR review suggestion: - `cuda.parallel` - device-wide algorithms/Thrust - `cuda.cooperative` - Cooperative algorithsm/CUB - `cuda.bench` - Benchmarking/NVBench
59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import sys
|
|
|
|
import cuda.bench as bench
|
|
import torch
|
|
|
|
|
|
def as_torch_cuda_Stream(
|
|
cs: bench.CudaStream, dev: int | None
|
|
) -> torch.cuda.ExternalStream:
|
|
return torch.cuda.ExternalStream(
|
|
stream_ptr=cs.addressof(), device=torch.cuda.device(dev)
|
|
)
|
|
|
|
|
|
def torch_bench(state: bench.State) -> None:
|
|
state.set_throttle_threshold(0.25)
|
|
|
|
dev_id = state.get_device()
|
|
tc_s = as_torch_cuda_Stream(state.get_stream(), dev_id)
|
|
|
|
dt = torch.float32
|
|
scalar_shape: tuple = tuple()
|
|
n = 2**28
|
|
with torch.cuda.stream(tc_s):
|
|
a3 = torch.randn(scalar_shape, dtype=dt)
|
|
a2 = torch.randn(scalar_shape, dtype=dt)
|
|
a1 = torch.randn(scalar_shape, dtype=dt)
|
|
a0 = torch.randn(scalar_shape, dtype=dt)
|
|
x = torch.linspace(-3, 3, n, dtype=dt)
|
|
y = torch.sin(x)
|
|
|
|
learning_rate = 1e-4
|
|
|
|
def launcher(launch: bench.Launch) -> None:
|
|
tc_s = as_torch_cuda_Stream(launch.get_stream(), dev_id)
|
|
with torch.cuda.stream(tc_s):
|
|
x2 = torch.square(x)
|
|
y_pred = (a3 + x2 * a1) + x * (a2 + a0 * x2)
|
|
|
|
_ = torch.square(y_pred - y).sum()
|
|
grad_y_pred = 2 * (y_pred - y)
|
|
grad_a3 = grad_y_pred.sum()
|
|
grad_a2 = (grad_y_pred * x).sum()
|
|
grad_a1 = (grad_y_pred * x2).sum()
|
|
grad_a0 = (grad_y_pred * x2 * x).sum()
|
|
|
|
_ = a3 - grad_a3 * learning_rate
|
|
_ = a2 - grad_a2 * learning_rate
|
|
_ = a1 - grad_a1 * learning_rate
|
|
_ = a0 - grad_a0 * learning_rate
|
|
|
|
state.exec(launcher, sync=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
bench.register(torch_bench)
|
|
|
|
bench.run_all_benchmarks(sys.argv)
|