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59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
import sys
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import cuda.nvbench as nvbench
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import torch
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def as_torch_cuda_Stream(
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cs: nvbench.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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def torch_bench(state: nvbench.State) -> None:
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state.set_throttle_threshold(0.25)
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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: nvbench.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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nvbench.register(torch_bench)
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nvbench.run_all_benchmarks(sys.argv)
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