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"""Example of the Timer and Fuzzer APIs: |
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$ python -m examples.fuzzer |
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""" |
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import sys |
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import torch.utils.benchmark as benchmark_utils |
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def main(): |
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add_fuzzer = benchmark_utils.Fuzzer( |
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parameters=[ |
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[ |
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benchmark_utils.FuzzedParameter( |
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name=f"k{i}", |
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minval=16, |
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maxval=16 * 1024, |
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distribution="loguniform", |
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) for i in range(3) |
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], |
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benchmark_utils.FuzzedParameter( |
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name="d", |
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distribution={2: 0.6, 3: 0.4}, |
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), |
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], |
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tensors=[ |
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[ |
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benchmark_utils.FuzzedTensor( |
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name=name, |
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size=("k0", "k1", "k2"), |
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dim_parameter="d", |
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probability_contiguous=0.75, |
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min_elements=64 * 1024, |
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max_elements=128 * 1024, |
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) for name in ("x", "y") |
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], |
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], |
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seed=0, |
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) |
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n = 250 |
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measurements = [] |
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for i, (tensors, tensor_properties, _) in enumerate(add_fuzzer.take(n=n)): |
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x, x_order = tensors["x"], str(tensor_properties["x"]["order"]) |
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y, y_order = tensors["y"], str(tensor_properties["y"]["order"]) |
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shape = ", ".join(tuple(f'{i:>4}' for i in x.shape)) |
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description = "".join([ |
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f"{x.numel():>7} | {shape:<16} | ", |
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f"{'contiguous' if x.is_contiguous() else x_order:<12} | ", |
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f"{'contiguous' if y.is_contiguous() else y_order:<12} | ", |
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]) |
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timer = benchmark_utils.Timer( |
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stmt="x + y", |
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globals=tensors, |
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description=description, |
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) |
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measurements.append(timer.blocked_autorange(min_run_time=0.1)) |
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measurements[-1].metadata = {"numel": x.numel()} |
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print(f"\r{i + 1} / {n}", end="") |
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sys.stdout.flush() |
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print() |
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print(f"Average attemts per valid config: {1. / (1. - add_fuzzer.rejection_rate):.1f}") |
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def time_fn(m): |
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return m.median / m.metadata["numel"] |
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measurements.sort(key=time_fn) |
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template = f"{{:>6}}{' ' * 19}Size Shape{' ' * 13}X order Y order\n{'-' * 80}" |
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print(template.format("Best:")) |
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for m in measurements[:15]: |
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print(f"{time_fn(m) * 1e9:>4.1f} ns / element {m.description}") |
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print("\n" + template.format("Worst:")) |
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for m in measurements[-15:]: |
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print(f"{time_fn(m) * 1e9:>4.1f} ns / element {m.description}") |
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if __name__ == "__main__": |
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main() |
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