为什么“numpy.any"有没有短路机制? [英] Why "numpy.any" has no short-circuit mechanism?

问题描述

我不明白为什么这么基本的优化还没有完成:

I don't understand why a so basic optimization has not yet be done:

In [1]: one_million_ones = np.ones(10**6)
In [2]: %timeit one_million_ones.any()
100 loops, best of 3: 693µs per loop

In [3]: ten_millions_ones = np.ones(10**7)
In [4]: %timeit ten_millions_ones.any()
10 loops, best of 3: 7.03 ms per loop

扫描整个数组，即使结论是第一项的证据.

The whole array is scanned, even if the conclusion is an evidence at first item.

推荐答案

这是一个未修复的性能回归.NumPy 问题 3446.实际上是 短路逻辑，但有变化ufunc.reduce 机制在短路逻辑周围引入了一个不必要的基于块的外循环，而该外循环不知道如何短路.您可以在此处看到对分块机制的一些解释.

It's an unfixed performance regression. NumPy issue 3446. There actually is short-circuiting logic, but a change to the ufunc.reduce machinery introduced an unnecessary chunk-based outer loop around the short-circuiting logic, and that outer loop doesn't know how to short circuit. You can see some explanation of the chunking machinery here.

不过，即使没有回归，短路效应也不会出现在您的测试中.首先，您正在为数组创建计时，其次，我认为他们从未为除布尔值之外的任何输入 dtype 设置短路逻辑.从讨论中可以看出，numpy.any 背后的 ufunc 归约机制的细节会让这变得困难.

The short-circuiting effects wouldn't have showed up in your test even without the regression, though. First, you're timing the array creation, and second, I don't think they ever put in the short-circuit logic for any input dtype but boolean. From the discussion, it sounds like the details of the ufunc reduction machinery behind numpy.any would have made that difficult.

讨论确实提出了一个令人惊讶的点，即 argmin 和 argmax 方法似乎对布尔输入短路.快速测试表明，从 NumPy 1.12(不是最新版本，但目前在 Ideone 上的版本)，x[x.argmax()] 短路，它在 1- 方面胜过 x.any() 和 x.max()维度布尔输入，无论输入是小还是大，也无论短路是否有回报.奇怪！

The discussion does bring up the surprising point that the argmin and argmax methods appear to short-circuit for boolean input. A quick test shows that as of NumPy 1.12 (not quite the most recent version, but the version currently on Ideone), x[x.argmax()] short-circuits, and it outcompetes x.any() and x.max() for 1-dimensional boolean input no matter whether the input is small or large and no matter whether the short-circuiting pays off. Weird!

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