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np.isnan doesn't work on CPU in fast-math mode

See original GitHub issue

as a result np.nan_to_num, np.nanmean, etc all don’t work

import jax.numpy as np
a = np.zeros(1) / np.zeros(1)
print(a.__array__())
print(np.isnan(a).__array__())

[nan] [False]

This bug only happens with the CPU-only build of JAX, when I see this warning: “warnings.warn(‘No GPU found, falling back to CPU.’)”

Issue Analytics

State:
Created 5 years ago
Comments:14 (7 by maintainers)

Top GitHub Comments

3reactions

hawkinspcommented, Apr 26, 2019

We just pushed out jaxlib 0.1.13, which should fix this problem.

Parts of fast math are still enabled by default for performance, but the semantics of NaNs and Infs should now be honored. Please file a new issue if you see any further problems!

2reactions

hawkinspcommented, Mar 2, 2019

A brief update on this bug: we tried disabling fastmath in XLA/CPU by default, but found it regressed performance for some neural network benchmarks significantly because it prevents vectorization in some important cases.

https://reviews.llvm.org/D57728 apparently fixes the performance problem, but it isn’t in yet. I’m hoping we can simply disable fast math by default when that change makes it into LLVM.

A warning makes sense until we do so, I guess.

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