BLD: Do not use clang default to ignore floating point exceptions

Remarkably numpy.log() gives a runtime overflow warning on a float32 array containing values larger than 1e9. Numpy 1.19.4, macOS 10.15.7, MacBookPro15,3. Warning does not appear for float64 array even for much larger values (e.g. 1e35).

Reproducing code example:

import numpy as np
a = np.array([1e9], np.float32)
np.log(a)

Error message:

RuntimeWarning: overflow encountered in log

NumPy/Python version information:

1.19.4 3.8.6 (default, Nov 20 2020, 18:29:40)
[Clang 12.0.0 (clang-1200.0.32.27)]

@tomgoddard

@tomgoddard, I cannot reproduce this on linux. Since you are on 1.19.x, could you print:

np.core._multiarray_umath.__cpu_features__

Just so we have the information?

The most likely cause will be that you have AVX512 instructions available and our AVX512 code has an error here. If it is in the AV512 path, @r-devulap could you have a look?

Of course it could also be a clang bug in the glibc version...

np.core._multiarray_umath.__cpu_features__ {'MMX': True, 'SSE': True, 'SSE2': True, 'SSE3': True, 'SSSE3': True, 'SSE41': True, 'POPCNT': True, 'SSE42': True, 'AVX': True, 'F16C': True, 'XOP': False, 'FMA4': False, 'FMA3': True, 'AVX2': True, 'AVX512F': False, 'AVX512CD': False, 'AVX512ER': False, 'AVX512PF': False, 'AVX5124FMAPS': False, 'AVX5124VNNIW': False, 'AVX512VPOPCNTDQ': False, 'AVX512VL': False, 'AVX512BW': False, 'AVX512DQ': False, 'AVX512VNNI': False, 'AVX512IFMA': False, 'AVX512VBMI': False, 'AVX512VBMI2': False, 'AVX512BITALG': False, 'AVX512_KNL': False, 'AVX512_KNM': False, 'AVX512_SKX': False, 'AVX512_CLX': False, 'AVX512_CNL': False, 'AVX512_ICL': False, 'VSX': False, 'VSX2': False, 'VSX3': False, 'NEON': False, 'NEON_FP16': False, 'NEON_VFPV4': False, 'ASIMD': False, 'FPHP': False, 'ASIMDHP': False, 'ASIMDDP': False, 'ASIMDFHM': False}

Numpy 1.19.4 was installed using brew on macOS 10.15.7.

could you run np.log(np.array([1e9]*8, np.float32)) and check if the overflow error still remains? newest versions of clang committing aggressive optimization to partial load operations lead to undefined the vector tail on AVX2.

I don't see this problem when using NumPy built with gcc 9.3 or clang 10.0. Makes me think it has to something to do with clang 12. Still figuring out how to install clang 12 on my Ubuntu to reproduce the error :/

>> a = np.array([1e9]*8, np.float32) >> np.log(a)

<stdin>:1: RuntimeWarning: overflow encountered in log array([20.723267, 20.723267, 20.723267, 20.723267, 20.723267, 20.723267, 20.723267, 20.723267], dtype=float32)

…

could you run a = np.array([1e9]*8, np.float32) and check if the overflow error still remains? newest versions of clang committing aggressive optimization to partial load operations lead to undefined the vector tail on AVX2.

No overflow warning for smaller values such as 1e8. Python 3.8.6 (default, Nov 20 2020, 18:29:40) [Clang 12.0.0 (clang-1200.0.32.27)] on darwin

>> import numpy as np >> a = np.array([1e8], np.float32) >> np.log(a)

array([18.420681], dtype=float32) No overflow warning with float64

>> import numpy as np >> a = np.array([1e9, 1e20, 1e30], np.float64) >> np.log(a)

array([20.72326584, 46.05170186, 69.07755279])

confirmed that this behavior is only with clang (clang 10 and clang 12), and does not happen with gcc. It decides to set an overflow flag for a blend instruction (which is weird and incorrect) x = _mm256_blendv_ps(x, temp, denormal_mask); here:

numpy/numpy/core/src/umath/simd.inc.src

Line 1554 in d7a75e8

x = _mm256_blendv_ps(x, temp, denormal_mask);

@r-devulap

@r-devulap thanks for digging this down, I am assuming it is a clang bug. Can we file it there? I am not sure there is a good way for us to work around it in NumPy to begin with...

Trying to narrow down to a simple C program to replicate the bug, unsuccessful so far ..

Found the culprit! clang optimizes out a blend instruction which was precisely meant to avoid this reported issue of an overflow warning. While the compiler was being clever, it is still a bug.

PR #18030 has a simple work around for this issue. Let me know if that works.

EDIT: didn't mean to include the code snippet.

Reopened and changed title. We should probably have a quick look around for clang workaround, but I don't think we need to backport getting rid of those.

This was the other time clang give me an incorrect FP flag: #13586 (comment) and the associated PR #13623

Hmmm, I thought I would look into this. But I am honestly not sure where we should be adding this flag, or if we actually should (additionally) even prod Python itself to add this flag. Am I right to think that most clang specific flags currently are inherited from Python itself?

What is the status of this?

I think this is important, but I am not very familiar with distutils, and am not sure where this flag should be inserted correctly. (It probably is also correct for SciPy, but do we want to set it by default for scipy as well?).
I am happy to check for the related code cleanups, but not too fond of digging through distutils to find the right place.

It shouldn't matter too much to push it off to 1.21 all known issues due to this are fixed after all. While the flag change is something we could do, the (probably) related code cleanup we do not want to backport anyway probably.

@rgommers

@rgommers I somewhat assume you just know where to put this. Can you point where we would add a clang specific compiler flag to distutils (maybe NumPy specific, although this flag is really also necessary for scipy, I bet).

In CCompiler_customize: https://github.com/numpy/numpy/blob/main/numpy/distutils/ccompiler.py#L366

maybe NumPy specific, although this flag is really also necessary for scipy, I bet

It's hard to make it NumPy-specific, and I think other libraries should get this flag too, so I'd say just add it unconditionally.

@seberb

@seberb,

Can you point where we would add a clang specific compiler flag to distutils?

for a quickfix, we can link the flag with one of baseline features flags, etc SSE on x86.`

numpy/numpy/distutils/ccompiler_opt.py

Lines 309 to 314 in ddbff08

    
           on_x86 = self.cc_on_x86 or self.cc_on_x64 
        
           is_unix = self.cc_is_gcc or self.cc_is_clang 
        
           if on_x86 and is_unix: return dict( 
        
               SSE    = dict(flags="-msse"), 
        
               SSE2   = dict(flags="-msse2"),

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BLD: Do not use clang default to ignore floating point exceptions #18005

Reproducing code example:

Error message:

NumPy/Python version information:

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	on_x86 = self.cc_on_x86 or self.cc_on_x64
	is_unix = self.cc_is_gcc or self.cc_is_clang

	if on_x86 and is_unix: return dict(
	SSE = dict(flags="-msse"),
	SSE2 = dict(flags="-msse2"),

BLD: Do not use clang default to ignore floating point exceptions #18005

Description

Reproducing code example:

Error message:

NumPy/Python version information:

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seberg commented on Dec 16, 2020

tomgoddard commented on Dec 16, 2020

tomgoddard commented on Dec 16, 2020

seiko2plus commented on Dec 16, 2020

r-devulap commented on Dec 16, 2020

tomgoddard commented on Dec 16, 2020

tomgoddard commented on Dec 16, 2020

r-devulap commented on Dec 16, 2020

seberg commented on Dec 17, 2020

r-devulap commented on Dec 18, 2020

r-devulap commented on Dec 19, 2020

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seberg commented on Dec 21, 2020

r-devulap commented on Dec 21, 2020

seberg commented on Jan 25, 2021

charris commented on Jan 29, 2021

seberg commented on Jan 29, 2021

seberg commented on Mar 18, 2021

rgommers commented on Mar 18, 2021

seiko2plus commented on Mar 18, 2021

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