numba v0.55.2

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Analysis by source code inspection shows a discrepancy between it and the the package's stated requirements in the meta.yaml.

Packages found by source code inspection but not in the meta.yaml:

• pygments
• arm_pyart

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[conda-forge-linter]

Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

[henryiii]

Looks like test_workqueue_aborts_on_nested_parallelism is broken on linux_aarch64 with:

lib/python3.9/site-packages/numba/np/ufunc/workqueue.cpython-39-aarch64-linux-gnu.so: undefined symbol: pthread_atfork

[henryiii]

numba/numba#8094 looks suspect. Similar to an issue in #83.

[henryiii]

Need to update limits still.

[conda-forge-linter]

Hi! This is the friendly automated conda-forge-linting service.

I wanted to let you know that I linted all conda-recipes in your PR (recipe) and found some lint.

Here's what I've got...

For recipe:

• Selectors are suggested to take a <two spaces>#<one space>[<expression>] form. See lines [59]

[conda-forge-linter]

Hi! This is the friendly automated conda-forge-linting service.

I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

[henryiii]

Are seven errors to be expected when using NumPy 1.22 on Linux? Guessing that's what caused the errors, but I can check.

[henryiii]

Yes, using NumPy 1.23 breaks 7 unit tests

[stuartarchibald]

@henryiii thanks for opening this. Just from a quick look at the failing tests and guessing... looks like numerical regressions using NumPy 1.22 on chips with AVX512 that are SkyLake+. The output from numba -s in the build logs has this new item:

2022-05-26T14:24:26.8511260Z __NumPy Information__
2022-05-26T14:24:26.8511575Z NumPy Version                                 : 1.22.4
2022-05-26T14:24:26.8512453Z NumPy Supported SIMD features                 : ('MMX', 'SSE', 'SSE2', 'SSE3', 'SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2', 'AVX512F', 'AVX512CD', 'AVX512VL', 'AVX512BW', 'AVX512DQ', 'AVX512_SKX')
2022-05-26T14:24:26.8513588Z NumPy Supported SIMD dispatch                 : ('SSSE3', 'SSE41', 'POPCNT', 'SSE42', 'AVX', 'F16C', 'FMA3', 'AVX2', 'AVX512F', 'AVX512CD', 'AVX512_KNL', 'AVX512_KNM', 'AVX512_SKX', 'AVX512_CLX', 'AVX512_CNL', 'AVX512_ICL')
2022-05-26T14:24:26.8514403Z NumPy Supported SIMD baseline                 : ('SSE', 'SSE2', 'SSE3')
2022-05-26T14:24:26.8514793Z NumPy AVX512_SKX support detected             : True

it's the last line that shows this particular condition is detected: NumPy AVX512_SKX support detected : True

This diagnostic was added to Numba in 0.55.2 as part of the NumPy 1.22 support, it was discovered here numba/numba#8030 (comment) that this dispatch-to-lower-precision-ufunc-loops feature could be disabled and the following was added to Numba's test scripts to do this:

https://github.com/numba/numba/blob/290a693641b85c327ab79e2430d60fe82666aa38/buildscripts/incremental/test.sh#L29-L41

I'm hoping if this PR contains similar at test time the test suite will pass again.

[njriasan]

@henryiii Do you have any estimate on when you think this will be available?

[henryiii]

I was traveling the last two days but am back and should have time to try the fix suggested above today.

[henryiii]

Ahh, I'm failing test_no_accidental_warnings (and two more, but they might be related).

Edit: Okay, I think I see what to add here.

[maxnoe]

Thanks guys! The incompatibility and the conda env solver that chose to install numpy 1.22 with numba 0.53 caused as some pain...