change to dynamic linking and add interface and threading layers

[vtavana]

resolves IntelPython/mkl_fft#165

Main branch

import numpy as np, mkl_umath
n = 2**31
data = np.ones(n)
mkl_umath.absolute(data)
# Intel oneMKL ERROR: Parameter 1 was incorrect on entry to vdAbs.
# array([0., 0., 0., ..., 0., 0., 0.], shape=(2147483648,))

This branch

import numpy as np, mkl_umath
n = 2**31
data = np.ones(n)
mkl_umath.absolute(data)
# array([1., 1., 1., ..., 1., 1., 1.], shape=(2147483648,))

[ndgrigorian]

In general I don't see a problem with this change, but changing a dependency is a pretty big tweak.

@ekomarova @AndresGuzman-Ballen is there any way Intel NumPy tests can be run with this branch to make sure it doesn't introduce any major failures?

[AndresGuzman-Ballen]

@ndgrigorian I suppose I could point mkl_umath's commit in numpy-2.2.5 branch to this branch, but I'd prefer doing that after I can get all subpackages to build and pass tests in our CI

[ekomarova]

This PR does not break testing with Intel Numpy 2.2.5. All checks are green. I will also check v1.26.4

[ekomarova]

@ndgrigorian I see some problems in build/tests in numpy 1.26.4. Could you please take a look at it here https://github.com/intel-innersource/libraries.python.intel.condarecipes.numpy-recipe/pull/152?

[vtavana]

@ndgrigorian I see some problems in build/tests in numpy 1.26.4. Could you please take a look at it here intel-innersource/libraries.python.intel.condarecipes.numpy-recipe#152?

@ekomarova To fix build failure you need to pin setuptools >= 77

[vtavana]

Linux test failure is because of tolerance, I think it should be patched to fix.

FAILED core/tests/test_umath.py::TestPower::test_power_float - AssertionError:
Arrays are not almost equal to 7 decimals
unary offset=(0, 0), size=8, dtype=<class 'numpy.float32'>, out of place
Mismatched elements: 1 / 8 (12.5%)
Max absolute difference: 2.3841858e-07
Max relative difference: 9.7333974e-08
x: array([0.       , 0.9999999, 1.4142134, 1.7320508, 1.9999999, 2.236068 ,
2.4494896, 2.6457512], dtype=float32)
y: array([0.       , 1.       , 1.4142135, 1.7320508, 2.       , 2.236068 ,
2.4494898, 2.6457512], dtype=float32)

FAILED core/tests/test_umath.py::TestAVXUfuncs::test_avx_based_ufunc - AssertionError:
Arrays are not equal
Mismatched elements: 1 / 2 (50%)
Max absolute difference: 4.7683716e-07
Max relative difference: 6.150229e-08
x: array([2.410054, 7.75316 ], dtype=float32)
y: array([2.410054, 7.75316 ], dtype=float32)

[AndresGuzman-Ballen]

Rather than tweak the precision, what if the numpy PR just include this? https://github.com/intel-innersource/libraries.python.intel.condarecipes.numpy-recipe/blob/2.2.5/patches/add_precision_flags.patch

[AndresGuzman-Ballen]

@ekomarova in case you're curious how I came to that conclusion, this is some context: #73

[vtavana]

Windows failure are not related to this PR, they are related to #73
Does numpy-1.26.4 works with main branch of mkl_umath? or we had not tested that recently?

[AndresGuzman-Ballen]

I think numpy 1.26.4 wasn't tested recently with recent mkl_umath changes. I'll push some tweaks so that way the numpy 1.26.4 PR works.

[AndresGuzman-Ballen]

@vtavana how strange...I saw a Windows failure for the ldexp function here when the changes from here seem to be included. Is the ilp64 PR introducing this issue again?

[vtavana]

@vtavana how strange...I saw a Windows failure for the ldexp function here when the changes from here seem to be included. Is the ilp64 PR introducing this issue again?

I believe there was a change in #73 that is not needed and should be reverted. I created a new PR to revert it #77

[vtavana]

numpy recipes are tested with this branch and everything works fine.
numpy-1.26.4: https://github.com/intel-innersource/libraries.python.intel.condarecipes.numpy-recipe/pull/153
numpy-2.2.5: https://github.com/intel-innersource/libraries.python.intel.condarecipes.numpy-recipe/pull/154

[ndgrigorian]

no further concerns from my side, LGTM

[vtavana]

@napetrov Any other suggestion/concern on this PR before merging it?

[napetrov]

@napetrov Any other suggestion/concern on this PR before merging it?

thanks, look good!

[jankrecke]

Thank you for the fix! 🙏

Can you comment on when this will be released? 👀

[ekomarova]

@jankrecke Hi! This should be released soon along with the 2025.2 release, I can't give exact dates, but most likely within a couple of weeks