ValueError: Internal error occurred: b'Intel MKL DFTI ERROR: Inconsistent configuration parameters' for certain shapes in _numpy_fft.rfft

[JesseLivezey]

For certain large 1d rffts, I get the following error. Tested this in anaconda python 3.7 and 3.8 on linux. If the long/fft axis is 1 instead of 0, I don't get this error.

ValueError: Internal error occurred: b'Intel MKL DFTI ERROR: Inconsistent configuration parameters'

This example generates the error below

import numpy as np
from mkl_fft._numpy_fft import rfft


X = np.random.randn(18134053, 1)
rfft(X, axis=0) # ok
print(18134052, 1)

X = np.random.randn(18134053, 16)
rfft(X, axis=0) # generates error
print(18134052, 16)

18134052 1
Traceback (most recent call last):
  File "mkl_test.py", line 10, in <module>
    rfft(X, axis=0)
  File "/home/jesse/anaconda3/envs/mkl_test/lib/python3.7/site-packages/mkl_fft/_numpy_fft.py", line 414, in rfft
    (x,), {'n': n, 'axis': axis})
  File "/home/jesse/anaconda3/envs/mkl_test/lib/python3.7/site-packages/mkl_fft/_numpy_fft.py", line 103, in trycall
    raise ve
  File "/home/jesse/anaconda3/envs/mkl_test/lib/python3.7/site-packages/mkl_fft/_numpy_fft.py", line 98, in trycall
    res = func(*args, **kwrds)
  File "mkl_fft/_pydfti.pyx", line 792, in mkl_fft._pydfti.rfft_numpy
  File "mkl_fft/_pydfti.pyx", line 701, in mkl_fft._pydfti._rc_fft1d_impl
ValueError: Internal error occurred: b'Intel MKL DFTI ERROR: Inconsistent configuration parameters'

This is in a basic conda environment with only numpy added.

$ conda list
# packages in environment at /home/jesse/anaconda3/envs/mkl_test:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
blas                      1.0                         mkl  
ca-certificates           2021.1.19            h06a4308_0  
certifi                   2020.12.5        py37h06a4308_0  
intel-openmp              2020.2                      254  
ld_impl_linux-64          2.33.1               h53a641e_7  
libedit                   3.1.20191231         h14c3975_1  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 9.1.0                hdf63c60_0  
libstdcxx-ng              9.1.0                hdf63c60_0  
mkl                       2020.2                      256  
mkl-service               2.3.0            py37he8ac12f_0  
mkl_fft                   1.3.0            py37h54f3939_0  
mkl_random                1.1.1            py37h0573a6f_0  
ncurses                   6.2                  he6710b0_1  
numpy                     1.19.2           py37h54aff64_0  
numpy-base                1.19.2           py37hfa32c7d_0  
openssl                   1.1.1j               h27cfd23_0  
pip                       21.0.1           py37h06a4308_0  
python                    3.7.10               hdb3f193_0  
readline                  8.1                  h27cfd23_0  
setuptools                52.0.0           py37h06a4308_0  
six                       1.15.0           py37h06a4308_0  
sqlite                    3.33.0               h62c20be_0  
tk                        8.6.10               hbc83047_0  
wheel                     0.36.2             pyhd3eb1b0_0  
xz                        5.2.5                h7b6447c_0  
zlib                      1.2.11               h7b6447c_3  

and

mkl.get_version()
# {'MajorVersion': 2020, 'MinorVersion': 0, 'UpdateVersion': 2, 'ProductStatus': b'Product',
# 'Build': b'20200624', 'Processor': b'Intel(R) Advanced Vector Extensions 2 (Intel(R) AVX2) enabled processors',
# 'Platform': b'Intel(R) 64 architecture'}

Possibly similar to #24.

cc @hlillemark

[JesseLivezey]

No errors with fft along axis 1

import numpy as np
from mkl_fft._numpy_fft import rfft


X = np.random.randn(1, 18134053)
rfft(X, axis=1)
print(1, 18134052)

X = np.random.randn(16, 18134053)
rfft(X, axis=1)
print(16, 18134052)

[JesseLivezey]

The same error happens for irfft, fft, ifft in _numpy_fft with this shape.

[oleksandr-pavlyk]

Thank you for the report @JesseLivezey.

This must be a recently introduced change.

In [1]: import numpy as np

In [2]: from mkl_fft._numpy_fft import rfft

In [3]: import mkl_fft

In [4]: X = np.random.randn(1, 18134053)

In [5]: Y = mkl_fft.rfft_numpy(X)

In [6]: Y.shape
Out[6]: (1, 9067027)

In [7]: Y = mkl_fft.rfft_numpy(X, axis=0)

In [8]: Y.shape
Out[8]: (1, 18134053)

In [9]: mkl_fft.__version__
Out[9]: '1.2.0'

Also, just having created an environment from conda-forge:

conda create -n cf_mkl_fft_issue_57 python ipython mkl numpy mkl_fft mkl-service

I can not reproduce the issue:

Python 3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 05:02:46)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.21.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import numpy as np, mkl_fft, mkl

In [2]: X = np.random.randn(1, 18134053)

In [3]: Y = mkl_fft.rfft_numpy(X)

In [4]: Y0 = mkl_fft.rfft_numpy(X, axis=0)

In [5]: mkl.get_version_string()
Out[5]: 'Intel(R) Math Kernel Library Version 2020.0.4 Product Build 20200917 for Intel(R) 64 architecture applications'

I also can not reproduce the issue using the latest oneMKL version 2020.1, and the development version 2020.2 to be released soon.

[JesseLivezey]

@oleksandr-pavlyk I only get errors when the first dim is the long/fft dimension and the second is >1

X = np.random.randn(18134053, 2) # for me, anything >=2 for the second dim causes an error

In your tests, it looks like the second dimension is long, but you're taking the fft over the first.

[oleksandr-pavlyk]

Thank you @JesseLivezey I have reproduced the issue. It appears like MKL raises this exception when the dimension is greater than 2^24.

In [31]: Y0 = mkl_fft.rfft_numpy(X[slice(0, 2**24, 1), :], axis=0)
MKL_VERBOSE FFT(drfo16777216:2:2*2:1:1,bScale:5.96046e-08,tLim:6,unaligned_input,unaligned_output,desc:0x56293beeb700) 221.30ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:6

In [32]: Y0 = mkl_fft.rfft_numpy(X[slice(0, 2**24 + 1, 1), :], axis=0)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-32-c91c2fa3cd09> in <module>()
----> 1 Y0 = mkl_fft.rfft_numpy(X[slice(0, 2**24 + 1, 1), :], axis=0)

mkl_fft/_pydfti.pyx in mkl_fft._pydfti.rfft_numpy()

mkl_fft/_pydfti.pyx in mkl_fft._pydfti._rc_fft1d_impl()

ValueError: Internal error occurred: b'Intel MKL DFTI ERROR: Inconsistent configuration parameters'

[oleksandr-pavlyk]

I reproduced the internal error using C programming language and reported the issue to MKL team.

[ahnitz]

@oleksandr-pavlyk Apologies for reopening this old issue, but was there a resolution to this problem? We think we might be seeing a very similar issue in our code which directly calls the mkl runtime, and it would be good to rule out if it's an error on our end. Is there a link to the issue reporting (if public)?

[oleksandr-pavlyk]

@ahnitz Once the issue was originally reported, I changed mkl_fft to do a better job at reporting the error that MKL itself is flagging. I also notified relevant MKL team about the problem.

I have just checked that nightly builds of MKL still flag the input generated from input In[32] in my excerpt from Mar 8 comment as problematic.

I would therefore think the problem has not found its resolution as yet.

[GeJulia]

I encounter a similar issue when using ifft2d. Is there any progress on this ticket?

[oleksandr-pavlyk]

@JesseLivezey @GeJulia @ahnitz I am happy to report that the error has been addressed in MKL 2024.2:

In [1]: import numpy as np
   ...: from mkl_fft._numpy_fft import rfft
   ...:
   ...:
   ...: X = np.random.randn(18134053, 1)
   ...: rfft(X, axis=0) # ok
   ...: print(18134052, 1)
   ...:
   ...: X = np.random.randn(18134053, 16)
   ...: rfft(X, axis=0) # generates error
   ...: print(18134052, 16)
18134052 1
18134052 16

In [2]: import mkl

In [3]: mkl.get_version_string()
Out[3]: 'Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications'

In [4]:

[JesseLivezey]

@oleksandr-pavlyk thanks for following up!