---------------------------------------------------------------------------
NoSectionError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/aesara/configparser.py](https://localhost:8080/#) in fetch_val_for_key(self, key, delete_key)
236 try:
--> 237 return self._aesara_cfg.get(section, option)
238 except InterpolationError:
12 frames
NoSectionError: No section: 'blas'
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
KeyError: 'blas__ldflags'
During handling of the above exception, another exception occurred:
ModuleNotFoundError Traceback (most recent call last)
ModuleNotFoundError: No module named 'mkl'
During handling of the above exception, another exception occurred:
RuntimeError Traceback (most recent call last)
[/usr/local/lib/python3.7/dist-packages/aesara/link/c/cmodule.py](https://localhost:8080/#) in check_mkl_openmp()
2691 you set this flag and don't set the appropriate environment or make
2692 sure you have the right version you *will* get wrong results.
-> 2693 """
2694 )
2695
RuntimeError:
Could not import 'mkl'. If you are using conda, update the numpy
packages to the latest build otherwise, set MKL_THREADING_LAYER=GNU in
your environment for MKL 2018.
If you have MKL 2017 install and are not in a conda environment you
can set the Aesara flag blas__check_openmp to False. Be warned that if
you set this flag and don't set the appropriate environment or make
sure you have the right version you *will* get wrong results.
Some results that you can compare against. They were 10 executions
of gemm in float64 with matrices of shape 2000x2000 (M=N=K=2000).
All memory layout was in C order.
CPU tested: Xeon E5345(2.33Ghz, 8M L2 cache, 1333Mhz FSB),
Xeon E5430(2.66Ghz, 12M L2 cache, 1333Mhz FSB),
Xeon E5450(3Ghz, 12M L2 cache, 1333Mhz FSB),
Xeon X5560(2.8Ghz, 12M L2 cache, hyper-threads?)
Core 2 E8500, Core i7 930(2.8Ghz, hyper-threads enabled),
Core i7 950(3.07GHz, hyper-threads enabled)
Xeon X5550(2.67GHz, 8M l2 cache?, hyper-threads enabled)
Libraries tested:
* numpy with ATLAS from distribution (FC9) package (1 thread)
* manually compiled numpy and ATLAS with 2 threads
* goto 1.26 with 1, 2, 4 and 8 threads
* goto2 1.13 compiled with multiple threads enabled
Xeon Xeon Xeon Core2 i7 i7 Xeon Xeon
lib/nb threads E5345 E5430 E5450 E8500 930 950 X5560 X5550
numpy 1.3.0 blas 775.92s
numpy_FC9_atlas/1 39.2s 35.0s 30.7s 29.6s 21.5s 19.60s
goto/1 18.7s 16.1s 14.2s 13.7s 16.1s 14.67s
numpy_MAN_atlas/2 12.0s 11.6s 10.2s 9.2s 9.0s
goto/2 9.5s 8.1s 7.1s 7.3s 8.1s 7.4s
goto/4 4.9s 4.4s 3.7s - 4.1s 3.8s
goto/8 2.7s 2.4s 2.0s - 4.1s 3.8s
openblas/1 14.04s
openblas/2 7.16s
openblas/4 3.71s
openblas/8 3.70s
mkl 11.0.083/1 7.97s
mkl 10.2.2.025/1 13.7s
mkl 10.2.2.025/2 7.6s
mkl 10.2.2.025/4 4.0s
mkl 10.2.2.025/8 2.0s
goto2 1.13/1 14.37s
goto2 1.13/2 7.26s
goto2 1.13/4 3.70s
goto2 1.13/8 1.94s
goto2 1.13/16 3.16s
Test time in float32. There were 10 executions of gemm in
float32 with matrices of shape 5000x5000 (M=N=K=5000)
All memory layout was in C order.
cuda version 8.0 7.5 7.0
gpu
M40 0.45s 0.47s
k80 0.92s 0.96s
K6000/NOECC 0.71s 0.69s
P6000/NOECC 0.25s
Titan X (Pascal) 0.28s
GTX Titan X 0.45s 0.45s 0.47s
GTX Titan Black 0.66s 0.64s 0.64s
GTX 1080 0.35s
GTX 980 Ti 0.41s
GTX 970 0.66s
GTX 680 1.57s
GTX 750 Ti 2.01s 2.01s
GTX 750 2.46s 2.37s
GTX 660 2.32s 2.32s
GTX 580 2.42s
GTX 480 2.87s
TX1 7.6s (float32 storage and computation)
GT 610 33.5s
Some Aesara flags:
blas__ldflags= -L/usr/local/lib -lmkl_rt -lpthread -lm -lm
compiledir= /root/.aesara/compiledir_Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic-x86_64-3.7.13-64
floatX= float64
device= cpu
Some OS information:
sys.platform= linux
sys.version= 3.7.13 (default, Apr 24 2022, 01:04:09)
[GCC 7.5.0]
sys.prefix= /usr
Some environment variables:
MKL_NUM_THREADS= None
OMP_NUM_THREADS= None
GOTO_NUM_THREADS= None
Numpy config: (used when the Aesara flag "blas__ldflags" is empty)
blas_mkl_info:
NOT AVAILABLE
blis_info:
NOT AVAILABLE
openblas_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
runtime_library_dirs = ['/usr/local/lib']
blas_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
runtime_library_dirs = ['/usr/local/lib']
lapack_mkl_info:
NOT AVAILABLE
openblas_lapack_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
runtime_library_dirs = ['/usr/local/lib']
lapack_opt_info:
libraries = ['openblas', 'openblas']
library_dirs = ['/usr/local/lib']
language = c
define_macros = [('HAVE_CBLAS', None)]
runtime_library_dirs = ['/usr/local/lib']
Supported SIMD extensions in this NumPy install:
baseline = SSE,SSE2,SSE3
found = SSSE3,SSE41,POPCNT,SSE42,AVX,F16C,FMA3,AVX2
not found = AVX512F,AVX512CD,AVX512_KNL,AVX512_KNM,AVX512_SKX,AVX512_CLX,AVX512_CNL,AVX512_ICL
Numpy dot module: numpy
Numpy location: /usr/local/lib/python3.7/dist-packages/numpy/__init__.py
Numpy version: 1.21.6
We executed 10 calls to gemm with a and b matrices of shapes (5000, 5000) and (5000, 5000).
Total execution time: 74.73s on CPU (with direct Aesara binding to blas).
Try to run this script a few times. Experience shows that the first time is not as fast as following calls. The difference is not big, but consistent.
Here's an example google colab that installs aesara 2.7.7 and then tries to import: https://colab.research.google.com/drive/1bPq3mcIlcw6mJQE8Gf8bcEdVhJdJSUpB#scrollTo=36fiA6TJjLJC
Error:
Setting
%env MKL_THREADING_LAYER=GNUallows importing, but it's not clear why aesara can't figure that out itself, or what the problem is exactly.Output of check_blas.py:
CC @canyon289 @junpenglao @ColCarroll