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Posted to issues@mxnet.apache.org by GitBox <gi...@apache.org> on 2021/02/20 00:37:11 UTC
[GitHub] [incubator-mxnet] waytrue17 opened a new issue #19938: [Numpy] np.percentile fails unittest
waytrue17 opened a new issue #19938:
URL: https://github.com/apache/incubator-mxnet/issues/19938
## Description
`test_np_percentile` fails assertion check after upgrading to numpy 1.20.0. The assertion error between `mx.np.percentile` and `numpy.percentile` indicates there might be computational changes in `numpy.percentile`.
## Occurrences
https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fwindows-cpu/detail/PR-19844/2/pipeline/
and
https://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fwindows-gpu/detail/PR-19813/1/pipeline/
## What have you tried to solve it?
1. Currently numpy falls back to < 1.20.0 to mitigate the issue in CI.
2. The error log for the above two occurences:
```
> raise AssertionError(msg)
E AssertionError:
E Items are not equal:
E Error 1.229105 exceeds tolerance rtol=3.000000e-02, atol=3.000000e-04 (mismatch 12.500000%).
E Location of maximum error: (0, 1, 2), a=-0.04492188, b=-0.04296875
*** Maximum errors for vector of size 24: rtol=0.03, atol=0.0003
1: Error 1.229105 Location of error: (0, 1, 2), a=-0.04492188, b=-0.04296875
2: Error 1.229105 Location of error: (1, 1, 2), a=-0.04492188, b=-0.04296875
3: Error 1.229105 Location of error: (2, 1, 2), a=-0.04492188, b=-0.04296875
```
and
```
> raise AssertionError(msg)
E AssertionError:
E Items are not equal:
E Error 1.743375 exceeds tolerance rtol=3.000000e-02, atol=3.000000e-04 (mismatch 12.500000%).
E Location of maximum error: (0, 1, 0, 0), a=-0.02539062, b=-0.02734375
*** Maximum errors for vector of size 24: rtol=0.03, atol=0.0003
1: Error 1.743375 Location of error: (0, 1, 0, 0), a=-0.02539062, b=-0.02734375
2: Error 1.743375 Location of error: (1, 1, 0, 0), a=-0.02539062, b=-0.02734375
3: Error 1.743375 Location of error: (2, 1, 0, 0), a=-0.02539062, b=-0.02734375
```
Although the output shapes and values are different between the two cases, the maximum mismatch is consistent - 12.5%.
3. Tried and failed to find what numpy changed `percentile` caused the issue
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