leezu opened a new issue #11271: nd.topk regression with nan values URL: https://github.com/apache/incubator-mxnet/issues/11271 mxnet master contains a regression in the nd.topk operator compared to mxnet v1.2. Likely https://github.com/apache/incubator-mxnet/pull/10997 is at fault (but I have not performed an bisection to proof that). Consider the following: Behavior in mxnet master: ``` In [1]: a = [np.nan] * 4 + list(range(2500)) ...: a = mx.nd.array(a) ...: print(a) ...: for k in range(3,10): ...: print(mx.nd.topk(a, k=k)) ...: ...: [ nan nan nan ... 2497. 2498. 2499.] <NDArray 2504 @cpu(0)> [2. 0. 1.] <NDArray 3 @cpu(0)> [2. 0. 1. 3.] <NDArray 4 @cpu(0)> [2. 4. 0. 1. 3.] <NDArray 5 @cpu(0)> [5. 4. 2. 0. 1. 3.] <NDArray 6 @cpu(0)> [2. 6. 5. 4. 0. 1. 3.] <NDArray 7 @cpu(0)> [ 2. 1. 10. 9. 8. 6. 0. 3.] <NDArray 8 @cpu(0)> [ 2. 1. 11. 10. 9. 6. 8. 0. 3.] <NDArray 9 @cpu(0)> ``` Behavior in mxnet v1.2: ``` In [1]: In [1]: a = [np.nan] * 4 + list(range(2500)) ...: ...: a = mx.nd.array(a) ...: ...: print(a) ...: ...: for k in range(3,10): ...: ...: print(mx.nd.topk(a, k=k)) ...: [ nan nan nan ... 2497. 2498. 2499.] <NDArray 2504 @cpu(0)> [0. 1. 2.] <NDArray 3 @cpu(0)> [0. 1. 2. 3.] <NDArray 4 @cpu(0)> [0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03] <NDArray 5 @cpu(0)> [0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03] <NDArray 6 @cpu(0)> [0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03] <NDArray 7 @cpu(0)> [0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03 2.500e+03] <NDArray 8 @cpu(0)> [0.000e+00 1.000e+00 2.000e+00 3.000e+00 2.503e+03 2.502e+03 2.501e+03 2.500e+03 2.499e+03] <NDArray 9 @cpu(0)> ``` Notice how the result is correct with mxnet version 1.2 but wrong on the master version. As a sidenote, even with mxnet version 1.2 the behavior of nd.topk is inconsistent with nd.max. The latter ignores 'nan' whereas the former considers it to be the maximum element. ``` n [9]: mx.nd.argmax(mx.nd.array([np.nan, 1]), axis=0) Out[9]: [1.] <NDArray 1 @cpu(0)> In [10]: mx.nd.topk(mx.nd.array([np.nan, 1]), axis=0) Out[10]: [0.] <NDArray 1 @cpu(0)> ``` Arguably there was never a guarantee that nd.topk works under the presence of nan values, but legacy code may rely on this behavior and can break in unexpected ways. @asmushetzel
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