chongruo opened a new issue #16101: [Bug, Feature Request] mx.nd.where() URL: https://github.com/apache/incubator-mxnet/issues/16101 ## Description ### Bug [mx.nd.where()](https://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html?highlight=where#mxnet.ndarray.where) shows an incorrect behavior when one of the inputs is an NDArray with zero size. Here is a reproducible example ```python cond = mx.nd.array([0]) # cond.shape: (1,) x = mx.nd.array([[10,10]]) # x.shape: (1, 2) y = mx.nd.array(4) # y.shape: () print( mx.nd.where(cond, x, y) ) # output: [[4.0000e+00 3.0773e-41]] ``` The output is weird and it seems that the NDArray with zero size has not been checked. We expect that it would raise an error showing the shape of x and y must be the same, according to [docs of mx.nd.where()](https://beta.mxnet.io/api/ndarray/_autogen/mxnet.ndarray.where.html?highlight=where#mxnet.ndarray.where). Broadcast is not supported in the latest version but where() still has an output. It is also a little dangerous as it outputs incorrect answers rather than error messages, when users forget to type [] for ``mx.nd.array([4])``. <br> ### Feature Request #### 1. Broadcast Currently, there are two limitations for mx.nd.where() - x and y must have the same shape - If condition does not have the same shape as x, it must be a 1D array whose size is the same as x’s first dimension size Similar to [np.where()](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.where.html), it would be great if mx.nd.where() supports broadcast to make sure (cond, x, y) have the same shape, even if they are in different shapes as input. <br> #### 2. Scalar inputs (cond, x and y) In some situations, we want to give a constant value for True/False. It would be user-friendly if programmers only need to type ```mx.nd.where(cond, x, 0)``` instead of ```mx.nd.where(cond, x, mx.nd.array([0]))``` <br> <br> <br> <br> <br> --- ## Environment info (Required) ``` ----------Python Info---------- Version : 3.6.9 Compiler : GCC 7.3.0 Build : ('default', 'Jul 30 2019 19:07:31') Arch : ('64bit', '') ------------Pip Info----------- Version : 19.2.2 Directory : /home/ubuntu/anaconda3/envs/new/lib/python3.6/site-packages/pip ----------MXNet Info----------- Version : 1.6.0 Directory : /home/ubuntu/new/my-mxnet/python/mxnet Commit hash file "/home/ubuntu/new/my-mxnet/python/mxnet/COMMIT_HASH" not found. Not installed from pre-built package or built from source. Library : ['/home/ubuntu/new/my-mxnet/python/mxnet/../../build/libmxnet.so'] Build features: No runtime build feature info available ----------System Info---------- Platform : Linux-4.4.0-1092-aws-x86_64-with-debian-stretch-sid system : Linux node : ip-172-31-14-150 release : 4.4.0-1092-aws version : #103-Ubuntu SMP Tue Aug 27 10:21:48 UTC 2019 ----------Hardware Info---------- machine : x86_64 processor : x86_64 Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz Stepping: 4 CPU MHz: 2499.998 BogoMIPS: 4999.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 1024K L3 cache: 33792K NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f rdseed adx smap clflushopt clwb avx512cd xsaveopt xsavec xgetbv1 ida arat pku ----------Network Test---------- Setting timeout: 10 Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0014 sec, LOAD: 0.4787 sec. Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.1707 sec, LOAD: 0.2402 sec. Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.0228 sec, LOAD: 0.3108 sec. Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0107 sec, LOAD: 0.1101 sec. Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0013 sec, LOAD: 0.3356 sec. Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0135 sec, LOAD: 0.0633 sec. ----------Environment---------- ``` ## Build info (Required if built from source) Compiler (gcc/clang/mingw/visual studio): gcc MXNet commit hash: 03f12f0fe706d35c93a2cf721b6101bcbffeb07d Build config: plain CMakeList.txt with USE_NCCL=1
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