Dear Community, A while ago, we sent out an RFC <https://github.com/apache/incubator-mxnet/issues/14253> discussing the initiative introducing NumPy compatibility into MXNet. As the first outcome of this initiative, we submitted the PR <https://github.com/apache/incubator-mxnet/pull/14661> providing the infrastructure of supporting zero-dim (scalar) and zero-size tensors, which have been long-missing in MXNet.
In our implementation, we have put the best efforts of keeping the promise of backward compatibility in all the language bindings. Nevertheless, we still would like to call out the changes explicitly that may impact your existing codebases developed on top of MXNet by calling C-APIs directly or implementing operators in your own repos. 1. In you application, if you called any one of the following shape-related C-APIs, you will need to change the data type of shape's ndim and dim_size from *unsigned int* to signed *int*, because we have to use -1 to represent unknown shape information, and reserve 0 for scalar and zero-size tensors. One example of such changes can be seen in the cpp-package <https://github.com/apache/incubator-mxnet/pull/14661/files#diff-c0e77771fcfe1619faa4ff5f59d94e8bR183> calling MXSymbolInferShape. - MXSymbolInfershape - MXSymbolInfershapePartial - MXExecutorSimpleBind - MXExecutorReshape - MXNDArrayGetShape - MXNDArrayCreaetFromSharedMem 2. If you have implemented operators in your own codebases, you will probably need to change every operator's shape inference function to use the following util functions to check whether shape information is known, instead of checking against 0 directly. One example of such changes can be seen in the shape inference function <https://github.com/apache/incubator-mxnet/pull/14661/files#diff-afa640c4653c59f00f43a84455f91ef9R35> of concat operator. - shape_is_known (include/mxnet/tuple.h) - ndim_is_known (include/mxnet/tuple.h) - dim_size_is_known (include/mxnet/tuple.h) If you are interested in knowing the value of scalar tensors, and hence understanding our motivation further, this thread <https://discuss.mxnet.io/t/rank-0-arrays-in-mxnet-aka-pi-is-wrong/108> of discussion provides very good insights from the view of data science. It was actually related to an opportunity for MXNet becoming the backend of PyMC <https://en.wikipedia.org/wiki/PyMC3>, but somehow it didn't go through due to missing several key features <https://discuss.mxnet.io/t/moving-pymc3-from-theano-to-mxnet/86>, and scalar tensors is one of them. Please leave comments in the PR <https://github.com/apache/incubator-mxnet/pull/14661> if you have any concerns or suggestions of our work. Thank you very much for your time and consideration. Best, Jun *References* [1] RFC of NumPy compatibility: https://github.com/apache/incubator-mxnet/issues/14253 [2] Pull request of supporting scalar and zero-size tensors: https://github.com/apache/incubator-mxnet/pull/14661 [3] The value of scalar tensors from the view of data science: https://discuss.mxnet.io/t/rank-0-arrays-in-mxnet-aka-pi-is-wrong/108 [4] Previous discussion for MXNet becoming the backend of PyMC: https://discuss.mxnet.io/t/moving-pymc3-from-theano-to-mxnet/86
