[GitHub] [incubator-mxnet] DickJC123 edited a comment on issue #14954: broadcast_to() behavior change after new 0D and uninitialized shape support added?

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DickJC123 edited a comment on issue #14954:  broadcast_to() behavior change after new 0D and uninitialized shape support added?
URL: https://github.com/apache/incubator-mxnet/issues/14954#issuecomment-492867737
 
 
   The 5 tests that fail do so because the test is trying to save and restore a 2D NDArray with a 0 size for one of the dimensions.  I have not played with numpy compatibility modes for the tests, but in each case the last 3 of the following code lines is causing problems by converting for example (0,1) -> (-1,1):
   https://github.com/apache/incubator-mxnet/blob/c5265fbf635965b5124df2d7dcea1b57c6508b73/src/ndarray/ndarray.cc#L1713-L1718
   
   So you might say 'put the 5 tests in numpy_compatibility_mode.'  However, I started thinking about the notion of TShape serialization.  Ideally, the representation (and what it means) should not depend on the mode of the writer.  Why not always save TShapes in the expanded 'numpy_compatibility mode' representation (unknown = -1, known-0 = 0, known-non-zero = N), regardless of the mode of the writer?
   
   The code in ndarray.cc might then be:
   ```
   void NDArray::Save(dmlc::Stream *strm) const {
   ...
     // save shape
     auto stored_shape = shape_;
     if (!Imperative::Get()->is_np_comp()) {
       common::ConvertToNumpyShape(&stored_shape);
     }
     stored_shape.Save(strm);
   ...
   }
   
   bool NDArray::Load(dmlc::Stream *strm) {
   ...
     // load shape
     mxnet::TShape shape;
     if (!shape.Load(strm)) return false;
     if (!Imperative::Get()->is_np_comp()) {
       common::ConvertToLegacyShape(&shape);
     }
   ...
   } 

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