[GitHub] [incubator-mxnet] szha commented on issue #20898: mxnet.numpy and numpy differ regarding binary op result type with broadcast 0-dim array input

[GitBox <gi...@apache.org>]

szha commented on issue #20898:
URL: https://github.com/apache/incubator-mxnet/issues/20898#issuecomment-1046412771


   In general we want to conform to the array API standard. While fp16 is not in the array standard yet, I tried the same with ints and the same discrepancy exists.
   
   ```
   In [17]: onp.array(-2, dtype=onp.int64) + onp.array([2, 0], dtype=onp.int16)
   Out[17]: array([ 0, -2], dtype=int16)
   
   In [18]: onp.array([-2], dtype=onp.int64) + onp.array([2, 0], dtype=onp.int16)
   Out[18]: array([ 0, -2])
   
   In [19]: np.array(-2, dtype=onp.int64) + np.array([2, 0], dtype=onp.int16)
   Out[19]: array([ 0, -2], dtype=int64)
   
   In [20]: np.array([-2], dtype=onp.int64) + np.array([2, 0], dtype=onp.int16)
   Out[20]: array([ 0, -2], dtype=int64)
   ```
   
   According to the [type promotion rules](https://data-apis.org/array-api/latest/API_specification/type_promotion.html) in array API standard, mxnet's current behavior is desired.
   
   > Type promotion rules must apply when determining the common result type for two array operands during an arithmetic operation, regardless of array dimension. Accordingly, zero-dimensional arrays must be subject to the same type promotion rules as dimensional arrays.
   
   @rgommers do you expect this to become a problem for API standard adoption on the numpy side?


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