[GitHub] stephenrawls commented on issue #14264: nd.reshape truncate values

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

stephenrawls commented on issue #14264: nd.reshape truncate values
URL: https://github.com/apache/incubator-mxnet/issues/14264#issuecomment-468970765
 
 
   @NonvolatileMemory Do you mean you think it's a bug because users should expect mx.nd.Reshape() to have identical behavior to NumPy's Reshape() operator?
   
   If so, as mentioned, I don't have an opinion at all, and as mentioned I am happy for this feature to be turned on optionally, or to be renamed to something else entirely, e.g. mx.nd.SomeNewOperator().
   
   If you mean it's a bug for users to want this feature then I disagree. For example, consider the world of NLP where you have dynamically shaped inputs. Maybe one of my inputs is only 5 words long, and another is 10 words long. I might have a routine I want to run at inference time that needs to use an NDArray with one of its dimensions sized to fit however many words are in my utterance. I would like to (1) not have to do dynamic memory allocations during inference; and (2) to not have to allocate extra space for each shape I want to support. To do this you can image it is convenient to be able to do this:
   
   ```
   def init():
     some_array = mx.nd.array( max_shape )
   
   def infer():
     ...
     some_array_local = some_array.Reshape( current_shape )
     ...
   ```
   
   Just to reiterate, it doesn't matter to me that Reshape() retains this behavior, just that MxNet has some way of spelling this behavior.

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