[GitHub] [singa] dcslin commented on issue #580: [WIP] An experiment for buffering operations

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

dcslin commented on issue #580: [WIP] An experiment for buffering operations
URL: https://github.com/apache/singa/pull/580#issuecomment-579575835
 
 
   Hi, this is regarding the `SumAll` in this PR. Referring to numpy, when no axis is given, `numpy.sum` returns a scalar value. Referring to torch, `torch.sum()` always return tensor. This difference is because there is no tensor in numpy.
   
   I suppose the ideal way for singa would be deprecating `float Sum()` and keeping `Tensor Sum()`.
   
   However this breaks the current code.
   
   The workaround might be extending `Tensor Sum(Tensor in, int axis)` to support arbitrary axes like `Tensor Sum(Tensor in, int[] axis)`. Then when we want `Tensor SumAll()`, we could call `Tensor Sum(in, in.shape())`, which effectively sum all (over all axes) and return a Tensor, while not breaking current code.

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services