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Subject [GitHub] [singa] dcslin commented on issue #580: [WIP] An experiment for buffering operations
Date Wed, 29 Jan 2020 03:29:18 GMT
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.

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