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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