I am wondering if it is expected that if I call numpy.sum on a PyTorch tensor I get an error. As an aside, it does work on Pandas DataFrames. data = [[1.1, 2.2], [1.1, 2.2], [1.1, 2.2]] a = np.array(data) d = pd.DataFrame(data) t = torch.tensor(data) np.sum(a, axis=1) # works obviously np.sum(d, axis=1) # works np.sum(t, axis=1) # fails If you dig a bit inside, you'll see that's quite simply because numpy.sum tries to call torch.Tensor.sum with keyword argumets which do not belong to it, like in the following (https://github.com/numpy/numpy/blob/main/numpy/_core/fromnumeric.py around line 80) return reduction(axis=axis, out=out, **passkwargs) which inevitably results in an error. This seems quite related: https://github.com/numpy/numpy/issues/28024 Can this be considered a bug in numpy? Why can't we just pass the non null arguments to the sum method of the object we are passing to numpy.sum? I wanted to post an issue on GitHub/numpy but I am not sure I could define this as a bug, and if i click "question" there, then I am told to post here... Continue reading...
