PyTorch tensor to fortran-style ordering

I have already asked a similar question, but I’ll try to reformulate it.

My question now is how to reorder PyTorch tensor data in Fortran-style order like np.reshape(order=‘F’) in numpy?

I need column-major ordering to use CUDA functions from cusparse library.

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How about this?

t = torch.Tensor(2,3)
f = t.numpy().reshape(2,3,order='F')

First of all, this function is not differentiable.
Secondly, it needs data to be transfered from CPU to GPU and back. This operation is very slow.

It’s a few more lines in PyTorch, but this will work if your input is C-style contiguous:

t = torch.randn(3, 5)
t = t.t().contiguous().t()
print(t.shape)  # torch.Size([3, 5])
print(t.stride())  # (1, 3)
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@spaceinvader You are right, the numpy solution will be slow and non-differentiable.

@colesbury How about reversing the stride manually to perform less operations like this:

t = torch.randn(3, 5)
t.set_(, t.storage_offset(), t.size(), tuple(reversed(t.stride())))

This should have the same effect as t = t.t().contiguous().t() but is potentially faster.

How about this?
then vp2 is the F order version of vp