# How to flatten a tensor in column-major order?

I would like to flatten a tensor into column-major order. What is the optimal way for achieving this in PyTorch? Row-major order seems to be the default in PyTorch’s flatten function, and I don’t think there is an order option like in Numpy’s flatten function. Thanks!

Use tranpose method to reshape your tensor then flatten i.e.

``````x = torch.tensor([[1,2,3],[4,5,6],[7,8,9]])
x.flatten()
tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
x.transpose(1, 0).flatten()
tensor([1, 4, 7, 2, 5, 8, 3, 6, 9])``````
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That works well for matrices (where the transpose can be written as simply `x.t().flatten()`)

However, I am dealing with ndarrays, so that a matrix transpose is not applicable.

I just found that the solution had already been posted on this forum, here:

``````t = torch.rand(3, 3, 3)

# convert to column-major order
t.set_(t.storage(), t.storage_offset(), t.size(), tuple(reversed(t.stride())))
t.flatten()  # 1D array in column-major order
``````

Note that if you just want a tensor’s 1D representation in column-major order, the above operation will change the ordering of the elements in tensor `t`. This function will pull out just the flattened array in column-major order:

``````def flatten_fortran_w_clone(t):
nt = t.clone()
nt = nt.set_(nt.storage(), nt.storage_offset(), nt.size(), tuple(reversed(nt.stride())))
return nt.flatten()
``````

But there might be a better way of doing it without having to create a copy…

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