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]])
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])
1 Like

Thank you for your reply!

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:

The answer was:

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

# convert to column-major order
t.set_(, 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_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…

1 Like