torch.hash_tensor

torch.hash_tensor(input, *, mode=0) → Tensor

Returns a hash of all elements in the input tensor.

Currently only mode=0 (reduction via xor) is supported. The output will always be of type torch.uint64. The elements of input are upcasted to their 64 bit float / integer equivalent and bitcasted to torch.uint64 before reduction via xor.

Parameters

input (Tensor) – the input tensor.

Keyword Arguments

mode (int) – The hash to use. Default: 0 (xor_reduction)

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 1.1918, -1.1813,  0.3373]])
>>> torch.hash_tensor(a)
tensor(13822780554648485888, dtype=torch.uint64)

torch.hash_tensor(input, dim, *, keepdim=False, mode=0) → Tensor

Returns the hash of each row of the input tensor in the given dimension dim given by mode. If dim is a list of dimensions, reduce over all of them.

If keepdim is True, the output tensor is of the same size as input except in the dimension(s) dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 (or len(dim)) fewer dimension(s).

Parameters

• input (Tensor) – the input tensor.

• dim (int or tuple of ints, optional) – the dimension or dimensions to reduce. If None, all dimensions are reduced.

• keepdim (bool, optional) – whether the output tensor has dim retained or not. Default: False.

Keyword Arguments

mode (int) – The hash to use. Default: 0 (xor_reduction)

Example:

>>> a = torch.randn(2, 4)
>>> a
tensor([[ 0.1317, -0.5554, -1.4724, -1.1391],
        [ 0.0778, -0.6070,  0.6375,  0.1798]])
>>> torch.hash_tensor(a, 1)
tensor([9233691267014066176, 9255993250844508160], dtype=torch.uint64)