sum

Documentation

treetensor.torch.sum(input, *args, reduce=None, **kwargs)

In treetensor, you can get the sum result of a whole tree with this function.

Example:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.sum(torch.tensor([1.0, 2.0, 1.5]))  # the same as torch.sum
tensor(4.5000)

>>> ttorch.sum(ttorch.tensor({
...     'a': [1.0, 2.0, 1.5],
...     'b': {'x': [[1.8, 0.9], [1.3, 2.5]]},
... }))
tensor(11.)

>>> ttorch.sum(ttorch.tensor({
...     'a': [1.0, 2.0, 1.5],
...     'b': {'x': [[1.8, 0.9], [1.3, 2.5]]},
... }), reduce=False)
<Tensor 0x7fd45b534898>
├── a --> tensor(4.5000)
└── b --> <Tensor 0x7fd45b5344e0>
    └── x --> tensor(6.5000)

>>> ttorch.sum(ttorch.tensor({
...     'a': [1.0, 2.0, 1.5],
...     'b': {'x': [[1.8, 0.9], [1.3, 2.5]]},
... }), dim=0)
<Tensor 0x7f3640703128>
├── a --> tensor(4.5000)
└── b --> <Tensor 0x7f3640703080>
    └── x --> tensor([3.1000, 3.4000])

Torch Version Related

This documentation is based on torch.sum in torch v2.4.1+cu121. Its arguments’ arrangements depend on the version of pytorch you installed.

If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.

python -c 'import torch;print(torch.__version__)'

The arguments and keyword arguments supported in torch v2.4.1+cu121 is listed below.

Description From Torch v2.4.1+cu121

torch.sum(input, *, dtype=None) → Tensor

Returns the sum of all elements in the input tensor.

Args:

input (Tensor): the input tensor.

Keyword args:

dtype (torch.dtype, optional): the desired data type of returned tensor.

If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.1133, -0.9567,  0.2958]])
>>> torch.sum(a)
tensor(-0.5475)

torch.sum(input, dim, keepdim=False, *, dtype=None) → Tensor

Returns the sum of each row of the input tensor in the given dimension dim. 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).

Args:

input (Tensor): the input tensor. dim (int or tuple of ints): the dimension or dimensions to reduce. keepdim (bool): whether the output tensor has dim retained or not.

Keyword args:

dtype (torch.dtype, optional): the desired data type of returned tensor.

If specified, the input tensor is casted to dtype before the operation is performed. This is useful for preventing data type overflows. Default: None.

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.0569, -0.2475,  0.0737, -0.3429],
        [-0.2993,  0.9138,  0.9337, -1.6864],
        [ 0.1132,  0.7892, -0.1003,  0.5688],
        [ 0.3637, -0.9906, -0.4752, -1.5197]])
>>> torch.sum(a, 1)
tensor([-0.4598, -0.1381,  1.3708, -2.6217])
>>> b = torch.arange(4 * 5 * 6).view(4, 5, 6)
>>> torch.sum(b, (2, 1))
tensor([  435.,  1335.,  2235.,  3135.])