all

Documentation

treetensor.torch.all(input, *args, reduce=None, **kwargs)[source]

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

Example:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.all(torch.tensor([True, True]))  # the same as torch.all
tensor(True)

>>> ttorch.all(ttorch.tensor({'a': [True, True], 'b': {'x': [True, True]}}))
tensor(True)

>>> ttorch.all(ttorch.tensor({'a': [True, True], 'b': {'x': [True, False]}}))
tensor(False)

>>> ttorch.all(ttorch.tensor({'a': [True, True], 'b': {'x': [True, False]}}), reduce=False)
<Tensor 0x7fcda55652b0>
├── a --> tensor(True)
└── b --> <Tensor 0x7fcda5565208>
    └── x --> tensor(False)

>>> ttorch.all(ttorch.tensor({'a': [True, True], 'b': {'x': [True, False]}}), dim=0)
<Tensor 0x7fcda5565780>
├── a --> tensor(True)
└── b --> <Tensor 0x7fcda55656d8>
    └── x --> tensor(False)

Torch Version Related

This documentation is based on torch.all in torch v1.10.0+cu102. 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.

1
python -c 'import torch;print(torch.__version__)'

The arguments and keyword arguments supported in torch v1.10.0+cu102 is listed below.

Description From Torch v1.10.0+cu102

torch.all(input) → Tensor

Tests if all elements in input evaluate to True.

Note

This function matches the behaviour of NumPy in returning output of dtype bool for all supported dtypes except uint8. For uint8 the dtype of output is uint8 itself.

Example:

>>> a = torch.rand(1, 2).bool()
>>> a
tensor([[False, True]], dtype=torch.bool)
>>> torch.all(a)
tensor(False, dtype=torch.bool)
>>> a = torch.arange(0, 3)
>>> a
tensor([0, 1, 2])
>>> torch.all(a)
tensor(False)
torch.all(input, dim, keepdim=False, *, out=None) → Tensor

For each row of input in the given dimension dim, returns True if all elements in the row evaluate to True and False otherwise.

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

Args:

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

Keyword args:

out (Tensor, optional): the output tensor.

Example:

>>> a = torch.rand(4, 2).bool()
>>> a
tensor([[True, True],
        [True, False],
        [True, True],
        [True, True]], dtype=torch.bool)
>>> torch.all(a, dim=1)
tensor([ True, False,  True,  True], dtype=torch.bool)
>>> torch.all(a, dim=0)
tensor([ True, False], dtype=torch.bool)