randint_like

Documentation

treetensor.torch.randint_like(input, *args, **kwargs)

In treetensor, you can use randint_like to create a tree of tensors with numbers in an integer range.

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.randint_like(torch.ones(2, 3), 10)  # the same as torch.randint_like(torch.ones(2, 3), 10)
tensor([[0., 5., 0.],
        [2., 0., 9.]])

>>> ttorch.randint_like({
...     'a': torch.ones(2, 3),
...     'b': {'x': torch.ones(4, )},
... }, 10)
<Tensor 0x7ff363bb6748>
├── a --> tensor([[3., 6., 1.],
│                 [8., 9., 5.]])
└── b --> <Tensor 0x7ff363bb6898>
    └── x --> tensor([4., 4., 7., 1.])

Torch Version Related

This documentation is based on torch.randint_like 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.

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.randint_like(input, low=0, high, \*, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) → Tensor

Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive).

Args:

input (Tensor): the size of input will determine size of the output tensor. low (int, optional): Lowest integer to be drawn from the distribution. Default: 0. high (int): One above the highest integer to be drawn from the distribution.

Keyword args:

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

Default: if None, defaults to the dtype of input.

layout (torch.layout, optional): the desired layout of returned tensor.

Default: if None, defaults to the layout of input.

device (torch.device, optional): the desired device of returned tensor.

Default: if None, defaults to the device of input.

requires_grad (bool, optional): If autograd should record operations on the

returned tensor. Default: False.

memory_format (torch.memory_format, optional): the desired memory format of

returned Tensor. Default: torch.preserve_format.