full

Documentation

treetensor.torch.full(*args, **kwargs)[source]

In treetensor, you can use ones to create a tree of tensors with the same value.

Example:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.full((2, 3), 2.3)  # the same as torch.full((2, 3), 2.3)
tensor([[2.3000, 2.3000, 2.3000],
        [2.3000, 2.3000, 2.3000]])

>>> ttorch.full({'a': (2, 3), 'b': {'x': (4, )}}, 2.3)
<Tensor 0x7ff363bbc7f0>
├── a --> tensor([[2.3000, 2.3000, 2.3000],
│                 [2.3000, 2.3000, 2.3000]])
└── b --> <Tensor 0x7ff363bbc8d0>
    └── x --> tensor([2.3000, 2.3000, 2.3000, 2.3000])

Torch Version Related

This documentation is based on torch.full in torch v2.0.1+cu117. 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 v2.0.1+cu117 is listed below.

Description From Torch v2.0.1+cu117

torch.full(size, fill_value, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Creates a tensor of size size filled with fill_value. The tensor’s dtype is inferred from fill_value.

Args:
size (int…): a list, tuple, or torch.Size of integers defining the

shape of the output tensor.

fill_value (Scalar): the value to fill the output tensor with.

Keyword args:

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

Default: if None, uses a global default (see torch.set_default_tensor_type()).

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

Default: torch.strided.

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

Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

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

returned tensor. Default: False.

Example:

>>> torch.full((2, 3), 3.141592)
tensor([[ 3.1416,  3.1416,  3.1416],
        [ 3.1416,  3.1416,  3.1416]])