mul

Documentation

treetensor.torch.mul(input, other, *args, **kwargs)

Multiplies each element of the input input with the scalar other and returns a new resulting tensor.

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.mul(
...     ttorch.tensor([1, 2, 3]),
...     ttorch.tensor([3, 5, 11]),
... )
tensor([ 3, 10, 33])

>>> ttorch.mul(
...     ttorch.tensor({
...         'a': [1, 2, 3],
...         'b': {'x': [[3, 5], [9, 12]]},
...     }),
...     ttorch.tensor({
...         'a': [3, 5, 11],
...         'b': {'x': [[31, -15], [13, 23]]},
...     })
... )
<Tensor 0x7f11b139ca58>
├── a --> tensor([ 3, 10, 33])
└── b --> <Tensor 0x7f11b139cb00>
    └── x --> tensor([[ 93, -75],
                      [117, 276]])

Torch Version Related

This documentation is based on torch.mul 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.mul(input, other, *, out=None) → Tensor

Multiplies input by other.

\[\text{out}_i = \text{input}_i \times \text{other}_i\]

Supports broadcasting to a common shape, type promotion, and integer, float, and complex inputs.

Args:

input (Tensor): the input tensor. other (Tensor or Number) - the tensor or number to multiply input by.

Keyword args:

out (Tensor, optional): the output tensor.

Examples:

>>> a = torch.randn(3)
>>> a
tensor([ 0.2015, -0.4255,  2.6087])
>>> torch.mul(a, 100)
tensor([  20.1494,  -42.5491,  260.8663])

>>> b = torch.randn(4, 1)
>>> b
tensor([[ 1.1207],
        [-0.3137],
        [ 0.0700],
        [ 0.8378]])
>>> c = torch.randn(1, 4)
>>> c
tensor([[ 0.5146,  0.1216, -0.5244,  2.2382]])
>>> torch.mul(b, c)
tensor([[ 0.5767,  0.1363, -0.5877,  2.5083],
        [-0.1614, -0.0382,  0.1645, -0.7021],
        [ 0.0360,  0.0085, -0.0367,  0.1567],
        [ 0.4312,  0.1019, -0.4394,  1.8753]])