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 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.

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.mul(input, other, *, out=None) → Tensor

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

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

If input is of type FloatTensor or DoubleTensor, other should be a real number, otherwise it should be an integer

Args:

input (Tensor): the input tensor. other (Number): the number to be multiplied to each element of input

Keyword args:

out (Tensor, optional): the output tensor.

Example:

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

torch.mul(input, other, *, out=None) → Tensor

Each element of the tensor input is multiplied by the corresponding element of the Tensor other. The resulting tensor is returned.

The shapes of input and other must be broadcastable.

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

Args:

input (Tensor): the first multiplicand tensor other (Tensor): the second multiplicand tensor

Keyword args:

out (Tensor, optional): the output tensor.

Example:

>>> a = torch.randn(4, 1)
>>> a
tensor([[ 1.1207],
        [-0.3137],
        [ 0.0700],
        [ 0.8378]])
>>> b = torch.randn(1, 4)
>>> b
tensor([[ 0.5146,  0.1216, -0.5244,  2.2382]])
>>> torch.mul(a, b)
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]])