mm

Documentation

treetensor.torch.mm(input, mat2, *args, **kwargs)

In treetensor, you can create a matrix multiplication with treetensor.torch.mm().

Examples:

>>> import torch
>>> import treetensor.torch as ttorch
>>> ttorch.mm(
...     torch.tensor([[1, 2], [3, 4]]),
...     torch.tensor([[5, 6], [7, 2]]),
... )
tensor([[19, 10],
        [43, 26]])

>>> ttorch.mm(
...     ttorch.tensor({
...         'a': [[1, 2], [3, 4]],
...         'b': {'x': [[3, 4, 5], [6, 7, 8]]},
...     }),
...     ttorch.tensor({
...         'a': [[5, 6], [7, 2]],
...         'b': {'x': [[6, 5], [4, 3], [2, 1]]},
...     }),
... )
<Tensor 0x7f2e7489f340>
├── a --> tensor([[19, 10],
│                 [43, 26]])
└── b --> <Tensor 0x7f2e74896e50>
    └── x --> tensor([[44, 32],
                      [80, 59]])

Torch Version Related

This documentation is based on torch.mm in torch v2.4.1+cu121. 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 v2.4.1+cu121 is listed below.

Description From Torch v2.4.1+cu121

torch.mm(input, mat2, *, out=None) → Tensor

Performs a matrix multiplication of the matrices input and mat2.

If input is a \((n \times m)\) tensor, mat2 is a \((m \times p)\) tensor, out will be a \((n \times p)\) tensor.

Note

This function does not broadcast. For broadcasting matrix products, see torch.matmul().

Supports strided and sparse 2-D tensors as inputs, autograd with respect to strided inputs.

This operator supports TensorFloat32.

Args:

input (Tensor): the first matrix to be matrix multiplied mat2 (Tensor): the second matrix to be matrix multiplied

Keyword args:

out (Tensor, optional): the output tensor.

Example:

>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.mm(mat1, mat2)
tensor([[ 0.4851,  0.5037, -0.3633],
        [-0.0760, -3.6705,  2.4784]])