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# torch.mm

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

  Performs a matrix multiplication of the matrices `input` and `mat2`.

  If `input` is a (n×m)(n \times m)(n×m) tensor, `mat2` is a (m×p)(m \times p)(m×p) tensor, `out` will be a (n×p)(n \times p)(n×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 operation has support for arguments with sparse layouts. If `out` is provided it’s layout will be used. Otherwise, the result layout will be deduced from that of `input`.

  Warning

  Sparse support is a beta feature and some layout(s)/dtype/device combinations may not be supported, or may not have autograd support. If you notice missing functionality please open a feature request.

  This operator supports TensorFloat32.

  On certain ROCm devices, when using float16 inputs this module will use different precision for backward.

  Parameters:

    - input (Tensor) – the first matrix to be matrix multiplied

    - mat2 (Tensor) – the second matrix to be matrix multiplied

  Keyword Arguments:

    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]])

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