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# torch.addmm

torch.addmm(input, mat1, mat2, *, beta=1, alpha=1, out=None) → Tensor

  Performs a matrix multiplication of the matrices `mat1` and `mat2`. The matrix `input` is added to the final result.

  If `mat1` is a (n×m)(n \times m)(n×m) tensor, `mat2` is a (m×p)(m \times p)(m×p) tensor, then `input` must be broadcastable with a (n×p)(n \times p)(n×p) tensor and `out` will be a (n×p)(n \times p)(n×p) tensor.

  `alpha` and `beta` are scaling factors on matrix-vector product between `mat1` and `mat2` and the added matrix `input` respectively.

  out=β input+α (mat1 i@mat2 i)\text{out} = \beta\ \text{input} + \alpha\ (\text{mat1}_i \mathbin{@} \text{mat2}_i) out=β input+α (mat1 i​@mat2 i​)

  If `beta` is 0, then `input` will be ignored, and nan and inf in it will not be propagated.

  For inputs of type FloatTensor or DoubleTensor, arguments `beta` and `alpha` must be real numbers, otherwise they should be integers.

  This operation has support for arguments with sparse layouts. If `input` is sparse the result will have the same layout and if `out` is provided it must have the same layout as `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) – matrix to be added

    - mat1 (Tensor) – the first matrix to be matrix multiplied

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

  Keyword Arguments:

    - beta (Number, optional) – multiplier for `input` (β\beta β)

    - alpha (Number, optional) – multiplier for m a t 1@m a t 2 mat1 @ mat2 ma t 1@ma t 2 (α\alpha α)

    - out (Tensor, optional) – the output tensor.

  Example:

      >>> M = torch.randn(2, 3)
      >>> mat1 = torch.randn(2, 3)
      >>> mat2 = torch.randn(3, 3)
      >>> torch.addmm(M, mat1, mat2)
      tensor([[-4.8716,  1.4671, -1.3746],
              [ 0.7573, -3.9555, -2.8681]])

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