Table of Contents
Shortcuts

# torch.bmm

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

  Performs a batch matrix-matrix product of matrices stored in `input` and `mat2`.

  `input` and `mat2` must be 3-D tensors each containing the same number of matrices.

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

  out i=input i@mat2 i\text{out}_i = \text{input}_i \mathbin{@} \text{mat2}_i out i​=input i​@mat2 i​

  This operator supports TensorFloat32.

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

  Note

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

  Parameters:

    - input (Tensor) – the first batch of matrices to be multiplied

    - mat2 (Tensor) – the second batch of matrices to be multiplied

  Keyword Arguments:

    out (Tensor, optional) – the output tensor.

  Example:

      >>> input = torch.randn(10, 3, 4)
      >>> mat2 = torch.randn(10, 4, 5)
      >>> res = torch.bmm(input, mat2)
      >>> res.size()
      torch.Size([10, 3, 5])

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.