torch.matmul (original) (raw)

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

Matrix product of two tensors.

The behavior depends on the dimensionality of the tensors as follows:

• If both tensors are 1-dimensional, the dot product (scalar) is returned.
• If both arguments are 2-dimensional, the matrix-matrix product is returned.
• If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.
• If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.
• If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiply and removed after.
  The first N-2 dimensions of each argument, the batch dimensions, arebroadcast (and thus must be broadcastable). The last 2, the matrix dimensions, are handled as in the matrix-matrix product.
  For example, if input is a(j×1×n×m)(j \times 1 \times n \times m) tensor and other is a (k×m×p)(k \times m \times p)tensor, the batch dimensions are (j×1)(j \times 1) and (k)(k), and the matrix dimensions are (n×m)(n \times m) and (m×p)(m \times p).out will be a (j×k×n×p)(j \times k \times n \times p) tensor.

This operation has support for arguments with sparse layouts. In particular the matrix-matrix (both arguments 2-dimensional) supports sparse arguments with the same restrictions as torch.mm()

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.

Note

The 1-dimensional dot product version of this function does not support an out parameter.

Parameters

• input (Tensor) – the first tensor to be multiplied
• other (Tensor) – the second tensor to be multiplied

Keyword Arguments

out (Tensor, optional) – the output tensor.

Example:

vector x vector

tensor1 = torch.randn(3) tensor2 = torch.randn(3) torch.matmul(tensor1, tensor2).size() torch.Size([])

matrix x vector

tensor1 = torch.randn(3, 4) tensor2 = torch.randn(4) torch.matmul(tensor1, tensor2).size() torch.Size([3])

batched matrix x broadcasted vector

tensor1 = torch.randn(10, 3, 4) tensor2 = torch.randn(4) torch.matmul(tensor1, tensor2).size() torch.Size([10, 3])

batched matrix x batched matrix

tensor1 = torch.randn(10, 3, 4) tensor2 = torch.randn(10, 4, 5) torch.matmul(tensor1, tensor2).size() torch.Size([10, 3, 5])

batched matrix x broadcasted matrix

tensor1 = torch.randn(10, 3, 4) tensor2 = torch.randn(4, 5) torch.matmul(tensor1, tensor2).size() torch.Size([10, 3, 5])