- torch.linalg.multi_dot(tensors, *, out=None)¶
Efficiently multiplies two or more matrices by reordering the multiplications so that the fewest arithmetic operations are performed.
Supports inputs of float, double, cfloat and cdouble dtypes. This function does not support batched inputs.
Every tensor in
tensorsmust be 2D, except for the first and last which may be 1D. If the first tensor is a 1D vector of shape (n,) it is treated as a row vector of shape (1, n), similarly if the last tensor is a 1D vector of shape (n,) it is treated as a column vector of shape (n, 1).
If the first and last tensors are matrices, the output will be a matrix. However, if either is a 1D vector, then the output will be a 1D vector.
Differences with numpy.linalg.multi_dot:
Unlike numpy.linalg.multi_dot, the first and last tensors must either be 1D or 2D whereas NumPy allows them to be nD
This function does not broadcast.
This function is implemented by chaining
torch.mm()calls after computing the optimal matrix multiplication order.
The cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c. Given matrices A, B, C with shapes (10, 100), (100, 5), (5, 50) respectively, we can calculate the cost of different multiplication orders as follows:
In this case, multiplying A and B first followed by C is 10 times faster.
tensors (Sequence[Tensor]) – two or more tensors to multiply. The first and last tensors may be 1D or 2D. Every other tensor must be 2D.
- Keyword Arguments:
out (Tensor, optional) – output tensor. Ignored if None. Default: None.
>>> from torch.linalg import multi_dot >>> multi_dot([torch.tensor([1, 2]), torch.tensor([2, 3])]) tensor(8) >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([2, 3])]) tensor() >>> multi_dot([torch.tensor([[1, 2]]), torch.tensor([, ])]) tensor([]) >>> A = torch.arange(2 * 3).view(2, 3) >>> B = torch.arange(3 * 2).view(3, 2) >>> C = torch.arange(2 * 2).view(2, 2) >>> multi_dot((A, B, C)) tensor([[ 26, 49], [ 80, 148]])