mindspore.mint.matmul

mindspore.mint.matmul(input, other)[source]

Returns the matrix product of two tensors.

Note

Numpy arguments out, casting, order, subok, signature, and extobj are not supported. The dtype of input and other must be same. On Ascend, the rank of input or other must be between 1 and 6.

Parameters

  • input (Tensor) – Input tensor, scalar not allowed. The last dimension of input must be the same size as the second last dimension of other. And the shape of input and other could be broadcast.

  • other (Tensor) – Input tensor, scalar not allowed. The last dimension of input must be the same size as the second last dimension of other. And the shape of input and other could be broadcast.

Returns

Tensor or scalar, the matrix product of the inputs. This is a scalar only when both input, other are 1-d vectors.

Raises

  • TypeError – If the dtype of input and the dtype of other are not the same.

  • ValueError – If the last dimension of input is not the same size as the second-to-last dimension of other, or if a scalar value is passed in.

  • ValueError – If the shape of input and input could not broadcast together.

  • RuntimeError – If the rank of input or other is less than 1 or greater than 6 on the Ascend platform.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> # case 1 : Reasonable application of broadcast mechanism
>>> input = Tensor(np.arange(2*3*4).reshape(2, 3, 4), mindspore.float32)
>>> other = Tensor(np.arange(4*5).reshape(4, 5), mindspore.float32)
>>> output = mint.matmul(input, other)
>>> print(output)
[[[  70.   76.   82.   88.   94.]
[ 190.  212.  234.  256.  278.]
[ 310.  348.  386.  424.  462.]]
[[ 430.  484.  538.  592.  646.]
[ 550.  620.  690.  760.  830.]
[ 670.  756.  842.  928. 1014.]]]
>>> print(output.shape)
(2, 3, 5)
>>> # case 2 : the rank of `input` is 1
>>> input = Tensor(np.ones([1, 2]), mindspore.float32)
>>> other = Tensor(np.ones([2,]), mindspore.float32)
>>> output = mint.matmul(input, other)
>>> print(output)
[2.]
>>> print(output.shape)
(1,)