mindspore.mint.matmul

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

Return the matrix product of two tensors.

Note

input and other must have same data type, and both of them must be not scalar and support broadcast.
On Ascend, the rank of input or other must be between 1 and 6.
input and other must not be empty tensor when executing the backward process for dynamic shape case in JIT mode.

Parameters

input (Tensor) – The first input tensor.
other (Tensor) – The second input tensor.

Returns

Tensor or scalar

Supported Platforms:: Ascend

Examples

>>> import mindspore
>>> # case 1 : Reasonable application of broadcast mechanism.
>>> input = mindspore.ops.arange(24, dtype=mindspore.float32).reshape(2, 3, 4)
>>> other = mindspore.ops.arange(20, dtype=mindspore.float32).reshape(4, 5)
>>> output = mindspore.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]]]
>>>
>>> # case 2 : The rank of `input` is 1.
>>> input = mindspore.ops.ones(([1, 2]))
>>> other = mindspore.ops.ones(([2]))
>>> mindspore.mint.matmul(input, other)
Tensor(shape=[1], dtype=Float32, value= [ 2.00000000e+00])