mindspore.ops.BatchMatMul

class mindspore.ops.BatchMatMul(transpose_a=False, transpose_b=False)[source]

Computes matrix multiplication between two tensors by batch.

\[\]

text{output}[…, :, :] = text{matrix}(x[…, :, :]) * text{matrix}(y[…, :, :])

The rank of the two input tensors must be at least 2, and the two input tensors must have the same rank if the environment is GPU or CPU.

Parameters

transpose_a (bool) – If True , the last two dimensions of x is transposed before multiplication. Default: False .
transpose_b (bool) – If True , the last two dimensions of y is transposed before multiplication. Default: False .

Inputs:

x (Tensor) - The first tensor to be multiplied. The shape of the tensor is \((*B, N, C)\), where \(*B\) represents the batch size which can be multidimensional, \(N\) and \(C\) are the size of the last two dimensions. If transpose_a is True , its shape must be \((*B, C, N)\).
y (Tensor) - The second tensor to be multiplied. The shape of the tensor is \((*B, C, M)\). If transpose_b is True , its shape must be \((*B, M, C)\).

Outputs:

Tensor, the shape of the output tensor is \((*B, N, M)\).

Raises

TypeError – If transpose_a or transpose_b is not a bool.
ValueError – If length of shape of x is not equal to length of shape of y or length of shape of inputs is less than 2.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.ones(shape=[2, 4, 1, 3]), mindspore.float32)
>>> y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
>>> batmatmul = ops.BatchMatMul()
>>> output = batmatmul(x, y)
>>> print(output.shape)
(2, 4, 1, 4)
>>> x = Tensor(np.ones(shape=[2, 4, 3, 1]), mindspore.float32)
>>> y = Tensor(np.ones(shape=[2, 4, 3, 4]), mindspore.float32)
>>> batmatmul = ops.BatchMatMul(transpose_a=True)
>>> output = batmatmul(x, y)
>>> print(output.shape)
(2, 4, 1, 4)