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 both two input tensors must be same and not less than 2.

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)