mindspore.ops.Einsum

class mindspore.ops.Einsum(equation)[source]

Sums the product of the elements of the input Tensor along dimensions specified notation based on the Einstein summation convention(Einsum). You can use this operator to perform diagonal/reducesum/transpose/matmul/mul/inner product operations, etc.

Parameters: equation (str) – An attribute, represent the operation you want to do. the value can contain only letters([a-z][A-Z]), commas(,), ellipsis(…), and arrow(->). the letters represent inputs’s tensor dimension, commas(,)represent separate tensors, ellipsis(…) indicates the tensor dimension that you do not care about, the left of the arrow(->) indicates the input tensors, and the right of it indicates the desired output dimension.

Inputs:

x () - Input tensor used for calculation. The inputs must be a tuple/list of Tensors. When the inputs are only one tensor, you can input (tensor, ). Dtypes of them should be float16/float32/float64 and dtype of the tensor(s) must be the same.

Outputs:

Tensor, the shape of it can be obtained from the equation, and the data type is the same as input tensors.

Raises

TypeError – If equation itself is invalid, or the equation does not match the input tensor.
TypeError – If dtype of the input Tensors are not the same or dtype is not float16, float32 or float64.

Supported Platforms:: GPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> equation = "i->"
>>> einsum = ops.Einsum(equation)
>>> output = einsum([x])
>>> print(output)
[7.]
>>>
>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
>>> equation = "i,i->i"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x, y))
>>> print(output)
[ 2. 8. 12.]
>>>
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> y = Tensor(np.array([[2.0, 3.0], [1.0, 2.0], [4.0, 5.0]]), mindspore.float32)
>>> equation = "ij,jk->ik"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x, y))
>>> print(output)
[[16. 22.]
[37. 52.]]
>>>
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "ij->ji"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x,))
>>> print(output)
[[1. 4.]
[2. 5.]
[3. 6.]]
>>>
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "ij->j"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x,))
>>> print(output)
[5. 7. 9.]
>>>
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "...->"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x,))
>>> print(output)
[21.]
>>>
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 1.0]), mindspore.float32)
>>> equation = "j,i->ji"
>>> einsum = ops.Einsum(equation)
>>> output = einsum((x, y))
>>> print(output)
[[ 2. 4. 1.]
[ 4. 8. 2.]
[ 6. 12. 3.]]