mindspore.ops.floor_mod

mindspore.ops.floor_mod(x, y)[source]

Computes the remainder of division element-wise. It’s a flooring divide. E.g. \(floor(x / y) * y + mod(x, y) = x\).

Inputs of x and y comply with the implicit type conversion rules to make the data types consistent. The inputs must be two tensors or one tensor and one scalar. When the inputs are two tensors, dtypes of them cannot be both bool, and the shapes of them could be broadcast. When the inputs are one tensor and one scalar, the scalar could only be a constant.

\[out_{i} =\text{floor}(x_{i} // y_{i})\]

where the \(floor\) indicates the Floor operator, for more details, please refer to the mindspore.ops.Floor operator.

Warning

  • Data of input y should not be 0, or the maximum value of its dtype will be returned.

  • When the elements of input exceeds 2048 , the accuracy of operator cannot guarantee the requirement of double thousandths in the mini form.

  • Due to different architectures, the calculation results of this operator on NPU and CPU may be inconsistent.

  • If shape is expressed as (D1,D2… ,Dn), then D1*D2… *DN<=1000000,n<=8.

Parameters

  • x (Union[Tensor, Number, bool]) – The first input is a number or a bool or a tensor whose data type is number or bool.

  • y (Union[Tensor, Number, bool]) – The second input is a number or a bool when the first input is a tensor, or it can be a tensor whose data type is number or bool.

Returns

Tensor, the shape is the same as the one after broadcasting, and the data type is the one with higher precision of the two inputs.

Raises

TypeError – If neither x nor y is a Tensor.

Supported Platforms:

Ascend GPU CPU

Examples

>>> x = Tensor(np.array([2, 4, -1]), mindspore.int32)
>>> y = Tensor(np.array([3, 3, 3]), mindspore.int32)
>>> output = ops.floor_mod(x, y)
>>> print(output)
[2 1 2]