pypto.bitwise_or

Applicable Products

Ascend 950PR/Ascend 950DT: Supported
Atlas A3 training products/Atlas A3 inference products: Supported
Atlas A2 training products/Atlas A2 inference products: Supported

Description

Performs a bitwise OR operation on input and other element by element. The computation formula is as follows:

res_i = input_i | other_i

Prototype

bitwise_or(input: Tensor, other: Union[Tensor, int]) -> Tensor

Parameters

Parameter	Input/Output	Description
input	Input	Source operand. Supported type: Tensor. Supported data types vary by model. For details, see Constraints. Empty tensors are not supported. The shape supports only 1 to 4 dimensions. Broadcasting along a single dimension to the same shape is supported. The shape size must not exceed 2147483647 (that is, INT32_MAX).
other	Input	Source operand. Supported types: int and Tensor. Supported data types vary by model. For details, see Constraints. Empty tensors are not supported. The shape supports only 1 to 4 dimensions. Broadcasting along a single dimension to the same shape is supported. The shape size must not exceed 2147483647 (that is, INT32_MAX).

Return Value

Returns an output tensor with the same data type as input and other, and with a shape equal to the broadcast size of input and other.

Constraints

When both input and other are tensors, their data types must be the same.
Tensor data type description:
- Ascend 950PR/Ascend 950DT: DT_INT16, DT_UINT16, DT_INT8, and DT_UINT8, DT_INT32.
- Atlas A3 training products/Atlas A3 inference products: DT_INT16, DT_UINT16, DT_INT8, and DT_UINT8.
- Atlas A2 training products/Atlas A2 inference products: DT_INT16, DT_UINT16, DT_INT8, and DT_UINT8.
Tensor inputs do not support the TileOpFormat.TILEOP_NZ format.

Examples

TileShape Setting Example

Before calling this operation API, set the TileShape through set_vec_tile_shapes.

The TileShape must have the same number of dimensions as the output.

In a non-broadcast scenario, if the input shape is [m, n], other is [m, n], the output is [m, n], and the TileShape is set to [m1, n1], then m1 and n1 are used to tile the m and n axes, respectively.

In a broadcast scenario, if the input shape is [m, n], other is [m, 1], the output is [m, n], and the TileShape is set to [m1, n1], then m1 and n1 are used to tile the m and n axes, respectively.

pypto.set_vec_tile_shapes(4, 16)

API Call Example

a = pypto.tensor([2], pypto.DT_INT16)
b = pypto.tensor([2], pypto.DT_INT16)
out = pypto.bitwise_or(a, b)

The results are as follows:

Input data a:  [2, 5]
Input data b: [1, 7]
Output data out: [3, 7]