BitwiseXor

Versioned name: BitwiseXor-13

Category: Bitwise binary

Short description: BitwiseXor performs a bitwise logical XOR operation with two given tensors element-wise, applying multi-directional broadcast rules.

Detailed description: Before performing the operation, input tensors a and b are broadcasted if their shapes are different and the auto_broadcast attribute is not none. Broadcasting is performed according to the auto_broadcast value.

After broadcasting input tensors a and b, BitwiseXor performs a bitwise logical XOR operation for each corresponding element in the given tensors, based on the following algorithm.

For boolean type tensors, BitwiseXor is equivalent to LogicalXor.

If tensor is of any supported integer type, for each element of the tensor:

1. Convert values from input tensors to their binary representation according to the input tensor datatype.

2. Perform a logical XOR on each bit in the binary representation of values from a and b, where value 0 represents false and value 1 represents true.

3. Convert the results of XOR in binary representation to the input datatype.

Example 1 - BitwiseXor output for boolean tensor:

# For given boolean inputs:
a = [True, False, False]
b = [True, True, False]
# Perform logical XOR operation same as in LogicalXor operator:
output = [False, True, False]

Example 2 - BitwiseXor output for uint8 tensor:

# For given uint8 inputs:
a = [21, 120]
b = [3, 37]
# Create a binary representation of uint8:
# binary a: [00010101, 01111000]
# binary b: [00000011, 00100101]
# Perform bitwise XOR of corresponding elements in a and b:
# [00010110, 01011101]
# Convert binary values to uint8:
output = [22, 93]

Attributes:

• auto_broadcast

  • Description: specifies the rules used for auto-broadcasting of input tensors.

  • Range of values:

    • none - no auto-broadcasting is allowed, all input shapes must match,

    • numpy - numpy broadcasting rules, description is available in Broadcast Rules For Elementwise Operations,

    • pdpd - PaddlePaddle-style implicit broadcasting, description is available in Broadcast Rules For Elementwise Operations.

  • Type: string

  • Default value: “numpy”

  • Required: no

Inputs

• 1: A tensor of type T and arbitrary shape. Required.

• 2: A tensor of type T and arbitrary shape. Required.

Outputs

• 1: The result of element-wise BitwiseXor operation. A tensor of type T and the same shape equal to the broadcasted shape of two inputs.

Types

• T: any supported integer or boolean type.

Examples

Example 1: no broadcast

<layer ... type="BitwiseXor">
    <input>
        <port id="0">
            <dim>256</dim>
            <dim>56</dim>
        </port>
        <port id="1">
            <dim>256</dim>
            <dim>56</dim>
        </port>
    </input>
    <output>
        <port id="2">
            <dim>256</dim>
            <dim>56</dim>
        </port>
    </output>
</layer>

Example 2: numpy broadcast

<layer ... type="BitwiseXor">
    <input>
        <port id="0">
            <dim>8</dim>
            <dim>1</dim>
            <dim>6</dim>
            <dim>1</dim>
        </port>
        <port id="1">
            <dim>7</dim>
            <dim>1</dim>
            <dim>5</dim>
        </port>
    </input>
    <output>
        <port id="2">
            <dim>8</dim>
            <dim>7</dim>
            <dim>6</dim>
            <dim>5</dim>
        </port>
    </output>
</layer>