BitwiseLeftShift

Versioned name: BitwiseLeftShift-15

Category: Bitwise binary

Short description: BitwiseLeftShift performs a bitwise left shift operation operation, 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, BitwiseLeftShift performs a bitwise left shift operation for each corresponding element in the given tensors, based on the following equation:

\[o_{i} = a_{i} \ll b_{i}\]

Note

If the number of shifts is negative, or if it equals or exceeds the total number of bits in the type T, the behavior can be undefined or implementation-defined (depends on the hardware).

Unsigned integer shift is always performed modulo 2^n where n is the number of bits in the type T.

When signed integer shift operation overflows (the result does not fit in the result type), the behavior is undefined.

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, containing data to be shifted. Required.

• 2: A tensor of type T and arbitrary shape, with the number of shifts. Required.

Outputs

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

Types

• T: any supported integer type.

Examples

Example 1: no broadcast

<layer ... type="BitwiseLeftShift">
    <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="BitwiseLeftShift">
    <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>