LogicalXor

Applicability

Product	Supported
Atlas 350 Accelerator Card	√
Atlas A3 training product/Atlas A3 inference product	x
Atlas A2 training product/Atlas A2 inference product	x
Atlas 200I/500 A2 inference product	x
Atlas inference product AI Core	x
Atlas inference product Vector Core	x
Atlas training product	x

Function Usage

Performs the logical exclusive OR operation by element. If the input data type is not bool, zero is regarded as False, and non-zero data is regarded as True.

$\text{[math]}$

Prototype

template <const LogicalXorConfig& config = DEFAULT_LOGICAL_XOR_CONFIG, typename T, typename U>
__aicore__ inline void LogicalXor(const LocalTensor<T>& dst, const LocalTensor<U>& src0, const LocalTensor<U>& src1, const uint32_t count)

Parameters

**Table 1** Template parameters
Parameter	Description
config	LogicalXor algorithm configuration. This is an optional parameter of the LogicalXorConfig type. The code below describes the definition. isReuseSource: This parameter is reserved. Pass the default value false.
T	Data type of the destination operand. For the Atlas 350 Accelerator Card, the supported data type is bool.
U	Data type of the source operand. For the Atlas 350 Accelerator Card, the supported data types are bool, int8_t, uint8_t, int16_t, uint16_t, half, bfloat16_t, int32_t, uint32_t, float, int64_t, and uint64_t.

struct LogicalXorConfig {
    bool isReuseSource;
};

**Table 2** Parameters
Parameter	Type	Description
dst	Output	Destination operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT.
src0, src1	Input	Source operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT.
count	Input	Number of elements involved in the computation.

Returns

None

Constraints

The source operand address must not overlap the destination operand address.
For details about the alignment requirements of the operand address offset, see General Description and Restrictions.

Examples

For details about a complete example, see logicalxor operator sample.

AscendC::LocalTensor<uint32_t> src0, src1;
AscendC::LocalTensor<bool> dst; 
uint32_t count = 512; // Number of elements involved in computation
AscendC::LogicalXor(dst, src0, src1, count);

Result example:

Input (src0):
[-4, 4, 1, -8, 0, ... 3]
Input (src1):
[-1, 3, 1, 5, 1, ... -3]
Output (dst):
[False, False, False, False, True, ... False]

Parent topic: LogicalXor APIs