Or

Applicability

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

Function Usage

Performs bitwise OR element-wise. The formula is as follows:

$\text{[math]}$

Prototype

Computation of the entire tensor
1

dst = src0 | src1;

Computation of the first n pieces of data of a tensor

        
             template <typename T>
__aicore__ inline void Or(const LocalTensor<T>& dst, const LocalTensor<T>& src0, const LocalTensor<T>& src1, const int32_t& count)

High-dimensional tensor sharding computation

Bitwise mask mode

          
               template <typename T, bool isSetMask = true>
__aicore__ inline void Or(const LocalTensor<T>& dst, const LocalTensor<T>& src0, const LocalTensor<T>& src1, uint64_t mask[], const uint8_t repeatTime, const BinaryRepeatParams& repeatParams)

Contiguous mask mode

          
               template <typename T, bool isSetMask = true>
__aicore__ inline void Or(const LocalTensor<T>& dst, const LocalTensor<T>& src0, const LocalTensor<T>& src1, uint64_t mask, const uint8_t repeatTime, const BinaryRepeatParams& repeatParams)

Parameters

**Table 1** Template parameters
Parameter	Description
T	Operand data type. For the Atlas training product , the supported data types are int16_t and uint16_t. For the Atlas inference product AI Core, the supported data types are int16_t and uint16_t. For the Atlas A2 training product / Atlas A2 inference product , the supported data types are int16_t and uint16_t. For the Atlas A3 training product / Atlas A3 inference product , the supported data types are int16_t and uint16_t. For the Atlas 200I/500 A2 inference product , the supported data types are int16_t and uint16_t. For the Atlas 350 Accelerator Card, the supported data types are int8_t, uint8_t, int16_t, uint16_t, int32_t, uint32_t, int64_t, and uint64_t.
isSetMask	Indicates whether to set mask inside the API. true: sets mask inside the API. false: sets mask outside the API. Developers need to use the SetVectorMask API to set the mask value. In this mode, the mask value in the input parameter of this API must be set to the placeholder MASK_PLACEHOLDER.

**Table 2** Parameters
Parameter	Input/Output	Description
dst	Output	Destination operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned.
src0/src1	Input	Source operands. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. The two source operands must have the same data type as the destination operand.
count	Input	Number of elements involved in the computation.
mask[]/mask	Input	mask controls the elements that participate in computation in each iteration. Bitwise mode: controls the elements that participate in computation by bit. If a bit is set to 1, the corresponding element participates in the computation. If a bit is set to 0, the corresponding element is masked from the computation. The mask value is an array. The array length and the value range of the array elements are related to the operand data type. When the operand is 16-bit, the array length is 2, with mask[0] and mask[1] each in the range [0, 2⁶⁴ – 1], and they cannot both be 0 at the same time. When the operand is 32-bit, the array length is 1, with mask[0] in the range (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1, with mask[0] in the range (0, 2³² – 1]. For example, if mask = [0, 8] and 8 = 0b1000, only the fourth element participates in computation. Contiguous mode: indicates the number of contiguous elements that participate in computation. The value range is related to the operand data type. The maximum number of elements that can be processed in each iteration varies according to the data type. When the operand is 16-bit, mask ∈ [1, 128]. When the operand is 32-bit, mask ∈ [1, 64]. When the operand is 64-bit, mask ∈ [1, 32].
repeatTime	Input	Number of iteration repeats. The Vector Unit reads 256 bytes of contiguous data for computation each time. To read the complete data for processing, the unit needs to read the input data in multiple repeats. repeatTime indicates the number of iterations. For details about this parameter, see High-dimensional Sharding APIs.
repeatParams	Input	Parameters that control the operand address strides. They are of the BinaryRepeatParams type, and contain such parameters as those that specify the address stride of the operand for the same data block between adjacent iterations and address stride of the operand between different data blocks in a single iteration. For details about the address stride of the operand between adjacent iterations, see repeatStride. For details about the address stride of the operand between different data blocks in a single iteration, see dataBlockStride.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the constraints on operand address overlapping, see General Address Overlapping Restrictions.

When the entire tensor computation API is used for symbol overloading, the computation workload is the total length of the destination LocalTensor.

For the Atlas 350 Accelerator Card, uint8_t, int8_t, uint64_t, and int64_t support only the APIs that compute the first n pieces of data in a tensor.
In particular, the OR operation of the uint32_t/int32_t type can be implemented by calling ReinterpretCast. That is, use ReinterpretCast of LocalTensor to convert the data type into the uint16_t/int16_t type, and then perform the OR operation. Directly passing data of the uint32_t/int32_t type varies depending on the version. Considering the compatibility of operators in different versions, you are not advised to directly passing data in such a way.
- The APIs for computing the entire tensor and for computing the first n pieces of data in a tensor can be of uint32_t/int32_t type. However, for the latter API, the precision can meet the expectation only if count is set to twice the expected number. For a high-dimensional tensor sharding computation API, direct compilation results in an error indicating that the data type is not supported. The preceding description applies to the following models:
  Atlas A2 training product / Atlas A2 inference product
  
  Atlas A3 training product / Atlas A3 inference product
- A compilation error message is displayed, indicating that these data types are not supported. The preceding description applies to the following models:
  Atlas training product
  
  Atlas inference product AI Core
- A CPU error message is displayed, indicating that these data types are not supported. The preceding description applies to the following models:
  Atlas 200I/500 A2 inference product

Examples

Example of high-dimensional tensor sharding computation (contiguous mask mode)

        
             uint64_t mask = 128;
// repeatTime = 4. 128 elements are computed in one iteration, and 512 elements are computed in total.
// dstBlkStride, src0BlkStride, src1BlkStride = 1. Data is continuously read and written in a single iteration.
// dstRepStride, src0RepStride, src1RepStride = 8. Data is continuously read and written between adjacent iterations.
AscendC::Or(dstLocal, src0Local, src1Local, mask, 4, { 1, 1, 1, 8, 8, 8 });

Example of high-dimensional tensor sharding computation (bitwise mask mode)

        
             uint64_t mask[2] = { UINT64_MAX, UINT64_MAX };
// repeatTime = 4. 128 elements are computed in one iteration, and 512 elements are computed in total.
// dstBlkStride, src0BlkStride, src1BlkStride = 1. Data is continuously read and written in a single iteration.
// dstRepStride, src0RepStride, src1RepStride = 8. Data is continuously read and written between adjacent iterations.
AscendC::Or(dstLocal, src0Local, src1Local, mask, 4, { 1, 1, 1, 8, 8, 8 });

Example of computing the first n pieces of data of a tensor

        
             AscendC::Or(dstLocal, src0Local, src1Local, 512);

Result example:

Input (src0Local): [1 2 3 ... 512]
Input (src1Local): [513 512 511 ... 2]
Output (dstLocal): [513 514 511 ... 514]

Parent topic: Logical Computation