Sub

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

Computes element-wise difference. The formula is as follows:

$\text{[math]}$

Prototype

Computation of the entire tensor
1

dst = src0 - src1;

Computation of the first n data elements of a tensor

        
             template <typename T>
__aicore__ inline void Sub(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 Sub(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 Sub(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 350 Accelerator Card, the supported data types are int8_t, uint8_t, int16_t, uint16_t, half, bfloat16_t, int32_t, uint32_t, float, complex32, int64_t, uint64_t, and complex64. For the Atlas A3 training product / Atlas A3 inference product , the supported data types are half, int16_t, int32_t, and float. For the Atlas A2 training product / Atlas A2 inference product , the supported data types are half, int16_t, int32_t, and float. For the Atlas 200I/500 A2 inference product , the supported data types are half, int16_t, int32_t, and float. For the Atlas inference product AI Core, the supported data types are half, int16_t, int32_t, and float. For the Atlas training product , the supported data types are half, int32_t, and float.
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 and src1	Input	Source operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. Both 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 compute unit reads 256 bytes of contiguous data for computation each time. To process the input data, the data needs to be read and computed over multiple repeats. repeatTime indicates the number of repeats. 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, int64_t, complex32, and complex64 support only the APIs that operate on the first n data elements of a tensor, as well as operator overloads where the entire tensor participates in the computation.

Examples

For more examples, see LINK.

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::Sub(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::Sub(dstLocal, src0Local, src1Local, mask, 4, { 1, 1, 1, 8, 8, 8 });

Example of computing the first n data elements of a tensor

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

Example computation of the entire tensor
1

dstLocal = src0Local - src1Local;

Result example:

Input (src0Local): [1 2 3 ... 512]
Input (src1Local): [513 514 515 ... 1024]
Output (dstLocal): [-512 -512 -512 ... -512]

Parent topic: Basic Arithmetic