Div

Applicability

Product	Supported/Unsupported
Atlas A3 training products / Atlas A3 inference products	√
Atlas A2 training products / Atlas A2 inference products	√
Atlas 200I/500 A2 inference products	√
Atlas inference product 's AI Core	√
Atlas inference product 's Vector Core	x
Atlas training products	√

Function Usage

Computes the quotient 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 Div(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 Div(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 Div(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** Parameters in the template
Parameter	Description
T	Operand data type. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are half and float. For the Atlas A2 training products / Atlas A2 inference products , the supported data types are half and float. For the Atlas 200I/500 A2 inference products , the supported data types are half and float. For the Atlas inference product 's AI Core, the supported data types are half and float. For the Atlas training products , the supported data types are half 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 the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the LocalTensor must be 32-byte aligned.
src0, src1	Input	Source operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the 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	The mask parameter is used to control the elements involved 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 in the computation. The mask is in array form. The array length and the value range of the array elements are related to the data type of the operand. When the operand is 16-bit, the array length is 2. In this case, mask[0] and mask[1] must be in the range of [0, 2⁶⁴ – 1] and cannot be 0 at the same time. When the operand is 32-bit, the array length is 1. In this case, mask[0] must be in the range of (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1. In this case, mask[0] must be in the range of (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 repeat 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 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 parameters between adjacent iterations, see repeatStride. For details about the address stride parameters of DataBlock in the same iteration, see dataBlockStride.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the operand address overlapping restrictions, see General Address Overlap Restrictions.

When the entire tensor computation API is used for symbol overloading, the computation workload is the total length of the destination LocalTensor.
Pay attention to division by zero errors.

Examples

For more examples, see here.

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

        
         
           
           
             #include "kernel_operator.h"
 
class KernelDiv {
public:
    __aicore__ inline KernelDiv() {}
    __aicore__ inline void Init(__gm__ uint8_t* src0Gm, __gm__ uint8_t* src1Gm, __gm__ uint8_t* dstGm)
    {
        src0Global.SetGlobalBuffer((__gm__ half*)src0Gm);
        src1Global.SetGlobalBuffer((__gm__ half*)src1Gm);
        dstGlobal.SetGlobalBuffer((__gm__ half*)dstGm);
        pipe.InitBuffer(inQueueSrc0, 1, 512 * sizeof(half));
        pipe.InitBuffer(inQueueSrc1, 1, 512 * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, 512 * sizeof(half));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }
private:
    __aicore__ inline void CopyIn()
    {
        AscendC::LocalTensor<half> src0Local = inQueueSrc0.AllocTensor<half>();
        AscendC::LocalTensor<half> src1Local = inQueueSrc1.AllocTensor<half>();
        AscendC::DataCopy(src0Local, src0Global, 512);
        AscendC::DataCopy(src1Local, src1Global, 512);
        inQueueSrc0.EnQue(src0Local);
        inQueueSrc1.EnQue(src1Local);
    }
    __aicore__ inline void Compute()
    {
        AscendC::LocalTensor<half> src0Local = inQueueSrc0.DeQue<half>();
        AscendC::LocalTensor<half> src1Local = inQueueSrc1.DeQue<half>();
        AscendC::LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();
        
        uint64_t mask = 128;
        AscendC::Div(dstLocal, src0Local, src1Local, mask, 4, { 1, 1, 1, 8, 8, 8 });

 
        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc0.FreeTensor(src0Local);
        inQueueSrc1.FreeTensor(src1Local);
    }
    __aicore__ inline void CopyOut()
    {
        AscendC::LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        AscendC::DataCopy(dstGlobal, dstLocal, 512);
        outQueueDst.FreeTensor(dstLocal);
    }
private:
    AscendC::TPipe pipe;
    AscendC::TQue<AscendC::TPosition::VECIN, 1> inQueueSrc0, inQueueSrc1;
    AscendC::TQue<AscendC::TPosition::VECOUT, 1> outQueueDst;
    AscendC::GlobalTensor<half> src0Global, src1Global, dstGlobal;
};
 
extern "C" __global__ __aicore__ void div_simple_kernel(__gm__ uint8_t* src0Gm, __gm__ uint8_t* src1Gm,
    __gm__ uint8_t* dstGm)
{
    KernelDiv op;
    op.Init(src0Gm, src1Gm, dstGm);
    op.Process();
}

            

          

        
       

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

Example of computing the first n data elements of a tensor

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

Example computation of the entire tensor
1

dstLocal = src0Local / src1Local;

Result example:

Input (src0Local): [1.0 2.0 3.0 ... 512.0]
Input (src1Local): [2.0 2.0 2.0 ... 2.0]
Output (dstLocal): [0.5 1.0 1.5 ... 256.0]

Parent topic: Basic arithmetic operations