Documentation

RepeatReduceSum

Applicability

Product

Supported/Unsupported

Atlas A3 training products / Atlas A3 inference products

x

Atlas A2 training products / Atlas A2 inference products

√

Atlas 200I/500 A2 inference products

x

Atlas inference product 's AI Core

√

Atlas inference product 's Vector Core

x

Atlas training products

√

Functions

Sums all data in each repeat. Compared with WholeReduceSum, it does not support the bitwise mask mode. You are advised to use WholeReduceSum with more comprehensive functions.

Prototype

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template <typename T, bool isSetMask = true>
__aicore__ inline void RepeatReduceSum(const LocalTensor<T>& dst, const LocalTensor<T>& src, const int32_t repeatTime, const int32_t mask, const int32_t dstBlkStride, const int32_t srcBlkStride, const int32_t dstRepStride, const int32_t srcRepStride);

Parameters

Table 1 Parameters in the template

Parameter

Description

T

Data type of the operand.

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 2-byte aligned (for data of the half type) or 4-byte aligned (for data of the float type).

For the Atlas A2 training products / Atlas 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.

src

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 source operand must have the same data type as the destination operand.

For the Atlas A2 training products / Atlas 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.

repeatTime

Input

Number of iteration repeats. The value range is [0, 255]. 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.

mask

Input

It is used to control the number of consecutive elements involved in computation in each iteration. 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].

dstBlkStride

Input

This parameter is invalid and can be set to any value.

srcBlkStride

Input

Address stride of data blocks in a single iteration. For details, see dataBlockStride.

dstRepStride

Input

Address stride between adjacent iterations of the destination operand. The unit is the length after reduction of a repeat.

The unit is the number of bytes occupied by the dst data type. For example, if dst is half, the unit is 2 bytes.

Note that this parameter cannot be set to 0 for the Atlas training products .

srcRepStride

Input

Address stride between adjacent iterations of the source operand, that is, the number of data blocks skipped of the source operand in each iteration. For details, see repeatStride.

Returns

None

Constraints

  • RepeatReduceSum uses the binary tree mode to implement internal adding.

    Assume that the source operand is 128 data elements of the half type [data0, data1, data2, ..., data127], the computation can be completed in one repeat. The computation process is as follows:

    1. Add data0 and data1 to obtain data00, add data2 and data3 to obtain data01, ..., add data124 and data125 to obtain data62, and add data126 and data127 to obtain data63.
    2. Add data00 and data01 to obtain data000, add data02 and data03 to obtain data001, ..., and add data62 and data63 to obtain data031.
    3. By analogy, the destination operand is one data element of the half type.

    When being greater than 65504, the computation result is truncated to 65504. For example, the source operand is [60000, 60000, –30000, 100], 60000 + 60000 > 65504, meaning that the result overflows. In this case, the maximum value 65504 will be used as the result. Similarly, –30000 + 100 = –29900, 65504 – 29900 = 35604.

Examples

  • Example segment: 
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    // Both dstLocal and srcLocal are of the half type. For srcLocal, the computation data is of size 512 and is continuously arranged. Its compute result is also continuously arranged. It uses the high-dimensional tensor sharding computation API. mask is set to 128, indicating that all elements are involved in the computation.
// Based on the preceding information, it can be inferred that repeatTimes is 4, dstRepStride is 1, srcBlkStride is 1, srcRepStride is 8, and dstBlkStride is invalid and is set to 0. Therefore, the API example is as follows:
AscendC::RepeatReduceSum<half>(dstLocal, srcLocal, 4, 128, 0, 1, 1, 8);
  • Complete example: 
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    #include "kernel_operator.h"
class KernelReduce {
public:
    __aicore__ inline KernelReduce() {}
    __aicore__ inline void Init(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
    {
        srcGlobal.SetGlobalBuffer((__gm__ half*)src);
        dstGlobal.SetGlobalBuffer((__gm__ half*)dstGm);

        repeat = srcDataSize / mask;
        pipe.InitBuffer(inQueueSrc, 1, srcDataSize * sizeof(half));
        pipe.InitBuffer(outQueueDst, 1, dstDataSize * sizeof(half));
    }
    __aicore__ inline void Process()
    {
        CopyIn();
        Compute();
        CopyOut();
    }

private:
    __aicore__ inline void CopyIn()
    {
        AscendC::LocalTensor<half> srcLocal = inQueueSrc.AllocTensor<half>();
        AscendC::DataCopy(srcLocal, srcGlobal, srcDataSize);
        inQueueSrc.EnQue(srcLocal);
    }
    __aicore__ inline void Compute()
    {
        AscendC::LocalTensor<half> srcLocal = inQueueSrc.DeQue<half>();
        AscendC::LocalTensor<half> dstLocal = outQueueDst.AllocTensor<half>();

        AscendC::RepeatReduceSum<half>(dstLocal, srcLocal, repeat, mask, 0, 1, 1, 8);

        outQueueDst.EnQue<half>(dstLocal);
        inQueueSrc.FreeTensor(srcLocal);
    }
    __aicore__ inline void CopyOut()
    {
        AscendC::LocalTensor<half> dstLocal = outQueueDst.DeQue<half>();
        AscendC::DataCopy(dstGlobal, dstLocal, dstDataSize);
        outQueueDst.FreeTensor(dstLocal);
    }

private:
    AscendC::TPipe pipe;
    AscendC::TQue<AscendC::TPosition::VECIN, 1> inQueueSrc;
    AscendC::TQue<AscendC::TPosition::VECOUT, 1> outQueueDst;
    AscendC::GlobalTensor<half> srcGlobal, dstGlobal;
    int srcDataSize = 2048;
    int dstDataSize = 16;
    int mask = 128;
    int repeat = 0;
};
extern "C" __global__ __aicore__ void reduce_kernel(__gm__ uint8_t* src, __gm__ uint8_t* dstGm)
{
    KernelReduce op;
    op.Init(src, dstGm);
    op.Process();
}

    Result example:

    Input (src_gm):
[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
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 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
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 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1. 1. 1.]
Output (dst_gm):
[128. 128. 128. 128. 128. 128. 128. 128. 128. 128. 128. 128. 128. 128.
 128. 128.]
Parent topic: Reduction computation
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