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Erf

Function Description

Computes error function or Gaussian error function element-wise. The specific calculation formula is as follows, where PAR represents the number of elements that can be processed by the vector unit in one iteration:

Prototype

  • Pass the temporary space through the sharedTmpBuffer input parameter.
    • All or part of the source operand tensors are involved in computation.
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      template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor, const LocalTensor<uint8_t> &sharedTmpBuffer, const uint32_t calCount)
    • All source operand tensors are involved in computation.
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      template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf( const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor, const LocalTensor<uint8_t> &sharedTmpBuffer)
  • Allocate the temporary space through the API framework.
    • All or part of the source operand tensors are involved in computation.
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      template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor, const uint32_t calCount)
    • All source operand tensors are involved in computation.
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      template <typename T, bool isReuseSource = false>
__aicore__ inline void Erf(const LocalTensor<T> &dstTensor, const LocalTensor<T> &srcTensor)
Due to the complex mathematical computation involved in the internal implementation of this API, additional temporary space is required to store intermediate variables generated during computation. The temporary space can be passed by developers through the sharedTmpBuffer input parameter or allocated through the API framework.
  • When the sharedTmpBuffer input parameter is used for passing the temporary space, the tensor serves as the temporary space. In this case, the API framework is not required for temporary space allocation. This enables developers to manage the sharedTmpBuffer space and reuse the buffer after calling the API, so that the buffer is not repeatedly allocated and deallocated, improving the flexibility and buffer utilization.
  • When the API framework is used for temporary space allocation, developers do not need to allocate the space, but must reserve the required size for the space.

If sharedTmpBuffer is used, developers must allocate space for the tensor. If the API framework is used, developers must reserve the temporary space. To obtain the size of the temporary space (BufferSize) to be reserved, use the GetErfMaxMinTmpSize API.

Parameters

Table 1 Parameters in the template

Parameter

Description

T

Data type of the operand.

isReuseSource

Whether the source operand can be modified. This parameter is reserved. Pass the default value false.
Table 2 API parameters

Parameter

Input/Output

Description

dstLocal

Output

Destination operand.

The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT.

srcLocal

Input

Source operand.

The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT.

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

sharedTmpBuffer

Input

Temporary buffer.

The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT.

For details about how to obtain the temporary space size (BufferSize), see 7.11.31-Erf Tiling.

calCount

Input

Number of actually computed data elements. The value range is (0, srcTensor.GetSize()].

Returns

None

Availability

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 Restrictions.

Example

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#include "kernel_operator.h"

template <typename srcType>
class KernelErf {
public:
    __aicore__ inline KernelErf()
    {}
    __aicore__ inline void Init(GM_ADDR srcGm, GM_ADDR dstGm, uint32_t srcSize)
    {
        srcGlobal.SetGlobalBuffer(reinterpret_cast<__gm__ srcType *>(srcGm), srcSize);
        dstGlobal.SetGlobalBuffer(reinterpret_cast<__gm__ srcType *>(dstGm), srcSize);

        pipe.InitBuffer(inQueueX, 1, srcSize * sizeof(srcType));
        pipe.InitBuffer(outQueue, 1, srcSize * sizeof(srcType));
    }
    __aicore__ inline void Process(uint32_t offset, uint32_t calSize)
    {
        bufferSize = calSize;
        CopyIn(offset);
        Compute();
        CopyOut(offset);
    }
private:
    __aicore__ inline void CopyIn(uint32_t offset)
    {
        AscendC::LocalTensor<srcType> srcLocal = inQueueX.AllocTensor<srcType>();
        AscendC::DataCopy(srcLocal, srcGlobal[offset], bufferSize);
        inQueueX.EnQue(srcLocal);
    }
    __aicore__ inline void Compute()
    {
        AscendC::LocalTensor<srcType> dstLocal = outQueue.AllocTensor<srcType>();
        AscendC::LocalTensor<srcType> srcLocal = inQueueX.DeQue<srcType>();
        for (int i = 0; i < 100; i++) {
            AscendC::Erf<srcType, false, true>(dstLocal, srcLocal);
        }
        outQueue.EnQue<srcType>(dstLocal);
        inQueueX.FreeTensor(srcLocal);
    }
    __aicore__ inline void CopyOut(uint32_t offset)
    {
        AscendC::LocalTensor<srcType> dstLocal = outQueue.DeQue<srcType>();
        AscendC::DataCopy(dstGlobal[offset], dstLocal, bufferSize);
        outQueue.FreeTensor(dstLocal);
    }
private:
    AscendC::GlobalTensor<srcType> srcGlobal;
    AscendC::GlobalTensor<srcType> dstGlobal;
    AscendC::TPipe pipe;
    AscendC::TQue<AscendC::QuePosition::VECIN, 1> inQueueX;
    AscendC::TQue<AscendC::QuePosition::VECOUT, 1> outQueue;
    uint32_t bufferSize = 0;
};

template <typename dataType>
__aicore__ void kernel_erf_operator(GM_ADDR srcGm, GM_ADDR dstGm, uint32_t srcSize)
{
    KernelErf<dataType> op;
    op.Init(srcGm, dstGm, srcSize);
    op.Process();
}
Result example:
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Input data (srcLocal):
[-9.609991  -1.8448765  9.609758   3.8447127 -1.1222854  9.229954
 -1.9746934 -3.7733989 -4.9434195  0.8424659  0.2051153 -9.630209
  9.585648   1.3517833 -7.195028   4.7777047]
Output data (dstLocal):
[-1.         -0.9909206   1.          0.99999994 -0.88752156  1.
 -0.994772   -0.9999999  -1.          0.7665139   0.22824255 -1.
  1.          0.9440866  -1.          1.        ]
Parent topic: Erf