Digamma

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	x
Atlas inference product 's AI Core	√
Atlas inference product 's Vector Core	x
Atlas training products	x

Function

Computes the logarithmic derivative of the gamma function of x element-wise. The formula is as follows, where is the Gamma function.

$\text{[math]}$

Prototype

Pass the temporary space through the sharedTmpBuffer input parameter.

        
             template <typename T, bool isReuseSource = false>
__aicore__ inline void Digamma(LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor, LocalTensor<uint8_t>& sharedTmpBuffer, const uint32_t calCount)

Allocate the temporary space through the API framework.

        
             template<typename T, bool isReuseSource = false>
__aicore__ inline void Digamma(LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor, const uint32_t calCount)

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 temporary space size (BufferSize), use the API provided in GetDigammaMaxMinTmpSize.

Parameters

**Table 1** Template parameters
Parameter	Description
T	Data type of the operand. 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 inference product 's AI Core, the supported data types are half and float.
isReuseSource	Whether the source operand can be modified. The default value is false. This parameter is valid only when the input data type is float. true: The source operand can be modified. In this case, this API can reuse srcTensor's buffer during internal computation, reducing buffer usage. false: The srcTensor's buffer cannot be reused during internal computation of this API. For details about how to use isReuseSource, see More Examples.

**Table 2** API parameters
Parameter	Input/Output	Description
dstTensor	Output	Destination operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT.
srcTensor	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. This parameter is used to store intermediate variables during complex computation in Digamma and is provided by developers. For details about how to obtain the temporary space size (BufferSize), see GetDigammaMaxMinTmpSize.
calCount	Input	Number of elements involved in the computation.

Returns

None

Restrictions

The source operand address must not overlap the destination operand address.
sharedTmpBuffer must not overlap the addresses of the source operand and destination operand.
For details about the operand address alignment requirements, see General Address Alignment Restrictions.

Example

For details about the complete call example, see More Examples.

      
           AscendC::TPipe pipe;
AscendC::TQue<AscendC::TPosition::VECCALC, 1> tmpQue;
pipe.InitBuffer(tmpQue, 1, bufferSize);  // bufferSize is obtained through the tiling parameter on the host.
AscendC::LocalTensor<uint8_t> sharedTmpBuffer = tmpQue.AllocTensor<uint8_t>();
// The input tensor length is 1024, the input data type of the operator is float, and the number of actually computed data elements is 1024.
AscendC::Digamma<float, false>(dstLocal, srcLocal, sharedTmpBuffer, 1024);

Result example:

Input data (srcLocal): [5.3675685 0.26528683 -2.872628 ... 2.9387941 9.001339]
Output data (dstLocal): [1.5843406 -3.978809 -6.2081366 ... 0.8983184 2.1407988]

Parent topic: Digamma APIs