Lgamma

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	x
Atlas inference product AI Core	√
Atlas inference product Vector Core	x
Atlas training product	x

Function Usage

Computes the natural logarithm of the absolute value of the Gamma function of x element-wise. The formula is as follows.

$\text{[math]}$

Prototype

Pass the temporary space through the sharedTmpBuffer input parameter.

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

The API framework allocates temporary space.

template <typename T, bool isReuseSource = false>
__aicore__ inline void Lgamma(const 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, extra 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 by 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 or deallocated, improving the flexibility and buffer utilization.
When the API framework allocates temporary space, developers do not need to allocate the space but must reserve the required size for the temporary 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 API provided in 6.1.55 Lgamma Tiling.

Parameters

**Table 1** Template parameters
Parameter	Description
T	Operand data type. For the Atlas 350 Accelerator Card, the supported data types are half and float. For the Atlas A3 training product/Atlas A3 inference product, the supported data types are half and float. For the Atlas A2 training product/Atlas A2 inference product, the supported data types are half and float. For the Atlas inference product 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 TPosition can be VECIN, VECCALC, or VECOUT.
srcTensor	Input	Source operand. The type is LocalTensor, and TPosition can be 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 TPosition can be VECIN, VECCALC, or VECOUT. This parameter is used to store intermediate variables during complex computation in Lgamma and is provided by developers. For details about how to obtain the temporary space size (BufferSize), see GetLgammaMaxMinTmpSize.
calCount	Input	Number of elements involved in the computation.

Returns

None

Constraints

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

Examples

For details about a complete example, see Lgamma operator sample.

// dstLocal: tensor for storing the computation result
// srcLocal: input tensor involved in computation
// sharedTmpBuffer: temporary buffer for storing intermediate variables during complex internal computation
// The input tensor length is 1,024, the input data type of the operator is half, and the number of actually computed data elements is 512.
AscendC::Lgamma(dstLocal, srcLocal, sharedTmpBuffer, 512);

Result example:

Input (srcLocal): [0.000000  0.010000  0.020000  0.030000  0.040000  ...]
Output (dstLocal): [inf       4.599480  3.900805  3.489971  3.197078  ...]

Parent topic: Lgamma APIs