Atanh

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

Performs element-wise inverse hyperbolic tangent cosine computation using the following formula:

$\text{[math]}$

Prototype

Pass the temporary space through the sharedTmpBuffer input parameter.

All or part of the source operand tensors are involved in computation.

template <typename T, bool isReuseSource = false>
__aicore__ inline void Atanh(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.

template <typename T, bool isReuseSource = false>
__aicore__ inline void Atanh(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.

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

All source operand tensors are involved in computation.

template <typename T, bool isReuseSource = false>
__aicore__ inline void Atanh(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 GetAtanhMaxMinTmpSize API.

Parameters

**Table 1** Template parameters
Parameter	Description
T	Data type of the operand. 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. This parameter is reserved. Pass the default value false.

**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 Atanh and is provided by developers. For details about how to obtain the temporary space size (BufferSize), see GetAtanhMaxMinTmpSize.
calCount	Input	Number of elements involved in the computation.

Returns

None

Restrictions

The value range of the input source data must be (–0.99, –0.001) or (0.001, 0.99). If the input is not within the range, the output is invalid.
The source operand address must not overlap the destination operand address.
The address of 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 a complete call example, see atanh operator sample.

// dstLocal: tensor for storing the Atanh computation result
// srcLocal: tensor for storing the Atanh computation input
// sharedTmpBuffer: tensor for storing the temporary buffer during Atanh computation
// The input data type of the operator is half, and the number of elements involved in the computation is 512.
AscendC::Atanh(dstLocal, srcLocal, sharedTmpBuffer, 512);

Result example:

Input (srcLocal): [0.000000 0.010000 0.020000 ... 0.990000 1.000000 1.010000 ...]
Output (dstLocal): [0.000000 0.010000 0.020003 ... 2.646650 Inf -NaN ...]

Parent topic: Atanh APIs