Atanh

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

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

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 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.
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      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.
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      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.
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      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 passed, developers must allocate space for the tensor. If the API framework is used, you 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 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. 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 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 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 details about the complete calling example, see More Examples.

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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 half, and the number of actually computed data elements is 512.
AscendC::Atanh(dstLocal, srcLocal, sharedTmpBuffer, 512);
Result example:
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Input data (srcLocal): [0.80541134 0.08385705 0 ...  -0.30962205 -0.28947052]
Output data (dstLocal): [1.11382794 0.08405444 0 ...  -0.32012733 -0.29798826]