Tan

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 tangent computation using the following formula:

The Taylor's Formula of Tan(x) is as follows:

B2n is a Bernoulli number.

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 Tan(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 Tan(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 Tan(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 Tan(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 allocated through the API framework or passed by developers through the sharedTmpBuffer input parameter.

  • 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.
  • 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.

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

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.

For details about how to obtain the temporary space size (BufferSize), see GetTanMaxMinTmpSize.

calCount

Input

Number of elements involved in the computation.

Returns

None

Restrictions

  • The input data must be within the range of (-65504.0, 65504.0).
  • 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.

Example

For a complete call example, see Tan operator sample.
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// dstLocal: tensor for storing the Tan computation result
// srcLocal: tensor for storing the Tan computation input
// sharedTmpBuffer: tensor for storing the temporary buffer during Tan computation

// Allocate the temporary space through the API framework, all of which is used for computation.
AscendC::Tan(dstLocal, srcLocal);
// Allocate the temporary space through the API framework, part of which is used for computation, with the number of elements involved in the computation being 512.
AscendC::Tan(dstLocal, srcLocal, 512);

// Pass the temporary space through the sharedTmpBuffer input parameter, all of which is used for computation.
AscendC::Tan(dstLocal, srcLocal, sharedTmpBuffer);
// Pass the temporary space through the sharedTmpBuffer input parameter, part of which is used for computation, with the number of elements involved in computation being 512.
AscendC::Tan(dstLocal, srcLocal, sharedTmpBuffer, 512);
Result example:
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Input (srcLocal):
[-2.56 -2.55 -2.54 ... 0. ... 2.53  2.54  2.55]
Output (dstLocal):
[0.6567  0.671  0.688 ... -0.702  -0.688  -0.671 ]