Tanh

Applicability

Function Usage

Performs element-wise logistic regression with Tanh 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.

    1 2	template <typename T, bool isReuseSource = false, const TanhConfig& config = DEFAULT_TANH_CONFIG> __aicore__ inline void Tanh(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.

    1 2	template <typename T, bool isReuseSource = false, const TanhConfig& config = DEFAULT_TANH_CONFIG> __aicore__ inline void Tanh(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.

    1 2	template <typename T, bool isReuseSource = false, const TanhConfig& config = DEFAULT_TANH_CONFIG> __aicore__ inline void Tanh(const LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor, const uint32_t calCount)

  • All source operand tensors are involved in computation.

    1 2	template <typename T, bool isReuseSource = false, const TanhConfig& config = DEFAULT_TANH_CONFIG> __aicore__ inline void Tanh(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 API provided in GetTanhMaxMinTmpSize.

Parameters

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enum class TanhAlgo {
  INTRINSIC = 0,
  SUBSECTION_COMPENSATION,
};
struct TanhConfig {
  TanhAlgo algo = TanhAlgo::INTRINSIC;
};

Returns

None

Restrictions

• 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

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// dstLocal: tensor for storing the Tanh computation result
// srcLocal: tensor for storing the Tanh computation input
// sharedTmpBuffer: tensor for storing the temporary buffer during Tanh computation

// Allocate the temporary space through the API framework, all of which is used for computation.
AscendC::Tanh(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::Tanh(dstLocal, srcLocal, 512);

// Pass the temporary space through the sharedTmpBuffer input parameter, all of which is used for computation.
AscendC::Tanh(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::Tanh(dstLocal, srcLocal, sharedTmpBuffer, 512);
static constexpr AscendC::TanhAlgo algo = AscendC::TanhAlgo::SUBSECTION_COMPENSATION;
static constexpr AscendC::TanhConfig config = { algo };
AscendC::Tanh<half, false, config>(dstLocal, srcLocal, sharedTmpBuffer, 512);

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Input (srcLocal):
[-2.56 -2.55 -2.54 ... 0. ... 2.53  2.54  2.55]
Output (dstLocal):
[-0.98813187 -0.9878992 -0.98761402 ... 0. ... 0.98737127 0.98761402 0.9878992]