Sigmoid

Applicability

Product	Supported
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	√

Function

Performs element-wise logistic regression with Sigmoid using the following formula:

$\text{[math]}$

Prototype

Pass to 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 Sigmoid(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 Sigmoid(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 Sigmoid(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 Sigmoid(const LocalTensor<T>& dstTensor, const LocalTensor<T>& srcTensor)

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 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 GetSigmoidMaxMinTmpSize.

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. For the Atlas training products, 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 Sigmoid and is provided by developers. For details about how to obtain the size of the temporary space (BufferSize), see GetSigmoidMaxMinTmpSize.
calCount	Input	Number of elements involved in the computation.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.

The source operand address must not overlap the destination operand address.
The address of sharedTmpBuffer must not overlap that of the source or destination operand.

Example

For details about the complete call example, see More Examples.

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 shape is 1024, the input data type of the operator is half, and the number of actually computed data elements is 512.
AscendC::Sigmoid(dstLocal, srcLocal, sharedTmpBuffer, 512);

Result example:

Input data (srcLocal): [1.762616 7.9542747 ... 7.8306146 6.3167496]
Output data (dstLocal):
[0.853537 0.996489 ... 0.996027 0.998197]

Parent topic: Sigmoid APIs