LeakyRelu

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	√
Atlas inference product AI Core	√
Atlas inference product Vector Core	x
Atlas training product	x

Function Usage

Performs an element-wise leaky rectified linear unit (Leaky ReLU) operation. The formula is as follows:

$\text{[math]}$

Leaky ReLU is a commonly used activation function in artificial neural networks. Its mathematical expression is as follows.

Leaky ReLU differs from ReLU by introducing a small slope for negative values, instead of setting them to zero.

Prototype

Computation of the first n pieces of data of a tensor

        
             template <typename T, bool isSetMask = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const T& scalarValue, const int32_t& count)

High-dimensional tensor sharding computation

Bitwise mask mode

          
               template <typename T, bool isSetMask = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const T& scalarValue, uint64_t mask[], const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

Contiguous mask mode

          
               template <typename T, bool isSetMask = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const T& scalarValue, uint64_t mask, const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

If dst and src use the TensorTrait data structure, their data type (represented by LiteType in TensorTrait) may be different from the data type of scalarValue. So, a new template parameter U needs to be created to indicate the data type of scalarValue. std::enable_if is used to check whether LiteType extracted from T is the same as U. If they are the same, the API passes the compilation. Otherwise, the compilation fails. The API prototype is defined as follows:

Computation of the first n pieces of data of a tensor

        
             template <typename T, typename U, bool isSetMask = true, typename Std::enable_if<Std::is_same<PrimT<T>, U>::value, bool>::type = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const U& scalarValue, const int32_t& count)

High-dimensional tensor sharding computation

Bitwise mask mode

          
               template <typename T, typename U, bool isSetMask = true, typename Std::enable_if<Std::is_same<PrimT<T>, U>::value, bool>::type = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const U& scalarValue, uint64_t mask[], const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

Contiguous mask mode

          
               template <typename T, typename U, bool isSetMask = true, typename Std::enable_if<Std::is_same<PrimT<T>, U>::value, bool>::type = true>
__aicore__ inline void LeakyRelu(const LocalTensor<T>& dst, const LocalTensor<T>& src, const U& scalarValue, uint64_t mask, const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

Parameters

**Table 1** Template parameters
Parameter	Description
T	Operand data type. For the Atlas inference product AI Core, 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 A3 training product / Atlas A3 inference product , the supported data types are half and float. For the Atlas 200I/500 A2 inference product , the supported data types are half and float. For the Atlas 350 Accelerator Card, the supported data types are half and float.
U	Data type of scalarValue. For the Atlas inference product AI Core, 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 A3 training product / Atlas A3 inference product , the supported data types are half and float. For the Atlas 200I/500 A2 inference product , the supported data types are half and float. For the Atlas 350 Accelerator Card, the supported data types are half and float.
isSetMask	Whether to set the mask mode and mask value inside the API. true: indicates that the settings are performed inside the API. The APIs for high-dimensional tensor sharding computation and for computing the first n pieces of data in a tensor use the Normal or Counter mode of the mask. Generally, retain the default value of isSetMask, indicating that the mask mode and mask value are set in the API based on the mask and count parameters passed by developers. false: indicates that the settings are performed outside the API. For high-dimensional tensor sharding computation APIs, in some scenarios that require high performance, developers need to use SetMaskNorm or SetMaskCount to set the mask mode and use the SetVectorMask API to set the mask value. The mask value in the input parameter of this API must be set to MASK_PLACEHOLDER. For the APIs that compute the first n pieces of data in a tensor, in some scenarios that require high performance, developers need to use SetMaskCount to set the mask mode to Counter and use the SetVectorMask API to set the mask value. The count value in the input parameter of this API does not take effect. You are advised to set it to 1. For the models below, isSetMask is invalid for the APIs that compute the first n pieces of data in a tensor. Retain the default value. Atlas 350 Accelerator Card Atlas 200I/500 A2 inference product

**Table 2** Parameters
Parameter	Input/Output	Meaning
dst	Output	Destination operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned.
src	Input	Source operand. The type is LocalTensor, and TPosition can be VECIN, VECCALC, or VECOUT. The start address of LocalTensor must be 32-byte aligned. The data type must be the same as that of the destination operand.
scalarValue	Input	Source operand, and its data type must be the same as the element type of the tensor in the destination operand.
count	Input	Number of elements involved in the computation.
mask/mask[]	Input	mask controls the elements that participate in computation in each iteration. Bitwise mode: controls the elements that participate in computation by bit. If a bit is set to 1, the corresponding element participates in the computation. If a bit is set to 0, the corresponding element is masked from the computation. The mask value is an array. The array length and the value range of the array elements are related to the operand data type. When the operand is 16-bit, the array length is 2, with mask[0] and mask[1] each in the range [0, 2⁶⁴ – 1], and they cannot both be 0 at the same time. When the operand is 32-bit, the array length is 1, with mask[0] in the range (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1, with mask[0] in the range (0, 2³² – 1]. For example, if mask = [0, 8] and 8 = 0b1000, only the fourth element participates in computation. Contiguous mode: indicates the number of contiguous elements that participate in computation. The value range is related to the operand data type. The maximum number of elements that can be processed in each iteration varies according to the data type. When the operand is 16-bit, mask ∈ [1, 128]. When the operand is 32-bit, mask ∈ [1, 64]. When the operand is 64-bit, mask ∈ [1, 32].
repeatTime	Input	Number of iteration repeats. The Vector Unit reads 256 bytes of contiguous data for computation each time. To read the complete data for processing, the unit needs to read the input data in multiple repeats. repeatTime indicates the number of iterations. For details about this parameter, see High-dimensional Sharding APIs.
repeatParams	Input	Structure for controlling element-wise operations. For details, see UnaryRepeatParams.

Returns

None

Restrictions

For details about the operand address alignment requirements, see General Address Alignment Restrictions.
For details about the constraints on operand address overlapping, see General Address Overlapping Restrictions.

Examples

Example of high-dimensional tensor sharding computation (contiguous mask mode)

        
             // dstLocal: input tensor for storing the LeakyReLU computation result
// srcLocal: input tensor for storing the LeakyReLU computation result
// scalar: negative slope coefficient

uint64_t mask = 128;
half scalar = 0.001;
// repeatTime = 4. 128 elements are processed in a single iteration. To compute 512 elements, four iterations are required.
// dstBlkStride, srcBlkStride = 1. The interval between src0 data addresses involved in computation in each iteration is one data block, indicating that data is continuously read and written in a single iteration.
// dstRepStride, srcRepStride = 8. The interval between addresses of adjacent iterations is eight data blocks, indicating that data is continuously read and written between adjacent iterations.
AscendC::LeakyRelu(dstLocal, srcLocal, scalar, mask, 4, {1, 1, 8, 8});

Example of high-dimensional tensor sharding computation (bitwise mask mode)

        
             uint64_t mask[2] = { UINT64_MAX, UINT64_MAX };
half scalar = 0.001;
// repeatTime = 4. 128 elements are processed in a single iteration. To compute 512 elements, four iterations are required.
// dstBlkStride, srcBlkStride = 1. The interval between src0 data addresses involved in computation in each iteration is one data block, indicating that data is continuously read and written in a single iteration.
// dstRepStride, srcRepStride = 8. The interval between addresses of adjacent iterations is eight data blocks, indicating that data is continuously read and written between adjacent iterations.
AscendC::LeakyRelu(dstLocal, srcLocal, scalar, mask, 4, {1, 1, 8, 8});

Example of computing the first n pieces of data of a tensor

        
             half scalar = 0.001;
// The input data type of the operator is half, and the number of elements involved in the computation is 512.
AscendC::LeakyRelu(dstLocal, srcLocal, scalar, 512);

Result example:

Input (src0Local): [-287.  246. -438.  177. 596. -950.  -293.  322. ... -900.]
Input (scalar) = 0.001
Output (dstLocal): [-0.287 246. -0.438 177. 596. -0.950 -0.293 322. ... -0.900]

Parent topic: Basic Arithmetic