Muls

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	√
Atlas inference product 's AI Core	√
Atlas inference product 's Vector Core	x
Atlas training products	√

Function Usage

Multiplies each element in the vector by a scalar. The calculation formula is as follows:

$\text{[math]}$

Prototype

Compute of the first n data elements of a tensor

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

Compute of the sharded high-dimensional tensor

Bitwise mask mode

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

Compute of the first n data elements 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 Muls(const LocalTensor<T>& dst, const LocalTensor<T>& src, const U& scalarValue, const int32_t& count)

Compute of the sharded high-dimensional tensor

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 Muls(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 Muls(const LocalTensor<T>& dst, const LocalTensor<T>& src, const U& scalarValue, uint64_t mask, const uint8_t repeatTime, const UnaryRepeatParams& repeatParams)

Parameters

**Table 1** Parameters in the template
Parameter	Description
T	Operand data type. For the Atlas training products , the supported data types are half and float. For the Atlas inference product 's AI Core, the supported data types are half, int16_t, float, and int32_t. For the Atlas A2 training products / Atlas A2 inference products , the supported data types are half, int16_t, float, and int32_t. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are half, int16_t, float, and int32_t. For the Atlas 200I/500 A2 inference products , the supported data types are half, int16_t, float, and int32_t.
U	Data type of scalarValue. For the Atlas training products , the supported data types are half and float. For the Atlas inference product 's AI Core, the supported data types are half, int16_t, float, and int32_t. For the Atlas A2 training products / Atlas A2 inference products , the supported data types are half, int16_t, float, and int32_t. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are half, int16_t, float, and int32_t. For the Atlas 200I/500 A2 inference products , the supported data types are half, int16_t, float, and int32_t.
isSetMask	Whether to set the mask mode and mask value inside the API. true: indicates that the settings are performed inside the API. The tensor high-dimensional tiling computation API or the API for computing the first n elements of a tensor uses the Normal/Counter mode of the mask. In general, you can retain the default value of isSetMask, which indicates that the mask mode and mask value are set inside the API based on the mask/count parameter passed by the developer. false: indicates that the settings are performed outside the API. For the tensor high-dimensional tiling computation API, in some scenarios with high performance requirements, you need to use SetMaskNorm or SetMaskCount to set the mask mode and use SetVectorMask 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 data elements in a tensor, in scenarios that require high performance, you need to use SetMaskCount to set the mask mode to counter mode and use the SetVectorMask API to set the mask value. The count parameter in the input parameter of this API does not take effect. You are advised to set it to 1. For the following models, the isSetMask parameter in the API for calculating the first n pieces of data in a tensor does not take effect. Retain the default value. Atlas 200I/500 A2 inference products

**Table 2** Parameters
Parameter	Input/Output	Meaning
dst	Output	Destination operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the LocalTensor must be 32-byte aligned.
src	Input	Source operand. The type is LocalTensor, and the supported TPosition is VECIN, VECCALC, or VECOUT. The start address of the LocalTensor must be 32-byte aligned. Its 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	The mask parameter is used to control the elements involved 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 in the computation. The mask is in array form. The array length and the value range of the array elements are related to the data type of the operand. When the operand is 16-bit, the array length is 2. In this case, mask[0] and mask[1] must be in the range of [0, 2⁶⁴ – 1] and cannot be 0 at the same time. When the operand is 32-bit, the array length is 1. In this case, mask[0] must be in the range of (0, 2⁶⁴ – 1]. When the operand is 64-bit, the array length is 1. In this case, mask[0] must be in the range of (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 repeat 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 repeats. For details about this parameter, see High-dimensional Sharding APIs.
repeatParams	Input	Control structure information of element 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 operand address overlapping restrictions, see General Address Overlap Restrictions.

Examples

For more examples, see here.

High-dimensional tensor segmentation and computation example (mask in contiguous mode)

        
             uint64_t mask = 128;
int16_t scalar = 2;
// 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 calculation 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::Muls(dstLocal, srcLocal, scalar, mask, 4, { 1, 1, 8, 8 });

High-dimensional tensor segmentation and computation example (mask in bitwise mode)

        
             uint64_t mask[2] = { UINT64_MAX, UINT64_MAX };
int16_t scalar = 2;
// 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 calculation 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::Muls(dstLocal, srcLocal, scalar, mask, 4, {1, 1, 8, 8});

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

        
             int16_t scalar = 2;
AscendC::Muls(dstLocal, srcLocal, scalar, 512);

Result example:

Input (srcLocal): [1 2 3 ... 512]
Input (scalar): 2
Output (dstLocal): [2 4 6 ... 1024]

Parent topic: Basic arithmetic operations