ShiftLeft

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

Function Usage

Shifts each element in the source operand leftwards by the number of bits specified by the scalar in the formula. The left shift operation is classified into the following types based on the data type of the source operand:

If the data type is unsigned, logical left shift is performed. In this case, the binary number is shifted leftward by a specified number of bits. The most significant bit is discarded, and the least significant bit is padded with 0. For example, after the binary number 1010101010101010 (uint16_t) is logically shifted leftward by one bit, the result is 0101010101010100.
If the data type is signed, arithmetic left shift is performed. In this case, the binary number is shifted leftward by a specified number of bits. The second most significant bit is discarded, and the least significant bit is padded with 0. For example, after the binary number 1010101010101010 (int16_t) is arithmetically shifted leftward by one bit, the result is 1101010101010100; after the binary number is arithmetically shifted leftward by three bits, the result is 1101010101010000.

$\text{[math]}$

Prototype

Computation of the first n pieces of data of a tensor

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

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 ShiftLeft(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 ShiftLeft(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 ShiftLeft(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 A2 training products / Atlas A2 inference products , the supported data types are uint16_t, int16_t, uint32_t, and int32_t. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are uint16_t, int16_t, uint32_t, and int32_t. For the Atlas 200I/500 A2 inference products , the supported data types are uint16_t, int16_t, uint32_t, and int32_t.
U	Data type of scalarValue. For the Atlas A2 training products / Atlas A2 inference products , the supported data types are uint16_t, int16_t, uint32_t, and int32_t. For the Atlas A3 training products / Atlas A3 inference products , the supported data types are uint16_t, int16_t, uint32_t, and int32_t. For the Atlas 200I/500 A2 inference products , the supported data types are uint16_t, int16_t, uint32_t, 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	Shift distance. Its data type must be the same as that of the tensor element in the destination operand. For the Atlas A2 training products / Atlas A2 inference products , when src is of the uint16_t or int16_t type, the value range of scalarValue is [0, 16]. When src is of the uint32_t or int32_t type, the value range of scalarValue is [0, 32]. For the Atlas A3 training products / Atlas A3 inference products , when src is of the uint16_t or int16_t type, the value range of scalarValue is [0, 16]. When src is of the uint32_t or int32_t type, the value range of scalarValue is [0, 32]. For the Atlas 200I/500 A2 inference products , when src is of the uint16_t or int16_t type, the value range of scalarValue is [0, 16]. When src is of the uint32_t or int32_t type, the value range of scalarValue is [0, 32].
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

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

        
             uint64_t mask = 128;
int16_t scalar = 2;
// repeatTime = 4. 128 elements are processed in each iteration. To calculate 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::ShiftLeft(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 };
int16_t scalar = 2;
// repeatTime = 4. 128 elements are processed in each iteration. To calculate 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::ShiftLeft(dstLocal, srcLocal, scalar, mask, 4, {1, 1, 8, 8});

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

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

Result example:

Input (src0Local): [1 2 3 ... 512]
Input (scalar): 2
Output (dstLocal): [4 8 12 ... 2048]

Parent topic: Logic-based computation