Compares

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

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Atlas inference product Vector Core

x

Atlas training product

x

T

Data type of the source operand.

U

Data type of the destination operand.

isSetMask

Indicates whether to set mask inside the API.

• true: sets mask inside the API.
• false: sets mask outside the API. Developers need to use the SetVectorMask API to set the mask value. In this mode, the mask value in the input parameter of this API must be set to the placeholder MASK_PLACEHOLDER.

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.

dst is used to store the comparison result. The uint8_t data in dst is expanded bit-wise, with each bit from left to right representing the comparison result of the corresponding positions in src0 and src1. If the comparison result is true, the corresponding bit is set to 1; otherwise, it is set to 0.

For the Atlas 350 Accelerator Card, the supported data type is uint8_t.

For the Atlas A3 training product / Atlas A3 inference product , the supported data type is uint8_t.

For the Atlas A2 training product / Atlas A2 inference product , the supported data type is uint8_t.

For the Atlas 200I/500 A2 inference product , the supported data type is uint8_t.

For the Atlas inference product AI Core, the supported data type is uint8_t.

src0

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.

For the Atlas 350 Accelerator Card, the supported data types are int8_t, uint8_t, int16_t, uint16_t, half, bfloat16_t, int32_t, uint32_t, float, int64_t, uint64_t, and double (double only supports CMPMODE::EQ).

For the Atlas A3 training product / Atlas A3 inference product , the supported data types are half/float (support all CMPMODE options) and int32_t (only supports CMPMODE::EQ).

For the Atlas A2 training product / Atlas A2 inference product , the supported data types are half/float (support all CMPMODE options) and int32_t (only supports CMPMODE::EQ).

For the Atlas 200I/500 A2 inference product , the supported data types are int16_t, uint16_t, half, float, int32_t, and uint32_t.

For the Atlas inference product AI Core, the supported data types are half and float.

src1Scalar

Input

Source operand (scalar). The data type is the same as that of src0.

cmpMode

Input

Comparison mode, including EQ, NE, GE, LE, GT, and LT.

• LT: src0 < src1
• GT: src0 > src1
• GE: src0 ≥ src1
• EQ: src0 = src1
• NE: src0 ≠ src1
• LE: src0 ≤ src1

mask/mask[]

Input

mask controls the elements that participate in computation in each iteration.

For the Atlas 350 Accelerator Card, setting this parameter is valid.

For the Atlas A3 training product / Atlas A3 inference product , this parameter is reserved. Setting this parameter is invalid.

For the Atlas A2 training product / Atlas A2 inference product , this parameter is reserved. Setting this parameter is invalid.

For the Atlas 200I/500 A2 inference product , setting this parameter is valid.

For the Atlas inference product AI Core, this parameter is reserved. Setting this parameter is invalid.

• Contiguous mode: indicates the number of contiguous elements that participate in computation. The value range depends on the operand data type. The maximum number of elements that can be processed in each iteration varies depending on the data type. If the operand is 16-bit, mask is in the range [1, 128]. If the operand is 32-bit, mask is in the range [1, 64].

• Bitwise mode: controls which elements participate in the computation on a per-bit basis. A bit value of 1 indicates participation, while 0 indicates non-participation. The parameter type is a uint64_t array of length 2 or 4.

  For example, if mask = [0, 8] and 8 = 0b1000, only the fourth element participates in computation.

  The value range depends on the operand data type. The maximum number of elements that can be processed in each iteration varies depending on the data type. If the operand is 16-bit, mask[0] and mask[1] are in the range [0, 2⁶⁴ – 1] and cannot be 0 at the same time. If the operand is 32-bit, mask[1] is 0 and mask[0] is in the range (0, 2⁶⁴ – 1].

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

Parameters that control the operand address strides. They are of the UnaryRepeatParams type (see UnaryRepeatParams), and contain parameters such as the address stride of the operand for the same Data Block between adjacent iterations and the address stride of the operand between different Data Blocks in a single iteration.

For details about the address stride of the operand between adjacent iterations, see repeatStride. For details about the address stride of the operand between different data blocks in a single iteration, see dataBlockStride.

count

Input

Number of elements involved in the computation. When setting count, the total memory occupied by the count elements must be 256-byte aligned.