Div

Atlas 350 Accelerator Card

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Atlas A3 training product / Atlas A3 inference product

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Atlas A2 training product / Atlas A2 inference product

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Atlas 200I/500 A2 inference product

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Atlas inference product AI Core

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Atlas inference product Vector Core

x

Atlas training product

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T

Operand data type.

For the Atlas 350 Accelerator Card, the supported data types are int16_t, uint16_t, half, int32_t, uint32_t, float, complex32, int64_t, uint64_t, and complex64.

For the Atlas A3 training product / Atlas A3 inference product , 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 200I/500 A2 inference product , the supported data types are half and float.

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

For the Atlas training product , the supported data types are half and float.

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.

config

This parameter is supported only by the Atlas 350 Accelerator Card.

Precision computation mode. The DivConfig type is defined as follows:

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enum class DivAlgo {
    INTRINSIC = 0,
    DIFF_COMPENSATION,
    PRECISION_1ULP_FTZ_TRUE,
    PRECISION_0ULP_FTZ_TRUE,
    PRECISION_0ULP_FTZ_FALSE,
    PRECISION_1ULP_FTZ_FALSE
};
struct DivConfig {
    DivAlgo algo = DivAlgo::INTRINSIC;
};

The precision computation mode is configured by using the algo parameter of the DivConfig structure. The options of algo are as follows:

• DivAlgo::INTRINSIC and DivAlgo::PRECISION_1ULP_FTZ_TRUE: The result is computed using a single instruction, with a maximum precision error of 1 ulp.
• DivAlgo::DIFF_COMPENSATION and DivAlgo::PRECISION_0ULP_FTZ_TRUE: The result is computed using the difference compensation algorithm, with a maximum precision error of 0 ulp. Currently, this algorithm supports only the float data type.
• DivAlgo::PRECISION_0ULP_FTZ_FALSE: Subnormal data computation is supported. The result is computed using the difference compensation algorithm, with a maximum precision error of 0 ulp. Currently, this algorithm supports only the float data type.
• DivAlgo::PRECISION_1ULP_FTZ_FALSE: Subnormal data computation is supported. The result is computed using a single instruction, with a maximum precision error of 1 ulp.

The default value of this parameter, DEFAULT_DIV_CONFIG, is defined as follows:

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constexpr DivConfig DEFAULT_DIV_CONFIG = { DivAlgo::INTRINSIC };

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.

src0 and src1

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.

Both source operands must have the same data type as 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 compute unit reads 256 bytes of contiguous data for computation each time. To process the input data, the data needs to be read and computed over multiple repeats. repeatTime indicates the number of repeats.

For details about this parameter, see High-dimensional Sharding APIs.

repeatParams

Input

Parameters that control the operand address strides. They are of the BinaryRepeatParams type, and contain such parameters as those that specify the address stride of the operand for the same data block between adjacent iterations and 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.