Mins (Flexible Scalar Position)

Atlas 350 Accelerator Card

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Atlas A3 training product / Atlas A3 inference product

x

Atlas A2 training product / Atlas A2 inference product

x

Atlas 200I/500 A2 inference product

x

Atlas inference product AI Core

x

Atlas inference product Vector Core

x

Atlas training product

x

T

Operand data type.

For an API with a flexible scalar position, this parameter is reserved for future use. If this parameter needs to be specified, pass the default value BinaryDefaultType.

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

config

Position of a single element. This parameter is of BinaryConfig type and takes effect when the scalar input is a LocalTensor single element. The default value is DEFAULT_BINARY_CONFIG, indicating that the scalar is after the tensor.

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struct BinaryConfig {
    int8_t scalarTensorIndex = 1; // Position of the scalar when the scalar input is a LocalTensor single element. 0 indicates the scalar is before the tensor and 1 indicates the scalar is after the tensor.
};
constexpr BinaryConfig DEFAULT_BINARY_CONFIG = {1};

U

LocalTensor data type. The data type is automatically inferred based on dst. Developers do not need to configure this parameter. Ensure that dst meets the data type requirements.

S

LocalTensor or scalar data type. The data type is automatically inferred based on src0. Developers do not need to configure this parameter. Ensure that src0 meets the data type requirements.

V

LocalTensor or scalar data type. The data type is automatically inferred based on src1. Developers do not need to configure this parameter. Ensure that src1 meets the data type requirements.

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.

For the Atlas 350 Accelerator Card, the supported data types are uint8_t, int8_t, half, bfloat16_t, int16_t, float, int32_t, uint64_t, and int64_t.

src0/src1

Input

Source operands.

• If the scalar input is a LocalTensor, the source operands are a vector operand and a single element from the LocalTensor. 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 uint8_t, int8_t, half, bfloat16_t, int16_t, float, int32_t, uint64_t, and int64_t.

• If the scalar input is an immediate value:

  For the Atlas 350 Accelerator Card, the supported data types are uint8_t, int8_t, half, bfloat16_t, int16_t, float, int32_t, uint64_t, and int64_t.

The data type must be the same as that of 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.