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[object Object][object Object]undefined[object Object]

• Description: Performs element-wise addition on the two tensor lists. The addition coefficient can be adjusted by the alpha parameter. Compared with , this API modifies the structure type of the input parameter alpha from aclTensor to aclScalar. Select the appropriate API based on actual requirements.
• Formula:$$x1 = [{x1_0}, {x1_1}, ... {x1_{n-1}}], x2 = [{x2_0}, {x2_1}, ... {x2_{n-1}}]\\ y = [{y_0}, {y_1}, ... {y_{n-1}}]\\$$ $$y_i = x1_i + x2_i * alpha (i=0,1,...n-1)$$

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Each operator has calls. First, aclnnForeachAddListV2GetWorkspaceSize is called to obtain the input parameters and compute the required workspace size based on the process. Then, aclnnForeachAddListV2 is called to perform computation.

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• Parameters

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  • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training series products/Atlas A3 inference series products[object Object]:

    The data type of [object Object] depends on that of [object Object]:

    • When [object Object] is of the FLOAT32 or BFLOAT16 type, the data type must be FLOAT32 or DOUBLE.
    • When [object Object] is of the FLOAT16 type, the data type must be FLOAT16 or DOUBLE.
    • When [object Object] is of the INT32 type, the data type must be INT32 or INT64.

• Returns

  aclnnStatus: status code. For details, see .

  The first-phase API implements input parameter verification. The following errors may be thrown.

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• Parameters

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• Returns

  aclnnStatus: status code. For details, see .

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• Deterministic computation:
  • aclnnForeachAddListV2 defaults to a deterministic implementation.

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The following example is for reference only. For details, see .