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Description: Maps a set of indices to a vector space and thereby quantizes the data. The 2D weight tensor of the embedding operator is weight, which has m+1 rows and n columns. For any input index tensor indices (for example, 1 row and 3 columns), the output out is a tensor with 3 rows and n columns, as shown below.

$$\begin{aligned} & weight = \begin{bmatrix} x_{01} & x_{02} & ... & x_{0n}\\ x_{11} & x_{12} & ... & x_{1n}\\ x_{21} & x_{22} & ... & x_{2n}\\ ...\\ x_{m1} & x_{m2} & ... & x_{mn}\\ \end{bmatrix}\\ & indices = \begin{bmatrix} 0,2,m \end{bmatrix}\\ & out = \begin{bmatrix} x_{01} & x_{02} & ... & x_{0n}\\ x_{21} & x_{22} & ... & x_{2n}\\ x_{m1} & x_{m2} & ... & x_{mn}\\ \end{bmatrix}\\ \end{aligned}$$ [object Object]

Each operator has calls. First, aclnnEmbeddingGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation flow. Then, aclnnEmbedding is called to perform computation.

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• Parameters

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  • [object Object]Atlas inference products[object Object], [object Object]Atlas training products[object Object], and [object Object]Atlas 200I/500 A2 inference products[object Object]: The data type cannot be BFLOAT16.

• Returns

  [object Object]: 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

  [object Object]: status code. For details, see .

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

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