[object Object]

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

Description: Computes the backward pass of , and accumulates rows of [object Object] corresponding to identical [object Object] entries into [object Object].

[object Object]

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

[object Object]

• Parameters

  [object Object]
  • [object Object]Atlas training series products[object Object]: The data type cannot be BFLOAT16.

• Returns

  aclnnStatus: status code. For details, see .

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

  [object Object]

[object Object]

• Parameters

  [object Object]

• Returns

  aclnnStatus: status code. For details, see .

[object Object]

• Deterministic computation:

  • aclnnEmbeddingDenseBackward defaults to a non-deterministic implementation. You can call aclrtCtxSetSysParamOpt to enable deterministic computation.

• [object Object]Atlas training series products[object Object]:

  • If scale is set to true, the last dimension of grad is defined as embeddingDim. An error is reported when its size exceeds the specified range. The valid ranges are as follows:
    • When indices is INT32, the following condition must be satisfied:
    $$embeddingDim < \frac{180192 - countsSize * 4}{36}$$
    • When indices is INT64, the following condition must be satisfied:
    $$embeddingDim < \frac{180192 - countsSize * 8}{20}$$
    • The formula for countsSize is as follows, where coreNum indicates the number of AI processor cores:
    $$countsSize = numWeights / coreNum + numWeights \% coreNum$$

• [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]:

  • When the parameter shape exceeds the following limits, high precision cannot be guaranteed. If deterministic computation is enabled, high performance cannot be guaranteed either.
    • After grad is collapsed to a 2D shape, the first dimension exceeds INT32_MAX (2147483647).
    • numWeights exceeds INT32_MAX (2147483647).
  • When the collapsed dimension of indices exceeds INT32_INF (2139095040), high performance cannot be guaranteed.

[object Object]

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