[object Object]

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

• Description: Computes the mean squared error between each element in input x and target y. reduction specifies the reduction to be applied to the output. The value can be none, mean, or sum. mean indicates that the output will be averaged by reducing axis 0, and sum indicates that the output will be summed by reducing axis 0.

• Formula:

  If [object Object] is [object Object]:

  $$\ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad l_n = \left( x_n - y_n \right)^2,$$

  $x$ is self, $y$ is target, and $N$ is the batch size. If [object Object] is not [object Object]:

  $$\ell(x, y) = \begin{cases} \operatorname{mean}(L), & \text{if reduction} = \text{'mean';}\\ \operatorname{sum}(L), & \text{if reduction} = \text{'sum'.} \end{cases}$$

[object Object]

Each operator has calls. First, aclnnMseLossOutGetWorkspaceSize is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, aclnnMseLossOut is called to perform computation.

• [object Object]
• [object Object]

[object Object]

• Parameters:

  • self (aclTensor*, compute input): [object Object] in the formula, aclTensor on the device. The shapes of self and target meet the . are supported. The can be ND. The shape supports zero to eight dimensions.

    • [object Object]Atlas training series products[object Object]: The data type can be FLOAT16 or FLOAT.
    • [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 can be BFLOAT16, FLOAT16, or FLOAT.

  • target (aclTensor*, compute input): [object Object] in the formula, aclTensor on the device. The shapes of self and target meet the . are supported. The can be ND. The shape supports zero to eight dimensions.

    • [object Object]Atlas training series products[object Object]: The data type can be FLOAT16 or FLOAT.
    • [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 can be BFLOAT16, FLOAT16, or FLOAT.

  • reduction (int64_t, compute input): [object Object] in the formula, which specifies the reduction to be applied to the output. The value can be 0 ('none') | 1 ('mean') | 2 ('sum').

    none indicates that no reduction is applied. mean indicates that the output will be averaged by reducing axis 0. sum indicates that the output will be summed by reducing axis 0.

  • out (aclTensor*, compute output): $\ell(x, y)$ in the formula, aclTensor on the device. are supported. The can be ND. When the value of reduction is 0, the shape of out is the same as that obtained after broadcasting self and target. When the value of reduction is 1 or 2, the shape of out is the same as that obtained by broadcasting self and target and then applying a reduction operation along axis 0.

    • [object Object]Atlas training series products[object Object]: The data type can be FLOAT16 or FLOAT.
    • [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 can be BFLOAT16, FLOAT16, or FLOAT.

  • workspaceSize (uint64_t*, output): size of the workspace to be allocated on the device.

  • executor (aclOpExecutor**, output): operator executor, containing the operator computation process.

• Returns:

  aclnnStatus: status code. For details, see .

[object Object]

• Parameters:

  • workspace (void*, input): address of the workspace to be allocated on the device.

  • workspaceSize (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by the first-phase API aclnnMseLossOutGetWorkspaceSize.

  • executor (aclOpExecutor*, input): operator executor, containing the operator computation process.

  • stream (aclrtStream, input): stream for executing the task.

• Returns:

  aclnnStatus: status code. For details, see .

[object Object]

• Deterministic compute:
  • aclnnMseLossOut defaults to a deterministic implementation.

[object Object]

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