[object Object]

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

• Description: Performs backpropagation of .

[object Object]

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

• [object Object]

• [object Object]

[object Object]

• Parameters:

  • [object Object] (aclTensor*, compute input): input of backpropagation, aclTensor on the device. The shape can be 4D or 5D and must be the same as that of self and gradInput. The shape and dtype must be the same as those of out in forward propagation aclnnReplicationPad3d. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, DOUBLE, COMPLEX64, or COMPLEX128.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT16, FLOAT32, BFLOAT16, DOUBLE, COMPLEX64, or COMPLEX128.
  • [object Object] (aclTensor*, input): input tensor for forward propagation, which is an aclTensor on the device. The shape can be 4D or 5D and must be the same as that of [object Object] and [object Object]. The shape and dtype must be the same as those of [object Object]. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, DOUBLE, COMPLEX64, or COMPLEX128.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT16, FLOAT32, BFLOAT16, DOUBLE, COMPLEX64, or COMPLEX128.
  • [object Object] (aclIntArray*, input): aclIntArray on the device. The length is 6, and the values indicate respectively the padding values on the left, right, top, bottom, front, and back. The first two values of [object Object] must be less than the value of the last dimension of [object Object], the middle two values must be less than the value of the second-to-last dimension of [object Object], and the last two values must be less than the value of the third-to-last dimension of [object Object].
  • [object Object] (aclTensor*, output): output of back propagation, which is an aclTensor on the device. The shape can be 4D or 5D and must be the same as that of [object Object] and [object Object]. The dtype must be the same as that of [object Object]. The can be ND.
    • [object Object]Atlas training products[object Object]: The data type can be FLOAT16, FLOAT32, DOUBLE, COMPLEX64, or COMPLEX128.
    • [object Object]Atlas A2 training products/Atlas A2 inference products[object Object] and [object Object]Atlas A3 training products/Atlas A3 inference products[object Object]: The data type can be FLOAT16, FLOAT32, BFLOAT16, DOUBLE, COMPLEX64, or COMPLEX128.
  • [object Object] (uint64_t*, output): size of the workspace to be allocated on the device.
  • [object Object] (aclOpExecutor**, output): operator executor, containing the operator computation process.

• Returns:

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

[object Object]

• Parameters:

  • [object Object] (void*, input): address of the workspace to be allocated on the device.
  • [object Object] (uint64_t, input): size of the workspace to be allocated on the device, which is obtained by the first-phase API aclnnReplicationPad3dBackwardGetWorkspaceSize.
  • [object Object] (aclOpExecutor*, input): operator executor, containing the operator computation process.
  • [object Object] (aclrtStream, input): stream for executing the task.

• Returns:

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

[object Object]

• Deterministic compute:
  • [object Object] defaults to a deterministic implementation.

If the number of elements in [object Object] is greater than 300*1024*1024, the execution may time out.

[object Object]

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